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Guo X, Liu F, Huang D. Migration through Resolution Cell Correction and Sparse Aperture ISAR Imaging for Maneuvering Target Based on Whale Optimization Algorithm-Fast Iterative Shrinkage Thresholding Algorithm. Sensors (Basel) 2024; 24:2148. [PMID: 38610359 PMCID: PMC11014137 DOI: 10.3390/s24072148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 03/06/2024] [Accepted: 03/23/2024] [Indexed: 04/14/2024]
Abstract
Targets faced by inverse synthetic aperture radar (ISAR) are often non-cooperative, with target maneuvering being the main manifestation of this non-cooperation. Maneuvers cause ISAR imaging results to be severely defocused, which can create huge difficulties in target identification. In addition, as the ISAR bandwidth continues to increase, the impact of migration through resolution cells (MTRC) on imaging results becomes more significant. Target non-cooperation may also result in sparse aperture, leading to the failure of traditional ISAR imaging algorithms. Therefore, this paper proposes an algorithm to realize MTRC correction and sparse aperture ISAR imaging for maneuvering targets simultaneously named whale optimization algorithm-fast iterative shrinkage thresholding algorithm (WOA-FISTA). In this algorithm, FISTA is used to perform MTRC correction and sparse aperture ISAR imaging efficiently and WOA is adopted to estimate the rotational parameter to eliminate the effects of maneuvering on imaging results. Experimental results based on simulation and measured datasets prove that the proposed algorithm implements sparse aperture ISAR imaging and MTRC correction for maneuvering targets simultaneously. The proposed algorithm achieves better results than traditional algorithms under different signal-to-noise ratio conditions.
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Affiliation(s)
- Xinrong Guo
- Science College, Armed Police Engineering University, Xi’an 710051, China;
| | - Fengkai Liu
- Air and Missile Defense College, Air Force Engineering University, Xi’an 710051, China;
| | - Darong Huang
- Air and Missile Defense College, Air Force Engineering University, Xi’an 710051, China;
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2
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Zhu X, Zhang Y, Lu W, Fang Y, He J. An ISAR Image Component Recognition Method Based on Semantic Segmentation and Mask Matching. Sensors (Basel) 2023; 23:7955. [PMID: 37766012 PMCID: PMC10535067 DOI: 10.3390/s23187955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 09/05/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023]
Abstract
The inverse synthetic aperture radar (ISAR) image is a kind of target feature data acquired by radar for moving targets, which can reflect the shape, structure, and motion information of the target, and has attracted a great deal of attention from the radar automatic target recognition (RATR) community. The identification of ISAR image components in radar satellite identification missions has not been carried out in related research, and the relevant segmentation methods of optical images applied to the research of semantic segmentation of ISAR images do not achieve ideal segmentation results. To address this problem, this paper proposes an ISAR image part recognition method based on semantic segmentation and mask matching. Furthermore, a reliable automatic ISAR image component labeling method is designed, and the satellite target component labeling ISAR image samples are obtained accurately and efficiently, and the satellite target component labeling ISAR image data set is obtained. On this basis, an ISAR image component recognition method based on semantic segmentation and mask matching is proposed in this paper. U-Net and Siamese Network are designed to complete the ISAR image binary semantic segmentation and binary mask matching, respectively. The component label of the ISAR image is predicted by the mask matching results. Experiments based on satellite component labeling ISAR image datasets confirm that the proposed method is feasible and effective, and it has greater comparative advantages compared to other classical semantic segmentation networks.
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Affiliation(s)
- Xinli Zhu
- Graduate School, Space Engineering University, Beijing 101416, China; (X.Z.); (J.H.)
| | - Yasheng Zhang
- Department of Aerospace Science and Technology, Space Engineering University, Beijing 101416, China; (Y.Z.); (Y.F.)
| | - Wang Lu
- Beijing Institute of Tracking and Telecommunication Technology, Beijing 100094, China
| | - Yuqiang Fang
- Department of Aerospace Science and Technology, Space Engineering University, Beijing 101416, China; (Y.Z.); (Y.F.)
| | - Jun He
- Graduate School, Space Engineering University, Beijing 101416, China; (X.Z.); (J.H.)
