Reliability of Retinal Layer Annotation with a Novel, High-Resolution Optical Coherence Tomography Device: A Comparative Study

Optical coherence tomography (OCT) enables in vivo diagnostics of individual retinal layers in the living human eye. However, improved imaging resolution could aid diagnosis and monitoring of retinal diseases and identify potential new imaging biomarkers. The investigational high-resolution OCT platform (High-Res OCT; 853 nm central wavelength, 3 µm axial-resolution) has an improved axial resolution by shifting the central wavelength and increasing the light source bandwidth compared to a conventional OCT device (880 nm central wavelength, 7 µm axial-resolution). To assess the possible benefit of a higher resolution, we compared the retest reliability of retinal layer annotation from conventional and High-Res OCT, evaluated the use of High-Res OCT in patients with age-related macular degeneration (AMD), and assessed differences of both devices on subjective image quality. Thirty eyes of 30 patients with early/intermediate AMD (iAMD; mean age 75 ± 8 years) and 30 eyes of 30 age-similar subjects without macular changes (62 ± 17 years) underwent identical OCT imaging on both devices. Inter- and intra-reader reliability were analyzed for manual retinal layer annotation using EyeLab. Central OCT B-scans were graded for image quality by two graders and a mean-opinion-score (MOS) was formed and evaluated. Inter- and intra-reader reliability were higher for High-Res OCT (greatest benefit for inter-reader reliability: ganglion cell layer; for intra-reader reliability: retinal nerve fiber layer). High-Res OCT was significantly associated with an improved MOS (MOS 9/8, Z-value = 5.4, p < 0.01) mainly due to improved subjective resolution (9/7, Z-Value 6.2, p < 0.01). The retinal pigment epithelium drusen complex showed a trend towards improved retest reliability in High-Res OCT in iAMD eyes but without statistical significance. Improved axial resolution of the High-Res OCT benefits retest reliability of retinal layer annotation and improves perceived image quality and resolution. Automated image analysis algorithms could also benefit from the increased image resolution.

SAR minimum entropy autofocusing based on Prewitt operator

Current autofocus algorithms utilizing image criteria impose a significant computational burden. Therefore, this paper proposes a computationally efficient autofocus algorithm combined with SAR image feature points, employing the Prewitt operator to obtain the SAR image features. The range cell with the number of feature points in the front row as the input of the autofocus method to perform motion error estimation and compensation on SAR imagery. Our method's key feature is to optimize the selection criteria of range cells by acquiring the feature points of SAR images,reduces the number of input range cell,reduce the computational complexity of the autofocus algorithm and ultimately enhance the focusing effect of SAR images. Trials involving simulation and measured data demonstrate the effectiveness of the developed method.

A deep residual attention-based U-Net with a biplane joint method for liver segmentation from CT scans

Liver tumours are diseases with high morbidity and high deterioration probabilities, and accurate liver area segmentation from computed tomography (CT) scans is a prerequisite for quick tumour diagnosis. While 2D network segmentation methods can perform segmentation with lower device performance requirements, they often discard the rich 3D spatial information contained in CT scans, limiting their segmentation accuracy. Hence, a deep residual attention-based U-shaped network (DRAUNet) with a biplane joint method for liver segmentation is proposed in this paper, where the biplane joint method introduces coronal CT slices to assist the transverse slices with segmentation, incorporating more 3D spatial information into the segmentation results to improve the segmentation performance of the network. Additionally, a novel deep residual block (DR block) and dual-effect attention module (DAM) are introduced in DRAUNet, where the DR block has deeper layers and two shortcut paths. The DAM efficiently combines the correlations of feature channels and the spatial locations of feature maps. The DRAUNet with the biplane joint method is tested on three datasets, Liver Tumour Segmentation (LiTS), 3D Image Reconstruction for Comparison of Algorithms Database (3DIRCADb), and Segmentation of the Liver Competition 2007 (Sliver07), and it achieves 97.3%, 97.4%, and 96.9% Dice similarity coefficients (DSCs) for liver segmentation, respectively, outperforming most state-of-the-art networks; this strongly demonstrates the segmentation performance of DRAUNet and the ability of the biplane joint method to obtain 3D spatial information from 3D images.

Translational Compensation Algorithm for Ballistic Targets in Midcourse Based on Template Matching

The high-speed movement of a ballistic target will cause folding and translation of the micro-Doppler, which will affect the extraction of micro-motion features. To address the adverse effects of high-speed movement of ballistic targets in midcourse on the extraction of micro-motion features, a novel translational compensation algorithm based on template matching is proposed. Firstly, a 512 × 512 time-frequency map is obtained by binarization and down-sampling. The matching template then convolves the time-frequency map to obtain contour-like points. Then, the upper and lower contour points are preliminarily determined by the extreme value, and all actual contour points are screened out through structural similarity. Lastly, the upper and lower trend lines are determined and translation parameters for compensation by polynomial fitting are estimated. Simulation results show that the proposed algorithm has lower requirements for time-frequency resolution, higher precision and lower time complexity as a whole. Furthermore, it is also applicable to spectral aliasing.

Rapid Evaluation Model of Endurance Performance and Its Application for Agricultural UAVs

Current developments in agricultural aviation technology have gradually increased the requirements for the endurance of agricultural unmanned aerial vehicles (UAVs). It is significant to establish an endurance evaluation model for different types of UAVs and rationalize the battery and operating load parameters on this basis, which play an important role in improving the operational efficiency of the whole UAV. Taking the quad-rotor UAV as the research object, firstly, the structural composition of the whole UAV is studied in this paper, and then the main influencing parameters of the UAV endurance evaluation model are determined, combined with the mass distribution data of specific models. Based on the above, the hovering output power of the UAV is modularly divided, and a fast measurement method is innovatively proposed to sample and fit the power data of each module. Finally, in combination with the variation law of the lithium battery discharge time, a rapid evaluation method of the endurance time of agricultural UAVs is derived and validated by the hovering test data of the agri-drone with a standard operating load of 5 kg. The results show that the mass distribution system of the UAV has the highest percentage of operational load mass (40.45% under the standard configuration); the power distribution system has the highest percentage of motor pull curve output power (91.12% under the standard configuration); the maximum error of the surface verification point in the validation experiment is 2.73%, and the model is of relatively high accuracy. The modeling idea and the supporting module power rapid measurement method can quickly model and evaluate the endurance of common agricultural drones, guide reasonable matching between the battery and operating load, and are of great significance in improving UAVs’ endurance and operational efficiency.

