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Radar Signal Processing for Target Tracking
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Radar Signal Processing for Target Tracking

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

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 45465

Special Issue Editors

Prof. Dr. Danilo Orlando

E-Mail Website
Guest Editor
Università degli Studi “Niccolò Cusano”, via Don Carlo Gnocchi 3, 00166 Rome, Italy
Interests: the field of statistical signal processing with applications to radar and sonar
Special Issues, Collections and Topics in MDPI journals
Dr. Filippo Biondi

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Guest Editor
University of L'Aquila (Italy), Piazzale E. Pontieri 67100 L'Aquila, Italy
Interests: the field of statistical signal processing with applications to synthetic aperture radar and synthetic aperture sonar
Special Issues, Collections and Topics in MDPI journals
Dr. Domenico Ciuonzo

E-Mail Website
Guest Editor
University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy
Interests: the field of statistical signal processing with applications to sensor networks, data fusion, radar, and network analytics
Dr. Carmine Clemente

E-Mail Website
Guest Editor
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G11XQ, UK
Interests: advanced radar signal processing algorithms; MIMO radars; passive radar systems and micro-Doppler analysis; extraction and classification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Last-generation radar systems are provided with a considerable abundance of computation power, which was inconceivable a few decades ago and has led to more and more sophisticated processing schemes at different system levels. In addition, technological development has allowed for a reduction in the costs of remote sensing devices and an increasing proliferation of sensing systems for both civilian and military applications facing challenging scenarios, which may include outliers, intentional interference, etc. In this context, one of the main tasks accomplished by a radar system is the active/passive tracking of multiple targets. Such a function can be fed by either compressed data, namely, detections along with the associated rough measurements at the output of the signal processing unit, or raw data at the output of the matched filter (track-before-detect paradigm and/or synthetic aperture radar tracking) and collected by means of sensor networks or multistatic radar systems with either a fusion center or distributed tracking architecture. Additionally, realistic and outlier unexpected effects may be detrimental to some model assumptions, paving the way to a need for (and possible integration with) data-driven approaches. Finally, it is important to highlight that tracking functions may play a primary role in several operating contexts as, for instance, space debris monitoring (due to high traffic density of satellites), tracking of icebergs (which are very spread because of climate changes), UAV (or agile targets) detection and tracking, etc.

This Special Issue is focused on the design of modern tracking algorithms for multiple targets that take advantage of both enhanced available computational power and recent approaches to statistical signal processing based upon machine learning and/or compressed sensing over possibly distributed system architectures.

Prof. Danilo Orlando
Dr. Filippo Biondi
Dr. Domenico Ciuonzo
Dr. Carmine Clemente
Guest Editor

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

  • Target Tracking for multistatic/MIMO radar
  • Bearing-only tracking
  • Tracking in sensor networks
  • LOCALIZATION Algorithms
  • Track-before-detect algorithms for multiple targets in conventional radars
  • Track-before-detect algorithms for synthetic aperture radar
  • Compressed-sensing-based tracking algorithms
  • Jamming platform tracking algorithms
  • Machine/Deep learning approaches to multiple targets tracking
  • Tracking space debris for space situational awareness (SSA) by radar and Inverse-SAR (ISAR)
  • Tracking icebergs by SAR multi-temporal images
  • Tracking of agile/fast-moving targets
  • Tracking of targets with kinematic characterized by transitories.

Published Papers (19 papers)

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19 pages, 1024 KiB  
Article
A Multi-Sensor Interacted Vehicle-Tracking Algorithm with Time-Varying Observation Error
by Jingjie Gao, Qian Zhang, Huachao Sun and Wei Wang
Remote Sens. 2022, 14(9), 2176; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092176 - 01 May 2022
Cited by 1 | Viewed by 1506
Abstract
Vehicle tracking in the field of intelligent transportation has received extensive attention in recent years. Multi-sensor-based vehicle tracking system is widely used in some critical environments. However, in the actual scenes, the observation error of each sensor is often different and time varying [...] Read more.