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3
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Shi C, Zhang Q, Lin T, Liu Z, Li S. Recognition of Micro-Motion Jamming Based on Complex-Valued Convolutional Neural Network. Sensors (Basel) 2023; 23:1118. [PMID: 36772157 PMCID: PMC9919671 DOI: 10.3390/s23031118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/13/2023] [Accepted: 01/16/2023] [Indexed: 06/18/2023]
Abstract
Micro-motion jamming is a new jamming method to inverse synthetic aperture radar (ISAR) in recent years. Compared with traditional jamming methods, it is more flexible and controllable, and is a great threat to ISAR. The prerequisite of taking relevant anti-jamming measures is to recognize the patterns of micro-motion jamming. In this paper, a method of micro-motion jamming pattern recognition based on complex-valued convolutional neural network (CV-CNN) is proposed. The micro-motion jamming echo signals are serialized and input to the network, and the result of recognition is output. Compared with real-valued convolutional neural network (RV-CNN), it can be found that the proposed method has a higher recognition accuracy rate. Additionally, the recognition accuracy rate is analyzed with different signal-to-noise ratio (SNR) and number of training samples. Simulation results prove the effectiveness of the proposed recognition method.
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Affiliation(s)
- Chongwei Shi
- Information and Navigation School, Air Force Engineering University, Xi’an 710077, China
| | - Qun Zhang
- Information and Navigation School, Air Force Engineering University, Xi’an 710077, China
| | - Tao Lin
- Information and Navigation School, Air Force Engineering University, Xi’an 710077, China
| | - Zhidong Liu
- Information and Navigation School, Air Force Engineering University, Xi’an 710077, China
| | - Shiliang Li
- Equipment Management and Unmanned Aerial Vehicle Engineering School, Air Force Engineering University, Xi’an 710051, China
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4
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Wang H, Yang Q, Wang H, Deng B. Autofocusing of Maneuvering Targets in Terahertz Inverse Synthetic Aperture Radar Imaging Based on Damped Newton Method. Sensors (Basel) 2022; 22:6883. [PMID: 36146231 PMCID: PMC9503409 DOI: 10.3390/s22186883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/04/2022] [Accepted: 09/07/2022] [Indexed: 06/16/2023]
Abstract
Maneuvering target imaging based on inverse synthetic aperture radar (ISAR) imaging has recently drawn significant attention. Among the many autofocusing technologies which are crucial in ISAR imaging, minimum-entropy-based autofocusing (MEA) is highly robust. However, traditional MEA is not suitable for terahertz (THz) ISAR imaging. For one thing, the iterative process in traditional MEA is too complicated to be utilized for THz-ISAR imaging with tremendous data. For another, THz wavelengths are very short and extremely sensitive to phase errors, so the compensation accuracy of the traditional MEA method can hardly meet the requirements of THz radar high-resolution imaging. Therefore, in this paper, the MEA algorithm based on the damped Newton method is proposed, which improves computational efficiency by approximating the first- and second-order partial derivatives of the image entropy function with respect to the phase errors, as well as by the fast Fourier transform (FFT). The search step size factor is introduced to ensure that the algorithm can converge along the declination direction of the entropy function and obtain the globally optimal ISAR image. The experimental results validated the efficiency of the proposed algorithm, which is promising in THz-ISAR imaging of maneuvering targets.
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Affiliation(s)
| | - Qi Yang
- Correspondence: ; Tel.: +86-731-8457-5714
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5
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Liu Y, Zhang Q, Liu Z, Li G, Xiong S, Luo Y. An Anti-Jamming Method against Interrupted Sampling Repeater Jamming Based on Compressed Sensing. Sensors (Basel) 2022; 22:2239. [PMID: 35336416 DOI: 10.3390/s22062239] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 03/09/2022] [Accepted: 03/12/2022] [Indexed: 11/30/2022]
Abstract
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.
