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Advanced Array Signal Processing for Target Imaging and Detection

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 19299

Special Issue Editors

College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China
Interests: radar and sonar target detection; waveform design
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: ultra-wide band radar; radar imaging; array signal processing
Faculty of Engineering, Department of Earth Science & Engineering, Imperial College London, London, UK
Interests: image processing techniques for data fusion; filtering and InSAR
College of Communication Engineering, Jilin University, Changchun 130012, China
Interests: sound array signal processing; wind parameter measurement
Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Napoli, Italy
Interests: electromagnetic propagation; electromagnetic modeling; microwave remote sensing and electromagnetics; SAR signal processing and simulation; information retrieval from SAR data
Special Issues, Collections and Topics in MDPI journals
Key Laboratory of Marine Information Acquisition and Security, Harbin Engineering University, Harbin 150001, China
Interests: underwater acoustic channel; sonar system; underwater acoustic target detection and location
College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China
Interests: underwater vector acoustic field analysis
College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China
Interests: adaptive signal processing; target detection and identification

Special Issue Information

Dear Colleagues,

In recent decades, considerable progress has been made in the theory and methodology of array signal processing for airborne, ground, marine, and underwater target detection and imaging. However, further development faces increasing challenges regarding improving target illumination performance due to the influences of clutter, interference, and noise. It is valuable to attain a comprehensive understanding of current array signal processing theory and approaches for detecting various targets in the air, on the land, in the sea, and under water, and thus to solve future problems exerted by the new application requirements.

This Special Issue will collect and promote advanced array signal processing methods for target detection, imaging, and recognition using radar and sonar in a wide range of complex adverse environments with strong background noise/jamming. The Special Issue will focus on (but is not limited to) the following aspects:

  • State-of-the-art array signal processing of radar and sonar;
  • Waveform/frequency diversity;
  • Artificial intelligence for aerial/underwater target characterization, analysis, and recognition under various interference, clutter, and noise conditions;
  • Novel modelling and analysis methods for complex target detection and imaging;
  • Methods and approaches for the optimization of target detection and imaging;
  • Practical validation notes and technical reviews of the related topics.

For this Special Issue, we welcome manuscripts on active and passive microwave/acoustic remote sensing, signal and image processing methods, and experimental applications of remote sensing.

Dr. Jiahua Zhu
Prof. Dr. Xiaotao Huang
Prof. Dr. Jianguo Liu
Prof. Dr. Xinbo Li
Dr. Gerardo Di Martino
Prof. Dr. Shengchun Piao
Dr. Junyuan Guo
Dr. Wei Guo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • radar and sonar array signal processing
  • waveform and frequency diversity
  • interference, clutter, and noise suppression
  • aerial/ground/marine/underwater target illumination
  • target resolution enhancement

Published Papers (21 papers)

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16 pages, 3572 KiB  
Article
Broadband Source Localization Using Asynchronous Distributed Hydrophones Based on Frequency Invariability of Acoustic Field in Shallow Water
by Hui Li, Jun Huang, Zhezhen Xu, Kunde Yang and Jixing Qin
Remote Sens. 2024, 16(6), 982; https://doi.org/10.3390/rs16060982 - 11 Mar 2024
Viewed by 396
Abstract
This paper introduces a model-independent passive source localization method, employing asynchronous distributed hydrophones in shallow water. Based on the frequency invariability of the acoustic field, assuming the correct source range information, the warped spectra of received signals at distributed hydrophones exhibit identical shapes. [...] Read more.
This paper introduces a model-independent passive source localization method, employing asynchronous distributed hydrophones in shallow water. Based on the frequency invariability of the acoustic field, assuming the correct source range information, the warped spectra of received signals at distributed hydrophones exhibit identical shapes. Subsequently, a cost function is formulated to mutually align the warped spectra, with its maximum point indicating the source location. The proposed method can locate the source in two-dimensional horizontal space without requiring either angle- or time-synchronization information. Numerical simulations are conducted to demonstrate the performance of the proposed method. Full article
(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection)
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16 pages, 5272 KiB  
Article
Transfer-Learning-Based Human Activity Recognition Using Antenna Array
by Kun Ye, Sheng Wu, Yongbin Cai, Lang Zhou, Lijun Xiao, Xuebo Zhang, Zheng Zheng and Jiaqing Lin
Remote Sens. 2024, 16(5), 845; https://doi.org/10.3390/rs16050845 - 28 Feb 2024
Viewed by 395
Abstract
Due to its low cost and privacy protection, Channel-State-Information (CSI)-based activity detection has gained interest recently. However, to achieve high accuracy, which is challenging in practice, a significant number of training samples are required. To address the issues of the small sample size [...] Read more.