Dense Oil Tank Detection and Classification via YOLOX-TR Network in Large-Scale SAR Images

Oil storage tank detection and classification in synthetic aperture radar (SAR) images play a vital role in monitoring energy distribution and consumption. Due to the SAR side-looking imaging geometry and multibouncing scattering mechanism, dense oil tank detection and classification tasks have faced more challenges, such as overlapping, blurred contours, and geometric distortion, especially for small-sized tanks. To address the above issues, this paper proposes YOLOX-TR, an improved YOLOX based on the Transformer encoder and structural reparameterized VGG-like (RepVGG) blocks, to achieve end-to-end oil tank detection and classification in densely arranged areas of large-scale SAR images. Based on YOLOX, the Transformer encoder, a self-attention-based architecture, is integrated to enhance the representation of feature maps and capture the region of interest of oil tanks in densely distributed scenarios. Furthermore, RepVGG blocks are employed to reparameterize the backbone with multibranch typologies to strengthen the distinguishable feature extraction of multi-scale oil tanks without increasing computation in inference time. Eventually, comprehensive experiments based on a Gaofen-3 1 m oil tank dataset (OTD) demonstrated the effectiveness of the Transformer encoder and RepVGG blocks, as well as the performance superiority of YOLOX-TR with a mAP and mAP0.5 of 60.8% and 94.8%, respectively.

Online Education Classroom Intelligent Management System Based on Tensor CS Reconstruction Model

To study a high-efficiency online classroom intelligent management system, this article builds an artificial intelligence classroom management system based on the tensor CS reconstruction model. Moreover, this study uses the cosine function to represent the data energy fitting of the traditional active contour model and proposes a local cosine fitting energy active contour model based on partial image restoration, which is used for image and composite image segmentation. Simultaneously, this study proposes a new type of super-resolution algorithm. This algorithm performs Fourier transform of a low-resolution image into a frequency range and then performs an inverse Fourier transform on the image expanded in the frequency range to obtain the initial high-resolution image and finally reconstructs a new super-resolution image using the frequency-domain compressed data of the high-resolution image. Finally, this study verifies and analyzes the performance of the model through experiments. The research results are basically consistent with the expectations of the model.

Bendlet Transform Based Adaptive Denoising Method for Microsection Images

Magnetic resonance imaging (MRI) plays an important role in disease diagnosis. The noise that appears in MRI images is commonly governed by a Rician distribution. The bendlets system is a second-order shearlet transform with bent elements. Thus, the bendlets system is a powerful tool with which to represent images with curve contours, such as the brain MRI images, sparsely. By means of the characteristic of bendlets, an adaptive denoising method for microsection images with Rician noise is proposed. In this method, the curve contour and texture can be identified as low-frequency components, which is not the case with other methods, such as the wavelet, shearlet, and so on. It is well known that the Rician noise belongs to a high-frequency channel, so it can be easily removed without blurring the clarity of the contour. Compared with other algorithms, such as the shearlet transform, block matching 3D, bilateral filtering, and Wiener filtering, the values of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) obtained by the proposed method are better than those of other methods.

Elevation Resolution Enhancement Method Using Non-Ideal Linear Motion Error of Airborne Array TomoSAR

Airborne array tomographic synthetic aperture radar (TomoSAR) is a major breakthrough, which can obtain three-dimensional (3D) information of layover scenes in a single pass. As a high-resolution SAR, airborne array TomoSAR has considerable potential for 3D applications. However, the original TomoSAR elevation resolution is limited by the baseline and platform length. In this study, a novel method for enhancing the elevation resolution is proposed. First, the actual curve trajectory observation model of airborne array TomoSAR is established. Subsequently, multi-channel image data are substituted into the model to obtain the observation equation. Furthermore, the azimuth and elevation directions of the two-dimensional observation scene are modeled uniformly. The scene reconstruction is realized through the two-dimensional joint solution. Finally, the observation equation is sparsely solved according to the sparse distribution characteristics of the target to obtain the image. The performance of the proposed method is verified via simulation and real-data experiments. The experimental results indicate that, compared with the traditional elevation resolution enhancement method, the proposed method improves the elevation resolution by two times. The proposed method also provides a new thinking for high-resolution SAR 3D imaging.

SQRT: A Secure Querying Scheme of Routing Table Based on Oblivious Transfer

The user equipment and directory server stay in the state of asymmetric information in anonymous networks, so that it is hard to coordinate information leakage prevention and information sharing when we explore the routing addressing technology. The severe security risk faced by existing anonymous networks, such as Tor and I2P, is the leakage of privacy information of routing nodes. This paper tries to resolve this problem and proposes a novel secure querying scheme of the routing table (SQRT) based on oblivious transfer, which can effectively ensure that both user equipment and directory server faithfully follow the routing querying protocol and protect the privacy information of both parties to the greatest extent. The SQRT scheme can realize that the directory server cannot only send the required routing nodes to the user equipment, but can also ensure that the directory server cannot know the exact routing nodes received by the user equipment and protect the information of other routing nodes in the directory server from disclosure. Security analysis shows that in the scenario where the directory server and user equipment are semi-honest, respectively, the SQRT scheme can ensure the privacy of both directory server and user equipment. The experimental simulation results show that compared with existing schemes, the SQRT scheme has obvious performance advantages in terms of the degree of anonymity, protocol running time, and communication traffic.

A Simulation Method of Two-Dimensional Sea-Surface Current Field for Trajectory Crossing Spaceborne SAR

The trajectory-crossing method is an important and effective method for spaceborne SAR (synthetic aperture radar) to detect a two-dimensional current field. However, in practical applications, there are few spaceborne SAR data that meet the requirements of close acquisition time and overlapping trajectories, which makes it difficult to comprehensively analyze the impact of different systems and environmental parameters on current measurement results from real data processing. With the proposal of a SAR constellation plan in the future, the data resources will be constantly enriched, and the trajectory-crossing method will be widely used. It is, thus, necessary to lay some theoretical foundation at present. A two-dimensional sea-surface-current-field simulation method for trajectory crossing spaceborne SAR is proposed in this paper. Based on the principles of the trajectory-crossing method, the proposed method can realize the simulation of two-dimensional sea-surface-current field under given spaceborne SAR system and environmental parameters. In this paper, the simulation process of this method is given, and the simulation experiment is performed. Compared with the current measurement results of SAR data, the simulation experiment shows that the current velocity error is less than 0.03 m/s and the direction error is less than 10 degrees, which proves the reliability of the proposed simulation method. The proposed method lays a foundation for analyzing the influence of various parameters in the application of the trajectory-crossing method.