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6
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Liu Z, Zhang Q, Li K. An Anti-Jamming Method against Two-Dimensional Deception Jamming by Spatial Location Feature Recognition. Sensors (Basel) 2021; 21:s21227702. [PMID: 34833774 PMCID: PMC8625882 DOI: 10.3390/s21227702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 11/12/2021] [Accepted: 11/18/2021] [Indexed: 11/16/2022]
Abstract
Interrupted sampling repeater jamming (ISRJ) is an effective method for implementing deception jamming on chirp radars. By means of frequency-shifting jamming processing of the target echo signal and pulse compression during image processing, a group of false targets will appear in different spatial locations around the true target. Extracting the features of these false targets is complex and limited to existing countering methods against ISRJ. This paper proposes an anti-jamming method to identify the spatial location characteristics of two-dimensional deception false targets. By adjusting the parameters of the radar transmitted signal, the method simultaneously transmits the anti-jamming signal and carries out false target identification and elimination in the range and azimuth dimensions. Eventually, the optimal signal parameter design of the anti-jamming signal is obtained by comparing different anti-jamming strategies in the range dimension. The validity of the proposed method is proved by deducing the mathematical model between the spatial distribution characteristics of the false targets and the radar transmitted signal parameters and demonstrated by simulations.
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Affiliation(s)
- Zhidong Liu
- The Institute of Information and Navigation, Air Force Engineering University, Xi’an 710077, China; (Q.Z.); (K.L.)
- The Collaborative Innovation Center of Information Sensing and Understanding, Xi’an 710077, China
- Correspondence:
| | - Qun Zhang
- The Institute of Information and Navigation, Air Force Engineering University, Xi’an 710077, China; (Q.Z.); (K.L.)
- The Collaborative Innovation Center of Information Sensing and Understanding, Xi’an 710077, China
- The Key Laboratory for Information Science of Electromagnetic Waves, Fudan University, Shanghai 200433, China
| | - Kaiming Li
- The Institute of Information and Navigation, Air Force Engineering University, Xi’an 710077, China; (Q.Z.); (K.L.)
- The Collaborative Innovation Center of Information Sensing and Understanding, Xi’an 710077, China
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7
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Wei J, Shao S, Ma H, Wang P, Zhang L, Liu H. High-Resolution ISAR Imaging with Modified Joint Range Spatial-Variant Autofocus and Azimuth Scaling. Sensors (Basel) 2020; 20:E5047. [PMID: 32899498 DOI: 10.3390/s20185047] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 08/28/2020] [Accepted: 09/01/2020] [Indexed: 11/17/2022]
Abstract
Well-focused and accurately scaled high-resolution inverse synthetic aperture radar (ISAR) images provide a sound basis for feature extraction and target recognition. This paper proposes a novel high-resolution ISAR imaging algorithm, namely modified joint range spatial-variant autofocus and azimuth scaling algorithm (MJAAS). After motion compensation, the shift of the equivalent rotational center (ERC) of the target destroys the linear relationship between the azimuth chirp rates (ACR) of echo signals and the range coordinates of scattering points, thereby leading to the failure of azimuth scaling. Accordingly, a new joint equivalent rotational center position and effective rotational velocity (JERCP-ERV) signal model is established, serving as the basis of MJAAS. By recourse to the Davidon-Fletcher-Powell (DFP) algorithm, MJAAS can jointly estimate the ERCP and ERV by solving a minimum entropy optimization problem, so as to simultaneously achieve accurate azimuth scaling and range spatial-variant autofocus, which further improves the image focusing performance. MJAAS is not restricted by the modes of motion errors (coherent or non-coherent) and the motion compensation methods, so it can be widely applied to real data with the advantages of strong practicality and high accuracy. Extensive experimental results based on both simulated and real data are provided to corroborate the effectiveness of the proposed algorithm.
