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20 pages, 3003 KiB

Open AccessArticle

Analysis of Electromagnetic Wave and Multipath Suppression from Overhead Perspective

by Haolan Luo, Wenqiang Zhang, Zhaoting Ren, Chuantian Tang, Yu Ou, Guolong Cui and Shisheng Guo

Remote Sens. 2023, 15(20), 4903; https://doi.org/10.3390/rs15204903 - 10 Oct 2023

Cited by 1 | Viewed by 1216

Abstract

The multipath problem in indoor target detection has always been a long-standing research hotspot. Although there are many solutions to the multipath problem in a horizontal line of sight, the multipath problem of single-station radar from an overhead perspective still needs to be [...] Read more.

The multipath problem in indoor target detection has always been a long-standing research hotspot. Although there are many solutions to the multipath problem in a horizontal line of sight, the multipath problem of single-station radar from an overhead perspective still needs to be solved. At present, there is a lack of detailed analysis on the multipath propagation law of electromagnetic waves from an overhead perspective. This paper first analyzes the multipath propagation law of overhead perspective and reveals a combination multipath propagation phenomenon that is easily overlooked, which is formed by walls, ground, and targets. In addition, during the analysis process, the influence of coherent sources generated by multipath on angle estimation was fully considered, and verified through simulation and measured data. Then, based on the result of propagation analysis, this paper proposes a multipath ghost target suppression method. This method first establishes a multipath ghost target location dictionary based on building information, and then matches the tracking results with the dictionary to suppress successfully matched multipath ghost targets. Finally, several experiments are carried out to verify the effectiveness of this method. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking)

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19 pages, 4095 KiB

Open AccessArticle

Range Sidelobe Iterative Suppression Algorithm for Extended Target with Non-Grid Multiple Scattering Points

by Jing Xu, Yanan Zhang, Jindong Zhang and Jiarui Chen

Remote Sens. 2023, 15(19), 4811; https://doi.org/10.3390/rs15194811 - 3 Oct 2023

Viewed by 972

Abstract

This paper concentrates on extended targets to suppress high sidelobes in non-grid multiple scattering points. Notice that the problem essentially resolves the high sidelobes of the non-grid multiple scatterers after locating positions; we proposed a novel iterative algorithm based on an adaptive pulse [...] Read more.

This paper concentrates on extended targets to suppress high sidelobes in non-grid multiple scattering points. Notice that the problem essentially resolves the high sidelobes of the non-grid multiple scatterers after locating positions; we proposed a novel iterative algorithm based on an adaptive pulse compression algorithm on the basis of an off-grid position estimation. First, the grid offsets of the multiple scattering points through decoherence and super-resolution techniques are estimated by employing an enhanced MUSIC algorithm. Then, an optimization model is constructed for the non-grid multiple scattering points of the target. Finally, a sidelobe suppression algorithm based on the minimum mean square error (MMSE) criterion is presented. Numerical results reveal that the proposed algorithm can achieve better estimation performance on the offsets of the non-grid multiple scattering points, while it suppresses range sidelobes effectively. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking)

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24 pages, 4338 KiB

Open AccessArticle

Weighted Maximum Correntropy Criterion-Based Interacting Multiple-Model Filter for Maneuvering Target Tracking

by Liangliang Huai, Bo Li, Peng Yun, Chao Song and Jiayuan Wang

Remote Sens. 2023, 15(18), 4513; https://doi.org/10.3390/rs15184513 - 13 Sep 2023

Cited by 2 | Viewed by 1284

Abstract

During the process of maneuvering target tracking, the measurement may be disturbed by outliers, which leads to a decrease in the state estimation performance of the classic interacting multiple-model (IMM) filter. To solve this problem, a weighted maximum correntropy criterion (WMCC)-based IMM filter [...] Read more.