Due to its low cost and privacy protection, Channel-State-Information (CSI)-based activity detection has gained interest recently. However, to achieve high accuracy, which is challenging in practice, a significant number of training samples are required. To address the issues of the small sample size and cross-scenario in neural network training, this paper proposes a WiFi human activity-recognition system based on transfer learning using an antenna array: Wi-AR. First, the Intel5300 network card collects CSI signal measurements through an antenna array and processes them with a low-pass filter to reduce noise. Then, a threshold-based sliding window method is applied to extract the signal of independent activities, which is further transformed into time–frequency diagrams. Finally, the produced diagrams are used as input to a pretrained ResNet18 to recognize human activities. The proposed Wi-AR was evaluated using a dataset collected in three different room layouts. The testing results showed that the suggested Wi-AR recognizes human activities with a consistent accuracy of about 94%, outperforming the other conventional convolutional neural network approach. Full article
(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection)
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12 pages, 3529 KiB  
Communication
A Fast Power Spectrum Sensing Solution for Generalized Coprime Sampling
by Kaili Jiang, Dechang Wang, Kailun Tian, Yuxin Zhao, Hancong Feng and Bin Tang
Remote Sens. 2024, 16(5), 811; https://doi.org/10.3390/rs16050811 - 26 Feb 2024
Viewed by 375
Abstract
With the growing scarcity of spectrum resources, wideband spectrum sensing is necessary to process a large volume of data at a high sampling rate. For some applications, only second-order statistics are required for spectrum estimation. In this case, a fast power spectrum sensing [...] Read more.
With the growing scarcity of spectrum resources, wideband spectrum sensing is necessary to process a large volume of data at a high sampling rate. For some applications, only second-order statistics are required for spectrum estimation. In this case, a fast power spectrum sensing solution is proposed based on the generalized coprime sampling. The solution involves the inherent structure of the sensing vector to reconstruct the autocorrelation sequence of inputs from sub-Nyquist samples, which requires only parallel Fourier transform and simple multiplication operations. Thus, it takes less time than the state-of-the-art methods while maintaining the same performance, and it achieves higher performance than the existing methods within the same execution time without the need to pre-estimate the number of inputs. Furthermore, the influence of the model mismatch has only a minor impact on the estimation performance, allowing for more efficient use of the spectrum resource in a distributed swarm scenario. Simulation results demonstrate the low complexity in sampling and computation, thus making it a more practical solution for real-time and distributed wideband spectrum sensing applications. Full article
(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection)
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14 pages, 708 KiB  
Communication
Underwater Acoustic Nonlinear Blind Ship Noise Separation Using Recurrent Attention Neural Networks
by Ruiping Song, Xiao Feng, Junfeng Wang, Haixin Sun, Mingzhang Zhou and Hamada Esmaiel
Remote Sens. 2024, 16(4), 653; https://doi.org/10.3390/rs16040653 - 09 Feb 2024
Viewed by 584
Abstract
Ship-radiated noise is the main basis for ship detection in underwater acoustic environments. Due to the increasing human activity in the ocean, the captured ship noise is usually mixed with or covered by other signals or noise. On the other hand, due to [...] Read more.
Ship-radiated noise is the main basis for ship detection in underwater acoustic environments. Due to the increasing human activity in the ocean, the captured ship noise is usually mixed with or covered by other signals or noise. On the other hand, due to the softening effect of bubbles in the water generated by ships, ship noise undergoes non-negligible nonlinear distortion. To mitigate the nonlinear distortion and separate the target ship noise, blind source separation (BSS) becomes a promising solution. However, underwater acoustic nonlinear models are seldom used in research for nonlinear BSS. This paper is based on the hypothesis that the recovery and separation accuracy can be improved by considering this nonlinear effect in the underwater environment. The purpose of this research is to explore and discover a method with the above advantages. In this paper, a model is used in underwater BSS to describe the nonlinear impact of the softening effect of bubbles on ship noise. To separate the target ship-radiated noise from the nonlinear mixtures, an end-to-end network combining an attention mechanism and bidirectional long short-term memory (Bi-LSTM) recurrent neural network is proposed. Ship noise from the database ShipsEar and line spectrum signals are used in the simulation. The simulation results show that, compared with several recent neural networks used for linear and nonlinear BSS, the proposed scheme has an advantage in terms of the mean square error, correlation coefficient and signal-to-distortion ratio. Full article
(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection)
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12 pages, 2327 KiB  
Communication
Source Depth Discrimination Using Intensity Striations in the Frequency–Depth Plane in Shallow Water with a Thermocline
by Xiaobin Li and Chao Sun
Remote Sens. 2024, 16(4), 639; https://doi.org/10.3390/rs16040639 - 08 Feb 2024
Viewed by 557
Abstract
A source depth discrimination method based on intensity striations in the frequency–depth plane with a vertical linear array in a shallow water environment is proposed and studied theoretically and experimentally. To quantify the orientation of the interference patterns, a generalized waveguide variant (GWV) [...] Read more.