Robust Space–Time Joint Sparse Processing Method with Airborne Active Array for Severely Inhomogeneous Clutter Suppression

Due to clutter inhomogeneity, the clutter suppression ability of space–time adaptive processing (STAP) is usually constrained by the insufficient number of independent and identically distributed (IID) clutter training samples and, as a result, is sacrificed to achieve the demanded sample reduction. Moreover, since clutter heterogeneity is exacerbated in the real environment, the IID training sample size can be heavily reduced, leading to the deterioration in clutter suppression. To solve this problem, a novel robust space–time joint sparse processing method with airborne active array is proposed. This method has several outstanding advantages: (1) only the single snapshot cell under test (CUT) data is used for the superior clutter suppression performance; and (2) the proposed method completely removes the dependence of the system processing ability on IID training samples. In this paper, the signal model of uniform transmitting subarray diversity is first established to obtain the single snapshot echo observed CUT data. Then, with the matched reconstruction, the single snapshot data are equivalently converted into multi-frame echo data. Finally, a fast multi-frame echo data joint sparse Bayesian algorithm is used to achieve heterogeneous clutter suppression. Numerous experiments were performed to verify the advantages of the proposed method.

Anthropogenic Influences on Soil Erosion since the Late Holocene and Contrasting Regional Sustainability in China

A multi-disciplinary investigation of loess sections in the southeast and northwest of the Chinese Loess Plateau (CLP) and a sediment core drilled in Zhejiang Province of southeast China was conducted. Discrepancies among grain size distribution, rock magnetic properties, geochemical characteristics and chroma features, and up-section weakening relation between various proxies in the Sanmenxia loess section were found. The results were compared with those of the Baicaoyuan loess section in the northwest of the CLP and the sediment records across the plateau and elsewhere. It was suggested that human impacts began to increase soil erosion on the CLP since the middle Holocene. In addition to the increased soil erosion being decoupled from drying climate after 4 ka, renewed intensification of soil erosion was suggested to occur within the interval of 1.5–2.5 ka as a result of enhanced human activities. The two detected increases in human-induced soil erosion on the CLP are consistent with the human-driven land use changes or human–land interactions at national or regional scales, including the anthropogenic influences on the changes in the sediment load of the Yellow River. In contrast, no human impacts overwhelming hydroclimate control of soil erosion was revealed in the Beihuqiao cores, Zhejiang. The population growth during the past 2400 years showed a relative decreasing trend on CLP and a relative increasing trend in Zhejiang. It is indicated that anthropogenic factors have played a key role in modulating the Earth’s surface environment. In particular, ecologically fragile areas, such as the CLP, would be much more susceptible to human disturbance and climate change. The current serious land degradation on the CLP mainly results from the negative feedback between human–land interactions. Regional heterogeneity should be taken into account for sustainable development.

Two Low-Level Feature Distributions Based No Reference Image Quality Assessment

No reference image quality assessment (NR IQA) aims to develop quantitative measures to automatically and accurately estimate perceptual image quality without any prior information about the reference image. In this paper, we introduce two low-level feature distributions (TLLFD) based method for NR IQA. Different from the deep learning method, the proposed method characterizes image quality with the distributions of low-level features, thus it has few parameters, simple model, high efficiency, and strong robustness. First, the texture change of distorted image is extracted by the weighted histogram of generalized local binary pattern. Second, the Weibull distribution of gradient is extracted to represent the structural change of the distorted image. Furthermore, support vector regression is adopted to model the complex nonlinear relationship between feature space and quality measure. Finally, numerical tests are performed on LIVE, CISQ, MICT, and TID2008 standard databases for five different distortion categories JPEG2000 (JP2K), JPEG, White Noise (WN), Gaussian Blur (GB), and Fast Fading (FF). The experimental results indicate that TLLFD method achieves superior performance and strong generalization for image quality prediction as compared to state-of-the-art full-reference, no reference, and even deep learning IQA methods.

An Improved Ground Moving Target Parameter Estimation and Imaging Method for Multichannel High Resolution SAR

With increasing demands from both military and civilian applications, ground moving-target imaging is becoming one of the important research topics for high-resolution SAR systems. However, the existing moving-target imaging methods are not suitable for high-resolution SAR because of their low parameter estimation accuracy and high computational complexity. To solve the problem, an improved ground moving-target parameter estimation and imaging method is proposed. First, the third-order phase model of the uniformly accelerated target signal is constructed, and the Hough transform and the second-order Keystone transform (SOKT) are used to correct the range cell migration into one range cell to achieve target coherent accumulation. Secondly, a delayed cross-correlation function (DCCF) is constructed to reduce the order of the range migration phase response in the slow time domain, and the coupling degree between the cross-correlation peak position and the range migration is reduced, so that the obtained DCCF has a higher gain, which ensures the accuracy of parameter estimation. Parameter estimation is simplified to peak detection by the Shift-And-Correlation (SAC) algorithm and two-dimensional Fourier transform (2D-FFT), avoiding parameter search. Compared with the existing methods, the proposed method has better focusing effect and lower computational complexity. Finally, simulation and measured data are given to verify the effectiveness of the proposed method.

A Real-Time Monitoring Method for Civil Aircraft Take-Off and Landing Based on Synthetic Aperture Microwave Radiation Technology

It is important to monitor the take-off and landing of civil aircraft using passive detection methods. Due to the strict aircraft safety requirements and the electromagnetic environment around an airport, using too many active detection methods should be avoided. Using an aircraft’s microwave radiation signal detection is very advantageous because it does not actively emit signals and has a strong cloud penetration, suitable for all-weather observation. This paper introduces a synthetic aperture microwave radiation system for monitoring the take-off and landing of civil aircraft, which is characterized by real-time two-dimensional imaging, and the image refresh rate can reach 10 ms, which meets the high refresh rate requirements for aircraft imaging. Applicable system parameters and antenna array distribution scheme and imaging algorithm are given. Then the paper focuses on the error analysis and correction method of the system. The correction method is simple and fast, which avoids the disadvantage that the error needs to be corrected regularly in the laboratory environment, and is suitable for airport application. Finally, the simulation and experimental results show that this technology can be used for real-time monitoring of civil aircraft during take-off and landing, and it is a practical means to assisting landing.