During the process of maneuvering target tracking, the measurement may be disturbed by outliers, which leads to a decrease in the state estimation performance of the classic interacting multiple-model (IMM) filter. To solve this problem, a weighted maximum correntropy criterion (WMCC)-based IMM filter is proposed. In the proposed filter, the fusion state is used as the input of each sub-model to reduce the computational complexity of state interaction and the WMCC is adopted to derive the sub-model state update and state fusion to improve the state estimation performance under outlier interference. Through principal analysis, the superiority of the proposed filter over the classic IMM filter in fusion strategy is revealed. The specific form of the proposed filter in radar maneuvering target tracking is provided. Two experimental cases of maneuvering target tracking are tested to illustrate the effectiveness of the proposed filter. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking)

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30 pages, 11538 KiB

Open AccessArticle

Integration and Detection of a Moving Target with Multiple Beams Based on Multi-Scale Sliding Windowed Phase Difference and Spatial Projection

by Rensu Hu, Dong Li, Jun Wan, Xiaohua Kang, Qinghua Liu, Zhanye Chen and Xiaopeng Yang

Remote Sens. 2023, 15(18), 4429; https://doi.org/10.3390/rs15184429 - 8 Sep 2023

Viewed by 1272

Abstract

Due to the fast scanning speed of the current phased-array radar and the moving characteristics of the target, the moving target usually spans multiple beams during coherent integration time, which results in severe performance loss for target focusing and parameter estimation because of [...] Read more.

Due to the fast scanning speed of the current phased-array radar and the moving characteristics of the target, the moving target usually spans multiple beams during coherent integration time, which results in severe performance loss for target focusing and parameter estimation because of the unknown entry/departure beam time within the coherent period. To solve this issue, a novel focusing and detection method based on the multi-beam phase compensation function (MBPCF), multi-scale sliding windowed phase difference (MSWPD), and spatial projection are proposed in this paper. The proposed method mainly includes the following three steps. First, the geometric and signal models of multiple beam integration with observed moving targets are accurately established where the range migration (RM), Doppler frequency migration (DFM), and beam migration (BM) are analyzed. Based on that, the BM is eliminated by the MBPCF, the second-order keystone transform (SOKT) is utilized to mitigate the RM, and then, a new MSWPD operation is developed to estimate the target’s entry/departure beam time, which realizes well-focusing output within the beam. After that, by dividing the radar detection area, the spatial projection (SP) method is adopted to obtain multiple-beams joint integration, and thus, improved detection performance can be obtained. Numerical experiments are carried out to evaluate the performance of the proposed method. The results show that the proposed method could achieve superior focusing and detection performances. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking)

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22 pages, 13465 KiB

Open AccessArticle

High Precision Motion Compensation THz-ISAR Imaging Algorithm Based on KT and ME-MN

by Wei Liu, Hongqiang Wang, Qi Yang, Bin Deng, Lei Fan and Jun Yi

Remote Sens. 2023, 15(18), 4371; https://doi.org/10.3390/rs15184371 - 5 Sep 2023

Cited by 3 | Viewed by 1213

Abstract

In recent years, terahertz (THz) radar has been widely researched for its high-resolution imaging. However, the traditional inverse synthetic aperture radar (ISAR) imaging algorithms in the microwave band perform unsatisfactorily in the THz band. Firstly, due to THz radar’s large bandwidth and short [...] Read more.

In recent years, terahertz (THz) radar has been widely researched for its high-resolution imaging. However, the traditional inverse synthetic aperture radar (ISAR) imaging algorithms in the microwave band perform unsatisfactorily in the THz band. Firstly, due to THz radar’s large bandwidth and short wavelength, the rotation of the target will result in serious space-varying(SV) range migration and space-varying phase error. Furthermore, it is challenging to accurately estimate the rotational velocity and compensate for phase errors in the presence of severe range migration effects. Therefore, in this paper, a high-precision THz-ISAR imaging algorithm is proposed. The algorithm includes the following step: First, the SV first-order range migration(FRM) is corrected using keystone transform (KT); then, the minimum entropy based on modified newton (ME-MN) is used to estimate the rotational velocity roughly, and the remaining SV second-order range migration(SRM) is corrected to obtain the range profile with the envelope alignment. Finally, the echo after the envelope alignment is processed for the second time based on ME-MN. The target rotation velocity is accurately estimated, and the phase error is compensated to obtain a well-focused imaging result. The validity of the proposed method is verified by numerical simulation and electromagnetic calculation data. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking)