A source depth discrimination method based on intensity striations in the frequency–depth plane with a vertical linear array in a shallow water environment is proposed and studied theoretically and experimentally. To quantify the orientation of the interference patterns, a generalized waveguide variant (GWV) η is introduced. Due to the different dominance of the mode groups, the GWV distribution in the surface source is sharply peaked, indicating the presence of striations in the interferogram and the slope associated with the source–array range, while the distribution of the submerged source is more diffuse, and its interferogram is chaotic. The existence or lack of a distinct peak is used to separate the surface and submerged source classes. The method does not demand prior knowledge of the sound speed profile or the relative movement between the source and the array. In addition, it is the presence of the striations, not the value of η, that is exploited to separate the surface and submerged source classes, which means the source–array range can be unknown. The proposed method is validated using experimental data on the towing ship in SWellEx–96 and numerical modeling. The method’s performance under noise situations and for different source–array ranges is also investigated. Full article
(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection)
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34 pages, 5142 KiB  
Article
Pentagram Arrays: A New Paradigm for DOA Estimation of Wideband Sources Based on Triangular Geometry
by Mohammed Khalafalla, Kaili Jiang, Kailun Tian, Hancong Feng, Ying Xiong and Bin Tang
Remote Sens. 2024, 16(3), 535; https://doi.org/10.3390/rs16030535 - 31 Jan 2024
Viewed by 1533
Abstract
Antenna arrays are used for signal processing in sonar and radar direction of arrival (DOA) estimation. The well-known array geometries used in DOA estimation are uniform linear array (ULA), uniform circular array (UCA), and rectangular grid array (RGA). In these geometries, the neighboring [...] Read more.
Antenna arrays are used for signal processing in sonar and radar direction of arrival (DOA) estimation. The well-known array geometries used in DOA estimation are uniform linear array (ULA), uniform circular array (UCA), and rectangular grid array (RGA). In these geometries, the neighboring elements are separated by a fixed distance λ/2 (λ is the wavelength), which does not perform well for d greater than λ/2. Uniform rectangular arrays introduce grating lobes, which cause poor DOA estimation performance, especially for wideband sources. Random sampling arrays are sometimes practically not realizable. Periodic geometries require numerous sensors. Based on the minimization of the number of sensors, this paper developed a novel pentagram array to address the problem of DOA estimation of wideband sources. The array has a fixed number of elements with variable element spacing and is abbreviated as (FNEVES), which offers a new idea for array design. In this study, the geometric structure is designed and mathematically analyzed. Also, a DOA signal model is designed based on a spherical radar coordinate system to derive its steering manifold matrix. The DOA estimation performance comparison with ULA and UCA geometries under the multiple signal classification (MUSIC) algorithm using different wideband scenarios is presented. For further investigation, more simulations are realized using the minimum variance distortionless (MVDR) technique (CAPON) and the subtracting signal subspace (SSS) algorithm. Simulation results demonstrate the effectiveness of the proposed geometry compared to its counterparts. In addition, the SSS, through the simulations, provided better results than the MUSIC and CAPON methods. Full article
(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection)
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19 pages, 1410 KiB  
Article
Detecting Weak Underwater Targets Using Block Updating of Sparse and Structured Channel Impulse Responses
by Chaoran Yang, Qing Ling, Xueli Sheng, Mengfei Mu and Andreas Jakobsson
Remote Sens. 2024, 16(3), 476; https://doi.org/10.3390/rs16030476 - 26 Jan 2024
Viewed by 538
Abstract
In this paper, we considered the real-time modeling of an underwater channel impulse response (CIR), exploiting the inherent structure and sparsity of such channels. Building on the recent development in the modeling of acoustic channels using a Kronecker structure, we approximated the CIR [...] Read more.
In this paper, we considered the real-time modeling of an underwater channel impulse response (CIR), exploiting the inherent structure and sparsity of such channels. Building on the recent development in the modeling of acoustic channels using a Kronecker structure, we approximated the CIR using a structured and sparse model, allowing for a computationally efficient sparse block-updating algorithm, which can track the time-varying CIR even in low signal-to-noise ratio (SNR) scenarios. The algorithm employs a conjugate gradient formulation, which enables a gradual refinement if the SNR is sufficiently high to allow for this. This was performed by gradually relaxing the assumed Kronecker structure, as well as the sparsity assumptions, if possible. The estimated CIR was further used to form a residual signal containing (primarily) information of the time-varying signal responses, thereby allowing for the detection of weak target signals. The proposed method was evaluated using both simulated and measured underwater signals, clearly illustrating the better performance of the proposed method. Full article
(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection)
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27 pages, 2688 KiB  
Article
On the 2D Beampattern Optimization of Sparse Group-Constrained Robust Capon Beamforming with Conformal Arrays
by Yan Dai, Chao Sun and Xionghou Liu
Remote Sens. 2024, 16(2), 421; https://doi.org/10.3390/rs16020421 - 21 Jan 2024
Viewed by 718
Abstract
To overcome the problems of the high sidelobe levels and low computational efficiency of traditional Capon-based beamformers in optimizing the two-dimensional (elevation–azimuth) beampatterns of conformal arrays, in this paper, we propose a robust Capon beamforming method with sparse group constraints that is solved [...] Read more.