Multi-Rotor UAV-Borne PolInSAR Data Processing and Preliminary Analysis of Height Inversion in Urban Area

Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) has many useful applications, especially in forest areas. With the development of SAR miniaturization technology, researchers can install PolInSAR on small unmanned aerial vehicles (UAV), which can reduce flight costs. Limited by size and power, UAV-borne SAR usually works in a high-frequency band, which restricts its application to such things as vegetation height inversion. While on the other hand, the high resolution acquired under a short wavelength promises its application in urban areas. However, there are fewer studies on the application of PolInSAR in urban areas compared with that in forest areas. In this paper, we propose a processing method for a Ku-band multi-rotor-UAV-borne PolInSAR and provide a preliminary analysis of height inversion results on its data from the Fudan campus in Shanghai. We obtain the digital surface model (DSM) of different polarization modes and the DSM of polarimetric interferometry optimal decomposition in this area, whose RMSE is 2.88m. On this basis, the elevation inversion results of targets such as buildings, lampposts, and trees are compared and analyzed. We preliminarily explore and analyze the reasons for the different results of different targets. To this end, we propose a mathematical derivation of the relationship between the interferometric phase between PolInSAR and InSAR of Pauli decomposition. We also perform a simulation to analyze the relationship between the phase center height of Pauli decomposition and PolInSAR under different cases. It provides a reference for the application of small UAV-borne PolInSAR in urban areas.

Research on an LEO Constellation Multi-Aircraft Collaborative Navigation Algorithm Based on a Dual-Way Asynchronous Precision Communication-Time Service Measurement System (DWAPC-TSM)

In order to solve the collaborative navigation problems in challenging environments such as insufficient visible satellites, obstacle reflections and multipath errors, and in order to improve the accuracy, usability, and stability of collaborative navigation and positioning, we propose a dual-way asynchronous precision communication–timing–measurement system (DWAPC-TSM) LEO constellation multi-aircraft cooperative navigation and positioning algorithm which gives the principle, algorithm structure, and error analysis of the DWAPC-TSM system. In addition, we also analyze the effect of vehicle separation range on satellite observability. The DWAPC-TSM system can achieve high-precision ranging and time synchronization accuracy. With the help of this system, by adding relative ranging and speed measurement observations in an unscented Kalman filter (UKF), the multi-aircraft coordinated navigation and positioning of aircraft is finally realized. The simulation results show that, even without the aid of an altimeter, the multi-aircraft cooperative navigation and positioning algorithm based on the DWAPC-TSM system can achieve good navigation and positioning results, and with the aid of the altimeter, the cooperative navigation and positioning accuracy can be effectively improved. For the formation flight configurations of horizontal collinear and vertical collinear, the algorithm is universal, and in the case of vertical collinear, the navigation performance of the formation members tends to be consistent. Under different relative measurement accuracy, the algorithm can maintain good robustness; compared with some existing classical algorithms, it can significantly improve the navigation and positioning accuracy. A reference scheme for exploring the feasibility of a new cooperative navigation and positioning mode for LEO communication satellites is presented.

Non-Line-of-Sight Moving Target Detection Method Based on Noise Suppression

At the present time, most of existing security systems only detect and track targets in line-of-sight (LOS). However, in practice, the locations of targets are often out of the line of sight. This article focuses on the non-line-of-sight (NLOS) moving target detection with low-power transmission signals by reflection. There are two key problems, the weak target echo signal and the multipath effect. In terms of the issues, this paper constructs the echo signal model of the NLOS target. On the basis of the echo model, the detection method of NLOS moving target based on millimeter-wave radar comes up, which is of great theoretical value and important practical significance for indoor security. This paper innovatively applies the polynomial fitting method to suppress static noise and range gating method to suppress noise from other range gates. Then, the location and velocity of the target are estimated by two-dimensional fast Fourier transform (FFT) and the multiple signal classification (MUSIC) method. Furthermore, in order to verify the accuracy of the NLOS target echo signal model proposed in this paper, we respectively simulated two important parts of the signal in the model, the target echo signal and the direct backscattered signal of the intermediate interface, both of which are multipath signals. We counted the echo path length distribution in these two parts, and applied the NLOS target detection method to process them respectively. In addition, we also simulated the NLOS target echo signal and obtained actual data in the actual scene, and processed both the simulated data and the actual data. Comparing the results of target detection with and without denoising methods, the effectiveness of the two denoising methods proposed in this paper is verified.

An Anti-Jamming Method against Interrupted Sampling Repeater Jamming Based on Compressed Sensing

Interrupted sampling repeater jamming (ISRJ) is an attracted coherent jamming method to inverse synthetic aperture radar (ISAR) in the past decades. By means of different jamming parameters settings, realistic dense false targets can be formed around the true target. This paper proposed an adaptive anti-jamming method against ISRJ by adjusting the number of measurements based on compressed sensing (CS). The jamming signal is energy concentrated and segmented sparse in the frequency domain. The measurements number of the reconstructed target signal and the jamming signal is different. According to the restricted isometry property (RIP) condition of CS theory, signal reconstructing performance depends on the number of measurements that varies with the sparsity of the vector. Thus, the jamming signal is suppressed, and the true target signal is retained by altering the measurements number of echo signals. Besides, the two-dimensional (2D) anti-jamming method is derived in detail. The anti-jamming effect is analyzed with different signal-to-noise ratios (SNR), sampling rates, and jam-to-signal ratios (JSR). Simulations prove the effectiveness of the proposed anti-jamming method.

Target Detection and DOA Estimation for Passive Bistatic Radar in the Presence of Residual Interference

With the development of radio technology, passive bistatic radar (PBR) will suffer from interferences not only from the base station that is used as the illuminator of opportunity (BS-IoO), but also from the base station with co-frequency or adjacent frequency (BS-CF/AF). It is difficult for clutter cancellation algorithm to suppress all the interferences, especially the interferences from BS-CF/AF. The residual interferences will seriously affect target detection and DOA estimation. To solve this problem, a novel target detection and DOA estimation method for PBR based on compressed sensing sparse reconstruction is proposed. Firstly, clutter cancellation algorithm is used to suppress the interferences from BS-IoO. Secondly, the residual interferences and target echo are separated in spatial domain based on the azimuth sparse reconstruction. Finally, target detection and DOA estimation method are given. The proposed method can achieve not only target detection and DOA estimation in the presence of residual interferences, but also better anti-mainlobe interferences and high-resolution DOA estimation performance. Numerical simulation and experimental results verify the effectiveness of the proposed algorithm.