Vehicle tracking in the field of intelligent transportation has received extensive attention in recent years. Multi-sensor-based vehicle tracking system is widely used in some critical environments. However, in the actual scenes, the observation error of each sensor is often different and time varying because of the environmental change and the channel difference. Therefore, in this paper, we propose a multi-sensor interacted vehicle-tracking algorithm with time-varying observation error (MI-TVOE). The algorithm establishes a jointed and time-varying observation error model for each sensor to indicate the variation of observation noise. Then, we develop a multi-sensor interacted vehicle-tracking algorithm which can predict the statistical information of a time-varying observation error and fuse the tracking result of each sensor to provide a global estimation. Simulation results show that the proposed MI-TVOE algorithm can significantly improve the tracking performance compared to the single-sensor-based tracking method, the traditional unscented Kalman filter (UKF), the apdative UKF method (AUKF) and the multi-error fused UKF method (MEF-UKF), which will be well applied to the complex tracking scenes and will reduce the computational complexity with time-varying observation error. The experiments in this paper also prove the superiority of the proposed MI-TVOE algorithm in complex environments. Full article
(This article belongs to the Special Issue Radar Signal Processing for Target Tracking)
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22 pages, 3089 KiB  
Communication
Improved GM-PHD Filter with Birth Intensity and Spawned Intensity Estimation Based on Trajectory Situation Feedback Control
by Chao Zhang, Zhengzhou Li, Yong Zhu, Zefeng Luo and Tianqi Qin
Remote Sens. 2022, 14(7), 1683; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071683 - 31 Mar 2022
Cited by 2 | Viewed by 1235
Abstract
The Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter can effectively track multiple targets in a single scenario. However, for GM-PHD, unknown target behavior, e.g., target birth or target intersection, produces difficulties in terms of accurate estimation. First of all, GM-PHD assumes the model [...] Read more.
The Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter can effectively track multiple targets in a single scenario. However, for GM-PHD, unknown target behavior, e.g., target birth or target intersection, produces difficulties in terms of accurate estimation. First of all, GM-PHD assumes the model parameters about the birth target are prior information, which results in the inability to detect the birth target that occurs at random in complex scenarios. Then, since the measurements generated by the intersected targets overlap each other, GM-PHD cannot distinguish these targets, resulting in a biased estimation of the state and number of targets. To solve these problems, this paper proposes an improved GM-PHD filter with a birth intensity and spawned intensity updating method based on the trajectory situation feedback. In the filtering process, the trajectory initiation feedback formed by the rule-based correlation of Gaussian components is introduced to GM-PHD to adjust the birth intensity in real time, which is used to improve the detection of birth targets. Simultaneously, the analysis of trajectory situation is designed to determine the relative motion trend between targets. On this basis, the filter improves the recognition of the intersected targets by enhancing the spawned intensity. Simulation results demonstrate that the proposed algorithm achieves better performance on the state and number of targets in complex scenarios, and shows superiority to other GM-PHD filters. Full article
(This article belongs to the Special Issue Radar Signal Processing for Target Tracking)
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25 pages, 790 KiB  
Article
Data-Driven Joint Beam Selection and Power Allocation for Multiple Target Tracking
by Yuchun Shi, Hao Zheng and Kang Li 
Remote Sens. 2022, 14(7), 1674; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071674 - 30 Mar 2022
Cited by 2 | Viewed by 1421
Abstract
For the problem of joint beam selection and power allocation (JBSPA) for multiple target tracking (MTT), existing works tend to allocate resources only considering the MTT performance at the current tracking time instant. However, in this way, it cannot guarantee the long-term MTT [...] Read more.
For the problem of joint beam selection and power allocation (JBSPA) for multiple target tracking (MTT), existing works tend to allocate resources only considering the MTT performance at the current tracking time instant. However, in this way, it cannot guarantee the long-term MTT performance in the future. If the JBSPA not only considers the tracking performance at the current tracking time instant but also at the future tracking time instant, the allocation results are theoretically able to enhance the long-term tracking performance and the robustness of tracking. Motivated by this, the JBSPA is formulated as a model-free Markov decision process (MDP) problem, and solved with a data-driven method in this article, i.e., deep reinforcement learning (DRL). With DRL, the optimal policy is given by learning from the massive interacting data of the DRL agent and environment. In addition, in order to ensure the information prediction performance of target state in maneuvering target scenarios, a data-driven method is developed based on Long-short term memory (LSTM) incorporating the Gaussian mixture model (GMM), which is called LSTM-GMM for short. This method can realize the state prediction by learning the regularity of nonlinear state transitions of maneuvering targets, where the GMM is used to describe the target motion uncertainty in LSTM. Simulation results have shown the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Radar Signal Processing for Target Tracking)
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20 pages, 2930 KiB  
Article
Tracking of Maneuvering Extended Target Using Modified Variable Structure Multiple-Model Based on Adaptive Grid Best Model Augmentation
by Lifan Sun, Jinjin Zhang, Haofang Yu, Zhumu Fu and Zishu He
Remote Sens. 2022, 14(7), 1613; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071613 - 28 Mar 2022
Cited by 8 | Viewed by 1728
Abstract
Maneuvering extended target tracking, an important but challenging research field, has attracted increasing attention in the field of radar signal processing. Variable structure multiple-model (VSMM) estimation is the current mainstream tracking algorithm, it possesses high tracking performance but depends largely on the model [...] Read more.