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8
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Wu K, Xu X. Two-Dimensional Augmented State-Space Approach with Applications to Sparse Representation of Radar Signatures. Sensors (Basel) 2019; 19:s19214631. [PMID: 31653113 PMCID: PMC6864621 DOI: 10.3390/s19214631] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 10/18/2019] [Accepted: 10/21/2019] [Indexed: 11/26/2022]
Abstract
In this work, we focus on sparse representation of two-dimensional (2-D) radar signatures for man-made targets. Based on the damped exponential (DE) model, a 2-D augmented state–space approach (ASSA) is proposed to estimate the parameters of scattering centers on complex man-made targets, i.e., the complex amplitudes and the poles in down-range and aspect dimensions. An augmented state–space approach is developed for pole estimation of down-range dimension. Multiple-range search strategy, which applies one-dimensional (1-D) state–space approach (SSA) to the 1-D data for each down-range cell, is used to alleviate the pole-pairing problem occurring in previous algorithms. Effectiveness of the proposed approach is verified by the numerical and measured inverse synthetic aperture radar (ISAR) data.
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Affiliation(s)
- Kejiang Wu
- School of Electronics and Information Engineering, Beihang University, Beijing 100191, China.
| | - Xiaojian Xu
- School of Electronics and Information Engineering, Beihang University, Beijing 100191, China.
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9
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Huang X, Ji K, Leng X, Dong G, Xing X. Refocusing Moving Ship Targets in SAR Images Based on Fast Minimum Entropy Phase Compensation. Sensors (Basel) 2019; 19:E1154. [PMID: 30866476 DOI: 10.3390/s19051154] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 02/27/2019] [Accepted: 03/04/2019] [Indexed: 11/16/2022]
Abstract
Moving ship targets appear blurred and defocused in synthetic aperture radar (SAR) images due to the translation motion during the coherent processing. Motion compensation is required for refocusing moving ship targets in SAR scenes. A novel refocusing method for moving ship is developed in this paper. The method is exploiting inverse synthetic aperture radar (ISAR) technique to refocus the ship target in SAR image. Generally, most cases of refocusing are for raw echo data, not for SAR image. Taking into account the advantages of processing in SAR image, the processing data are SAR image rather than raw echo data in this paper. The ISAR processing is based on fast minimum entropy phase compensation method, an iterative approach to obtain the phase error. The proposed method has been tested using Spaceborne TerraSAR-X, Gaofeng-3 images and airborne SAR images of maritime targets.
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10
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Shi H, Xia S, Qin Q, Yang T, Qiao Z. Non-Stationary Platform Inverse Synthetic Aperture Radar Maneuvering Target Imaging Based on Phase Retrieval. Sensors (Basel) 2018; 18:E3333. [PMID: 30301157 DOI: 10.3390/s18103333] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 10/01/2018] [Accepted: 10/03/2018] [Indexed: 11/19/2022]
Abstract
As a powerful signal processing tool for imaging moving targets, placing radar on a non-stationary platform (such as an aerostat) is a future direction of Inverse Synthetic Aperture Radar (ISAR) systems. However, more phase errors are introduced into the received signal due to the instability of the radar platform, making it difficult for popular algorithms to accurately perform motion compensation, which leads to severe effects in the resultant ISAR images. Moreover, maneuvering targets may have complex motion whose motion parameters are unknown to radar systems. To overcome the issue of non-stationary platform ISAR autofocus imaging, a high-resolution imaging method based on the phase retrieval principle is proposed in this paper. Firstly, based on the spatial geometric and echo models of the ISAR maneuvering target, we can deduce that the radial motion of the radar platform or the vibration does not affect the modulus of the ISAR echo signal, which provides a theoretical basis for the phase recovery theory for the ISAR imaging. Then, we propose an oversampling smoothness (OSS) phase retrieval algorithm with prior information, namely, the phase of the blurred image obtained by the classical imaging algorithm replaces the initial random phase in the original OSS algorithm. In addition, the size of the support domain of the OSS algorithm is set with respect to the blurred target image. Experimental simulation shows that compared with classical imaging methods, the proposed method can obtain the resultant motion-compensated ISAR image without estimating the radar platform and maneuvering target motion parameters, wherein the fictitious target is perfectly focused.
