A Bayesian Filtering Approach for Error Mitigation in Ultra-Wideband Ranging

Ultra-wideband (UWB) sensors have been widely used in multi-robot systems for cooperative tracking and positioning purposes due to their advantages such as high ranging accuracy and good real-time performance. In order to reduce the influence of non-line-of-sight (NLOS) UWB communication caused by the presence of obstacles on ranging accuracy in indoor environments, the paper proposes a novel Bayesian filtering approach for UWB ranging error mitigation. Nonparametric UWB sensor models, namely received signal strength (RSS) model and time of arrival (TOA) model, are constructed to capture the probabilistic noise characteristics under the influence of different obstruction conditions and materials within a typical indoor environment. The proposed Bayesian filtering approach can be used either as a standalone error mitigation approach for peer-to-peer (P2P) ranging, or as a part of a higher level Bayesian state estimation framework. Experiments were conducted to validate and evaluate the proposed approach in two configurations, i.e., inter-robot ranging, and mobile robot tracking in a wireless sensor network. The experimental results show that the proposed method can accurately identify the line-of-sight (LOS) and NLOS scenarios with wood and metal obstacles in a probabilistic representation and effectively improve the ranging/tracking accuracy. In addition, the low computational overhead of the approach makes it attractive in real-time systems.

TSV Manufacturing Fault Modeling and Diagnosis Based on Multi-Tone Dither

The faults in through-silicon via (TSV) have a critical impact on the reliability and yield of a three-dimensional integrated circuit (3-D IC). With the significant increase in the number of TSVs used in 3-D IC, the testing of TSVs for manufacturing faults poses certain serious challenges especially weak fault testing, and therefore it is important to have effective Design-For-Test (DFT) techniques. In this paper, we present a method for TSV testing using multi-tone dither signal, based on electrical characteristic analysis. This method mainly observes the differences in the root mean square (RMS) value of the output signal voltage between faultless and faulty TSV circuits to detect manufacturing faults, and uses only passive components such as metal lines, without consuming additional power for the testing. With regard to the common manufacturing faults such as voids and pinholes, the electrical characteristics of faulty TSVs are modeled and analyzed, and analytic equations of the faults, which are based on characteristic parameters, are explored. The ground-signal-TSV (GS-TSV) equivalent electrical model with manufacturing faults is simulated and tested by using a multi-tone dither test signal, which is generated by modulating an RF signal with an optimized multi-tone signal. The peak-to-average ratio (PAR) is used as the test evaluation parameter to determine the type and size of the fault. The simulation results demonstrate the effectiveness of the multi-tone dither test method in the detection of voids (as low as ohm level) and pinholes (up to mega ohm level). It is obvious that this method performs better in the diagnosis of weak manufacturing faults in TSVs.

Parameter Estimation Based on Sigmoid Transform in Wideband Bistatic MIMO Radar System under Impulsive Noise Environment

Since second-order statistics-based methods rely heavily on Gaussianity assumption and fractional lower-order statistics-based methods depend on a priori knowledge of non-Gaussian noise, there remains a void in wideband bistatic multiple-input/multiple-output (MIMO) radar systems under impulsive noise. In this paper, a novel method based on Sigmoid transform was used to estimate target parameters, which do not need a priori knowledge of the noise in an impulsive noise environment. Firstly, a novel wideband ambiguity function, termed Sigmoid wideband ambiguity function (Sigmoid-WBAF), is proposed to estimate the Doppler stretch and time delay by searching the peak of the Sigmoid-WBAF. A novel Sigmoid correlation function is proposed. Furthermore, a new MUSIC algorithm based on the Sigmoid correlation function (Sigmoid-MUSIC) is proposed to estimate the direction-of-departure (DOD) and direction-of-arrival (DOA). Then, the boundness of the Sigmoid-WBAF to the symmetric alpha stable (SαS) noise, the feasibility analysis of the Sigmoid-WBAF, and complexity analysis of the Sigmoid-WBAF and Sigmoid-MUSIC are presented to evaluate the performance of the proposed method. In addition, the Cramér–Rao bound for parameter estimation was derived and computed in closed form, which shows that better performance was achieved. Simulation results and theoretical analyses are presented to verify the effectiveness of the proposed method.