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18 pages, 9274 KiB

Open AccessArticle

Joint Direction of Arrival-Polarization Parameter Tracking Algorithm Based on Multi-Target Multi-Bernoulli Filter

by Zhikun Chen, Bin’an Wang, Ruiheng Yang and Yuchao Lou

Remote Sens. 2023, 15(16), 3929; https://doi.org/10.3390/rs15163929 - 8 Aug 2023

Cited by 1 | Viewed by 1111

Abstract

This paper presents a tracking algorithm for joint estimation of direction of arrival (DOA) and polarization parameters, which exhibit dynamic behavior due to the movement of signal source carriers. The proposed algorithm addresses the challenge of real-time estimation in multi-target scenarios with an [...] Read more.

This paper presents a tracking algorithm for joint estimation of direction of arrival (DOA) and polarization parameters, which exhibit dynamic behavior due to the movement of signal source carriers. The proposed algorithm addresses the challenge of real-time estimation in multi-target scenarios with an unknown number. This algorithm is built upon the Multi-target Multi-Bernoulli (MeMBer) filter algorithm, which makes use of a sensor array called Circular Orthogonal Double-Dipole (CODD). The algorithm begins by constructing a Minimum Description Length (MDL) principle, taking advantage of the characteristics of the polarization-sensitive array. This allows for adaptive estimation of the number of signal sources and facilitates the separation of the noise subspace. Subsequently, the joint parameter Multiple Signal Classification (MUSIC) spatial spectrum function is employed as the pseudo-likelihood function, overcoming the limitations imposed by unknown prior information constraints. To approximate the posterior distribution of MeMBer filters, Sequential Monte Carlo (SMC) method is utilized. The simulation results demonstrate that the proposed algorithm achieves excellent tracking accuracy in joint DOA-polarization parameter estimation, whether in scenarios with known or unknown numbers of signal sources. Moreover, the algorithm demonstrates robust tracking convergence even under low Signal-to-Noise Ratio (SNR) conditions. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking)

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18 pages, 3390 KiB

Open AccessArticle

Target Detection of Passive Bistatic Radar under the Condition of Impure Reference Signal

by Yong Wu, Zhikun Chen and Dongliang Peng

Remote Sens. 2023, 15(15), 3876; https://doi.org/10.3390/rs15153876 - 4 Aug 2023

Cited by 4 | Viewed by 1396

Abstract

This paper addresses the issue of the reference signal being contaminated by target echoes due to factors such as wide beamwidths, beam pointing errors, or site errors in passive bistatic radar (PBR). Such contamination can lead to reference signal impurity and subsequently degrade [...] Read more.

This paper addresses the issue of the reference signal being contaminated by target echoes due to factors such as wide beamwidths, beam pointing errors, or site errors in passive bistatic radar (PBR). Such contamination can lead to reference signal impurity and subsequently degrade the performance of traditional object detection methods. To overcome this challenge, a novel target detection method centered around the reference signal’s purification is presented. The proposed method involves the modeling, analysis, localization, and purification of the impure reference signal. First, a new signal model incorporating the impure reference signal is established, followed by a comprehensive analysis of its impact on target detection. Then, leveraging the valuable information hidden in the impure reference signal, target echoes within the impure reference signal are localized and reconstructed. Finally, the reconstructed target echoes are subtracted from the original impure reference signal to purify the reference signal. Based on the purified reference signal, traditional target detection is performed to detect the expected targets. The core advantage of this method lies in its efficient reconstruction and removal of the impure terms within the reference signal. The effectiveness of the proposed method is demonstrated through a series of simulation results. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking)