Maneuvering extended target tracking, an important but challenging research field, has attracted increasing attention in the field of radar signal processing. Variable structure multiple-model (VSMM) estimation is the current mainstream tracking algorithm, it possesses high tracking performance but depends largely on the model set used. However, the existing model set design methods still cannot offer higher tracking performance due to the complex maneuverability of the target. The current best model augmentation (BMA) algorithm is an efficient and universal model set design method, but it cannot adapt well to complex maneuvering situations because of its fixed basic and candidate model set. Hence, this paper proposes a modified BMA algorithm for VSMM based on adaptive grid, it can make full use of the previous information to realize an adaptation and time variation of the model set. Overall, two different scenarios were considered, wherein the digital simulation shows that the proposed method can provide a more reliable estimation of the kinematic state and shape of the extended target. Full article
(This article belongs to the Special Issue Radar Signal Processing for Target Tracking)
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19 pages, 864 KiB  
Article
Smoothing Linear Multi-Target Tracking Using Integrated Track Splitting Filter
by Sufyan Ali Memon, Ihsan Ullah, Uzair Khan and Taek Lyul Song
Remote Sens. 2022, 14(5), 1289; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14051289 - 06 Mar 2022
Cited by 5 | Viewed by 2339
Abstract
Multi-target tracking (MTT) is a challenging issue due to an unknown number of real targets, motion uncertainties, and coalescence behavior of sensor (such as radar) measurements. The conventional MTT systems deal with intractable computational complexities because they enumerate all feasible joint measurement-to-track association [...] Read more.
Multi-target tracking (MTT) is a challenging issue due to an unknown number of real targets, motion uncertainties, and coalescence behavior of sensor (such as radar) measurements. The conventional MTT systems deal with intractable computational complexities because they enumerate all feasible joint measurement-to-track association hypotheses and recursively calculate the a posteriori probabilities of each of these joint hypotheses. Therefore, the state-of-art MTT system demands bypassing the entire joint data association procedure. This research work utilizes linear multi-target (LM) tracking to treat feasible target detections followed by neighbored tracks as clutters. The LM integrated track splitting (LMITS) algorithm was developed without a smoothing application that produces substantial estimation errors. Smoothing refines the state estimation in order to reduce estimation errors for an efficient MTT. Therefore, we propose a novel Fixed Interval Smoothing LMITS (FIsLMITS) algorithm in the existing LMITS algorithm framework to improve MTT performance. This algorithm initializes forward and backward tracks employing LMITS separately using measurements collected from the sensor in each scan. The forward track recursion starts after the smoothing. Therefore, each forward track acquires backward multi-tracks that arrived from upcoming scans (future scans) while simultaneously associating them in a forward track for fusion and smoothing. Thus, forward tracks become more reliable for multi-target state estimation in difficult cluttered environments. Monte Carlo simulations are carried out to demonstrate FIsLMITS with improved state estimation accuracy and false track discrimination (FTD) in comparison to the existing MTT algorithms. Full article
(This article belongs to the Special Issue Radar Signal Processing for Target Tracking)
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27 pages, 53026 KiB  
Article
A Novel 4D Track-before-Detect Approach for Weak Targets Detection in Clutter Regions
by Bo Yan, Hua Zhang, Luping Xu, Yu Chen and Hongmin Lu
Remote Sens. 2021, 13(23), 4942; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13234942 - 05 Dec 2021
Cited by 3 | Viewed by 1781
Abstract
A 4D TBD approach is developed here for closely weak extended target tracking and overcoming heterogeneous clutter background and various clutter regions. The 4D measurements in this work are the points containing three positional information in spatial space and corresponding timestamp. The proposed [...] Read more.