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11
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Zeng C, Qin M, Li D, Liu H, Chai Y. An Efficient ISAR Imaging of Targets with Complex Motions Based on a Quasi-Time-Frequency Analysis Bilinear Coherent Algorithm. Sensors (Basel) 2018; 18:s18092814. [PMID: 30149683 PMCID: PMC6164856 DOI: 10.3390/s18092814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 08/17/2018] [Accepted: 08/24/2018] [Indexed: 11/16/2022]
Abstract
The inverse synthetic aperture radar (ISAR) imaging for targets with complex motions has always been a challenging task due to the time-varying Doppler parameter, especially at the low signal-to-noise ratio (SNR) condition. In this paper, an efficient ISAR imaging algorithm for maneuvering targets based on a noise-resistance bilinear coherent integration is developed without the parameter estimation. First, the received signals of the ISAR in a range bin are modelled as a multicomponent quadratic frequency-modulated (QFM) signal after the translational motion compensation. Second, a novel quasi-time-frequency representation noise-resistance bilinear Radon-cubic phase function (CPF)-Fourier transform (RCFT) is proposed, which is based on the coherent integration of the energy of auto-terms along the slope line trajectory. In doing so, the RCFT also effectively suppresses the cross-terms and spurious peaks interference at no expense of the time-frequency resolution loss. Third, the cross-range positions of target's scatters in ISAR image are obtained via a simple maximization projection from the RCFT result to the Doppler centroid axis, and the final high-resolution ISAR image is thus produced by regrouping all the range-Doppler frequency centroids. Compared with the existing time-frequency analysis-based and parameter estimation-based ISAR imaging algorithms, the proposed method presents the following features: (1) Better cross-term interference suppression at no time-frequency resolution loss; (2) computationally efficient without estimating the parameters of each scatters; (3) higher signal processing gain because of 2-D coherent integration realization and its bilinear function feature. The simulation results are provided to demonstrate the performance of the proposed method.
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Affiliation(s)
- Cao Zeng
- National Laboratory of Radar Signal Processing, Xidian University, Xian 710071, China.
| | - Mengyi Qin
- Center of Communication and Tracking Telemetering Command, Chongqing University, Chongqing 400044, China.
| | - Dong Li
- Center of Communication and Tracking Telemetering Command, Chongqing University, Chongqing 400044, China.
- Key Laboratory of Complex System Safety and Control, Ministry of Education, Chongqing University, Chongqing 400044, China.
- Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing, Guilin University of Electronic Technology 541004, China.
| | - Hongqing Liu
- Chongqing Key Laboratory of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
| | - Yi Chai
- Key Laboratory of Complex System Safety and Control, Ministry of Education, Chongqing University, Chongqing 400044, China.
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12
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Wang B, Xu S, Wu W, Hu P, Chen Z. Adaptive ISAR Imaging of Maneuvering Targets Based on a Modified Fourier Transform. Sensors (Basel) 2018; 18:s18051370. [PMID: 29702626 PMCID: PMC5981441 DOI: 10.3390/s18051370] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 04/25/2018] [Accepted: 04/26/2018] [Indexed: 11/16/2022]
Abstract
Focusing on the inverse synthetic aperture radar (ISAR) imaging of maneuvering targets, this paper presents a new imaging method which works well when the target's maneuvering is not too severe. After translational motion compensation, we describe the equivalent rotation of maneuvering targets by two variables-the relative chirp rate of the linear frequency modulated (LFM) signal and the Doppler focus shift. The first variable indicates the target's motion status, and the second one represents the possible residual error of the translational motion compensation. With them, a modified Fourier transform matrix is constructed and then used for cross-range compression. Consequently, the imaging of maneuvering is converted into a two-dimensional parameter optimization problem in which a stable and clear ISAR image is guaranteed. A gradient descent optimization scheme is employed to obtain the accurate relative chirp rate and Doppler focus shift. Moreover, we designed an efficient and robust initialization process for the gradient descent method, thus, the well-focused ISAR images of maneuvering targets can be achieved adaptively. Human intervention is not needed, and it is quite convenient for practical ISAR imaging systems. Compared to precedent imaging methods, the new method achieves better imaging quality under reasonable computational cost. Simulation results are provided to validate the effectiveness and advantages of the proposed method.
