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Auxiliary Particle Flow track-before-detect algorithm for Marine Neighboring Weak Targets

REMOTE SENSING 2025年第9期17卷 1547-1547页

作者： Zhang, Fan Liu, Chang Chinese Acad Sci Aerosp Informat Res Inst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

detection and tracking of marine weak targets can be effectively solved by track-before-detect (TBD) algorithms based on particle filtering. However, these algorithms are susceptible to influence from neighboring targets, leading to potential issues like misassociation and tracking failure. In this paper, an auxiliary particle flow track-before-detect algorithm designed for marine neighboring weak targets is proposed which can effectively track marine neighboring weak targets under long-tail sea clutter. Firstly, marine neighboring targets are modeled by the generalized Pareto model, and an offline lookup table is utilized to obtain a non-closed solution, decreasing calculation cost. Subsequently, prediction is employed to classify targets, and measurement information is iteratively used to determine the sequence of target updates, effectively suppressing influence from neighboring targets. Finally, particles with higher measurement energy are chosen, and the Geodesic particle flow is employed to guide the particles toward better importance distribution, which enhances the accuracy of target trajectory estimation. Simulation experiments indicate that compared with track-before-detect algorithms based on parallel partition (PP) and auxiliary parallel partition (APP), the proposed algorithm shows an increase of 43.1% and 25.8% in detection probability at 6 dB, and a reduction of 76.6% and 66.2% in Root Mean Square Error (RMSE). detection ability and trajectory estimation performance are effectively improved in the simulation, and excellent tracking performance is also confirmed in real clutter experiments.

关键词： target tracking track-before-detect algorithm marine clutter parallel partition technology particle flow filter

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Dynamic Programming-Based track-before-detect algorithm for Weak Maneuvering Targets in Range-Doppler Plane

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REMOTE SENSING 2024年第14期16卷 2639-2639页

作者： Wu, Xinghui Ding, Jieru Wang, Zhiyi Wang, Min Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Xian Univ Finance & Econ Ctr Informat & Educ Technol Xian 710064 Peoples R China

This paper focuses on detecting and tracking maneuvering weak targets in the range-Doppler (RD) plane with the track-before-detect (TBD) algorithm based on dynamic programming (DP). Traditional DP-TBD algorithms integrate target detection and tracking in their framework while searching the paths provided by a predefined model of the kinematic properties within the constraints allowed. However, both the approximate motion model used in the RD plane and the model mismatch caused when the target undergoes a maneuver can degrade the TBD performance sharply. To address these issues, this paper accurately describes the evolution of the RD equation based on Constant Acceleration (CA) and Coordinated Turn (CT) motion models with the process noise in the Cartesian coordinate system, and it also employs a recursive method to estimate the parameters in the equations for efficient energy accumulation and path searches. Facing the situation that targets energy accumulation during the DP iteration process will lead to an expansion of the target energy accumulation process. This paper designs a more efficient Optimization Function (OF) to inhibit the expansion effect, improve the resolution of the nearby targets, and increase the detection probability of the weak targets simultaneously. In addition, to search the trajectory more efficiently and accurately, we improved the process of DP multi-frame accumulation, thus reducing the computation amount of large-scale searches. Finally, the effectiveness of the proposed method for CA and CT motion target detection and tracking is verified by many of the simulation experiments that were conducted in this paper.

关键词： dynamic programming track-before-detect algorithm range-Doppler evolution equation maneuvering target detection

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Adaptive real-time recursive radial distance-time plane Hough transform track-before-detect algorithm for hypersonic target

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IET RADAR SONAR AND NAVIGATION 2020年第1期14卷 138-146页

作者： Li, Lin Wang, Guohong Zhang, Xiangyu Yu, Hongbo Naval Aviat Univ Inst Informat Fus Yantai 264001 Peoples R China

Under the condition of low signal-to-noise ratio (SNR), the track-before-detect algorithm based on Hough transform (HT-TBD) is an effective means to detect and track hypersonic targets. In view of the problem that the existing HT-TBD algorithms cannot detect hypersonic multi-target in real time and some targets may be missed, an adaptive real-time recursive radial distance-time plane HT-TBD (RTHT-TBD) algorithm for a hypersonic target is proposed in this study. Firstly, RTHT-TBD technology is used to perform the non-coherent integration detection of the first n frames measurement data, and the initial accumulation matrix and storage array are obtained. Then the data of 2 similar to n + 1, 3 similar to n + 2, ... frames are processed recursively in turn. In order to solve the problem that the parameter space accumulation matrix cannot accumulate normally when the parameter space accumulation matrix changes in the recursive process, the adaptive technology is used to adjust the parameter space to realise the recursive detection at all times. The simulation results show that the proposed algorithm can realise real-time detection and tracking of hypersonic multi-target under low SNR, and effectively reduce the computational complexity.