A 4D TBD approach is developed here for closely weak extended target tracking and overcoming heterogeneous clutter background and various clutter regions. The 4D measurements in this work are the points containing three positional information in spatial space and corresponding timestamp. The proposed method is mainly designed to address two issues. The first one is the dilemma between the weak target detection and difficult computation originating from the high dimensions of measurement. The second issue is the suppression of inhomogeneous background clutter and various clutter regions. The extension experiment using synthetic data showcases that no false alarm track would be built in the clutter regions, and the detection rate of close targets exceeds 94%. The experiments using real 3D radar also prove that the method works well in tracking closely maneuvering extended targets even if a clutter region exists. Full article
(This article belongs to the Special Issue Radar Signal Processing for Target Tracking)
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27 pages, 4634 KiB  
Article
An Improved Smooth Variable Structure Filter for Robust Target Tracking
by Yu Chen, Luping Xu, Guangmin Wang, Bo Yan and Jingrong Sun
Remote Sens. 2021, 13(22), 4612; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224612 - 16 Nov 2021
Cited by 7 | Viewed by 1634
Abstract
As a new-style filter, the smooth variable structure filter (SVSF) has attracted significant interest. Based on the predictor-corrector method and sliding mode concept, the SVSF is more robust in the face of modeling errors and uncertainties compared to the Kalman filter. Since the [...] Read more.
As a new-style filter, the smooth variable structure filter (SVSF) has attracted significant interest. Based on the predictor-corrector method and sliding mode concept, the SVSF is more robust in the face of modeling errors and uncertainties compared to the Kalman filter. Since the estimation performance is usually insufficient in real cases where the measurement vector is of fewer dimensions than the state vector, an improved SVSF (ISVSF) is proposed by combining the existing SVSF with Bayesian theory. The ISVSF contains two steps: firstly, a preliminary estimation is performed by SVSF. Secondly, Bayesian formulas are adopted to improve the estimation for higher accuracy. The ISVSF shows high robustness in dealing with modeling uncertainties and noise. It is noticeable that ISVSF could deliver satisfying performance even if the state of the system is undergoing a sudden change. According to the simulation results of target tracking, the proposed ISVSF performance can be better than that obtained with existing filters. Full article
(This article belongs to the Special Issue Radar Signal Processing for Target Tracking)
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19 pages, 2266 KiB  
Article
Tracking a Low-Angle Isolated Target via an Elevation-Angle Estimation Algorithm Based on Extended Kalman Filter with an Array Antenna
by Hossein Darvishi, Mohammad Ali Sebt, Domenico Ciuonzo and Pierluigi Salvo Rossi
Remote Sens. 2021, 13(19), 3938; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13193938 - 01 Oct 2021
Cited by 5 | Viewed by 2701
Abstract
In a low-angle tracking situation, estimating the elevation angle is challenging due to the entrance of the multipath signals in the antenna’s main lobe. In this article, we propose two methods based on the extended Kalman filter (EKF) and frequency diversity (FD) process [...] Read more.
In a low-angle tracking situation, estimating the elevation angle is challenging due to the entrance of the multipath signals in the antenna’s main lobe. In this article, we propose two methods based on the extended Kalman filter (EKF) and frequency diversity (FD) process to estimate the elevation angle of a low-angle isolated target. In the first case, a simple weighting of the per-frequency estimates is obtained (termed WFD). Differently, in the second case, a matrix-based elaboration of the per-frequency estimates is proposed (termed MFD). The proposed methods are completely independent of prior knowledge of geometrical information and the physical parameters. The simulation results show that both methods have excellent performance and guarantee accurate elevation angle estimation in different multipath environments and even in very-low SNR conditions. Hence, they are both suitable for low-peak-power radars. Full article
(This article belongs to the Special Issue Radar Signal Processing for Target Tracking)
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29 pages, 4803 KiB  
Article
Fusing Measurements from Wi-Fi Emission-Based and Passive Radar Sensors for Short-Range Surveillance
by Ileana Milani, Carlo Bongioanni, Fabiola Colone and Pierfrancesco Lombardo
Remote Sens. 2021, 13(18), 3556; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183556 - 07 Sep 2021
Cited by 7 | Viewed by 1990
Abstract
In this work, we consider the joint use of different passive sensors for the localization and tracking of human targets and small drones at short ranges, based on the parasitic exploitation of Wi-Fi signals. Two different sensors are considered in this paper: (i) [...] Read more.
