Tracking multiple space objects using multiple surveillance sensors is a critical approach in many Space Situation Awareness(SSA) applications. In this process, the uncertainties of targets,dynamics, and observations ...Tracking multiple space objects using multiple surveillance sensors is a critical approach in many Space Situation Awareness(SSA) applications. In this process, the uncertainties of targets,dynamics, and observations are usually represented by the probability distributions. However, precise characterization of uncertainty becomes challenging due to imperfect knowledge about some key aspects, such as birth targets and sensor detection profiles. To overcome this challenge, this paper proposes a multi-sensor possibility PHD filter based on the theory of outer probability measures. An effective compensation method is introduced to tackle variations in the fields of view of SSA sensors or instances of missed detections, aiming to mitigate the inconsistency in localized information. The proposed method is adapted to centralized and distributed sensor networks, offering effective solutions for multi-sensor multi-target tracking. The major innovation of the proposed method compared with typical methods is the proper description of epistemic uncertainty, which yields more robust performance in the scenarios of lacking some information about the system.The effectiveness of the multi-sensor possibility PHD filter is demonstrated by a comparison with conventional methods in two simulated scenarios.展开更多
The ability to extract state-estimates for each target of a multi-target posterior, referred to as multi-estimate extraction（MEE）, is an essential requirement for a multi-target filter, whose key performance assessm...The ability to extract state-estimates for each target of a multi-target posterior, referred to as multi-estimate extraction（MEE）, is an essential requirement for a multi-target filter, whose key performance assessments are based on accuracy, computational efficiency and reliability. The probability hypothesis density（PHD） filter, implemented by the sequential Monte Carlo approach,affords a computationally efficient solution to general multi-target filtering for a time-varying number of targets, but leaves no clue for optimal MEE. In this paper, new data association techniques are proposed to distinguish real measurements of targets from clutter, as well as to associate particles with measurements. The MEE problem is then formulated as a family of parallel singleestimate extraction problems, facilitating the use of the classic expected a posteriori（EAP） estimator, namely the multi-EAP（MEAP） estimator. The resulting MEAP estimator is free of iterative clustering computation, computes quickly and yields accurate and reliable estimates. Typical simulation scenarios are employed to demonstrate the superiority of the MEAP estimator over existing methods in terms of faster processing speed and better estimation accuracy.展开更多
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
As a typical implementation of the probability hypothesis density(PHD) filter, sequential Monte Carlo PHD(SMC-PHD) is widely employed in highly nonlinear systems. However, the particle impoverishment problem introduce...As a typical implementation of the probability hypothesis density(PHD) filter, sequential Monte Carlo PHD(SMC-PHD) is widely employed in highly nonlinear systems. However, the particle impoverishment problem introduced by the resampling step, together with the high computational burden problem, may lead to performance degradation and restrain the use of SMC-PHD filter in practical applications. In this work, a novel SMC-PHD filter based on particle compensation is proposed to solve above problems. Firstly, according to a comprehensive analysis on the particle impoverishment problem, a new particle generating mechanism is developed to compensate the particles. Then, all the particles are integrated into the SMC-PHD filter framework. Simulation results demonstrate that, in comparison with the SMC-PHD filter, proposed PC-SMC-PHD filter is capable of overcoming the particle impoverishment problem, as well as improving the processing rate for a certain tracking accuracy in different scenarios.展开更多
考虑到存活目标与新生目标在动态演化特性上的差异性,提出了面向快速多目标跟踪的协同概率假设密度(collaborative probability hypothesis density,CoPHD)滤波框架。该框架利用存活目标的状态信息,将量测动态划分为存活目标量测集与新...考虑到存活目标与新生目标在动态演化特性上的差异性,提出了面向快速多目标跟踪的协同概率假设密度(collaborative probability hypothesis density,CoPHD)滤波框架。该框架利用存活目标的状态信息,将量测动态划分为存活目标量测集与新生目标量测集,在两个量测集分别运用PHD组处理更新基础上建立了处理模块的交互与协同机制,力图在保证跟踪精度的同时提高计算效率。该框架由于采用PHD组处理方式而具有状态自动提取功能。进一步给出了该框架的序贯蒙特卡罗算法实现。仿真结果表明,该算法在计算效率以及状态提取精度上具有明显优势。展开更多
基金funded by the National Natural Science Foundation of China(No.12202049)the Beijing Institute of Technology Research Fund Program for Young Scholars,China.