To overcome the problems of the high sidelobe levels and low computational efficiency of traditional Capon-based beamformers in optimizing the two-dimensional (elevation–azimuth) beampatterns of conformal arrays, in this paper, we propose a robust Capon beamforming method with sparse group constraints that is solved using the alternating-direction method of multipliers (ADMM). A robustness constraint based on worst-case performance optimization (WCPO) is imposed on the standard Capon beamformer (SCB) and then the sparse group constraints are applied to reduce the sidelobe level. The constraints are two sparsity constraints: the group one and the individual one. The former was developed to exploit the sparsity between groups based on the fact that the sidelobe can be divided into several different groups according to spatial regions in two-dimensional beampatterns, rather than different individual points in one-dimensional (azimuth-only) beampatterns. The latter is considered to emphasize the sparsity within groups. To solve the optimization problem, we introduce the ADMM to obtain the closed-form solution iteratively, which requires less computational complexity than the existing methods, such as second-order cone programming (SOCP). Numerical examples show that the proposed method can achieve flexible sidelobe-level control, and it is still effective in the case of steering vector mismatch. Full article
(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection)
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20 pages, 1407 KiB  
Article
Adaptive Beamforming with Sidelobe Level Control for Multiband Sparse Linear Array
by Hongtao Li, Longyao Ran, Cheng He, Zhoupeng Ding and Shengyao Chen
Remote Sens. 2023, 15(20), 4929; https://doi.org/10.3390/rs15204929 - 12 Oct 2023
Cited by 1 | Viewed by 603
Abstract
Multiband antenna arrays have the capability of effectively working in multiple frequency bands and thus significantly simplify the antenna system. To further reduce the system overhead, this paper discusses the joint design of antenna selection and adaptive beamforming for multiband antenna arrays, where [...] Read more.
Multiband antenna arrays have the capability of effectively working in multiple frequency bands and thus significantly simplify the antenna system. To further reduce the system overhead, this paper discusses the joint design of antenna selection and adaptive beamforming for multiband antenna arrays, where the sidelobe level is also controlled so as to alleviate the effect of unknown sporadic interference. Based on the maximum signal-to-interference-plus-noise ratio (SINR) criterion and sidelobe level constraints, the proposed multiband sparse array design is formulated into a nonconvex constrained nonlinear optimization problem with an l0,2-mixed norm regularization. This problem ensures that the same antenna positions are selected at all operating frequencies while the beamformer weights of each frequency are optimized independently. By exploiting the semi-definite relaxation and the reweighted l1,-norm approximation, the problem is converted into a series of convex subproblems and is then effectively solved by the proposed iterative reweighted method. Numerical results show that the proposed multiband sparse array significantly reduces the sidelobe levels in all operating frequencies while maintaining the maximum SINR, so it provides superior performance of interference suppression. Full article
(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection)
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19 pages, 7785 KiB  
Article
An Image Quality Improvement Method in Side-Scan Sonar Based on Deconvolution
by Jia Liu, Yan Pang, Lengleng Yan and Hanhao Zhu
Remote Sens. 2023, 15(20), 4908; https://doi.org/10.3390/rs15204908 - 11 Oct 2023
Cited by 1 | Viewed by 837
Abstract
Side-scan sonar (SSS) is an important underwater imaging method that has high resolutions and is convenient to use. However, due to the restriction of conventional pulse compression technology, the side-scan sonar beam sidelobe in the range direction is relatively high, which affects the [...] Read more.
Side-scan sonar (SSS) is an important underwater imaging method that has high resolutions and is convenient to use. However, due to the restriction of conventional pulse compression technology, the side-scan sonar beam sidelobe in the range direction is relatively high, which affects the definition and contrast of images. When working in a shallow-water environment, image quality is especially influenced by strong bottom reverberation or other targets on the seabed. To solve this problem, a method for image-quality improvement based on deconvolution is proposed herein. In this method, to increase the range resolution and lower the sidelobe, a deconvolution algorithm is employed to improve the conventional pulse compression. In our simulation, the tolerance of the algorithm to different signal-to-noise ratios (SNRs) and the resolution ability of multi-target conditions were analyzed. Furthermore, the proposed method was applied to actual underwater data. The experimental results showed that the quality of underwater acoustic imaging could be effectively improved. The ratios of improvement for the SNR and contrast ratio (CR) were 32 and 12.5%, respectively. The target segmentation results based on this method are also shown. The accuracy of segmentation was effectively improved. Full article
(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection)
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18 pages, 4849 KiB  
Article
A Space–Time–Range Joint Adaptive Focusing and Detection Method for Multiple Input Multiple Output Radar
by Jian Guan, Xiaoqian Mu, Yong Huang, Baoxin Chen, Ningbo Liu and Xiaolong Chen
Remote Sens. 2023, 15(18), 4509; https://doi.org/10.3390/rs15184509 - 13 Sep 2023
Viewed by 700
Abstract
The Multiple Input Multiple Output (MIMO) radar, as a new type of radar, emits orthogonal waveforms, which provide it with waveform diversity characteristics, leading to increased degrees of freedom and improved target detection performance. However, it also poses challenges such as difficulty in [...] Read more.