In this work, we consider the joint use of different passive sensors for the localization and tracking of human targets and small drones at short ranges, based on the parasitic exploitation of Wi-Fi signals. Two different sensors are considered in this paper: (i) Passive Bistatic Radar (PBR) that exploits the Wi-Fi Access Point (AP) as an illuminator of opportunity to perform uncooperative target detection and localization and (ii) Passive Source Location (PSL) that uses radio frequency (RF) transmissions from the target to passively localize it, assuming that it is equipped with Wi-Fi devices. First, we show that these techniques have complementary characteristics with respect to the considered surveillance applications that typically include targets with highly variable motion parameters. Therefore, an appropriate sensor fusion strategy is proposed, based on a modified version of the Interacting Multiple Model (IMM) tracking algorithm, in order to benefit from the information diversity provided by the two sensors. The performance of the proposed strategy is evaluated against both simulated and experimental data and compared to the performance of the single sensors. The results confirm that the joint exploitation of the considered sensors based on the proposed strategy largely improves the positioning accuracy, target motion recognition capability and continuity in target tracking. Full article
(This article belongs to the Special Issue Radar Signal Processing for Target Tracking)
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27 pages, 60882 KiB  
Article
Generalized Dechirp-Keystone Transform for Radar High-Speed Maneuvering Target Detection and Localization
by Jibin Zheng, Kangle Zhu, Zhiyong Niu, Hongwei Liu and Qing Huo Liu
Remote Sens. 2021, 13(17), 3367; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173367 - 25 Aug 2021
Cited by 9 | Viewed by 2074
Abstract
The multivariate range function of the high-speed maneuvering target induces modulations on both the envelop and phase, i.e., the range cell migration (RCM) and Doppler frequency migration (DFM) which degrade the long-time coherent integration used for detection and localization. To solve this problem, [...] Read more.
The multivariate range function of the high-speed maneuvering target induces modulations on both the envelop and phase, i.e., the range cell migration (RCM) and Doppler frequency migration (DFM) which degrade the long-time coherent integration used for detection and localization. To solve this problem, many long-time coherent integration methods have been proposed. Based on mechanisms of typical methods, this paper names two signal processing modes, i.e., processing unification (PU) mode and processing separation (PS) mode, and presents their general forms. Thereafter, based on the principle of the PS mode, a novel long-time coherent integration method, known as the generalized dechirp-keystone transform (GDKT), is proposed for radar high-speed maneuvering target detection and localization. The computational cost, energy integration, peak-to-sidelobe level (PSL), resolution, and anti-noise performance of the GDKT are analyzed and compared with those of the maximum likelihood estimation (MLE) method and keystone transform-dechirp (KTD) method. With mathematical analyses and numerical simulations, we validate two main superiorities of the GDKT, including (1) the statistically optimal anti-noise performance, and (2) the low computational cost. The real radar data is also used to validate the GDKT. It is worthwhile noting that, based on closed analytical formulae of the MLE method, KTD method, and GDKT, several doubts in radar high-speed maneuvering target detection and localization are mathematically interpreted, such as the blind speed sidelobe (BSSL) and the relationship between the PU and PS modes. Full article
(This article belongs to the Special Issue Radar Signal Processing for Target Tracking)
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11 pages, 1523 KiB  
Communication
Angle Estimation for MIMO Radar in the Presence of Gain-Phase Errors with One Instrumental Tx/Rx Sensor: A Theoretical and Numerical Study
by Fangqing Wen, Junpeng Shi, Xinhai Wang and Lin Wang
Remote Sens. 2021, 13(15), 2964; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152964 - 28 Jul 2021
Cited by 7 | Viewed by 1523
Abstract
Ideal transmitting and receiving (Tx/Rx) array response is always desirable in multiple-input multiple-output (MIMO) radar. In practice, nevertheless, Tx/Rx arrays may be susceptible to unknown gain-phase errors (GPE) and yield seriously decreased positioning accuracy. This paper focuses on the direction-of-departure (DOD) and direction-of-arrival [...] Read more.
