An algorithm to track multiple sharply maneuvering targets without prior knowledge about new target birth is proposed. These targets are capable of achieving sharp maneuvers within a short period of time, such as dron...An algorithm to track multiple sharply maneuvering targets without prior knowledge about new target birth is proposed. These targets are capable of achieving sharp maneuvers within a short period of time, such as drones and agile missiles.The probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems. However, the standard PHD filter operates on the single dynamic model and requires prior information about target birth distribution, which leads to many limitations in terms of practical applications. In this paper,we introduce a nonzero mean, white noise turn rate dynamic model and generalize jump Markov systems to multitarget case to accommodate sharply maneuvering dynamics. Moreover, to adaptively estimate newborn targets’information, a measurement-driven method based on the recursive random sampling consensus (RANSAC) algorithm is proposed. Simulation results demonstrate that the proposed method achieves significant improvement in tracking multiple sharply maneuvering targets with adaptive birth estimation.展开更多
In multiple extended targets tracking, replacing traditional multiple measurements with a rectangular region of the nonzero volume in the state space inspired by the box-particle idea is exactly suitable to deal with ...In multiple extended targets tracking, replacing traditional multiple measurements with a rectangular region of the nonzero volume in the state space inspired by the box-particle idea is exactly suitable to deal with extended targets, without distinguishing the measurements originating from the true targets or clutter.Based on our recent work on extended box-particle probability hypothesis density(ET-BP-PHD) filter, we propose the extended labeled box-particle cardinalized probability hypothesis density(ET-LBP-CPHD) filter, which relaxes the Poisson assumptions of the extended target probability hypothesis density(PHD) filter in target numbers, and propagates not only the intensity function but also cardinality distribution. Moreover, it provides the identity of individual target by adding labels to box-particles. The proposed filter can improve the precision of estimating target number meanwhile achieve targets' tracks. The effectiveness and reliability of the proposed algorithm are verified by the simulation results.展开更多
The key challenge of the extended target probability hypothesis density (ET-PHD) filter is to reduce the computational complexity by using a subset to approximate the full set of partitions. In this paper, the influen...The key challenge of the extended target probability hypothesis density (ET-PHD) filter is to reduce the computational complexity by using a subset to approximate the full set of partitions. In this paper, the influence for the tracking results of different partitions is analyzed, and the form of the most informative partition is obtained. Then, a fast density peak-based clustering (FDPC) partitioning algorithm is applied to the measurement set partitioning. Since only one partition of the measurement set is used, the ET-PHD filter based on FDPC partitioning has lower computational complexity than the other ET-PHD filters. As FDPC partitioning is able to remove the spatially close clutter-generated measurements, the ET-PHD filter based on FDPC partitioning has good tracking performance in the scenario with more clutter-generated measurements. The simulation results show that the proposed algorithm can get the most informative partition and obviously reduce computational burden without losing tracking performance. As the number of clutter-generated measurements increased, the ET-PHD filter based on FDPC partitioning has better tracking performance than other ET-PHD filters. The FDPC algorithm will play an important role in the engineering realization of the multiple extended target tracking filter.展开更多
In this paper, we consider the problem of irregular shapes tracking for multiple extended targets by introducing the Gaussian surface matrix（GSM） into the framework of the random finite set（RFS） theory. The Gaussi...In this paper, we consider the problem of irregular shapes tracking for multiple extended targets by introducing the Gaussian surface matrix（GSM） into the framework of the random finite set（RFS） theory. The Gaussian surface function is constructed first by the measurements, and it is used to define the GSM via a mapping function. We then integrate the GSM with the probability hypothesis density（PHD） filter, the Bayesian recursion formulas of GSM-PHD are derived and the Gaussian mixture implementation is employed to obtain the closed-form solutions. Moreover, the estimated shapes are designed to guide the measurement set sub-partition, which can cope with the problem of the spatially close target tracking. Simulation results show that the proposed algorithm can effectively estimate irregular target shapes and exhibit good robustness in cross extended target tracking.展开更多