The Multiple Input Multiple Output (MIMO) radar, as a new type of radar, emits orthogonal waveforms, which provide it with waveform diversity characteristics, leading to increased degrees of freedom and improved target detection performance. However, it also poses challenges such as difficulty in meeting higher data demand, separating waveforms, and suppressing the multidimensional sidelobes (range sidelobes, Doppler sidelobes, and angle sidelobes) of targets. Phase-coded signals are frequently employed as orthogonal transmission signals in the MIMO radar. However, these signals exhibit poor Doppler sensitivity, and the intra-pulse Doppler frequency shift can have an impact on the effectiveness of the matching filtering process. To address the aforementioned concerns, this paper presents a novel approach called the Space–Time–Range Joint Adaptive Focusing and Detection (STRJAFD) method. The proposed method utilizes the Mean Square Error (MSE) criterion and integrates spatial, temporal, and waveform dimensions to achieve efficient adaptive focusing and detection of targets. The experimental results demonstrate that the proposed method outperforms conventional cascaded adaptive methods in effectively addressing the matching mismatch issue caused by Doppler frequency shift, achieving super-resolution focusing, possessing better suppression effects on three-dimensional sidelobes and clutter, and exhibiting better detection performance in low signal-to-clutter ratio and low signal-to-noise ratio environments. Furthermore, STRJAFD is unaffected by coherent sources and demands less data. Full article
(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection)
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20 pages, 8003 KiB  
Article
A Novel 3D ArcSAR Sensing System Applied to Unmanned Ground Vehicles
by Yangsheng Hua, Jian Wang, Dong Feng and Xiaotao Huang
Remote Sens. 2023, 15(16), 4089; https://doi.org/10.3390/rs15164089 - 20 Aug 2023
Viewed by 717
Abstract
Microwave radar has advantages in detection accuracy and robustness, and it is an area of active research in unmanned ground vehicles. However, the existing conventional automotive corner radar, which employs real-aperture antenna arrays, has limitations in terms of observable angle and azimuthal resolution. [...] Read more.
Microwave radar has advantages in detection accuracy and robustness, and it is an area of active research in unmanned ground vehicles. However, the existing conventional automotive corner radar, which employs real-aperture antenna arrays, has limitations in terms of observable angle and azimuthal resolution. This paper proposes a novel 3D ArcSAR method to address this issue, which combines rotational synthetic aperture radar (SAR) and direction estimation algorithms. The method aims to reconstruct 3D images of 360° scenes and offers distinctive advantages in both azimuthal and altitudinal sensing. Nevertheless, due to the unique structural characteristics of vehicle SAR, it is limited to receiving only a single snapshot signal for 3D sensing. We propose a resolution algorithm based on ArcSAR and the iterative adaptive approach (IAA) to resolve the limitation. Furthermore, the errors in altitude angle estimation of the proposed algorithm and conventional algorithms are analyzed under various conditions, including different target spacing and signal-to-noise ratio (SNR). Finally, we design and implement a prototype of the 3D ArcSAR sensing system, which utilizes a millimeter-wave MIMO radar system and a rotating scanning mechanical system. The experimental results obtained from this prototype effectively validate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection)
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19 pages, 2452 KiB  
Article
Neural-Network-Based Equalization and Detection for Underwater Acoustic Orthogonal Frequency Division Multiplexing Communications: A Low-Complexity Approach
by Mingzhang Zhou, Junfeng Wang, Xiao Feng, Haixin Sun, Jie Qi and Rongbin Lin
Remote Sens. 2023, 15(15), 3796; https://doi.org/10.3390/rs15153796 - 30 Jul 2023
Cited by 1 | Viewed by 943
Abstract
The performance of the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system is often restrained by time-varying channels with large delays. The existing frequency domain equalizers do not work well because of the high complexity and difficulty of finding the real-time signal-to-noise [...] Read more.