Ideal transmitting and receiving (Tx/Rx) array response is always desirable in multiple-input multiple-output (MIMO) radar. In practice, nevertheless, Tx/Rx arrays may be susceptible to unknown gain-phase errors (GPE) and yield seriously decreased positioning accuracy. This paper focuses on the direction-of-departure (DOD) and direction-of-arrival (DOA) problem in bistatic MIMO radar with unknown gain-phase errors (GPE). A novel parallel factor (PARAFAC) estimator is proposed. The factor matrices containing DOD and DOA are firstly obtained via PARAFAC decomposition. One DOD-DOA pair estimation is then accomplished from the spectrum searching. Thereafter, the remainder DOD and DOA are achieved by the least squares technique with the previous estimated angle pair. The proposed estimator is analyzed in detail. It only requires one instrumental Tx/Rx sensor, and it outperforms the state-of-the-art algorithms. Numerical simulations verify the theoretical advantages. Full article
(This article belongs to the Special Issue Radar Signal Processing for Target Tracking)
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19 pages, 561 KiB  
Article
Bearings-Only Target Tracking with an Unbiased Pseudo-Linear Kalman Filter
by Zihao Huang, Shijin Chen, Chengpeng Hao and Danilo Orlando
Remote Sens. 2021, 13(15), 2915; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152915 - 24 Jul 2021
Cited by 12 | Viewed by 2957
Abstract
In bearings-only target tracking, the pseudo-linear Kalman filter (PLKF) attracts much attention because of its stability and its low computational burden. However, the PLKF’s measurement vector and the pseudo-linear noise are correlated, which makes it suffer from bias problems. Although the bias-compensated PLKF [...] Read more.
In bearings-only target tracking, the pseudo-linear Kalman filter (PLKF) attracts much attention because of its stability and its low computational burden. However, the PLKF’s measurement vector and the pseudo-linear noise are correlated, which makes it suffer from bias problems. Although the bias-compensated PLKF (BC–PLKF) and the instrumental variable-based PLKF (IV–PLKF) can eliminate the bias, they only work well when the target behaves with non-manoeuvring movement. To extend the PLKF to the manoeuvring target tracking scenario, an unbiased PLKF (UB–PLKF) algorithm, which splits the noise away from the measurement vector directly, is proposed. Based on the results of the UB–PLKF, we also propose its velocity-constrained version (VC–PLKF) to further improve the performance. Simulations show that the UB–PLKF and VC–PLKF outperform the BC–PLKF and IV–PLKF both in non-manoeuvring and manoeuvring scenarios. Full article
(This article belongs to the Special Issue Radar Signal Processing for Target Tracking)
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19 pages, 478 KiB  
Article
DOA and Range Estimation for FDA-MIMO Radar with Sparse Bayesian Learning
by Qi Liu, Xianpeng Wang, Mengxing Huang, Xiang Lan and Lu Sun
Remote Sens. 2021, 13(13), 2553; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132553 - 29 Jun 2021
Cited by 8 | Viewed by 2342
Abstract
Due to grid division, the existing target localization algorithms based on sparse signal recovery for the frequency diverse array multiple-input multiple-output (FDA-MIMO) radar not only suffer from high computational complexity but also encounter significant estimation performance degradation caused by off-grid gaps. To tackle [...] Read more.
Due to grid division, the existing target localization algorithms based on sparse signal recovery for the frequency diverse array multiple-input multiple-output (FDA-MIMO) radar not only suffer from high computational complexity but also encounter significant estimation performance degradation caused by off-grid gaps. To tackle the aforementioned problems, an effective off-grid Sparse Bayesian Learning (SBL) method is proposed in this paper, which enables the calculation the direction of arrival (DOA) and range estimates. First of all, the angle-dependent component is split by reconstructing the received data and contributes to immediately extract rough DOA estimates with the root SBL algorithm, which, subsequently, are utilized to obtain the paired rough range estimates. Furthermore, a discrete grid is constructed by the rough DOA and range estimates, and the 2D-SBL model is proposed to optimize the rough DOA and range estimates. Moreover, the expectation-maximization (EM) algorithm is utilized to update the grid points iteratively to further eliminate the errors caused by the off-grid model. Finally, theoretical analyses and numerical simulations illustrate the effectiveness and superiority of the proposed method. Full article
(This article belongs to the Special Issue Radar Signal Processing for Target Tracking)
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13 pages, 1480 KiB  
Communication
A Novel MIMO Radar Orthogonal Waveform Design Algorithm Based on Intelligent Ions Motion
by Lei Zhang and Fangqing Wen
Remote Sens. 2021, 13(10), 1968; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101968 - 18 May 2021
Cited by 10 | Viewed by 2171
Abstract
Orthogonal waveform design is one of the key technologies that affects the detection performance of MIMO radars. Most of the existing methods indirectly tackle this problem as an intractable nonconvex optimization and an NP-hard problem. In this work, we propose a novel waveform [...] Read more.
