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

Deadline for manuscript submissions: 30 April 2025 | Viewed by 9810

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Special Issue Editors

Prof. Dr. Junpeng Shi

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Guest Editor

College of Electronic Engineering, The National University of Defense Technology, Hefei 230027, China
Interests: radar signal processing; direction of arrival

Dr. Yuan Liu

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Guest Editor

The School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: radar imaging (SAR/ISAR); sparse signal recovery techniques; array signal processing and machine learning techniques

Dr. Weijian Liu

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Guest Editor

Wuhan Electronic Information Institute, Wuhan 410039, China
Interests: multichannel signal detection; statistical and array signal processing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jingwei Xu

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Guest Editor

National Lab of Radar Signal Processing, School of Electronic Engineering, Xidian University, Xi’an 710071, China
Interests: spaceborne/airborne radar technique; space-time adaptive processing; frequency diverse array radar
Special Issues, Collections and Topics in MDPI journals

Dr. Haowei Zhang

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Guest Editor

Air and Missile Defense College, Air Force Engineering University, Xi’an, China
Interests: radar resource management; target detetcion and tracking; integrated radar and communication system

Special Issue Information

Dear Colleagues,

Inrecent years, considerable progress has been made in developing theories and methodologies for array and signal processing for radar. However, further improved detection, estimation, and imaging performance has been shown to be challenging due to problems associated with the increasingly complex signal environments, such as clutter, jamming, etc. It is important to explore new theories, technologies, and applications in this case.

Specifically, in large-scale multi-input and multi-output systems, the significant increase in array antenna size has brought unprecedented resolution while also creating new requirements for mathematical tools such as signal optimization, feature analysis, and performance evaluation. With the widespread application of artificial intelligence, data-driven machine learning methods have also been introduced into the fields of sensor arrays and signal processing to overcome the performance limitations of traditional model-based methods under non-ideal signal conditions, thus deriving a new pattern of model and data driving. The resource management theory is facing challenges related to ideal environment to electronic antagonism environment, single resource to multiple resource, and convex optimization to non-convex optimization shifts. The above examples all demonstrate the active development trends and enormous application potential in the fields of array and signal processing for radar.

In order to further promote the innovative development of basic theories, key technologies, and applications of array and signal processing for radar, this Special Issue aims to gather the latest research progress in the field of array and signal processing for radar, especially for remote sensing. The types of collected papers will include academic papers and review articles discussing the latest technological achievements. These papers will cover subjects including, but not limited to, the following:

Target detection, tracking and imaging;
Direction of arrival estimation;
Adaptive beamforming ;
Radar signal processing based on compressive sensing and sparse optimization theory;
Performance bound of parameter estimation;
MIMO radar array signal processing;
Tensor optimization theory and learning strategies for multidimensional signals;
Space-time adaptive signal processing;
Machine learning-based radar array and signal processing;
Radar array and signal processing for IoT applications.

Prof. Dr. Junpeng Shi
Dr. Yuan Liu
Dr. Weijian Liu
Dr. Jingwei Xu
Dr. Haowei Zhang
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 array and signal processing
waveform and frequency diversity
interference, clutter and noise suppression
radar imaging
radar resource management
machine learning
space-based radar (SBR) system

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Published Papers (12 papers)

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17 pages, 605 KiB

Open AccessCommunication

Coherent Signal DOA Estimation Method Based on Space–Time–Coding Metasurface

by Guanchao Chen, Xiaolong Su, Lida He, Dongfang Guan and Zhen Liu

Remote Sens. 2025, 17(2), 218; https://doi.org/10.3390/rs17020218 - 9 Jan 2025

Viewed by 443

Abstract

A novel method for the direction of arrival (DOA) estimation of coherent signals under a space–time–coding metasurface (STCM) is proposed in this paper. Noticeably, the STCM can replace multi-channel arrays with a single channel, which can be utilized to modulate incident electromagnetic waves and generate harmonics. However, coherent signals are overlapping in the frequency spectrum and cannot achieve DOA estimation through subspace methods. Therefore, the proposed method transforms the angle information in the time domain into amplitude and phase information at harmonics in the frequency domain by modulating incident coherent signals using the STCM and performing a fast Fourier transform (FFT) on these signals. Based on the harmonics in the frequency spectrum of the coherent signals, appropriate harmonics are selected. Finally, the ℓ₁ norm singular value decomposition (ℓ₁-SVD) algorithm is utilized for achieving high-precision DOA estimation. Simulation experiments are conducted to show the performance of the proposed method under the condition of different incident angles, harmonic numbers, signal-to-noise ratios (SNRs), etc. Compared to the traditional algorithms, the performance of the proposed algorithm can achieve more accurate DOA estimation under a low SNR. Full article