A Novel High-Squint Spotlight SAR Raw Data Simulation Scheme in 2-D Frequency Domain

Raw data simulation is the front-end work of synthetic aperture radar (SAR), which is of great significance. For high-squint spotlight SAR, the frequency domain simulation algorithm is invalid because of the range-azimuth coupling effect. In order to realize high-squint spotlight SAR raw data simulation in the frequency domain, an algorithm based on coordinate transformation and non-uniform fast Fourier transform (NUFFT) is proposed. This algorithm generates broadside raw data using a two-dimensional (2-D) frequency simulation algorithm; then, coordinate transformation is used by analyzing the characteristics of broadside and high-squint spotlight SAR. After coordinate transformation, NUFFT is carried out to realize the coupling relation in the 2-D frequency domain. Since the coordinate transformation ignores the influence of range walk, the range walk is compensated after NUFFT. As a result, compared with the traditional squint spotlight SAR frequency domain simulation algorithm, the proposed algorithm can improve the accuracy of point and distributed target imaging results, and the efficiency of the proposed algorithm can be significantly improved in contrast the traditional time domain algorithm.

An Unsupervised Port Detection Method in Polarimetric SAR Images Based on Three-Component Decomposition and Multi-Scale Thresholding Segmentation

It is difficult to detect ports in polarimetric SAR images due to the complicated components, morphology, and coastal environment. This paper proposes an unsupervised port detection method by extracting the water of the port based on three-component decomposition and multi-scale thresholding segmentation. Firstly, the polarimetric characteristics of the port water are analyzed using modified three-component decomposition. Secondly, the volume scattering power and the power ratio of the double-bounce scattering power to the volume scattering power (PRDV) are used to extract the port water. Water and land are first separated by a global thresholding segmentation of the volume scattering power, in which the sampling region used for the threshold calculation is automatically selected by a proposed homogeneity measure. The interference water regions in the ports are then separated from the water by segmenting the PRDV using the multi-scale thresholding segmentation method. The regions of interest (ROIs) of the ports are then extracted by determining the connected interference water regions with a large area. Finally, ports are recognized by examining the area ratio of strong scattering pixels to the land in the extracted ROIs. Seven single quad-polarization SAR images acquired by RADARSAT-2 covering the coasts of Dalian, Zhanjiang, Fujian, Tianjin, Lingshui, and Boao in China and Berkeley in America are used to test the proposed method. The experimental results show that all ports are correctly and quickly detected. The false alarm rates are zero, the intersection of union section (IoU) indexes between the detected port and the ground truth can reach 75%, and the average processing time can be less than 100 s.

A Survey of UAV Swarm Task Allocation Based on the Perspective of Coalition Formation

Coalition formation of unmanned aerial vehicle (UAV) swarms, an effective solution for UAV swarm task allocation, is an important technology for UAV swarms to perform real-time and efficient collaborative task allocation in a dynamic and unknown environment. This paper summarizes the task allocation methods of UAV swarm coalition comprehensively and systematically. First, starting with the related work of UAV swarm coalition task allocation, this paper introduces the basic concept, general model, and constraint index of UAV swarm coalition task allocation. Then, the specific content, research status, advantages, and disadvantages of the coalition formation methods are analyzed, respectively. Third, the commonly used solution algorithms and research status of coalition task allocation are introduced, and the advantages and disadvantages of the existing coalition formation solution algorithms are compared and analyzed. Finally, it provides significant guidance for future related research.

Robust Burst Detection Algorithm for Distributed Unique Word TDMA Signal

In recent years, distributed unique word (DUW) has been widely used in satellite single carrier TDMA signals, such as very small aperture terminal (VSAT) satellite systems. Different from the centralized structure of traditional unique word, DUW is uniformly dispersed in a burst signal, where the traditional unique word detection methods are not applicable anymore. For this, we propose a robust burst detection algorithm based on DUW. Firstly, we allocated the sliding detection windows with the same structures as DUW in order to effectively detect it. Secondly, we adopt the method of time delay conjugate multiplication to eliminate the influence of frequency offset on detection performance. Due to the uniform dispersion of DUW, it naturally has two different kinds of time delays, namely the delay within the group and the delay between the two groups. So, we divide the traditional dual correlation formula into two parts to calculate them separately and obtain a dual correlation detection algorithm, which is suitable for DUW. Simulation and experimental results demonstrate that when the distribution structure of DUW changes, detection probability of the proposed algorithm fluctuates little, and its variance is 1.56×10−5, which is 99.83% lower than the existing DUW detection algorithms. In addition, its signal to noise ratio (SNR) threshold is about 1 dB lower than the existing algorithms under the same circumstance of the missed detection probability.

A multi-objective optimization for resource allocation of emergent demands in cloud computing

Cloud resource demands, especially some unclear and emergent resource demands, are growing rapidly with the development of cloud computing, big data and artificial intelligence. The traditional cloud resource allocation methods do not support the emergent mode in guaranteeing the timeliness and optimization of resource allocation. This paper proposes a resource allocation algorithm for emergent demands in cloud computing. After building the priority of resource allocation and the matching distances of resource performance and resource proportion to respond to emergent resource demands, a multi-objective optimization model of cloud resource allocation is established based on the minimum number of the physical servers used and the minimum matching distances of resource performance and resource proportion. Then, an improved evolutionary algorithm, RAA-PI-NSGAII, is presented to solve the multi-objective optimization model, which not only improves the quality and distribution uniformity of the solution set but also accelerates the solving speed. The experimental results show that our algorithm can not only allocate resources quickly and optimally for emergent demands but also balance the utilization of all kinds of resources.

Coastline Extraction from GF-3 SAR Images Using LKDACM and GMM Algorithms

Coastline detection using a Gaussian Mixture Model (GMM) applied to synthetic aperture radar (SAR) imagery is usually inaccurate due to the inherent noise of SAR data. In addition, the traditional active counter model is sensitive to the initial position of the contour line and requires a large number of iterations to converge to a solution. In this study, we first used the GMM algorithm to segment the SAR images and obtain a coarse land and sea segmentation map. This map is then used as the initial position for a subsequent active contour model. The K distribution was introduced into the local statistical active contour model to better model the SAR image. The Gaussian distribution-based local active contour model and the algorithm detailed in this paper were used to perform coastline extraction experiments on four SAR images. Four GF-3 SAR images with different modes were collected to validate the efficiency of the proposed method. The experimental results show that the coastline extraction methods from SAR images based on the GMM algorithm and the K distribution-based local statistical active contour model (LKDACM) overcame the shortcomings of the traditional active contour model to accurately and quickly detect coastlines, thus enabling the detection of coastline changes.