The performance of the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system is often restrained by time-varying channels with large delays. The existing frequency domain equalizers do not work well because of the high complexity and difficulty of finding the real-time signal-to-noise ratio. To solve these problems, we propose a low-complexity neural network (NN)-based scheme for joint equalization and detection. A simple NN structure is built to yield the detected symbols with the joint input of the segmented channel response and received symbol. The coherence bandwidth is investigated to find the optimal hyperparameters. By being completely trained offline with real channels, the proposed detector is applied independently in both simulations and sea trials. The results show that the proposed detector outperforms the ZF and MMSE equalizers and extreme learning machine (ELM)-based detectors in both the strongly reflected channels of the pool and time-variant channels of the shallow sea. The complexity of the proposed network is lower than the MMSE and ELM-based receiver. Full article
(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection)
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22 pages, 10552 KiB  
Article
Joint Detection and Reconstruction of Weak Spectral Lines under Non-Gaussian Impulsive Noise with Deep Learning
by Zhen Li, Junyuan Guo and Xiaohan Wang
Remote Sens. 2023, 15(13), 3268; https://doi.org/10.3390/rs15133268 - 25 Jun 2023
Viewed by 888
Abstract
Non-Gaussian impulsive noise in marine environments strongly influences the detection of weak spectral lines. However, existing detection algorithms based on the Gaussian noise model are futile under non-Gaussian impulsive noise. Therefore, a deep-learning method called AINP+LR-DRNet is proposed for joint detection and the [...] Read more.
Non-Gaussian impulsive noise in marine environments strongly influences the detection of weak spectral lines. However, existing detection algorithms based on the Gaussian noise model are futile under non-Gaussian impulsive noise. Therefore, a deep-learning method called AINP+LR-DRNet is proposed for joint detection and the reconstruction of weak spectral lines. First, non-Gaussian impulsive noise suppression was performed by an impulsive noise preprocessor (AINP). Second, a special detection and reconstruction network (DRNet) was proposed. An end-to-end training application learns to detect and reconstruct weak spectral lines by adding into an adaptive weighted loss function based on dual classification. Finally, a spectral line-detection algorithm based on DRNet (LR-DRNet) was proposed to improve the detection performance. The simulation indicated that the proposed AINP+LR-DRNet can detect and reconstruct weak spectral line features under non-Gaussian impulsive noise, even for a mixed signal-to-noise ratio as low as −26 dB. The performance of the proposed method was validated using experimental data. The proposed AINP+LR-DRNet detects and reconstructs spectral lines under strong background noise and interference with better reliability than other algorithms. Full article
(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection)
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19 pages, 5813 KiB  
Article
Research on High Robustness Underwater Target Estimation Method Based on Variational Sparse Bayesian Inference
by Libin Du, Huming Li, Lei Wang, Xu Lin and Zhichao Lv
Remote Sens. 2023, 15(13), 3222; https://doi.org/10.3390/rs15133222 - 21 Jun 2023
Cited by 1 | Viewed by 929
Abstract
Pulse noise (such as glacier fracturing and offshore pile driving), commonly seen in the marine environment, seriously affects the performance of Direction-of-Arrival (DOA) estimation methods in sonar systems. To address this issue, this paper proposes a high robustness underwater target estimation method based [...] Read more.
Pulse noise (such as glacier fracturing and offshore pile driving), commonly seen in the marine environment, seriously affects the performance of Direction-of-Arrival (DOA) estimation methods in sonar systems. To address this issue, this paper proposes a high robustness underwater target estimation method based on variational sparse Bayesian inference by studying and analyzing the sparse prior assumption characteristics of signals. This method models pulse noise to build an observation signal, completes the derivation of the conditional distribution of the observed variables and the prior distribution of the sparse signals, and combines Variational Bayes (VB) theory to approximate the posterior distribution, thereby obtaining the recovered signal of the sparse signals and reducing the impact of pulse noise on the estimation system. Our simulation results showed that the proposed method achieved higher estimation accuracy than traditional methods in both single and multiple snapshot scenarios and has practical potential. Full article
(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection)
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10 pages, 2896 KiB  
Communication
Enhanced Doppler Resolution and Sidelobe Suppression Performance for Golay Complementary Waveforms
by Jiahua Zhu, Yongping Song, Nan Jiang, Zhuang Xie, Chongyi Fan and Xiaotao Huang
Remote Sens. 2023, 15(9), 2452; https://doi.org/10.3390/rs15092452 - 06 May 2023
Cited by 33 | Viewed by 1410
Abstract
An enhanced Doppler resolution and sidelobe suppression have long been practical issues for moving target detection using Golay complementary waveforms. In this paper, Golay complementary waveform radar returns are combined with a proposed processor, the pointwise thresholding processor (PTP). Compared to the pointwise [...] Read more.
An enhanced Doppler resolution and sidelobe suppression have long been practical issues for moving target detection using Golay complementary waveforms. In this paper, Golay complementary waveform radar returns are combined with a proposed processor, the pointwise thresholding processor (PTP). Compared to the pointwise minimization processor (PMP) illustrated in a previous work, which could only achieve a Doppler resolution comparable to existing methods, this approach essentially increases the Doppler resolution to a very high level in theory. This study also introduced a further filtering process for the delay-Doppler map of the PTP, and simulations verified that the method results in a delay-Doppler map virtually free of range sidelobes. Full article
(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection)
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20 pages, 7002 KiB  
Article
High-Resolution SAR Imaging with Azimuth Missing Data Based on Sub-Echo Segmentation and Reconstruction
by Nan Jiang, Jiahua Zhu, Dong Feng, Zhuang Xie, Jian Wang and Xiaotao Huang
Remote Sens. 2023, 15(9), 2428; https://doi.org/10.3390/rs15092428 - 05 May 2023
Cited by 3 | Viewed by 963
Abstract
Due to the substantial electromagnetic interference, radar interruptions, and other factors, the SAR system may fail to receive valid data in some azimuth areas. This phenomenon is known as Azimuth Missing Data (AMD). If classical SAR imaging algorithms are performed directly using AMD [...] Read more.