Flood Hazard Mapping and Assessment on the Angkor World Heritage Site, Cambodia

World Heritage sites in general are exposed to the impacts of natural hazards, which threaten their integrity and may compromise their value. Floods are a severe threat to the Angkor World Heritage site. Studies of regional floods and flood hazard zoning have played an increasingly important role in ensuring sustainability of the Angkor site. This study developed a flood hazard index (FHI) model based on a geographic information system (GIS) and used synthetic aperture radar (SAR) data to extract historical floods at Angkor from 2007 to 2013. Four indices (flood affected frequency, absolute elevation, elevation standard deviation, drainage density) were used to identify flood-prone areas. The Analytic Hierarchy Process (AHP) and the Delphi method were employed to determine the weight of each index. The weighted indices were then used to develop a distribution map of flood hazards at Angkor. The results show that 9 monuments are at risk by potential floods among the 52 components of the Angkor monuments. The high hazard and moderate-to-high hazard areas in the core zone are mainly located surrounding the West Baray but will not bring direct risk impact on the monuments located in the core archaeological zone. The moderate hazard areas are located on both sides of the Siem Reap and Roluos rivers and in the flooded area of the Tonle Sap Lake in the core archaeological zone. These areas cover 19.4 km2, accounting for 9.13% of the total area of the core zone. This moderate hazard zone poses a greater flood threat to the core zone and must be given higher attention. The buffer zone is a small area with fewer sites. As such, flooding has a low impact on the buffer zone. The methods used in this study can be applied to flood hazard assessments of other heritage sites in Southeast Asia.

Dynamic Provable Data Possession of Multiple Copies in Cloud Storage Based on Full-Node of AVL Tree

This article describes how to protect the security of cloud storage, a provable data possession scheme based on full-nodes of an AVL tree for multiple data copies in cloud storage. In the proposed scheme, a Henon chaotic map is first implemented for the node calculation of the AVL tree, and then the location of the data in the cloud is verified by AVL tree. As an AVL tree can keep the balance even with multiple dynamic operations made on the data in the cloud, it can improve the search efficiency of the data block, and reduce the length of the authentication path. Simulation results and analysis confirm that it can achieve good security and high efficiency.

Integrated Ground-Based SAR Interferometry, Terrestrial Laser Scanner, and Corner Reflector Deformation Experiments

An integrated sensor system comprised of a terrestrial laser scanner (TLS), corner reflectors (CRs), and high precision linear rail is utilized to validate ground-based synthetic aperture radar (GB-SAR) interferometric micro-displacement measurements. A rail with positioning accuracy of 0.1 mm is deployed to ensure accurate and controllable deformation. The rail is equipped with a CR on a sliding platform for mobility. Three smaller CRs are installed nearby, each with a reflective sticker attached to the CR’s vertex; the CRs present as high-amplitude points both in the GB-SAR images and the TLS point cloud to allow for accurate data matching. We analyze the GB-SAR zero-baseline repeated rail differential interferometry signal model to obtain 2D interferograms of the test site in time series, and then use TLS to obtain a 3D surface model. The model is matched with interferograms to produce more intuitive 3D products. The CR displacements can also be extracted via surface reconstruction algorithm. Finally, we compared the rail sensor measurement and TLS results to optimize coherent scatterer selection and filter the data. The proposed method yields accurate target displacement results via quantitative analysis of GB-SAR interferometry.

Precision Downward-Looking 3D Synthetic Aperture Radar Imaging with Sparse Linear Array and Platform Motion Parameters Estimation

The downward-looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3D SAR) has attracted a great deal of attention, due to the ability to obtain three-dimensional (3D) images. However, if the velocity and the yaw rate of the platform are not measured with enough accuracy, the azimuth signal cannot be compressed and then the 3D image of the scene cannot be obtained. In this paper, we propose a method for platform motion parameter estimation, and downward-looking 3D SAR imaging. A DLSLA 3D SAR imaging model including yaw rate was established. We then calculated the Doppler frequency modulation, which is related to the cross-track coordinates rather than the azimuth coordinates. Thus, the cross-track signal reconstruction was realized. Furthermore, based on the minimum entropy criterion (MEC), the velocity and yaw rate of the platform were accurately estimated, and the azimuth signal compression was also realized. Moreover, a deformation correction procedure was designed to improve the quality of the image. Simulation results were given to demonstrate the validity of the proposed method.

An Improved BAQ Encoding and Decoding Method for Improving the Quantized SNR of SAR Raw Data

When the original echo data of SAR are saturated for quantization, the performance of the commonly used block adaptive quantization (BAQ) algorithm will be degraded, which will degrade the imaging quality. This article proposes an improved Llody-Max codec method, which only needs to change the codec look-up table to get better quantization performance when the original echo is saturated. The simulation results show that the proposed method can reduce the quantization power loss, improve the echo signal-to-noise ratio (SNR), and reduce the influence of quantization saturation on the scattering mechanism of polarized SAR data, which have good practical application value.