Accurate Localization of Oil Tanks in Remote Sensing Images via FGMRST-Based CNN

Object localization is an important application of remote sensing images and the basis of information extraction. The acquired accuracy is the key factor to improve the accuracy of object structure information inversion. The floating roof oil tank is a typical cylindrical artificial object, and its top cover fluctuates up and down with the change in oil storage. Taking the oil tank as an example, this study explores the localization by combining the traditional feature parameter method and convolutional neural networks (CNNs). In this study, an improved fast radial symmetry transform (FRST) algorithm called fast gradient modulus radial symmetry transform (FGMRST) is proposed and an approach based on FGMRST combined with CNN is proposed. It effectively adds the priori of circle features to the calculation process. Compared with only using CNN, it achieves higher precision localization with fewer network layers. The experimental results based on SkySat data show that the method can effectively improve the calculation accuracy and efficiency of the same order of magnitude network, and by increasing the network depth, the accuracy still has a significant improvement.

An Improved Phase Unwrapping Method Based on Hierarchical Networking and Constrained Adjustment

Accurate phase unwrapping (PU) is a precondition and key for using synthetic aperture radar interferometry (InSAR) technology to successfully invert topography and monitor surface deformations. However, most interferograms are seriously polluted by noise in the low-quality regions, which poses difficulties for PU. Therefore, using the strategy of leveling network adjustment, this paper proposes an improved PU method based on hierarchical networking and constrained adjustment. This method not only limits the phase error transfer of low-quality points, but also takes the PU results of high-quality points as control points and uses the network adjustment method with constraints to unwrap low-quality points, which effectively inhibits the influence of noise and improves the accuracy of unwrapping. Regardless of the unwrapping method used for high-quality points, the unwrapping accuracy of low-quality points can always be improved. Compared with other traditional two-dimensional phase unwrapping workflows, this method can more accurately recover the phase of low-coherence regions only through the interferogram. A simulation experiment showed that the local noise of the interferogram was effectively inhibited, and the PU accuracy of the low-quality regions was improved by 16–46% compared with different traditional methods. For a real-data experiment of mining area with low coherence, the PU result of our proposed method had fewer residues and lower phase standard deviation than traditional methods, further indicating the practicability and robustness of the proposed method. The work in this paper has considerable practical significance for recovering the decoherence phase with serious local noise such as mining centers and groundwater subsidence centers.

Velocity Estimation of Ocean Surface Currents in along-Track InSAR System Based on Conditional Generative Adversarial Networks

Velocity estimation of ocean surface currents is of great significance in the fields of the fishery, shipping, sewage discharge, and military affairs. Over the last decade, along-track interferometric synthetic aperture radar (along-track InSAR) has been demonstrated to be one of the important instruments for large-area and high-resolution ocean surface current velocity estimation. The calculation method of the traditional ocean surface current velocity, as influenced by the large-scale wave orbital velocity and the Bragg wave phase velocity, cannot easily separate the current velocity, characterized by large error and low efficiency. In this paper, a novel velocity estimation method of ocean surface currents is proposed based on Conditional Generative Adversarial Networks (CGANs). The main processing steps are as follows: firstly, the known ocean surface current field diagrams and their corresponding interferometric phase diagrams are constructed as the training dataset; secondly, the estimation model of the ocean surface current field is constructed based on the pix2pix algorithm and trained by the training dataset; finally, the interferometric phase diagrams in the test dataset are input into the trained model. In the simulation experiment, processing results of the proposed method are compared with those of traditional ocean surface current velocity estimation methods, which demonstrate the efficiency and effectiveness of the novel method.

Comparison of Atmospheric Circulation Anomalies between Dry and Wet Extreme High-Temperature Days in the Middle and Lower Reaches of the Yellow River

Many previous studies have reported that atmospheric circulation anomalies are generally the direct cause of extreme high-temperature (EHT). However, the atmospheric circulation anomalies of EHT days with different humidity and the differences between them are less often discussed, while humidity plays an important role in how people feel in a high-temperature environment. Therefore, this study uses 1961–2016 CN05.1 daily observational data and NCEP/NCAR reanalysis data to classify summer EHT days in China into dry and wet. Furthermore, we investigate the atmospheric circulation anomalies associated with the dry and wet EHT days in the middle and lower reaches of the Yellow River (MLRYR). The results reveal that dry EHT days are likely to be caused by adiabatic heating from anomalous subsidence, while wet EHT days are more likely caused by the low-latitude water vapor and heat anomalies brought by the Western Pacific Subtropical High (WPSH). This may be due to a remarkable westward/southward/narrowed extension of the Continental High (CH)/WPSH/South Asian High (SAH) accompanied by an occurrence of dry EHT day. The opposite pattern is observed for wet EHT days. Moreover, a wave train like the Silk Road pattern from the midlatitudes could affect the dry EHT days, while wet EHT days are more likely to be affected by a wave train from high latitudes. Knowing the specific characteristics of dry and wet EHT days and their associated atmospheric circulations could offer new insights into disaster risk prevention and reduction.

Research on a Segmentation Algorithm for the Tujia Brocade Images Based on Unsupervised Gaussian Mixture Clustering

Tujia brocades are important carriers of Chinese Tujia national culture and art. It records the most detailed and real cultural history of Tujia nationality and is one of the National Intangible Cultural Heritage. Classic graphic elements are separated from Tujia brocade patterns to establish the Tujia brocade graphic element database, which is used for the protection and inheritance of traditional national culture. Tujia brocade dataset collected a total of more than 200 clear Tujia brocade patterns and was divided into seven categories, according to traditional meanings. The weave texture of a Tujia brocade is coarse, and the textural features of the background are obvious, so classical segmentation algorithms cannot achieve good segmentation effects. At the same time, deep learning technology cannot be used because there is no standard Tujia brocade dataset. Based on the above problems, this study proposes a method based on an unsupervised clustering algorithm for the segmentation of Tujia brocades. First, the cluster number K is calculated by fusing local binary patterns (LBP) and gray-level co-occurrence matrix (GLCM) characteristic values. Second, clustering and segmentation are conducted on each input Tujia brocade image by adopting a Gaussian mixture model (GMM) to obtain a preliminary segmentation image, wherein the image yielded after preliminary segmentation is rough. Then, a method based on voting optimization and dense conditional random field (DenseCRF) (CRF denotes conditional random filtering) is adopted to optimize the image after preliminary segmentation and obtain the image segmentation results. Finally, the desired graphic element contour is extracted through interactive cutting. The contributions of this study include: (1) a calculation method for the cluster number K wherein the experimental results show that the effect of the clustering number K chosen in this paper is ideal; (2) an optimization method for the noise points of Tujia brocade patterns based on voting, which can effectively eliminate isolated noise points from brocade patterns.