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Affiliation(s)
- Binbin Wang
- Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China.
| | - Shiyou Xu
- Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China.
| | - Wenzhen Wu
- Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China.
| | - Pengjiang Hu
- Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China.
| | - Zengping Chen
- Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China.
- College of Information Engineering, Shenzhen University, Shenzhen 518060, China.
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13
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Zhou X, Wei G, Wu S, Wang D. Three-Dimensional ISAR Imaging Method for High-Speed Targets in Short-Range Using Impulse Radar Based on SIMO Array. Sensors (Basel) 2016; 16:s16030364. [PMID: 26978372 PMCID: PMC4813939 DOI: 10.3390/s16030364] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Revised: 03/04/2016] [Accepted: 03/08/2016] [Indexed: 11/16/2022]
Abstract
This paper proposes a three-dimensional inverse synthetic aperture radar (ISAR) imaging method for high-speed targets in short-range using an impulse radar. According to the requirements for high-speed target measurement in short-range, this paper establishes the single-input multiple-output (SIMO) antenna array, and further proposes a missile motion parameter estimation method based on impulse radar. By analyzing the motion geometry relationship of the warhead scattering center after translational compensation, this paper derives the receiving antenna position and the time delay after translational compensation, and thus overcomes the shortcomings of conventional translational compensation methods. By analyzing the motion characteristics of the missile, this paper estimates the missile's rotation angle and the rotation matrix by establishing a new coordinate system. Simulation results validate the performance of the proposed algorithm.
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Affiliation(s)
- Xinpeng Zhou
- School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China.
- Beijing Key Laboratory of Fractional Signals and Systems, Beijing 100081, China.
| | - Guohua Wei
- School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China.
- Beijing Key Laboratory of Fractional Signals and Systems, Beijing 100081, China.
| | - Siliang Wu
- School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China.
- Beijing Key Laboratory of Fractional Signals and Systems, Beijing 100081, China.
| | - Dawei Wang
- School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China.
- Beijing Key Laboratory of Fractional Signals and Systems, Beijing 100081, China.
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14
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Zhou W, Yeh CM, Jin K, Yang J, Lu YB. ISAR Imaging Based on the Wideband Hyperbolic Frequency-Modulation Waveform. Sensors (Basel) 2015; 15:23188-204. [PMID: 26389901 PMCID: PMC4610492 DOI: 10.3390/s150923188] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Revised: 08/28/2015] [Accepted: 09/09/2015] [Indexed: 11/16/2022]
Abstract
The hyperbolic frequency-modulated (HFM) waveform has an inherent Doppler-invariant property. It is more conducive than the conventional linear frequency-modulated (LFM) waveform to high speed moving target imaging. In order to apply the HFM waveform to existing inverse synthetic aperture radar (ISAR) imaging systems, a new pulse compression algorithm is proposed. First, the received HFM echoes are demodulated with the transmitted signal, which is called “decurve” in this paper. By this operation, the bandwidth of the demodulated echoes is effectively reduced and can be processed by the existing narrow-band receiver. Then, the phase of the decurved HFM echoes is analyzed, and thus, the pulse compression is accomplished by space-variant phase compensation. In addition, the space-variant phase compensation is realized by resampling and fast Fourier transform (FFT) with high computational efficiency. Finally, numerical results illustrate the effectiveness of the proposed method.
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Affiliation(s)
- Wei Zhou
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
| | - Chun-mao Yeh
- Beijing Institute of Radio Measurement, Beijing 100039, China.
| | - Kan Jin
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
| | - Jian Yang
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
| | - Yao-bin Lu
- Beijing Institute of Radio Measurement, Beijing 100039, China.
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