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20 pages, 7727 KiB

Open AccessArticle

Coalition Game Theoretic Power Allocation Strategy for Multi-Target Detection in Distributed Radar Networks

by Xiangrong Dai, Chenguang Shi, Ziwei Wang and Jianjiang Zhou

Remote Sens. 2023, 15(15), 3804; https://doi.org/10.3390/rs15153804 - 31 Jul 2023

Cited by 3 | Viewed by 1072

Abstract

This paper studies a coalition game theoretic power allocation algorithm for multi-target detection in radar networks based on low probability of intercept (LPI). The main goal of the algorithm is to reduce the total radiated power of the radar networks while satisfying the [...] Read more.

This paper studies a coalition game theoretic power allocation algorithm for multi-target detection in radar networks based on low probability of intercept (LPI). The main goal of the algorithm is to reduce the total radiated power of the radar networks while satisfying the predetermined target detection performance of each radar. Firstly, a utility function that comprehensively considers both target detection performance and the radiated power of the radar networks is designed with LPI performance as the guiding principle. Secondly, it causes a coalition to form between cooperating radars, and radars within the same coalition share information. On this basis, a mathematical model for power allocation in radar networks based on coalition game theory is established. The model takes the given target detection performance as a constraint and maximizing system energy efficiency and optimal power allocation as the optimization objective. Furthermore, this paper proposes a game algorithm for joint coalition formation and power allocation in a multi-target detection scenario. Finally, the existence and uniqueness of the Nash equilibrium (NE) solution are proven through strict mathematical deduction. Simulation results validate the effectiveness and feasibility of the proposed algorithm. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking)

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28 pages, 850 KiB

Open AccessArticle

Collaborative Trajectory Planning and Resource Allocation for Multi-Target Tracking in Airborne Radar Networks under Spectral Coexistence

by Chenguang Shi, Jing Dong, Sana Salous, Ziwei Wang and Jianjiang Zhou

Remote Sens. 2023, 15(13), 3386; https://doi.org/10.3390/rs15133386 - 3 Jul 2023

Cited by 2 | Viewed by 1285

Abstract

This paper develops a collaborative trajectory planning and resource allocation (CTPRA) strategy for multi-target tracking (MTT) in a spectral coexistence environment utilizing airborne radar networks. The key mechanism of the proposed strategy is to jointly design the flight trajectory and optimize the radar [...] Read more.

This paper develops a collaborative trajectory planning and resource allocation (CTPRA) strategy for multi-target tracking (MTT) in a spectral coexistence environment utilizing airborne radar networks. The key mechanism of the proposed strategy is to jointly design the flight trajectory and optimize the radar assignment, transmit power, dwell time, and signal effective bandwidth allocation of multiple airborne radars, aiming to enhance the MTT performance under the constraints of the tolerable threshold of interference energy, platform kinematic limitations, and given illumination resource budgets. The closed-form expression for the Bayesian Cramér–Rao lower bound (BCRLB) under the consideration of spectral coexistence is calculated and adopted as the optimization criterion of the CTPRA strategy. It is shown that the formulated CTPRA problem is a mixed-integer programming, non-linear, non-convex optimization model owing to its highly coupled Boolean and continuous parameters. By incorporating semi-definite programming (SDP), particle swarm optimization (PSO), and the cyclic minimization technique, an iterative four-stage solution methodology is proposed to tackle the formulated optimization problem efficiently. The numerical results validate the effectiveness and the MTT performance improvement of the proposed CTPRA strategy in comparison with other benchmarks. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking)

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23 pages, 11892 KiB

Open AccessArticle

A Software-Defined Radar for Low-Altitude Slow-Moving Small Targets Detection Using Transmit Beam Control

by Lingping Cai, Haonan Qian, Linger Xing, Yang Zou, Linkang Qiu, Zihan Liu, Sirui Tian and Hongtao Li

Remote Sens. 2023, 15(13), 3371; https://doi.org/10.3390/rs15133371 - 30 Jun 2023

Cited by 4 | Viewed by 2454

Abstract

Low-altitude slow-moving small (LSS) targets are defined as flying at altitudes less than 1000 m with speeds less than 55 m/s and a radar crossing-section (RCS) less than 2

m^{2}

. The detection performance of ground-based radar using the LSS target detection [...] Read more.