Optimization of a Multi-Energy Complementary Distributed Energy System Based on Comparisons of Two Genetic Optimization Algorithms

The development and utilization of low-carbon energy systems has become a hot topic of energy research in the international community. The construction of a multi-energy complementary distributed energy system (MCDES) is researched in this paper. Based on the multi-objective optimization theory, the planning optimization of an MCDES is studied, and a three-dimensional objective-optimization model is constructed by considering the constraints of the objective function and decision variables. Aiming at the optimization problem of building terminals for the MCDES studied in the paper, two genetic optimization algorithms—Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Non-Dominated Sorting Genetic Algorithm III (NSGA-III)—are used for calculation based on an example analysis. The constraint conditions of practical problems were added to the existing algorithms. Combined with the comparison of the solution quality and the optimal compromise solution of the two algorithms, a multi-decision method is proposed to obtain the optimal solution based on the Pareto optimal frontier of the two algorithms. Finally, the optimal decision scheme of the example is determined and the effectiveness and reliability of the optimization model are verified. Under the application of the MCDES optimization model studied in this paper, the iteration speed and hypervolume index of NSGA-III are found to be better than those of NSGA-II. The values of the life cycle cost and life cycle carbon emission objectives after the optimization of NSGA-III are indicated as 2% and 14% lower, respectively, than those of NSGA-II. The primary energy efficiency of NSGA-III is shown to be 20% higher than that of NSGA-II. According to the optimal decision, the energy operation strategies of the example MCDES with each typical day in the four seasons indicate that good integrated energy application and low-carbon operation performance are shown during the four-seasons operation process. The consumption of renewable energy is significant, which effectively reduces the application of high-grade energy. Thus, the theoretical guidance and engineering application reference are provided for MCDES design planning and operation optimization.

CARA: A Congestion-Aware Routing Algorithm for Wireless Sensor Networks

Congestion control is one of the key research topics in relation to the routing algorithms of wireless sensor networks (WSNs). In this paper, we propose a congestion-aware routing algorithm (CARA) for unlimited-lifetime wireless sensor networks by integrating the geographic distance and traffic load of sensor nodes. The algorithm takes alleviating congestion as the primary purpose and considers the traffic of the node itself and local network traffic. According to the geographic distance between nodes, CARA defines four decision parameters (node load factor, forward rate, cache remaining rate, and forward average cache remaining rate), selecting the best node as the next-hop through the multi-attribute decision-making method. Compared with the two existing algorithms for congestion control, our simulation results suggest that the CARA algorithm alleviates network congestion and meets reasonable network delay and energy consumption requirements.

A Nonlinear Fingerprint-Level Radar Simulation Modeling Method for Specific Emitter Identification

With the development of information technology for modern military confrontations, radar emitter fingerprint identification has become a hot and difficult topic in the field of electronic warfare, especially in the field of electronic reconnaissance. Owing to the confidentiality of military systems, most of the existing studies use simulation data for radar emitter fingerprint identification experiments and analysis. However, most of the existing modeling methods focus on the mechanism analysis of the nonlinear fingerprint characteristics of a single independent component. Its main disadvantage is that it can only represent the nonlinear fingerprint characteristics of some components in the radar emitter system but cannot fully reflect the nonlinear fingerprint characteristics of the whole radar emitter system. In this paper, a nonlinear fingerprint-level radar simulation modeling method is proposed. In contrast to the previous single component modeling method, the systematic nonlinear characteristic modeling method of this model can provide individual radar signal data under different modulation modes and working parameters, and provide experimental conditions for data support and theoretical analysis of radar emitter fingerprint identification.

Hyperchaotic Image Encryption Based on Multiple Bit Permutation and Diffusion

Image security is a hot topic in the era of Internet and big data. Hyperchaotic image encryption, which can effectively prevent unauthorized users from accessing image content, has become more and more popular in the community of image security. In general, such approaches conduct encryption on pixel-level, bit-level, DNA-level data or their combinations, lacking diversity of processed data levels and limiting security. This paper proposes a novel hyperchaotic image encryption scheme via multiple bit permutation and diffusion, namely MBPD, to cope with this issue. Specifically, a four-dimensional hyperchaotic system with three positive Lyapunov exponents is firstly proposed. Second, a hyperchaotic sequence is generated from the proposed hyperchaotic system for consequent encryption operations. Third, multiple bit permutation and diffusion (permutation and/or diffusion can be conducted with 1-8 or more bits) determined by the hyperchaotic sequence is designed. Finally, the proposed MBPD is applied to image encryption. We conduct extensive experiments on a couple of public test images to validate the proposed MBPD. The results verify that the MBPD can effectively resist different types of attacks and has better performance than the compared popular encryption methods.

Research on Optimization of Array Honeypot Defense Strategies Based on Evolutionary Game Theory

Honeypot has been regarded as an active defense technology that can deceive attackers by simulating real systems. However, honeypot is actually a static network trap with fixed disposition, which is easily identified by anti-honeypot technology. Thus, honeypot is a “passive” active defense technology. Dynamic honeypot makes up for the shortcomings of honeypot, which dynamically adjusts defense strategies with the attack of hackers. Therefore, the confrontation between defenders and attackers is a strategic game. This paper focuses on the non-cooperative evolutionary game mechanism of bounded rationality, aiming to improve the security of the array honeypot system through the evolutionarily stable strategies derived from the evolutionary game model. First, we construct a three-party evolutionary game model of array honeypot, which is composed of defenders, attackers and legitimate users. Secondly, we formally describe the strategies and revenues of players in the game, and build the three-party game payoff matrices. Then the evolutionarily stable strategy is obtained by analyzing the Replicator Dynamics of various parties. In addition, we discuss the equilibrium condition to get the influence of the number of servers N on the stability of strategy evolution. MATLAB and Gambit simulation experiment results show that deduced evolutionarily stable strategies are valid in resisting attackers.