文摘Tracking multiple space objects using multiple surveillance sensors is a critical approach in many Space Situation Awareness(SSA) applications. In this process, the uncertainties of targets,dynamics, and observations are usually represented by the probability distributions. However, precise characterization of uncertainty becomes challenging due to imperfect knowledge about some key aspects, such as birth targets and sensor detection profiles. To overcome this challenge, this paper proposes a multi-sensor possibility PHD filter based on the theory of outer probability measures. An effective compensation method is introduced to tackle variations in the fields of view of SSA sensors or instances of missed detections, aiming to mitigate the inconsistency in localized information. The proposed method is adapted to centralized and distributed sensor networks, offering effective solutions for multi-sensor multi-target tracking. The major innovation of the proposed method compared with typical methods is the proper description of epistemic uncertainty, which yields more robust performance in the scenarios of lacking some information about the system.The effectiveness of the multi-sensor possibility PHD filter is demonstrated by a comparison with conventional methods in two simulated scenarios.
基金partly supported by the Marie SklodowskaCurie Individual Fellowship (No. 709267)under the European Union’s Framework Programme for ResearchInnovation Horizon 2020 and National Natural Science Foundation of China (No. 51475383)
文摘The ability to extract state-estimates for each target of a multi-target posterior, referred to as multi-estimate extraction（MEE）, is an essential requirement for a multi-target filter, whose key performance assessments are based on accuracy, computational efficiency and reliability. The probability hypothesis density（PHD） filter, implemented by the sequential Monte Carlo approach,affords a computationally efficient solution to general multi-target filtering for a time-varying number of targets, but leaves no clue for optimal MEE. In this paper, new data association techniques are proposed to distinguish real measurements of targets from clutter, as well as to associate particles with measurements. The MEE problem is then formulated as a family of parallel singleestimate extraction problems, facilitating the use of the classic expected a posteriori（EAP） estimator, namely the multi-EAP（MEAP） estimator. The resulting MEAP estimator is free of iterative clustering computation, computes quickly and yields accurate and reliable estimates. Typical simulation scenarios are employed to demonstrate the superiority of the MEAP estimator over existing methods in terms of faster processing speed and better estimation accuracy.
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.
基金Projects(61671462,61471383,61671463,61304103)supported by the National Natural Science Foundation of ChinaProject(ZR2012FQ004)supported by the Natural Science Foundation of Shandong Province,China
文摘As a typical implementation of the probability hypothesis density(PHD) filter, sequential Monte Carlo PHD(SMC-PHD) is widely employed in highly nonlinear systems. However, the particle impoverishment problem introduced by the resampling step, together with the high computational burden problem, may lead to performance degradation and restrain the use of SMC-PHD filter in practical applications. In this work, a novel SMC-PHD filter based on particle compensation is proposed to solve above problems. Firstly, according to a comprehensive analysis on the particle impoverishment problem, a new particle generating mechanism is developed to compensate the particles. Then, all the particles are integrated into the SMC-PHD filter framework. Simulation results demonstrate that, in comparison with the SMC-PHD filter, proposed PC-SMC-PHD filter is capable of overcoming the particle impoverishment problem, as well as improving the processing rate for a certain tracking accuracy in different scenarios.
文摘考虑到存活目标与新生目标在动态演化特性上的差异性,提出了面向快速多目标跟踪的协同概率假设密度(collaborative probability hypothesis density,CoPHD)滤波框架。该框架利用存活目标的状态信息,将量测动态划分为存活目标量测集与新生目标量测集,在两个量测集分别运用PHD组处理更新基础上建立了处理模块的交互与协同机制,力图在保证跟踪精度的同时提高计算效率。该框架由于采用PHD组处理方式而具有状态自动提取功能。进一步给出了该框架的序贯蒙特卡罗算法实现。仿真结果表明,该算法在计算效率以及状态提取精度上具有明显优势。