(This article belongs to the Special Issue Array and Signal Processing for Radar)

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27 pages, 24936 KiB

Open AccessArticle

Multipath and Deep Learning-Based Detection of Ultra-Low Moving Targets Above the Sea

by Zhaolong Wang, Xiaokuan Zhang, Weike Feng, Binfeng Zong, Tong Wang, Cheng Qi and Xixi Chen

Remote Sens. 2024, 16(24), 4773; https://doi.org/10.3390/rs16244773 - 21 Dec 2024

Viewed by 424

Abstract

An intelligent approach is proposed and investigated in this paper for the detection of ultra-low-altitude sea-skimming moving targets for airborne pulse Doppler radar. Without suppressing interferences, the proposed method uses both target and multipath information for detection based on their distinguishable image features and deep learning (DL) techniques. First, the image features of the target, multipath, and sea clutter in the real-measured range-Doppler (RD) map are analyzed, based on which the target and multipath are defined together as the generalized target. Then, based on the composite electromagnetic scattering mechanism of the target and the ocean surface, a scattering-based echo generation model is established and validated to generate sufficient data for DL network training. Finally, the RD features of the generalized target are learned by training the DL-based target detector, such as you-only-look-once version 7 (YOLOv7) and Faster R-CNN. The detection results show the high performance of the proposed method on both simulated and real-measured data without suppressing interferences (e.g., clutter, jamming, and noise). In particular, even if the target is submerged in clutter, the target can still be detected by the proposed method based on the multipath feature. Full article

(This article belongs to the Special Issue Array and Signal Processing for Radar)

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17 pages, 4491 KiB

Open AccessArticle

Height Measurement for Meter-Wave MIMO Radar Based on Sparse Array Under Multipath Interference

by Cong Qin, Qin Zhang, Guimei Zheng, Gangsheng Zhang and Shiqiang Wang

Remote Sens. 2024, 16(22), 4331; https://doi.org/10.3390/rs16224331 - 20 Nov 2024

Viewed by 623

Abstract

For meter-wave multiple-input multiple-output (MIMO) radar, the multipath of target echoes may cause severe errors in height measurement, especially in the case of complex terrain where terrain fluctuation, ground inclination, and multiple reflection points exist. Inspired by a sparse array with greater degrees of freedom and low mutual coupling, a height measurement method based on a sparse array is proposed. First, a practical signal model of MIMO radar based on a sparse array is established. Then, the modified multiple signal classification (MUSIC) and maximum likelihood (ML) estimation algorithms based on two classical sparse arrays (coprime array and nested array) are proposed. To reduce the complexity of the algorithm, a real-valued processing algorithm for generalized MUSIC (GMUSIC) and maximum likelihood is proposed, and a reduced dimension matrix is introduced into the real-valued processing algorithm to further reduce computation complexity. Finally, sufficient simulation results are provided to illustrate the effectiveness and superiority of the proposed technique. The simulation results show that the height measurement accuracy can be efficiently improved by using our proposed technique for both simple and complex terrain. Full article

(This article belongs to the Special Issue Array and Signal Processing for Radar)

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21 pages, 626 KiB

Open AccessArticle

Algorithm for Designing Waveforms Similar to Linear Frequency Modulation Using Polyphase-Coded Frequency Modulation