ISAR Autofocus Imaging Algorithm for Maneuvering Targets Based on Phase Retrieval and Gabor Wavelet Transform

The imaging issue of a rotating maneuvering target with a large angle and a high translational speed has been a challenging problem in the area of inverse synthetic aperture radar (ISAR) autofocus imaging, in particular when the target has both radial and angular accelerations. In this paper, on the basis of the phase retrieval algorithm and the Gabor wavelet transform (GWT), we propose a new method for phase error correction. The approach first performs the range compression on ISAR raw data to obtain range profiles, and then carries out the GWT transform as the time-frequency analysis tool for the rotational motion compensation (RMC) requirement. The time-varying terms, caused by rotational motion in the Doppler frequency shift, are able to be eliminated at the selected time frame. Furthermore, the processed backscattered signal is transformed to the one in the frequency domain while applying the phase retrieval to run the translational motion compensation (TMC). Phase retrieval plays an important role in range tracking, because the ISAR echo module is not affected by both radial velocity and the acceleration of the target. Finally, after the removal of both the rotational and translational motion errors, the time-invariant Doppler shift is generated, and radar returned signals from the same scatterer are always kept in the same range cell. Therefore, the unwanted motion effects can be removed by applying this approach to have an autofocused ISAR image of the maneuvering target. Furthermore, the method does not need to estimate any motion parameters of the maneuvering target, which has proven to be very effective for an ideal range–Doppler processing. Experimental and simulation results verify the feasibility of this approach.

Mapping connectomes with diffusion MRI: deterministic or probabilistic tractography?

PURPOSE

Human connectomics necessitates high-throughput, whole-brain reconstruction of multiple white matter fiber bundles. Scaling up tractography to meet these high-throughput demands yields new fiber tracking challenges, such as minimizing spurious connections and controlling for gyral biases. The aim of this study is to determine which of the two broadest classes of tractography algorithms-deterministic or probabilistic-is most suited to mapping connectomes.

METHODS

This study develops numerical connectome phantoms that feature realistic network topologies and that are matched to the fiber complexity of in vivo diffusion MRI (dMRI) data. The phantoms are utilized to evaluate the performance of tensor-based and multi-fiber implementations of deterministic and probabilistic tractography.

RESULTS

For connectome phantoms that are representative of the fiber complexity of in vivo dMRI, multi-fiber deterministic tractography yields the most accurate connectome reconstructions (F-measure = 0.35). Probabilistic algorithms are hampered by an abundance of false-positive connections, leading to lower specificity (F = 0.19). While omitting connections with the fewest number of streamlines (thresholding) improves the performance of probabilistic algorithms (F = 0.38), multi-fiber deterministic tractography remains optimal when it benefits from thresholding (F = 0.42).

CONCLUSIONS

Multi-fiber deterministic tractography is well suited to connectome mapping, while connectome thresholding is essential when using probabilistic algorithms.

Laser Printing Files Detection Method Based on Double Features

A novel laser printing files detection method is proposed in this paper to solve the problem of low efficiency and difficulty in traditional detection. The new method is based on improved scale-invariant feature transform (SIFT) feature and histogram feature. Firstly, analyze the graphical features of different laser printing files. Different files have different printing texture features in valid data area. So segment the valid data area to remove the interference of background. Secondly, extract the histogram feature of the same character in the printing file. Normalize the histogram and then calculate the Bhattacharyya coefficient between the detected file and the original file to determine whether the detected file is right or fake. At the same time, calculate the SIFT features and match the detected file and the original file. To focus on the letter or character region, the SIFT features which are out of contour are deleted. Finally, the results of the two different methods are both used as the result of the identification. When any of the result is fake, the end result will be fake. In the self-built database experiment, in different printing files from different printers, the inkjet areas possess different image features. When scanning different files using 600 dpi, the detect accuracy is higher than 97%. This method was able to meet the reliability requirements of law.

A Channel Phase Error Correction Method Based on Joint Quality Function of GF-3 SAR Dual-Channel Images

Multichannel SAR is an effective approach to solving the contradiction between high azimuth resolution and wide swath. The goal of this paper is to obtain a new and effective method for estimating and compensating the interchannel phase error of the Chinese GF-3 Synthetic aperture radar (SAR). A channel phase error correction method based on the optimal value of the image domain quality function is proposed. In this method, the phase error is initially compensated using the correlation function method. In the fine correction of dual-channel phase error, a heuristic search algorithm is used to estimate the residual phase by searching the extremum of the quality function. After phase compensation in the image domain, the azimuth ambiguities caused by the remaining phase are eliminated. The proposed image domain processing method provides a new idea for channel phase error correction. The measured data of high-resolution GF-3 dual-channel ultrafine imaging mode verifies the validity of this method.