Speech enhancement based on perceptually motivated guided spectrogram filtering

In this paper, a single-channel speech enhancement algorithm is proposed by using guided spectrogram filtering based on masking properties of human auditory system when considering a speech spectrogram as an image. Guided filtering is capable of sharpening details and estimating unwanted textures or background noise from the noisy speech spectrogram. If we consider the noisy spectrogram as a degraded image, we can estimate the spectrogram of the clean speech signal using guided filtering after subtracting noise components. Combined with masking properties of human auditory system, the proposed algorithm adaptively adjusts and reduces the residual noise of the enhanced speech spectrogram according to the corresponding masking threshold. Because the filtering output is a local linear transform of the guidance spectrogram, the local mask window slides can be efficiently implemented via box filter with O(N) computational complexity. Experimental results show that the proposed algorithm can effectively suppress noise in different noisy environments and thus can greatly improve speech quality and speech intelligibility.

Effects of Motion Compensation Residual Error and Polarization Distortion on UAV-Borne PolInSAR

Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) can improve interferometric coherence and phase quality, which has good application potential. With the development of the Mini-SAR system, Unmanned Aerial Vehicle borne (UAV-borne) PolInSAR systems became a reality. However, UAV-borne PolInSAR is easily affected by air currents and other factors, which may cause large motion errors and polarization distortion inevitably exists. However, there are few pieces of research which are about motion compensation residual error (MCRE) and polarization distortion effects on PolInSAR. Though the effects of MCRE on Interferometric SAR (InSAR) and polarization distortion on PolInSAR were studied, respectively, these two parts are independently modeled and analyzed. In this paper, a model that simultaneously considers the effects of these two kinds of errors is proposed, and simulation results are given to validate the model. Then, a quantitative analysis based on a real Quadcopter UAV PolInSAR system is performed according to the model, which is valuable for system design and practical application of the UAV-borne PolInSAR system.

Improved weighted local linear embedding algorithm based on Laplacian eigenmaps

To solve the shortcomings of local linear embedding (LLE), such as sensitive to noise and poor generalization ability for new samples, an improved weighted local linear embedding algorithm based on Laplacian eigenmaps (IWLLE-LE) is proposed in this paper. In the proposed algorithm, Laplacian eigenmaps are used to reconstruct the objective function of dimensionality reduction. The weights of it are introduced by combining the geodesic distance with Euclidean distance, which can effectively represent the manifold structure of nonlinear data. Compared the existing LLE algorithm, the proposed one better maintains the original manifold structure of the data. The merit of the proposal is enhanced by the theoretical analysis and numerical experiments, where the classification recognition rate is 2%–8% higher than LLE.

Speech enhancement algorithm of improved OMLSA based on bilateral spectrogram filtering

In this paper, a bilateral spectrogram filtering (BSF)-based optimally modified log-spectral amplitude (OMLSA) estimator for single-channel speech enhancement is proposed, which can significantly improve the performance of OMLSA, especially in highly non-stationary noise environments, by taking advantage of bilateral filtering (BF), a widely used technology in image and visual processing, to preprocess the spectrogram of the noisy speech. BSF is capable of not only sharpening details, removing unwanted textures or background noise from the noisy speech spectrogram, but also preserving edges when considering a speech spectrogram as an image. The a posteriori signal-to-noise ratio (SNR) of OMLSA algorithm is estimated after applying BSF to the noisy speech. Besides, in order to reduce computing costs, a fast and accurate BF is adopted to reduce the algorithm complexity O(1) for each time-frequency bin. Finally, the proposed algorithm is compared with the original OMLSA and other classic denoising methods using various types of noise with different signal-to-noise ratios in terms of objective evaluation metrics such as segmental signal-to-noise ratio improvement and perceptual evaluation of speech quality. The results show the validity of the improved BSF-based OMLSA algorithm.

A Novel MIMO-SAR System Based on Simultaneous Digital Beam Forming of Both Transceiver and Receiver

Orthogonal frequency division multiplexing (OFDM) chirp waveform, which is composed of two or more successive identical linear frequency modulated sub pulses, is a newly proposed orthogonal waveform scheme for multi-input multi-output (MIMO) synthetic aperture radar (SAR) systems. However, according to the waveform model, there will be range ambiguity if the mapping width exceeds the maximum unambiguous width determined by the transmitted signal. This greatly limits its application in high-resolution wide-swath (HRWS) remote sensing. The traditional system divides the echoes by digital beam forming (DBF) to solve this problem, but the energy utilization rate is low. A MIMO-SAR system using simultaneous digital beam forming of both transceiver and receiver to avoid range ambiguity is designed in this paper. Compared with traditional system, the novel system designed in this paper obtain higher energy utilization and waveform orthogonality.

GF-1 Satellite Observations of Suspended Sediment Injection of Yellow River Estuary, China

We analyzed the distribution of suspended sediments concentration (SSC) in the Yellow River Estuary based on data from GaoFen-1 (GF-1), which is a high-resolution satellite carrying a wide field-of-view (WFV) sensor and panchromatic and a multispectral (PMS) sensor on it. A new SSC retrieval model for the wide field-of-view sensor (M-WFV) was established based on the relationship between in-situ SSC and the reflectance in blue and near infrared bands. SSC obtained from 16 WFV1 images were analyzed in the Yellow River Estuary. The results show that (1) SSC in the study area is mainly 100–3500 mg/L, with the highest value being around 4500 mg/L. (2) The details of suspended sediment injection phenomenon were found in the Yellow River Estuary. The SSC distribution in the coastal water has two forms. One is that the high SSC water evenly distributes near the coast and the gradient of the SSC is similar. The other is that the high SSC water concentrates at the right side of the estuary (Laizhou Bay) with a significantly large area. Usually, there is a clear-water notch at the left side of the estuary. (3) Currents clearly influenced the SSC distribution in the Yellow River Estuary. The SSC gradient in the estuary was high against the local current direction. On the contrary, the SSC gradient in the estuary was small towards the local current direction. Eroding the coast and resuspension of the bottom sediments, together with currents, are the major factors influencing the SSC distribution in nearshore water in the Yellow River Estuary.