by Pengpeng Wang, Zhan Wang, Peng You and Mengyun An

Remote Sens. 2024, 16(19), 3664; https://doi.org/10.3390/rs16193664 - 1 Oct 2024

Cited by 1 | Viewed by 994

Abstract

Linear frequency modulation (LFM) waveforms have been widely adopted due to their excellent performance characteristics, such as good Doppler tolerance and ease of physical implementation. However, LFM waveforms suffer from high autocorrelation sidelobes (ACSLs) and limited design flexibility. Phase-coded frequency modulation (PCFM) waveforms can be used to design waveforms similar to LFM, offering greater design flexibility to optimize ACSLs. However, it has been found that the initial PCFM waveform experiences spectral expansion during the ACSL optimization process, which reduces its similarity to LFM. Therefore, this article jointly optimizes the ACSLs and spectrum of the initial PCFM waveform, establishes an optimized mathematical model, and then solves it using the heavy-ball gradient descent algorithm. Numerical experiments indicate that the proposed method effectively addresses the problem of waveform similarity degradation caused by spectral expansion while reducing waveform ACSLs. At the same time, a balance between reducing waveform ACSLs and preserving waveform similarity can be achieved by adjusting the parameters. Full article

(This article belongs to the Special Issue Array and Signal Processing for Radar)

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17 pages, 1797 KiB

Open AccessArticle

Central Difference Variational Filtering Based on Conjugate Gradient Method for Distributed Imaging Application

by Wen Ye, Fubo Zhang and Hongmei Chen

Remote Sens. 2024, 16(18), 3541; https://doi.org/10.3390/rs16183541 - 23 Sep 2024

Viewed by 704

Abstract

The airborne distributed position and orientation system (ADPOS), which integrates multi-inertia measurement units (IMUs), a data-processing computer, and a Global Navigation Satellite System (GNSS), serves as a key sensor in new higher-resolution airborne remote sensing applications, such as array SAR and multi-node imaging loads. ADPOS can provide reliable, high-precision and high-frequency spatio-temporal reference information to realize multinode motion compensation with the various nonlinear filter estimation methods such as Central Difference Kalman Filtering (CDKF), and modified CDKF. Although these known nonlinear models demonstrate good performance, their noise estimation performance with its linear minimum variance estimation criterion is limited for ADPOS. For this reason, in this paper, Central Difference Variational Filtering (CDVF) based on the variational optimization process is presented. This method adopts the conjugate gradient algorithm to enhance the estimation performance for mean correction in the filtering update stage. On one hand, the proposed method achieves adaptability by estimating noise covariance through the variational optimization method. On the other hand, robustness is implemented under the minimum variance estimation criterion based on the conjugate gradient algorithm to suppress measurement noise. We conducted a real ADPOS flight test, and the experimental results show that the accuracy of the slave motion parameters has significantly improved compared to the current CDKF. Moreover, the compensation performance shows a clear enhancement. Full article

(This article belongs to the Special Issue Array and Signal Processing for Radar)

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24 pages, 4359 KiB

Open AccessArticle

Resource Allocation of Netted Opportunistic Array Radar for Maneuvering Target Tracking under Uncertain Conditions

by Qinghua Han, Weijun Long, Zhen Yang, Xishang Dong, Jun Chen, Fei Wang and Zhiheng Liang

Remote Sens. 2024, 16(18), 3499; https://doi.org/10.3390/rs16183499 - 20 Sep 2024

Viewed by 839

Abstract

The highly dynamic properties of maneuvering targets make it intractable for radars to predict the target motion states accurately and quickly, and low-grade predicted states depreciate the efficiency of resource allocation. To overcome this problem, we introduce the modified current statistical (MCS) model, which incorporates the input-acceleration transition matrix into the augmented state transition matrix, to predict the motion state of a maneuvering target. Based on this, a robust resource allocation strategy is developed for maneuvering target tracking (MTT) in a netted opportunistic array radar (OAR) system under uncertain conditions. The mechanism of the strategy is to minimize the total transmitting power conditioned on the desired tracking performance. The predicted conditional Cramér–Rao lower bound (PC-CRLB) is deemed as the optimization criterion, which is derived based on the recently received measurement so as to provide a tighter lower bound than the posterior CRLB (PCRLB). For the uncertainty of the target reflectivity, we encapsulate the determined resource allocation model with chance-constraint programming (CCP) to balance resource consumption and tracking performance. A hybrid intelligent optimization algorithm (HIOA), which integrates a stochastic simulation and a genetic algorithm (GA), is employed to solve the CCP problem. Finally, simulations demonstrate the efficiency and robustness of the presented algorithm. Full article

(This article belongs to the Special Issue Array and Signal Processing for Radar)