Perceptual Hashing Based Forensics Scheme for the Integrity Authentication of High Resolution Remote Sensing Image

High resolution remote sensing (HRRS) images are widely used in many sensitive fields, and their security should be protected thoroughly. Integrity authentication is one of their major security problems, while the traditional techniques cannot fully meet the requirements. In this paper, a perceptual hashing based forensics scheme is proposed for the integrity authentication of a HRRS image. The proposed scheme firstly partitions the HRRS image into grids and adaptively pretreats the grid cells according to the entropy. Secondly, the multi-scale edge features of the grid cells are extracted by the edge chains based on the adaptive strategy. Thirdly, principal component analysis (PCA) is applied on the extracted edge feature to get robust feature, which is then normalized and encrypted with secret key set by the user to receive the perceptual hash sequence. The integrity authentication procedure is achieved via the comparison between the recomputed perceptual hash sequence and the original one. Experimental results have shown that the proposed scheme has good robustness to normal content-preserving manipulations, has good sensitivity to detect local subtle and illegal tampering of the HRRS image, and has the ability to locate the tampering area.

Modeling and Performance of the IEEE 802.11p Broadcasting for Intra-Platoon Communication

Road capacity, traffic safety, and energy efficiency can be extremely improved by forming platoons with a small intra-vehicle spacing. Automated controllers obtain vehicle speed, acceleration, and position through vehicular ad hoc networks (VANETs), which allows the performance of platoon communication to make a significant impact on the stability of the platoon. To the best of our knowledge, there is not much research relating to packet delay and packet dropping rate of platoon communication based on the IEEE 802.11p broadcasting. In this paper, we introduce platoon structure model, vehicle control model, and communication model for a single platoon scenario. By utilizing Markov process and M/G/1/K queuing theory, we put forward an analytical model to assess the property of intra-vehicle communication. The analytical model is validated by simulations and the influence of communication parameters on intra-vehicle communication performance are discussed. In addition, the experimental results demonstrate that the IEEE 802.11p-based intra-vehicle communication guarantee the stability of platoon.

Residual U-Net Convolutional Neural Network Architecture for Low-Dose CT Denoising

Low-dose CT has received increasing attention in the recent years and is considered a promising method to reduce the risk of cancer in patients. However, the reduction of the dosage leads to quantum noise in the raw data, which is carried on in the reconstructed images. Two different multilayer convolutional neural network (CNN) architectures for the denoising of CT images are investigated. ResFCN is based on a fully-convolutional network that consists of three blocks of 5×5 convolutions filters and a ResUNet that is trained with 10 convolutional blocks that are arranged in a multi-scale fashion. Both architectures feature a residual connection of the input image to ease learning. Training images are based on realistic simulations by using the XCAT phantom. The ResUNet approach shows the most promising results with a peak signal to noise ratio of 44.00 compared to ResFCN with 41.79.

Variants of the Low-Energy Adaptive Clustering Hierarchy Protocol: Survey, Issues and Challenges

A wireless sensor network (WSN) is a modern technology in radio communication. A WSN comprises a number of sensors that are randomly spread in a specific area for sensing and monitoring physical attributes that are difficult to monitor by humans, such as temperature, humidity, and pressure. Many problems, including data routing, power consumption, clustering, and selecting cluster heads (CHs), may occur due to the nature of WSNs. Various protocols have been conducted to resolve these issues. One of the important hierarchical protocols that are used to reduce power consumption in WSNs is low-energy adaptive clustering hierarchy (LEACH). This paper presents a comprehensive study of clustering protocols for WSNs that are relevant to LEACH. This paper is the first to emphasis on cluster formation and CHs selection methods and their strengths and weaknesses. A new taxonomy is presented to discuss LEACH variants on the basis of different classes, and the current survey is compared with other existing surveys. A complete comparison of the location, energy, complexity, reliability, multi–hop path, and load balancing characteristics of LEACH variants is conducted. Future research guidelines for CHs selection and cluster formation in WSNs are also discussed.

