This paper studies the dynamic estimation problem for multitarget tracking. A novel gat- ing strategy that is based on the measurement likelihood of the target state space is proposed to improve the overall effectiven...This paper studies the dynamic estimation problem for multitarget tracking. A novel gat- ing strategy that is based on the measurement likelihood of the target state space is proposed to improve the overall effectiveness of the probability hypothesis density （PHD） filter. Firstly, a measurement-driven mechanism based on this gating technique is designed to classify the measure- ments. In this mechanism, only the measurements for the existing targets are considered in the update step of the existing targets while the measurements of newborn targets are used for exploring newborn targets. Secondly, the gating strategy enables the development of a heuristic state estima- tion algorithm when sequential Monte Carlo （SMC） implementation of the PHD filter is investi- gated, where the measurements are used to drive the particle clustering within the space gate. The resulting PHD filter can achieve a more robust and accurate estimation of the existing targets by reducing the interference from clutter. Moreover, the target birth intensity can be adaptive to detect newborn targets, which is in accordance with the birth measurements. Simulation results demonstrate the computational efficiency and tracking performance of the proposed algorithm.展开更多
We propose an efficient measurement-driven sequential Monte Carlo multi-Bernoulli(SMC-MB) filter for multi-target filtering in the presence of clutter and missing detection. The survival and birth measurements are dis...We propose an efficient measurement-driven sequential Monte Carlo multi-Bernoulli(SMC-MB) filter for multi-target filtering in the presence of clutter and missing detection. The survival and birth measurements are distinguished from the original measurements using the gating technique. Then the survival measurements are used to update both survival and birth targets, and the birth measurements are used to update only the birth targets.Since most clutter measurements do not participate in the update step, the computing time is reduced significantly.Simulation results demonstrate that the proposed approach improves the real-time performance without degradation of filtering performance.展开更多
基金supported by the Aeronautical Science Foundation of China(No.201401P6001)
文摘This paper studies the dynamic estimation problem for multitarget tracking. A novel gat- ing strategy that is based on the measurement likelihood of the target state space is proposed to improve the overall effectiveness of the probability hypothesis density （PHD） filter. Firstly, a measurement-driven mechanism based on this gating technique is designed to classify the measure- ments. In this mechanism, only the measurements for the existing targets are considered in the update step of the existing targets while the measurements of newborn targets are used for exploring newborn targets. Secondly, the gating strategy enables the development of a heuristic state estima- tion algorithm when sequential Monte Carlo （SMC） implementation of the PHD filter is investi- gated, where the measurements are used to drive the particle clustering within the space gate. The resulting PHD filter can achieve a more robust and accurate estimation of the existing targets by reducing the interference from clutter. Moreover, the target birth intensity can be adaptive to detect newborn targets, which is in accordance with the birth measurements. Simulation results demonstrate the computational efficiency and tracking performance of the proposed algorithm.
基金Project supported by the National Natural Science Foundationof China(Nos.61174142,61222310,and 61374021)the Specialized Research Fund for the Doctoral Program of Higher Education of China(Nos.20120101110115 and 20130101110109)+3 种基金theZhejiang Provincial Science and Technology Planning Projects ofChina(No.2012C21044)the Marine Interdisciplinary ResearchGuiding Funds for Zhejiang University(No.2012HY009B)theFundamental Research Funds for the Central Universities(No.2014XZZX003-12)the Aeronautical Science Foundation ofChina(No.20132076002)
文摘We propose an efficient measurement-driven sequential Monte Carlo multi-Bernoulli(SMC-MB) filter for multi-target filtering in the presence of clutter and missing detection. The survival and birth measurements are distinguished from the original measurements using the gating technique. Then the survival measurements are used to update both survival and birth targets, and the birth measurements are used to update only the birth targets.Since most clutter measurements do not participate in the update step, the computing time is reduced significantly.Simulation results demonstrate that the proposed approach improves the real-time performance without degradation of filtering performance.