Low-altitude slow-moving small (LSS) targets are defined as flying at altitudes less than 1000 m with speeds less than 55 m/s and a radar crossing-section (RCS) less than 2

m^{2}

. The detection performance of ground-based radar using the LSS target detection technique can be significantly deteriorated by the diversity of LSS targets, background clutter, and the occurrence of false alarms caused by multipath interference. To address the LSS target detection problem, we have devised a novel two-dimensional electronic scanning active phased array radar system that is implemented in the software-defined radar architecture and propose a transmit beam control algorithm based on the low peak-to-average ratio (PAPR). Meanwhile, we devised a flexible arbitrary radar waveform generator to adapt to complex environmental situations. Field experiment results effectively demonstrate that our radar can be used to detect LSS targets. Moreover, an ablation experiment was conducted to verify the role played by transmit beam control and adaptive waveform optimization and generation in improving the system performance. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking)

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24 pages, 4134 KiB

Open AccessArticle

Mitigation of Millimeter-Wave Radar Mutual Interference Using Spectrum Sub-Band Analysis and Synthesis

by Mingye Yin, Bo Feng and Yanbing Li

Remote Sens. 2023, 15(13), 3210; https://doi.org/10.3390/rs15133210 - 21 Jun 2023

Cited by 1 | Viewed by 1828

Abstract

Millimeter-wave radars are widely used in automotive radars because of their all-weather and all-day operation capability. However, as more and more radar sensors are used, the possibility of mutual interference between radars increases dramatically. Severe interference increases the noise level, affects target detection [...] Read more.

Millimeter-wave radars are widely used in automotive radars because of their all-weather and all-day operation capability. However, as more and more radar sensors are used, the possibility of mutual interference between radars increases dramatically. Severe interference increases the noise level, affects target detection performance, and can lead to missed detection and wrong detection. In this study, a novel solution to the problem of mutual radar interference is introduced. The method is based on the analysis and synthesis of spectrum sub-bands. Specifically, the received radar signal is partitioned into sub-bands, after which interference mitigation is carried out in each sub-band. Finally, the signals are reconstructed to obtain interference-free data. The effectiveness of this approach is evaluated using both a simulated multi-target scenario and a real-life experimental environment. The results demonstrate that the proposed method outperforms existing techniques in terms of interference mitigation while exhibiting rapid processing speeds. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking)

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20 pages, 3203 KiB

Open AccessArticle

Polarimetric Range Extended Target Detection via Adaptive Range Weighted Feature Extraction

by Mingchen Yuan, Liang Zhang, Yanhua Wang and Chang Han

Remote Sens. 2023, 15(11), 2929; https://doi.org/10.3390/rs15112929 - 4 Jun 2023

Cited by 3 | Viewed by 1581

Abstract

In ground static target detection, polarimetric high-resolution radar can distinguish the target from the strong ground clutter by reducing the clutter power in the range cell and providing additional polarimetric features. Since the energy of a target is split over several range cells, [...] Read more.