Transmitting Pulse Encoding for Beyond-PRT Retransmitting Deception Jamming Detection in Spaceborne Synthetic Aperture Radar (SAR)

Retransmitting deception jamming (RDJ) degrades and misleads the Synthetic Aperture Radar (SAR) image interpretation by forming false targets. The beyond-Pulse Repetition Time (PRT) RDJ enlarges the effective jamming area without constraining the jammer location to reduce the spaceborne SAR working effectiveness. In order to detect the beyond-PRT RDJ and enhance the working efficiency in electronic countermeasure environment, the transmitting pulse encoding method for use in spaceborne SAR is proposed based on the geometry and signal models of beyond-PRT RDJ. Optimum binary codes with maximum number of detection windows are determined by the encoding procedure. The detected area is found to be proportional to the code length and the encoding efficiencies of even and odd codes are analyzed. The simulation results validate the effectiveness of the transmitting pulse encoding method for beyond-PRT RDJ detection in spaceborne SAR.

A High-Resolution SAR Focusing Experiment Based on GF-3 Staring Data

Spotlight synthetic aperture radar (SAR) is a proven technique, which can provide high-resolution images as compared to those produced by traditional stripmap SAR. This paper addresses a high-resolution SAR focusing experiment based on Gaofen-3 satellite (GF-3) staring data with about 55 cm azimuth resolution and 240 MHz range bandwidth. In staring spotlight (ST) mode, the antenna always illuminates the same scene on the ground, which can extend the synthetic aperture. Based on a two-step processing algorithm, some special aspects such as curved-orbit model error correction, stop-and-go correction, and antenna pattern demodulation must be considered in image focusing. We provide detailed descriptions of all these aspects and put forward corresponding solutions. Using these suggested methods directly in an imaging module without any modification for other data processing software can make the most of the existing ground data processor. Finally, actual data acquired in GF-3 ST mode is used to validate these methodologies, and a well-focused, high-resolution image is obtained as a result of this focusing experiment.

The GF-3 SAR Data Processor

The Gaofen-3 (GF-3) data processor was developed as a workstation-based GF-3 synthetic aperture radar (SAR) data processing system. The processor consists of two vital subsystems of the GF-3 ground segment, which are referred to as data ingesting subsystem (DIS) and product generation subsystem (PGS). The primary purpose of DIS is to record and catalogue GF-3 raw data with a transferring format, and PGS is to produce slant range or geocoded imagery from the signal data. This paper presents a brief introduction of the GF-3 data processor, including descriptions of the system architecture, the processing algorithms and its output format.

Robust Angle Estimation for MIMO Radar with the Coexistence of Mutual Coupling and Colored Noise

This paper deals with joint estimation of direction-of-departure (DOD) and direction-of- arrival (DOA) in bistatic multiple-input multiple-output (MIMO) radar with the coexistence of unknown mutual coupling and spatial colored noise by developing a novel robust covariance tensor-based angle estimation method. In the proposed method, a third-order tensor is firstly formulated for capturing the multidimensional nature of the received data. Then taking advantage of the temporal uncorrelated characteristic of colored noise and the banded complex symmetric Toeplitz structure of the mutual coupling matrices, a novel fourth-order covariance tensor is constructed for eliminating the influence of both spatial colored noise and mutual coupling. After a robust signal subspace estimation is obtained by using the higher-order singular value decomposition (HOSVD) technique, the rotational invariance technique is applied to achieve the DODs and DOAs. Compared with the existing HOSVD-based subspace methods, the proposed method can provide superior angle estimation performance and automatically jointly perform the DODs and DOAs. Results from numerical experiments are presented to verify the effectiveness of the proposed method.

A nighttime image enhancement method based on Retinex and guided filter for object recognition of apple harvesting robot

In order to improve the working efficiency of robot promptly picking ripe apples, the harvesting robot must have the ability of continuous recognition and operation at night. Nighttime apple image has many dark spaces and shadows with low resolution, and therefore, a Retinex algorithm based on guided filter is presented to enhance nighttime image in this article. According to color feature of image, the illumination component is estimated by using guided filter which can be applied as an edge-preserving smoothing operator. And the reflection component with image itself characteristics is obtained by employing single-scale Retinex algorithm. After gamma correction, these two components of image are synthesized into a new enhanced nighttime apple image. Fifty nighttime images acquired under fluorescent lighting are selected to make experiment. Experimental results show that the image enhancement performance indexes processed by the proposed algorithm, such as average gray value, standard deviation, information entropy, average gradient, and segmentation error are superior to those of histogram equalization algorithms and Retinex algorithm based on bilateral filter. In addition, compared with the Retinex algorithm based on bilateral filter, the proposed algorithm has an average reduction of 74.56% in running time with better real-time and higher efficiency. So it can realize the continuous operation of apple harvesting robot at night.