Due to the substantial electromagnetic interference, radar interruptions, and other factors, the SAR system may fail to receive valid data in some azimuth areas. This phenomenon is known as Azimuth Missing Data (AMD). If classical SAR imaging algorithms are performed directly using AMD echo, the imaging results may be defocused or even display false targets, which seriously affects the accuracy of the image. Thus, we proposed a Sub-echo Segmentation and Reconstruction Azimuth Missing Data SAR Imaging Algorithm (SSR-AMDIA) to solve the problem of incomplete echo SAR imaging in this article. Instead of using the motion compensation step of the Polar Format algorithm (PFA) to recover the full echo from the AMD echo, the proposed SSR-AMDIA eliminates the effect of the planar approximation in PFA and expands the maximum depth of focus (DOF). The raw AMD echo was first subjected to range compression and Range Cell Migration Correction (RCMC), after which the AMD-RCMC echo was divided along the range direction. Then, we constructed a series of phase compensation functions based on the sub-segment AMD-RCMC echoes to guarantee the perfect recovery of the full RCMC echoes corresponding to the sub-scenes. Finally, we combined them to obtain the complete RCMC echo, and an excellent focused imaging result was then obtained via azimuth compression. Simulation and experimental data verified the effectiveness of the proposed algorithm. Furthermore, we derived the mathematical expressions for the two-dimensional maximum DOFs of the proposed algorithm. In contrast to the State-Of-the-Art (SOA) AMDIA, the SSR-AMDIA can obtain a superior imaging performance in a larger imaging scope under the conditions of most AMD cases. Full article
(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection)
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26 pages, 3340 KiB  
Article
Radar and Jammer Intelligent Game under Jamming Power Dynamic Allocation
by Jie Geng, Bo Jiu, Kang Li, Yu Zhao, Hongwei Liu and Hailin Li
Remote Sens. 2023, 15(3), 581; https://doi.org/10.3390/rs15030581 - 18 Jan 2023
Cited by 6 | Viewed by 2028
Abstract
In modern electronic warfare, the intelligence of the jammer greatly worsens the anti-jamming performance of traditional passive suppression methods. How to actively design anti-jamming strategies to deal with intelligent jammers is crucial to the radar system. In the existing research on radar anti-jamming [...] Read more.
In modern electronic warfare, the intelligence of the jammer greatly worsens the anti-jamming performance of traditional passive suppression methods. How to actively design anti-jamming strategies to deal with intelligent jammers is crucial to the radar system. In the existing research on radar anti-jamming strategies’ design, the assumption of jammers is too ideal. To establish a model that is closer to real electronic warfare, this paper explores the intelligent game between a subpulse-level frequency-agile (FA) radar and a transmit/receive time-sharing jammer under jamming power dynamic allocation. Firstly, the discrete allocation model of jamming power is established, and the multiple-round sequential interaction between the radar and the jammer is described based on an extensive-form game. A detection probability calculation method based on the signal-to-interference-pulse-noise ratio (SINR) accumulation gain criterion (SAGC) is proposed to evaluate the game results. Secondly, considering that the competition between the radar and the jammer has the feature of imperfect information, we utilized neural fictitious self-play (NFSP), an end-to-end deep reinforcement learning (DRL) algorithm, to find the Nash equilibrium (NE) of the game. Finally, the simulation results showed that the game between the radar and the jammer can converge to an approximate NE under the established model. The approximate NE strategies are better than the elementary strategies from the perspective of detection probability. In addition, comparing NFSP and the deep Q-network (DQN) illustrates the effectiveness of NFSP in solving the NE of imperfect information games. Full article
(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection)
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17 pages, 6462 KiB  
Article
Influence of Range-Dependent Sound Speed Profile on Position of Convergence Zones
by Ziyang Li, Shengchun Piao, Minghui Zhang and Lijia Gong
Remote Sens. 2022, 14(24), 6314; https://doi.org/10.3390/rs14246314 - 13 Dec 2022
Viewed by 1028
Abstract
Based on the Wentze–Kramers–Brillouin approximation, we derive formulae to calculate the position of convergence zones in a range-dependent environment with sound speed profiles varying in linear and ellipsoidal Gaussian eddy cases. Simulation results are provided for the linear and ellipsoidal Gaussian eddy cases. [...] Read more.