In ground static target detection, polarimetric high-resolution radar can distinguish the target from the strong ground clutter by reducing the clutter power in the range cell and providing additional polarimetric features. Since the energy of a target is split over several range cells, the resulting detection problem is called polarimetric range extended target (RET) detection, where all target scattering centers should be considered. In this paper, we propose a novel polarimetric RET detection method via adaptive range weighted feature extraction. Specifically, polarimetric features of range cells are extracted, and a pretrained attention-mechanism-based module is used to adaptively calculate range cells weights, which are used to accumulate the range cells features as detection statistics. While calculating weights, both amplitude and polarimetric features are considered. This method can make the most of polarization information and improve the accumulation effect, thus increasing the discrimination between targets and clutter. The effectiveness of the proposed method is verified compared to both popular energy-domain detection methods and existing feature-domain detection methods, and the results show that our method exhibits superior detection performance. Moreover, we further analyze our method on different target models and different clutter distributions to prove that our method is suitable for different types of targets and clutter. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking)

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19 pages, 7183 KiB

Open AccessArticle

A Lightweight Radar Ship Detection Framework with Hybrid Attentions

by Nanjing Yu, Haohao Ren, Tianmin Deng and Xiaobiao Fan

Remote Sens. 2023, 15(11), 2743; https://doi.org/10.3390/rs15112743 - 25 May 2023

Cited by 14 | Viewed by 2050

Abstract

One of the current research areas in the synthetic aperture radar (SAR) processing fields is deep learning-based ship detection in SAR imagery. Recently, ship detection in SAR images has achieved continuous breakthroughs in detection precision. However, determining how to strike a better balance [...] Read more.

One of the current research areas in the synthetic aperture radar (SAR) processing fields is deep learning-based ship detection in SAR imagery. Recently, ship detection in SAR images has achieved continuous breakthroughs in detection precision. However, determining how to strike a better balance between the precision and complexity of the algorithm is very meaningful for real-time object detection in real SAR application scenarios, and has attracted extensive attention from scholars. In this paper, a lightweight object detection framework for radar ship detection named multiple hybrid attentions ship detector (MHASD) with multiple hybrid attention mechanisms is proposed. It aims to reduce the complexity without loss of detection precision. First, considering that the ship features in SAR images are not inconspicuous compared with other images, a hybrid attention residual module (HARM) is developed in the deep-level layer to obtain features rapidly and effectively via the local channel attention and the parallel self-attentions. Meanwhile, it is also capable of ensuring high detection precision of the model. Second, an attention-based feature fusion scheme (AFFS) is proposed in the model neck to further heighten the features of the object. Meanwhile, AFFS constructs and develops a fresh hybrid attention feature fusion module (HAFFM) upon the local channel and spatial attentions to guarantee the applicability of the detection model. The Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) experimental results demonstrate that MHASD can balance detection speed and precision (improving average precision by 1.2% and achieving 13.7 GFLOPS). More importantly, extensive experiments on the SAR Ship Detection Dataset (SSDD) demonstrate that the proposed method is less affected by the background such as ports and rocks. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking)

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19 pages, 6038 KiB

Open AccessArticle

Remote Sensing Small Object Detection Network Based on Attention Mechanism and Multi-Scale Feature Fusion

by Junsuo Qu, Zongbing Tang, Le Zhang, Yanghai Zhang and Zhenguo Zhang

Remote Sens. 2023, 15(11), 2728; https://doi.org/10.3390/rs15112728 - 24 May 2023

Cited by 15 | Viewed by 3241

Abstract

In remote sensing images, small objects have too few discriminative features, are easily confused with background information, and are difficult to locate, leading to a degradation in detection accuracy when using general object detection networks for aerial images. To solve the above problems, [...] Read more.

In remote sensing images, small objects have too few discriminative features, are easily confused with background information, and are difficult to locate, leading to a degradation in detection accuracy when using general object detection networks for aerial images. To solve the above problems, we propose a remote sensing small object detection network based on the attention mechanism and multi-scale feature fusion, and name it AMMFN. Firstly, a detection head enhancement module (DHEM) was designed to strengthen the characterization of small object features through a combination of multi-scale feature fusion and attention mechanisms. Secondly, an attention mechanism based channel cascade (AMCC) module was designed to reduce the redundant information in the feature layer and protect small objects from information loss during feature fusion. Then, the Normalized Wasserstein Distance (NWD) was introduced and combined with Generalized Intersection over Union (GIoU) as the location regression loss function to improve the optimization weight of the model for small objects and the accuracy of the regression boxes. Finally, an object detection layer was added to improve the object feature extraction ability at different scales. Experimental results from the Unmanned Aerial Vehicles (UAV) dataset VisDrone2021 and the homemade dataset show that the AMMFN improves the