Orthogonal waveform design is one of the key technologies that affects the detection performance of MIMO radars. Most of the existing methods indirectly tackle this problem as an intractable nonconvex optimization and an NP-hard problem. In this work, we propose a novel waveform design algorithm based on intelligent ions motion optimization (IMO) to directly obtain a set of polyphase codes with good orthogonality. The autocorrelation sidelobe and cross-correlation sidelobe are first derived and subsequently integrated into evaluation functions for evaluating the orthogonality of polyphase codes. In order to effectively cope with the aforementioned problem, we present a strengthened IMO that is highly robust and converges rapidly. In the liquid state, an optimal guiding principle of same-charge ions is suggested to enhance global search ability and avoid falling into local optima. An ion updating strategy based on fitness ranking is presented to improve the search efficiency in the crystal state. Finally, the improved algorithm is employed to optimize the polyphase codes. The experimental results, compared with other state-of-the-art algorithms, show that the polyphase codes obtained by the proposed algorithm have better orthogonality. Full article
(This article belongs to the Special Issue Radar Signal Processing for Target Tracking)
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19 pages, 13577 KiB  
Article
Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network
by Jingyu Cong, Xianpeng Wang, Xiang Lan, Mengxing Huang and Liangtian Wan
Remote Sens. 2021, 13(10), 1956; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101956 - 17 May 2021
Cited by 21 | Viewed by 3019
Abstract
The traditional frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar two-dimensional (2D) super-resolution (SR) estimation algorithm for target localization has high computational complexity, which runs counter to the increasing demand for real-time radar imaging. In this paper, a fast joint direction-of-arrival (DOA) and [...] Read more.
The traditional frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar two-dimensional (2D) super-resolution (SR) estimation algorithm for target localization has high computational complexity, which runs counter to the increasing demand for real-time radar imaging. In this paper, a fast joint direction-of-arrival (DOA) and range estimation framework for target localization is proposed; it utilizes a very deep super-resolution (VDSR) neural network (NN) framework to accelerate the imaging process while ensuring estimation accuracy. Firstly, we propose a fast low-resolution imaging algorithm based on the Nystrom method. The approximate signal subspace matrix is obtained from partial data, and low-resolution imaging is performed on a low-density grid. Then, the bicubic interpolation algorithm is used to expand the low-resolution image to the desired dimensions. Next, the deep SR network is used to obtain the high-resolution image, and the final joint DOA and range estimation is achieved based on the reconstructed image. Simulations and experiments were carried out to validate the computational efficiency and effectiveness of the proposed framework. Full article
(This article belongs to the Special Issue Radar Signal Processing for Target Tracking)
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19 pages, 3585 KiB  
Article
Group Target Tracking Based on MS-MeMBer Filters
by Zhiguo Zhang, Jinping Sun, Huiyu Zhou and Congan Xu
Remote Sens. 2021, 13(10), 1920; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101920 - 14 May 2021
Cited by 12 | Viewed by 2581
Abstract
This paper presents a new group target tracking method based on the standard multi-sensor multi-target multi-Bernoulli (MS-MeMBer) filter. In the prediction step, the group structure is used to constrain the movement of the constituent members within the respective groups. Specifically, the group of [...] Read more.
This paper presents a new group target tracking method based on the standard multi-sensor multi-target multi-Bernoulli (MS-MeMBer) filter. In the prediction step, the group structure is used to constrain the movement of the constituent members within the respective groups. Specifically, the group of members is considered as an undirected random graph. Combined with the virtual leader-follower model, the motion equation of the members within groups is formulated. In the update step, the partitioning problem of multiple sensors is transformed into a multi-dimensional assignment (MDA) problem. Compared with the original two-step greedy partitioning mechanism, the MDA algorithm achieves better measurement partitions in group target tracking scenarios. To evaluate the performance of the proposed method, a simulation scenario including group splitting and merging is established. Results show that, compared with the standard MS-MeMBer filter, our method can effectively estimate the cardinality of members and groups at the cost of increasing computational load. The filtering accuracy of the proposed method outperforms that of the MS-MeMBer filter. Full article
(This article belongs to the Special Issue Radar Signal Processing for Target Tracking)
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19 pages, 1971 KiB  
Article
A Track-Before-Detect Strategy Based on Sparse Data Processing for Air Surveillance Radar Applications
by Nicomino Fiscante, Pia Addabbo, Carmine Clemente, Filippo Biondi, Gaetano Giunta and Danilo Orlando
Remote Sens. 2021, 13(4), 662; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040662 - 12 Feb 2021
Cited by 15 | Viewed by 4168
Abstract
In this paper we consider the tracking problem of a moving target competing against noise and clutter in a surveillance radar scenario. For a single array-antenna multiple-target tracking system and according to the Track-Before-Detect paradigm, we present a novel approach based on a [...] Read more.