Sum of the Magnitude for Hard Decision Decoding Algorithm Based on Loop Update Detection

In order to improve the performance of non-binary low-density parity check codes (LDPC) hard decision decoding algorithm and to reduce the complexity of decoding, a sum of the magnitude for hard decision decoding algorithm based on loop update detection is proposed. This will also ensure the reliability, stability and high transmission rate of 5G mobile communication. The algorithm is based on the hard decision decoding algorithm (HDA) and uses the soft information from the channel to calculate the reliability, while the sum of the variable nodes’ (VN) magnitude is excluded for computing the reliability of the parity checks. At the same time, the reliability information of the variable node is considered and the loop update detection algorithm is introduced. The bit corresponding to the error code word is flipped multiple times, before this is searched in the order of most likely error probability to finally find the correct code word. Simulation results show that the performance of one of the improved schemes is better than the weighted symbol flipping (WSF) algorithm under different hexadecimal numbers by about 2.2 dB and 2.35 dB at the bit error rate (BER) of 10⁻⁵ over an additive white Gaussian noise (AWGN) channel, respectively. Furthermore, the average number of decoding iterations is significantly reduced.

Single Carrier with Frequency Domain Equalization for Synthetic Aperture Underwater Acoustic Communications

Phase-coherent underwater acoustic (UWA) communication systems typically employ multiple hydrophones in the receiver to achieve spatial diversity gain. However, small underwater platforms can only carry a single transducer which can not provide spatial diversity gain. In this paper, we propose single-carrier with frequency domain equalization (SC-FDE) for phase-coherent synthetic aperture acoustic communications in which a virtual array is generated by the relative motion between the transmitter and the receiver. This paper presents synthetic aperture acoustic communication results using SC-FDE through data collected during a lake experiment in January 2016. The performance of two receiver algorithms is analyzed and compared, including the frequency domain equalizer (FDE) and the hybrid time frequency domain equalizer (HTFDE). The distances between the transmitter and the receiver in the experiment were about 5 km. The bit error rate (BER) and output signal-to-noise ratio (SNR) performances with different receiver elements and transmission numbers were presented. After combining multiple transmissions, error-free reception using a convolution code with a data rate of 8 kbps was demonstrated.

An Improved RD Algorithm for Maneuvering Bistatic Forward-Looking SAR Imaging with a Fixed Transmitter

In order to improve the azimuth resolution beyond what monostatic synthetic aperture radar (SAR) can achieve in the forward-looking area, an asymmetric configuration bistatic SAR system and its imaging algorithm are proposed in this paper. The transmitter is mounted on a fixed platform in side-looking mode while the receiver moves along a nonlinear trajectory in forward-looking mode. Due to the high velocity and acceleration of the maneuvering platform in both along-track and height direction, the traditional algorithms are no longer applicable. In this paper, a new algorithm based on the high precise 2-D frequency spectrum is proposed, which takes high-order Taylor series expansion terms of the slant range into consideration. The proposed algorithm compensates high-order range-azimuth coupling terms to guarantee the focus accuracy in SAR imaging. The simulation results and error analysis validate the effectiveness of the proposed algorithm and the correctness of our analysis.

Sparsity Adaptive Matching Pursuit Detection Algorithm Based on Compressed Sensing for Radar Signals

In this paper, the application of the emerging compressed sensing (CS) theory and the geometric characteristics of the targets in radar images are investigated. Currently, the signal detection algorithms based on the CS theory require knowing the prior knowledge of the sparsity of target signals. However, in practice, it is often impossible to know the sparsity in advance. To solve this problem, a novel sparsity adaptive matching pursuit (SAMP) detection algorithm is proposed. This algorithm executes the detection task by updating the support set and gradually increasing the sparsity to approximate the original signal. To verify the effectiveness of the proposed algorithm, the data collected in 2010 at Pingtan, which located on the coast of the East China Sea, were applied. Experiment results illustrate that the proposed method adaptively completes the detection task without knowing the signal sparsity, and the similar detection performance is close to the matching pursuit (MP) and orthogonal matching pursuit (OMP) detection algorithms.

Testing a Firefly-Inspired Synchronization Algorithm in a Complex Wireless Sensor Network

Data acquisition is the foundation of soft sensor and data fusion. Distributed data acquisition and its synchronization are the important technologies to ensure the accuracy of soft sensors. As a research topic in bionic science, the firefly-inspired algorithm has attracted widespread attention as a new synchronization method. Aiming at reducing the design difficulty of firefly-inspired synchronization algorithms for Wireless Sensor Networks (WSNs) with complex topologies, this paper presents a firefly-inspired synchronization algorithm based on a multiscale discrete phase model that can optimize the performance tradeoff between the network scalability and synchronization capability in a complex wireless sensor network. The synchronization process can be regarded as a Markov state transition, which ensures the stability of this algorithm. Compared with the Miroll and Steven model and Reachback Firefly Algorithm, the proposed algorithm obtains better stability and performance. Finally, its practicality has been experimentally confirmed using 30 nodes in a real multi-hop topology with low quality links.