{A P}_{s}

values by 2.4% and 3.2%, respectively, compared with YOLOv5s, which represents an effective improvement in the detection accuracy of small objects. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking)

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18 pages, 1213 KiB

Open AccessArticle

An Overview of the PAKF-JPDA Approach for Elliptical Multiple Extended Target Tracking Using High-Resolution Marine Radar Data

by Jaya Shradha Fowdur, Marcus Baum, Frank Heymann and Pawel Banys

Remote Sens. 2023, 15(10), 2503; https://doi.org/10.3390/rs15102503 - 10 May 2023

Cited by 2 | Viewed by 1747

Abstract

Ground radar stations observing specific regions of interest nowadays provide detections in the form of point-clouds. This article focuses on a framework that consists of an elliptical multitarget tracker, referred to as Principal-Axes based Kalman Filter (PAKF)-based Joint Probabilistic Data Association (JPDA) (PAKF-JPDA), [...] Read more.

Ground radar stations observing specific regions of interest nowadays provide detections in the form of point-clouds. This article focuses on a framework that consists of an elliptical multitarget tracker, referred to as Principal-Axes based Kalman Filter (PAKF)-based Joint Probabilistic Data Association (JPDA) (PAKF-JPDA), to enable maritime traffic monitoring. The framework touches on two major stages, target detection and target tracking. For the former, we employed a clustering approach and for the latter, we presented a data-association-based version of the PAKF tracker with an automatic track management functionality. The framework’s benefits are demonstrated when it is applied to the radar streaming in a harbor setting based on a homogeneous multisensor tracking system by comparing our results against their corresponding reference data with visualizations, including performance measures. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking)

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24 pages, 1401 KiB

Open AccessArticle

A Robust Target Tracking Method for Crowded Indoor Environments Using mmWave Radar

by Meiqiu Jiang, Shisheng Guo, Haolan Luo, Yu Yao and Guolong Cui

Remote Sens. 2023, 15(9), 2425; https://doi.org/10.3390/rs15092425 - 5 May 2023

Cited by 7 | Viewed by 2747

Abstract

Millimeter-wave-based extended target tracking has attracted extensive interest recently because of its privacy, high precision, and low cost. This paper concentrated on crowded indoor situations and presents a novel method for group tracking. First, the proposed alpha-extended Kalman filter and the group association [...] Read more.

Millimeter-wave-based extended target tracking has attracted extensive interest recently because of its privacy, high precision, and low cost. This paper concentrated on crowded indoor situations and presents a novel method for group tracking. First, the proposed alpha-extended Kalman filter and the group association were carried out, which can constantly estimate the target expansion and the number of reflection points, consequently modifying the measurement noise and covariance estimation. Then, to initialize the actual targets, we employed a density-based spatial clustering approach that includes false target suppression. After the targets have been updated, the track re-association and estimation procedure is conducted to account for the unanticipated break of moving and near-static targets. Finally, various experiments involving fewer than 11 participants were designed to assess the robustness of the method. As a result, continuous and steady tracking results, as well as high counting accuracy were obtained. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking)

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28 pages, 5946 KiB

Open AccessTechnical Note

HCM-LMB Filter: Pedestrian Number Estimation with Millimeter-Wave Radar in Closed Spaces

by Yang Li, You Li, Yanping Wang, Yun Lin, Wenjie Shen, Wen Jiang and Jinping Sun

Remote Sens. 2023, 15(19), 4698; https://doi.org/10.3390/rs15194698 - 25 Sep 2023

Cited by 1 | Viewed by 1054

Abstract

The electromagnetic wave transmitted by the millimeter-wave radar can penetrate flames, smoke, and the high-temperature field, and is the main sensor for detecting disaster victims in closed spaces. However, a moving target in the closed space will produce a considerable number of false [...] Read more.