In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Int...In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Interactive Multiple Model (IMM) estimator and feature fusion. New algorithm greatly improves the tracking performance due to the fact that IMM estimator provides better estimation and feature information enhances the accuracy of data association. The new algorithm is tested by tracking tropical fish in fish container. Experimental result shows that this algorithm can significantly reduce tracking lost rate and restrain the noises with higher computational effectiveness when compares with traditional MHT.展开更多
传统多假设跟踪(Multiple Hypothesis Tracker,MHT)算法假定杂波强度先验已知,在未知杂波的观测场景中,杂波强度误差将导致数据关联的准确性急剧下降。针对这一问题,本文提出一种基于核密度估计(Kernel Density Estimation,KDE)的在线...传统多假设跟踪(Multiple Hypothesis Tracker,MHT)算法假定杂波强度先验已知,在未知杂波的观测场景中,杂波强度误差将导致数据关联的准确性急剧下降。针对这一问题,本文提出一种基于核密度估计(Kernel Density Estimation,KDE)的在线杂波估计MHT算法。首先利用核密度函数拟合未知的杂波密度函数,并自适应地估计出该时刻波门内的杂波强度;然后利用杂波强度估计值计算假设航迹的得分函数,提高了数据关联的准确性和目标跟踪的稳定性。仿真结果表明,在未知杂波观测场景中,MHT-KDE算法有效改善了航迹的连续性,减少了虚假航迹数。展开更多
Considering the problem of multiple ballistic missiles tracking of boost-phase ballistic missile defense, a boost-phase tracking algorithm based on multiple hypotheses tracking （MHT） concept is proposed. This paper ...Considering the problem of multiple ballistic missiles tracking of boost-phase ballistic missile defense, a boost-phase tracking algorithm based on multiple hypotheses tracking （MHT） concept is proposed. This paper focuses on the tracking algo- rithm for hypothesis generation, hypothesis probability calculation, hypotheses reduction and pruning and other sectors. From an engineering point of view, a technique called the linear assignment problem （LAP） used in the implementation of M-best feasible hypotheses generation, the number of the hypotheses is relatively small compared with the total number that may exist in each scan, also the N-scan back pruning is used, the algorithm＇s efficiency and practicality have been improved. Monte Carlo simulation results show that the proposed algorithm can track the boost phase of multiple ballistic missiles and it has a good tracking performance compared with joint probability data association （JPDA）.展开更多
基金supported by the National Natural Science Foundation of China (61773142)。
文摘An algorithm to track multiple sharply maneuvering targets without prior knowledge about new target birth is proposed. These targets are capable of achieving sharp maneuvers within a short period of time, such as drones and agile missiles.The probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems. However, the standard PHD filter operates on the single dynamic model and requires prior information about target birth distribution, which leads to many limitations in terms of practical applications. In this paper,we introduce a nonzero mean, white noise turn rate dynamic model and generalize jump Markov systems to multitarget case to accommodate sharply maneuvering dynamics. Moreover, to adaptively estimate newborn targets’information, a measurement-driven method based on the recursive random sampling consensus (RANSAC) algorithm is proposed. Simulation results demonstrate that the proposed method achieves significant improvement in tracking multiple sharply maneuvering targets with adaptive birth estimation.
文摘In multiple extended targets tracking, replacing traditional multiple measurements with a rectangular region of the nonzero volume in the state space inspired by the box-particle idea is exactly suitable to deal with extended targets, without distinguishing the measurements originating from the true targets or clutter.Based on our recent work on extended box-particle probability hypothesis density(ET-BP-PHD) filter, we propose the extended labeled box-particle cardinalized probability hypothesis density(ET-LBP-CPHD) filter, which relaxes the Poisson assumptions of the extended target probability hypothesis density(PHD) filter in target numbers, and propagates not only the intensity function but also cardinality distribution. Moreover, it provides the identity of individual target by adding labels to box-particles. The proposed filter can improve the precision of estimating target number meanwhile achieve targets' tracks. The effectiveness and reliability of the proposed algorithm are verified by the simulation results.