关键词： radar detection target tracking signal detection object detection Hough transforms computational complexity hypersonic target low signal-to-noise ratio track-before-detect algorithm Hough transform existing HT-TBD algorithms hypersonic multitarget real-time recursive radial distance-time plane HT-TBD algorithm RTHT-TBD technology noncoherent integration detection initial accumulation matrix storage array parameter space accumulation matrix changes recursive process recursive detection real-time detection tracking

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track-before-detect algorithm for airborne radar system

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JOURNAL OF ENGINEERING-JOE 2019年第20期2019卷 6620-6624页

作者： Wang, Yaru Wang, Jinghe Wen, Ming Yi, Wei Kong, Lingjiang Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Sichuan Peoples R China

This study considers the detection of dim target via track-before-detect (TBD) in airborne radar system. Dim target detection by TBD is well-studied while aircraft ego-motion is not taken into account. Ego-motion of the aircraft (platform) causes traditional TBD method useless in airborne radar system by two reasons. On one hand, the movement of the aircraft causes measurement misaligned among different frames. On the other hand, state space and measurement space is non-one-to-one correspondence. Considering these two issues, the authors proposed a TBD algorithm for airborne radar based on plots (AR-P-TBD). First, they derived coordinate rotation and motion compensation formula, and convert each frame of measurement into a unified coordinate system to make these measurements aligned. Then, TBD processing in measurement space is implemented. It avoids the non-one-to-one correspondence between state space and measurement space. Simulation results demonstrate the effectiveness of AR-P-TBD.

关键词： target tracking object detection motion compensation radar tracking airborne radar radar detection airborne radar based on plots aircraft movement AR-P-TBD TBD processing unified coordinate system motion compensation formula TBD algorithm measurement space state space traditional TBD method aircraft ego-motion dim target detection airborne radar system track-before-detect algorithm

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Multi-frame track-before-detect algorithm for Passive Sonar System 8

Multi-frame Track-before-detect Algorithm for Passive Sonar ...

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8th International Conference on Control, Automation and Information Sciences (ICCAIS)

作者： Peng, Qiyun Li, Wujun Kong, Lingjiang Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9781728123110

It has been widely verified superiority of multiframe track-before-detect (MF-TBD) for weak-target detection and tracking. In this paper we apply MF-TBD approach to solve weak unknown and time-varying number of targets detection and tracking problem for passive sonar system with excellent concealment. MF-TBD signal processing method is exclusive developed for the passive sonar system. Compared with conventional tracking structures, MF-TBD establishes the joint estimation strategy of target kinematic states based on maximum a posteriori probability (MAP) criteria. By directly processing multi-frame unthresholded raw bearing time record (BTR) data, MF-TBD achieves energy accumulation for weak targets and improves detection performance. Finally, simulation results are performed to verify its efficiency for passive sonar system.

关键词： Passive sonar system dim target detection and tracking track-before-detect algorithm

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Coupled sequential detection and estimation procedure for track-before-detect

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IET RADAR SONAR AND NAVIGATION 2016年第7期10卷 1168-1177页

作者： Cui, Guolong Kong, Lingjiang Yi, Wei Wang, Bailu Yang, Xiaobo Univ Elect Sci & Technol China Sch Elect Engn Chengdu Peoples R China

In this study, the authors consider the coupled sequential detection and estimation of weak targets for track-before-detect algorithm in long range early warning radar. They present a new strategy and develop a coupled cost function consisting of detection, position estimation and tracking estimation loss. In the proposed procedure, the sequential stopping and detection rules are simplified. Moreover, they derive the lower bounds of the detection and estimation probabilities, which can be used to design the suitable related parameter values. Finally, several numerical simulations of the new strategy and the comparisons are presented and discussed.

关键词： radar detection object detection probability target tracking numerical analysis sequential estimation radar tracking coupled sequential detection procedure coupled sequential estimation procedure track-before-detect algorithm weak targets long range early warning radar coupled cost function position estimation loss tracking estimation loss sequential stopping rule estimation probabilities numerical simulations

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Code reordering using local random extraction and insertion (LREI) operator for GPGPU-based track-before-detect systems

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SOFT COMPUTING 2013年第6期17卷 1095-1106页

作者： Mazurek, Przemyslaw West Pomeranian Univ Technol Dept Signal Proc & Multimedia Engn PL-71126 Szczecin Poland

track-before-detect (TBD) algorithms are used for tracking systems, where the object's signal is below the noise floor (low-SNR objects). A lot of computations and memory transfers for real-time signal processing are necessary. GPGPU in parallel processing devices for TBD algorithms is well suited. Finding optimal or suboptimal code, due to lack of documentation for low-level programming of GPGPUs is not possible. High-level code optimization is necessary and the evolutionary approach, based on the single parent and single child is considered, that is local search approach. Brute force search technique is not feasible, because there are N! code variants, where N is the number of motion vectors components. The proposed evolutionary operator-LREI (local random extraction and insertion) allows source code reordering for the reduction of computation time due to better organization of memory transfer and the texture cache content. The starting point, based on the sorting and the minimal execution time metric is proposed. The unbiased random and biased sorting techniques are compared using experimental approach. Tests shows significant improvements of the computation speed, about 8 % over the conventional code for CUDA code. The time period of optimization for the sample code is about 1 h (1,000 iterations) for the considered recursive spatio-temporal TBD algorithm.

关键词： Evolutionary programming Code optimization GPGPU track-before-detect algorithm

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