The electromagnetic wave transmitted by the millimeter-wave radar can penetrate flames, smoke, and the high-temperature field, and is the main sensor for detecting disaster victims in closed spaces. However, a moving target in the closed space will produce a considerable number of false detections in the point cloud data collected by the radar due to multipath scattering. The false detections lead to false trajectories generated by multi-target tracking filters, such as the labeled multi-Bernoulli (LMB) filter, which, therefore, leads to inaccurate estimation of the number of pedestrians. Addressing this problem, in this paper, a three-class combination of the clutter point clouds model is proposed: static clutter, non-continuous dynamic clutter (NCDC), and continuous dynamic clutter (CDC). The model is based on the spatial and temporal distribution characteristics of the CDC sequence captured by a two-dimensional (2D) millimeter-wave (MMW) radar. However, in open space, CDC appears infrequently in radar tracking applications, and thus has not been considered in multi-target tracking filters such as the LMB filter. This leads to confusion between the CDC point cloud collected by the high-resolution radar in closed spaces and the real-target point cloud. To solve this problem, the impact mechanism of the LMB filter on prediction, update, and state estimation is modeled in this paper in different stages based on the temporal and spatial distribution characteristics of CDC. Finally, a hybrid clutter model-based LMB filter (HCM-LMB) is proposed, which focuses on scenes where NCDC and CDC are mixed. The filter introduces the temporal and spatial distribution characteristics of NCDC based on the original LMB filter, and improves the prediction, update, and state estimation of the original filter by combining the impact mechanism model and the new CDC prediction, CDC estimation, and false trajectory label management algorithm. Experiments were conducted on pedestrians in building corridors using 2D MMW radar perception. The experimental results show that under the influence of CDC, the total number of pedestrians estimated by the HCM-LMB filter was reduced by 22.5% compared with that estimated by the LMB filter. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking)

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17 pages, 5675 KiB

Open AccessTechnical Note

A Coherent Integration and Parameter Estimation Method for Constant Radial Acceleration Weak Target via SOKT-IAR-LVD

by Renli Zhang and Nan Xu

Remote Sens. 2023, 15(17), 4227; https://doi.org/10.3390/rs15174227 - 28 Aug 2023

Viewed by 961

Abstract

In order to enhance the detection and parameter estimation capacity to the maneuvering target with complex motions, a low complexity coherent integration and parameter estimation method named SOKT-IAR-LVD is proposed in this paper. In SOKT-IAR-LVD, first, the second-order keystone transform (SOKT) is utilized [...] Read more.

In order to enhance the detection and parameter estimation capacity to the maneuvering target with complex motions, a low complexity coherent integration and parameter estimation method named SOKT-IAR-LVD is proposed in this paper. In SOKT-IAR-LVD, first, the second-order keystone transform (SOKT) is utilized to eliminate the range curvature induced by target acceleration. Second, improved axis rotation (IAR) is applied to regulate the linear range migration by rotating the fast time axis and the target envelope is aligned along the slow time axis with a quadratic phase characteristic. At last, the target signal is coherently integrated via the Lv’s Distribution (LVD) transform. The target motion parameters, including range, velocity, and acceleration, are estimated by the IAR and LVD results. The integration gain and computational load of SOKT-IAR-LVD are analyzed. Without needing to estimate the Doppler ambiguity number and target acceleration, the computational burden of SOKT-IAR-LVD is three orders of magnitude lower than that of the Radon-Lv’s Distribution (RLVD) method. Simulation results demonstrate that the detection performance of SOKT-IAR-LVD is almost the same as that of RLVD and that the required input SNR of SOKT-IAR-LVD is 17.4 dB lower than that of SOKT–Radon Fourier transform (SOKT-RFT) when the detection threshold is set to 12 dB. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking)

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