In this paper we consider the tracking problem of a moving target competing against noise and clutter in a surveillance radar scenario. For a single array-antenna multiple-target tracking system and according to the Track-Before-Detect paradigm, we present a novel approach based on a three-stage processing chain that involves the Sparse Learning via Iterative Minimization algorithm, the k-means clustering method and the ad hoc detector by exploiting the sparse nature of the operating scenario. Under the latter assumption, the detection strategy declares the presence of targets subsequently to the retrieval of their corresponding tracks performed by jointly processing the received echoes of multiple consecutive radar scans. Simulation results show that the proposed approach is able to provide good tracking and detection capabilities for different multiple target trajectories with low Signal-to-Interference-plus-Noise ratio and results in providing advantages when compared to a number of other reference Track-Before-Detect strategies based on sparse data processing techniques. Full article
(This article belongs to the Special Issue Radar Signal Processing for Target Tracking)
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24 pages, 1772 KiB  
Article
Non-Cooperative Passive Direct Localization Based on Waveform Estimation
by Tao Zhou, Wei Yi and Lingjiang Kong
Remote Sens. 2021, 13(2), 264; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13020264 - 13 Jan 2021
Cited by 3 | Viewed by 1920
Abstract
This paper considers a non-cooperative passive localization system wherein widely distributed receivers are used to localize a transmitter radiating a periodical pulse pair signal. Two possible pulse modulation models, noncoherent and coherent pulses, are fully considered for practical application, and are effectively unified [...] Read more.
This paper considers a non-cooperative passive localization system wherein widely distributed receivers are used to localize a transmitter radiating a periodical pulse pair signal. Two possible pulse modulation models, noncoherent and coherent pulses, are fully considered for practical application, and are effectively unified as a general model for the algorithm design. To achieve highly accurate and robust localization performance, an enhanced direct position determination (DPD) algorithm based on waveform estimation (WE) is devised to jointly estimate the transmitter position and the waveform profile. The optimal objective function based on a least square (LS) principle is first derived to directly determine the position of the transmitter. Due to the complete lack of knowledge on the transmitted signal, the processing center calculates the objective function at each searched grid of interest by using estimated pulses instead of the real ones, while extraction of pulse samples and estimation of waveform are executed. Theoretical derivation gives the solution to estimate the non-parameterized waveform with a structure of maximum Rayleigh quotient. Additionally, simulation results verify the effectiveness of the proposed algorithm for many common waveform types in the cases of transmitting noncoherent and coherent pulses, and also show the excellent advantage over the classical DPD algorithm at low signal-to-noise ratio (SNR). Full article
(This article belongs to the Special Issue Radar Signal Processing for Target Tracking)
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18 pages, 3762 KiB  
Technical Note
High Accuracy Motion Detection Algorithm via ISM Band FMCW Radar
by Kui Qu, Rongfu Zhang and Zhijun Fang
Remote Sens. 2022, 14(1), 58; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14010058 - 23 Dec 2021
Cited by 2 | Viewed by 2611
Abstract
The conventional frequency modulated continuous wave (FMCW) radar accuracy range detection algorithm is based on the frequency estimation and additional phase evaluation which contains Fourier transform and frequency refining analysis in each chirp, so it has the disadvantages of being computationally expensive, and [...] Read more.
The conventional frequency modulated continuous wave (FMCW) radar accuracy range detection algorithm is based on the frequency estimation and additional phase evaluation which contains Fourier transform and frequency refining analysis in each chirp, so it has the disadvantages of being computationally expensive, and not being suitable for real-time motion measurement. In addition, if there are other objects near the target, the spectra of the clutter and the target will be adjacent and affect each other, making it more challenging to estimate the frequency of the target. In this paper, the analytical expression of the Fourier transform of the beat signal is presented and it can be seen that spectrum leakage makes the phase of Fourier transform no longer consistent with the real phase of signal. The change regularities of real and imaginary parts of Fourier transform are studied, and the corrected phase of ellipse approximation is given in the industrial, scientific, and medical (ISM) band. Accurate displacement can be obtained by accurate phase. The algorithm can filter the direct current (DC) offset which is mainly caused by stationary objects. The performance of the algorithm is evaluated by a radar system whose center frequency is 24.075 GHz and the bandwidth is 0.15 GHz; the measurement accuracy of displacement is 0.087 mm and the accuracy of distance is 0.043 m. Full article
(This article belongs to the Special Issue Radar Signal Processing for Target Tracking)
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