Improved Chirp Scaling Algorithm for Processing Squinted Mode Synthetic Aperture Sonar Data

The conventional Chirp Scaling Algorithm is mainly used in side looking or small squint angle mode of synthetic aperture imaging. In application of synthetic sonar, the large squint angle imaging is often required and the range-azimuth coupling is serious. On the basis of studying the principle of the conventional Chirp Scaling Algorithm, we improved the imaging algorithm in large squint mode. We analysed the structure of the echo frequency spectrum and the compensation of phase factor. The improved imaging algorithm designs a more precision of phase compensation factor, and eliminates the high order degree of range and azimuth coupling in the specific mapping band. Any target point is simulated in imaging region by using the improved algorithm. The simulation conclusion showed that the improved Chirp Scaling Algorithm is able to meet the imaging focus and is more suitable to slant imaging as compared with the traditional algorithm.

Detection of Multiple Stationary Humans Using UWB MIMO Radar

Remarkable progress has been achieved in the detection of single stationary human. However, restricted by the mutual interference of multiple humans (e.g., strong sidelobes of the torsos and the shadow effect), detection and localization of the multiple stationary humans remains a huge challenge. In this paper, ultra-wideband (UWB) multiple-input and multiple-output (MIMO) radar is exploited to improve the detection performance of multiple stationary humans for its multiple sight angles and high-resolution two-dimensional imaging capacity. A signal model of the vital sign considering both bi-static angles and attitude angle of the human body is firstly developed, and then a novel detection method is proposed to detect and localize multiple stationary humans. In this method, preprocessing is firstly implemented to improve the signal-to-noise ratio (SNR) of the vital signs, and then a vital-sign-enhanced imaging algorithm is presented to suppress the environmental clutters and mutual affection of multiple humans. Finally, an automatic detection algorithm including constant false alarm rate (CFAR), morphological filtering and clustering is implemented to improve the detection performance of weak human targets affected by heavy clutters and shadow effect. The simulation and experimental results show that the proposed method can get a high-quality image of multiple humans and we can use it to discriminate and localize multiple adjacent human targets behind brick walls.

A Novel 2-D Coherent DOA Estimation Method Based on Dimension Reduction Sparse Reconstruction for Orthogonal Arrays

Based on sparse representations, the problem of two-dimensional (2-D) direction of arrival (DOA) estimation is addressed in this paper. A novel sparse 2-D DOA estimation method, called Dimension Reduction Sparse Reconstruction (DRSR), is proposed with pairing by Spatial Spectrum Reconstruction of Sub-Dictionary (SSRSD). By utilizing the angle decoupling method, which transforms a 2-D estimation into two independent one-dimensional (1-D) estimations, the high computational complexity induced by a large 2-D redundant dictionary is greatly reduced. Furthermore, a new angle matching scheme, SSRSD, which is less sensitive to the sparse reconstruction error with higher pair-matching probability, is introduced. The proposed method can be applied to any type of orthogonal array without requirement of a large number of snapshots and a priori knowledge of the number of signals. The theoretical analyses and simulation results show that the DRSR-SSRSD method performs well for coherent signals, which performance approaches Cramer–Rao bound (CRB), even under a single snapshot and low signal-to-noise ratio (SNR) condition.

The Incremental Knowledge Acquisition Based on Hash Algorithm

In this paper, a new incremental knowledge acquisition method is proposed based on rough set theory, decision tree and granular computing. In order to effectively process dynamic data, describing the data by rough set theory, computing equivalence classes and calculating positive region with hash algorithm are analyzed respectively at first. Then, attribute reduction, value reduction and the extraction of rule set by hash algorithm are completed efficiently. Finally, for each new additional data, the incremental knowledge acquisition method is proposed and used to update the original rules. Both algorithm analysis and experiments show that for processing the dynamic information systems, compared with the traditional algorithms and the incremental knowledge acquisition algorithms based on granular computing, the time complexity of the proposed algorithm is lower due to the efficiency of hash algorithm and also this algorithm is more effective when it is used to deal with the huge data sets.