Based on the Wentze–Kramers–Brillouin approximation, we derive formulae to calculate the position of convergence zones in a range-dependent environment with sound speed profiles varying in linear and ellipsoidal Gaussian eddy cases. Simulation results are provided for the linear and ellipsoidal Gaussian eddy cases. Experiment data are used for calculations considering linearly varying sound speed, and the findings suitably agree with the simulation results. According to the evaluated environment, the influence of the range-dependent sound speed profile on the position of the upper and lower convergence zones for different source depths is analyzed through simulations. The corresponding results show that the influence of the sound speed profile on the position of the upper convergence zone is greater for a shallower source. In contrast, the position of the lower convergence zone for large-depth reception is less affected. Full article
(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection)
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13 pages, 1156 KiB  
Technical Note
Gridless Underdetermined DOA Estimation for Mobile Agents with Limited Snapshots Based on Deep Convolutional Generative Adversarial Network
by Yue Cui, Feiyu Yang, Mingzhang Zhou, Lianxiu Hao, Junfeng Wang, Haixin Sun, Aokun Kong and Jiajie Qi
Remote Sens. 2024, 16(4), 626; https://doi.org/10.3390/rs16040626 - 08 Feb 2024
Viewed by 538
Abstract
Deep learning techniques have made certain breakthroughs in direction-of-arrival (DOA) estimation in recent years. However, most of the current deep-learning-based DOA estimation methods view the direction finding problem as a grid-based multi-label classification task and require multiple samplings with a uniform linear array [...] Read more.
Deep learning techniques have made certain breakthroughs in direction-of-arrival (DOA) estimation in recent years. However, most of the current deep-learning-based DOA estimation methods view the direction finding problem as a grid-based multi-label classification task and require multiple samplings with a uniform linear array (ULA), which leads to grid mismatch issues and difficulty in ensuring accurate DOA estimation with insufficient sampling and in underdetermined scenarios. In order to solve these challenges, we propose a new DOA estimation method based on a deep convolutional generative adversarial network (DCGAN) with a coprime array. By employing virtual interpolation, the difference co-array derived from the coprime array is extended to a virtual ULA with more degrees of freedom (DOFs). Then, combining with the Hermitian and Toeplitz prior knowledge, the covariance matrix is retrieved by the DCGAN. A backtracking method is employed to ensure that the reconstructed covariance matrix has a low-rank characteristic. We performed DOA estimation using the MUSIC algorithm. Simulation results demonstrate that the proposed method can not only distinguish more sources than the number of physical sensors but can also quickly and accurately solve DOA, especially with limited snapshots, which is suitable for fast estimation in mobile agent localization. Full article
(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection)
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16 pages, 8955 KiB  
Technical Note
Information Extraction and Three-Dimensional Contour Reconstruction of Vehicle Target Based on Multiple Different Pitch-Angle Observation Circular Synthetic Aperture Radar Data
by Jian Zhang, Hongtu Xie, Lin Zhang and Zheng Lu
Remote Sens. 2024, 16(2), 401; https://doi.org/10.3390/rs16020401 - 20 Jan 2024
Viewed by 601
Abstract
The circular synthetic aperture radar (CSAR) has the ability of all-round continuous observation and high-resolution imaging detection, and can obtain all-round scattering information and higher-resolution images of the observation scene, so as to realize the target information extraction and three-dimensional (3D) contour reconstruction [...] Read more.
The circular synthetic aperture radar (CSAR) has the ability of all-round continuous observation and high-resolution imaging detection, and can obtain all-round scattering information and higher-resolution images of the observation scene, so as to realize the target information extraction and three-dimensional (3D) contour reconstruction of the observation targets. However, the existing methods are not accurate enough to extract the information of vehicle targets. Through the analysis of the vehicle target scattering model and CSAR image characteristics, this paper proposes a vehicle target information extraction and 3D contour reconstruction method based on multiple different pitch-angle observation CSAR data. The proposed method creatively utilizes the projection relationship of the vehicle in 2D CSAR imaging to reconstruct the 3D contour of the vehicle, without prior information. Firstly, the CSAR data obtained from multiple different pitch-angle observations are fully utilized, and the scattering points of odd-bounce reflection and even-bounce reflection echoes are extracted from the two-dimensional (2D) coherent CSAR images of the vehicle target. Secondly, the basic contour of the vehicle body is extracted from the scattering points of the even-bounce reflected echoes. Then, the geometric projection relationship of the “top–bottom shifting” effect of odd-bounce reflection is used to calculate the height and position information of the scattering points of odd-bounce reflection, so as to extract the multi-layer 3D contour of the vehicle target. Finally, the basic contour and the multi-layer 3D contour of the vehicle are fused to realize high-precision 3D contour reconstruction of the vehicle target. The correctness and effectiveness of the proposed method are verified by using the CVDomes simulation dataset of the American Air Force Research Laboratory (AFRL), and the experimental results show that the proposed method can achieve high-precision information extraction and realize distinct 3D contour reconstruction of the vehicle target. Full article
(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection)
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