基金supported by the National Natural Science Foundation of China(61401475)
文摘The key challenge of the extended target probability hypothesis density (ET-PHD) filter is to reduce the computational complexity by using a subset to approximate the full set of partitions. In this paper, the influence for the tracking results of different partitions is analyzed, and the form of the most informative partition is obtained. Then, a fast density peak-based clustering (FDPC) partitioning algorithm is applied to the measurement set partitioning. Since only one partition of the measurement set is used, the ET-PHD filter based on FDPC partitioning has lower computational complexity than the other ET-PHD filters. As FDPC partitioning is able to remove the spatially close clutter-generated measurements, the ET-PHD filter based on FDPC partitioning has good tracking performance in the scenario with more clutter-generated measurements. The simulation results show that the proposed algorithm can get the most informative partition and obviously reduce computational burden without losing tracking performance. As the number of clutter-generated measurements increased, the ET-PHD filter based on FDPC partitioning has better tracking performance than other ET-PHD filters. The FDPC algorithm will play an important role in the engineering realization of the multiple extended target tracking filter.
基金supported by the National Natural Science Foundation of China(6130501761304264+1 种基金61402203)the Natural Science Foundation of Jiangsu Province(BK20130154)
文摘In this paper, we consider the problem of irregular shapes tracking for multiple extended targets by introducing the Gaussian surface matrix（GSM） into the framework of the random finite set（RFS） theory. The Gaussian surface function is constructed first by the measurements, and it is used to define the GSM via a mapping function. We then integrate the GSM with the probability hypothesis density（PHD） filter, the Bayesian recursion formulas of GSM-PHD are derived and the Gaussian mixture implementation is employed to obtain the closed-form solutions. Moreover, the estimated shapes are designed to guide the measurement set sub-partition, which can cope with the problem of the spatially close target tracking. Simulation results show that the proposed algorithm can effectively estimate irregular target shapes and exhibit good robustness in cross extended target tracking.
基金Supported by the National Natural Science Foundation of China (No. 60772154)the President Foundation of Graduate University of Chinese Academy of Sciences (No. 085102GN00)
文摘In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Interactive Multiple Model (IMM) estimator and feature fusion. New algorithm greatly improves the tracking performance due to the fact that IMM estimator provides better estimation and feature information enhances the accuracy of data association. The new algorithm is tested by tracking tropical fish in fish container. Experimental result shows that this algorithm can significantly reduce tracking lost rate and restrain the noises with higher computational effectiveness when compares with traditional MHT.
文摘传统多假设跟踪(Multiple Hypothesis Tracker,MHT)算法假定杂波强度先验已知,在未知杂波的观测场景中,杂波强度误差将导致数据关联的准确性急剧下降。针对这一问题,本文提出一种基于核密度估计(Kernel Density Estimation,KDE)的在线杂波估计MHT算法。首先利用核密度函数拟合未知的杂波密度函数,并自适应地估计出该时刻波门内的杂波强度;然后利用杂波强度估计值计算假设航迹的得分函数,提高了数据关联的准确性和目标跟踪的稳定性。仿真结果表明,在未知杂波观测场景中,MHT-KDE算法有效改善了航迹的连续性,减少了虚假航迹数。
文摘Considering the problem of multiple ballistic missiles tracking of boost-phase ballistic missile defense, a boost-phase tracking algorithm based on multiple hypotheses tracking （MHT） concept is proposed. This paper focuses on the tracking algo- rithm for hypothesis generation, hypothesis probability calculation, hypotheses reduction and pruning and other sectors. From an engineering point of view, a technique called the linear assignment problem （LAP） used in the implementation of M-best feasible hypotheses generation, the number of the hypotheses is relatively small compared with the total number that may exist in each scan, also the N-scan back pruning is used, the algorithm＇s efficiency and practicality have been improved. Monte Carlo simulation results show that the proposed algorithm can track the boost phase of multiple ballistic missiles and it has a good tracking performance compared with joint probability data association （JPDA）.