A Power-Efficient Clustering Protocol for Coal Mine Face Monitoring with Wireless Sensor Networks Under Channel Fading Conditions

This study proposes a novel power-efficient and anti-fading clustering based on a cross-layer that is specific to the time-varying fading characteristics of channels in the monitoring of coal mine faces with wireless sensor networks. The number of active sensor nodes and a sliding window are set up such that the optimal number of cluster heads (CHs) is selected in each round. Based on a stable expected number of CHs, we explore the channel efficiency between nodes and the base station by using a probe frame and the joint surplus energy in assessing the CH selection. Moreover, the sending power of a node in different periods is regulated by the signal fade margin method. The simulation results demonstrate that compared with several common algorithms, the power-efficient and fading-aware clustering with a cross-layer (PEAFC-CL) protocol features a stable network topology and adaptability under signal time-varying fading, which effectively prolongs the lifetime of the network and reduces network packet loss, thus making it more applicable to the complex and variable environment characteristic of a coal mine face.

High capacity data hiding scheme based on (7, 4) Hamming code

Aiming to embed large amount of data while minimize the sum of costs of all changed pixels, a novel high capacity data hiding scheme based on (7, 4) Hamming code is realized by a family of algorithms. Firstly, n (n = 1, 2, 3) cover pixels are assigned to one set according to the payload. Then, 128 binary strings of length seven are divided into eight sets according to the syndrome of every binary string. Binary strings that share the same syndrome are classified into one set. Finally, a binary string in a certain set determined by the data to be embedded is chosen to modify some of the least significant bits of the n cover pixels. The experimental results demonstrate that the image quality of the proposed method with high embedding payload is superior to those of the related schemes.

AUV Positioning Method Based on Tightly Coupled SINS/LBL for Underwater Acoustic Multipath Propagation

This paper researches an AUV (Autonomous Underwater Vehicle) positioning method based on SINS (Strapdown Inertial Navigation System)/LBL (Long Base Line) tightly coupled algorithm. This algorithm mainly includes SINS-assisted searching method of optimum slant-range of underwater acoustic propagation multipath, SINS/LBL tightly coupled model and multi-sensor information fusion algorithm. Fuzzy correlation peak problem of underwater LBL acoustic propagation multipath could be solved based on SINS positional information, thus improving LBL positional accuracy. Moreover, introduction of SINS-centered LBL locating information could compensate accumulative AUV position error effectively and regularly. Compared to loosely coupled algorithm, this tightly coupled algorithm can still provide accurate location information when there are fewer than four available hydrophones (or within the signal receiving range). Therefore, effective positional calibration area of tightly coupled system based on LBL array is wider and has higher reliability and fault tolerance than loosely coupled. It is more applicable to AUV positioning based on SINS/LBL.

Hybrid Histogram of Oriented Optical Flow for Abnormal Behavior Detection in Crowd Scenes

Abnormal behavior detection in crowd scenes has received considerable attention in the field of public safety. Traditional motion models do not account for the continuity of motion characteristics between frames. In this paper, we present a new feature descriptor, called the hybrid optical flow histogram. By importing the concept of acceleration, our method can indicate the change of speed in different directions of a movement. Therefore, our descriptor contains more information on the movement. We also introduce a spatial and temporal region saliency determination method to extract the effective motion area only for samples, which could effectively reduce the computational costs, and we apply a sparse representation to detect abnormal behaviors via sparse reconstruction costs. Sparse representation has a high rate of recognition performance and stability. Experiments involving the UMN datasets and the videos taken by us show that our method can effectively identify various types of anomalies and that the recognition results are better than existing algorithms.

A Context-Aware Mobile User Behavior-Based Neighbor Finding Approach for Preference Profile Construction

In this paper, a new approach is adopted to update the user preference profile by seeking users with similar interests based on the context obtainable for a mobile network instead of from desktop networks. The trust degree between mobile users is calculated by analyzing their behavior based on the context, and then the approximate neighbors are chosen by combining the similarity of the mobile user preference and the trust degree. The approach first considers the communication behaviors between mobile users, the mobile network services they use as well as the corresponding context information. Then a similarity degree of the preference between users is calculated with the evaluation score of a certain mobile web service provided by a mobile user. Finally, based on the time attenuation function, the users with similar preference are found, through which we can dynamically update the target user’s preference profile. Experiments are then conducted to test the effect of the context on the credibility among mobile users, the effect of time decay factors and trust degree thresholds. Simulation shows that the proposed approach outperforms two other methods in terms of Recall Ratio, Precision Ratio and Mean Absolute Error, because neither of them consider the context mobile information.