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18 pages, 7339 KiB

Open AccessArticle

Thorough Understanding and 3D Super-Resolution Imaging for Forward-Looking Missile-Borne SAR via a Maneuvering Trajectory

by Tong Gu, Yifan Guo, Chen Zhao, Jian Zhang, Tao Zhang and Guisheng Liao

Remote Sens. 2024, 16(18), 3378; https://doi.org/10.3390/rs16183378 - 11 Sep 2024

Viewed by 1229

Abstract

For missile-borne platforms, traditional SAR technology consistently encounters two significant shortcomings: geometric distortion of 2D images and the inability to achieve forward-looking imaging. To address these issues, this paper explores the feasibility of using a maneuvering trajectory to enable forward-looking and three-dimensional imaging by analyzing the maneuvering characteristics of an actual missile-borne platform. Additionally, it derives the corresponding resolution characterization model, which lays a theoretical foundation for future applications. Building on this, the paper proposes a three-dimensional super-resolution imaging algorithm that combines axis rotation with compressed sensing. The axis rotation not only realizes the dimensionality reduction of data, but also can expand the observation scenario in the cross-track dimension. The proposed algorithm first focuses on the track-vertical plane to extract 2D position parameters. Then, a compressed sensing-based process is applied to extract reflection coefficients and super-resolution cross-track position parameters, thereby achieving precise 3D imaging reconstruction. Finally, numerical simulation results confirm the effectiveness and accuracy of the proposed algorithm. Full article

(This article belongs to the Special Issue Array and Signal Processing for Radar)

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14 pages, 539 KiB

Open AccessCommunication

Four-Dimensional Parameter Estimation for Mixed Far-Field and Near-Field Target Localization Using Bistatic MIMO Arrays and Higher-Order Singular Value Decomposition

by Qi Zhang, Hong Jiang and Huiming Zheng

Remote Sens. 2024, 16(18), 3366; https://doi.org/10.3390/rs16183366 - 10 Sep 2024

Viewed by 766

Abstract

In this paper, we present a novel four-dimensional (4D) parameter estimation method to localize the mixed far-field (FF) and near-field (NF) targets using bistatic MIMO arrays and higher-order singular value decomposition (HOSVD). The estimated four parameters include the angle-of-departure (AOD), angle-of-arrival (AOA), range-of-departure (ROD), and range-of-arrival (ROA). In the method, we store array data in a tensor form to preserve the inherent multidimensional properties of the array data. First, the observation data are arranged into a third-order tensor and its covariance tensor is calculated. Then, the HOSVD of the covariance tensor is performed. From the left singular vector matrices of the corresponding module expansion of the covariance tensor, the subspaces with respect to transmit and receive arrays are obtained, respectively. The AOD and AOA of the mixed FF and NF targets are estimated with signal-subspace, and the ROD and ROA of the NF targets are achieved using noise-subspace. Finally, the estimated four parameters are matched via a pairing method. The Cramér–Rao lower bound (CRLB) of the mixed target parameters is also derived. The numerical simulations demonstrate the superiority of the tensor-based method. Full article

(This article belongs to the Special Issue Array and Signal Processing for Radar)

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19 pages, 4249 KiB

Open AccessArticle

Robust Direction-of-Arrival Estimation in the Presence of Outliers and Noise Nonuniformity

by Bin Gao, Xing Shen, Zhengqiang Li and Bin Liao

Remote Sens. 2024, 16(17), 3140; https://doi.org/10.3390/rs16173140 - 26 Aug 2024

Viewed by 857

Abstract

In direction-of-arrival (DOA) estimation with sensor arrays, the background noise is usually modeled to be uncorrelated uniform white noise, such that the related algorithms can be greatly simplified by making use of the property of the noise covariance matrix being a diagonal matrix with identical diagonal entries. However, this model can be easily violated by the nonuniformity of sensor noise and the presence of outliers that may arise from unexpected impulsive noise. To tackle this problem, we first introduce an exploratory factor analysis (EFA) model for DOA estimation in nonuniform noise. Then, to deal with the outliers, a generalized extreme Studentized deviate (ESD) test is applied for outlier detection and trimming. Based on the trimmed data matrix, a modified EFA model, which belongs to weighted least-squares (WLS) fitting problems, is presented. Furthermore, a monotonic convergent iterative reweighted least-squares (IRLS) algorithm, called the iterative majorization approach, is introduced to solve the WLS problem. Simulation results show that the proposed algorithm offers improved robustness against nonuniform noise and observation outliers over traditional algorithms. Full article

(This article belongs to the Special Issue Array and Signal Processing for Radar)

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28 pages, 4464 KiB

Open AccessArticle

Joint Antenna Scheduling and Power Allocation for Multi-Target Tracking under Range Deception Jamming in Distributed MIMO Radar System

by Zhengjie Li, Yang Yang, Ruijun Wang, Cheng Qi and Jieyu Huang

Remote Sens. 2024, 16(14), 2616; https://doi.org/10.3390/rs16142616 - 17 Jul 2024

Cited by 1 | Viewed by 973

Abstract

The proliferation of electronic countermeasure (ECM) technology has presented military radar with unprecedented challenges as it remains the primary method of battlefield situational awareness. In this paper, a joint antenna scheduling and power allocation (JASPA) scheme is put forward for multi-target tracking (MTT) in the distributed multiple-input multiple-output (D-MIMO) radar. Aiming at radar resource scheduling in the presence of range deception jamming (RDJ), the false target discriminator is designed based on the Cramer–Rao lower bound (CRLB) in terms of the spoofing range, and the predicted conditional CRLB (PC-CRLB) plays a role in evaluating tracking accuracy. The JASPA scheme integrates the quality of service (QoS) principle to develop an optimization model based on false target discrimination, with the objective of enhancing both the discrimination probability of false targets and the tracking accuracy of real targets concurrently. Since the optimal variables can be separated in constraints, a four-step optimization cycle (FSOC)-based algorithm is developed to solve the multidimensional non-convex problem. Numerical simulation results are provided to illustrate the effectiveness of the proposed JASPA scheme in dealing with MTT in the RDJ environment. Full article

(This article belongs to the Special Issue Array and Signal Processing for Radar)

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15 pages, 1439 KiB

Open AccessTechnical Note

An Optimized Diffuse Kalman Filter for Frequency and Phase Synchronization in Distributed Radar Networks

by Xueyin Geng, Jun Wang, Bin Yang and Jinping Sun

Remote Sens. 2025, 17(3), 497; https://doi.org/10.3390/rs17030497 - 31 Jan 2025

Viewed by 244

Abstract

Distributed radar networks have emerged as a key technology in remote sensing and surveillance due to their high transmission power and robustness against node failures. When performing coherent beamforming with multiple radars, frequency and phase deviations introduced by independent oscillators lead to a decrease in transmission power. This paper proposes an optimized diffuse Kalman filter (ODKF) for the frequency and phase synchronization. Specifically, each radar locally estimates its frequency and phase, then shares this information with neighboring nodes, which are used for incremental update and diffusion update to adjust local estimates. To further reduce synchronization errors, we incorporate a self-feedback strategy in the diffusion step, in which each node balances its own estimate with neighbor information by optimizing the diagonal weights in the diffusion matrix. Numerical simulations demonstrate the superior performance of the proposed method in terms of mean squared deviation (MSD) and convergence speed. Full article

(This article belongs to the Special Issue Array and Signal Processing for Radar)

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11 pages, 3424 KiB

Open AccessTechnical Note

Enhancing Calibration Precision in MIMO Radar with Initial Parameter Optimization

by Yonghwi Kwon, Kanghyuk Seo and Chul Ki Kim

Remote Sens. 2025, 17(3), 389; https://doi.org/10.3390/rs17030389 - 23 Jan 2025

Viewed by 383

Abstract

For Advanced Driver Assistance Systems (ADASs), lots of researchers have been constantly researching various devices that can become the eyes of a vehicle. Currently represented devices are LiDAR, camera, and radar. This paper suggests one of the operation processes to study radar, which can be used regardless of climate change or weather, day or night. Thus, we propose a simple and easy calibration method for Multi-Input Multi-Output (MIMO) radar to guarantee performance with initial calibration parameters. Based on a covariance matrix, the modified signals of all channels improve performance, reducing unexpected interferences. Therefore, using the proposed coupling matrix, we can reduce unexpected interference and generate accurately calibrated results. To prove and verify the improvement in our method, a practical experiment is conducted with Frequency-Modulated Continuous-Wave (FMCW) MIMO radar, mounted on an automotive. Full article

(This article belongs to the Special Issue Array and Signal Processing for Radar)

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