Integrated Expected Likelihood Particle Filters
Integrated Expected Likelihood Particle Filters
复制标题
集成预期似然粒子滤波器
DOI：
10.23919/fusion45008.2020.9190387
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
S. Maskell
中科院分区：
文献类型：
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作者：
M. J. Ransom;Lyudmil Vladimirov;P. Horridge;J. Ralph;S. Maskell
本文讨论了单目标单传感器系统的目标跟踪算法的推导、实现和性能，估计了目标的状态、协方差和存在概率。假设目标存在，我们以给定的检测概率模拟目标的测量，以及由杂波密度参数化的错误测量（杂波）。我们通过计算接收器工作特征（ROC）曲线下的面积来评估算法的性能。我们特别关注正确推断目标存在与否的有效性。我们选择了综合概率数据关联滤波器（IPDAF）和综合期望似然粒子滤波器（IELPF）算法，IELPF实现了一个近乎最优的建议，它使用当前的测量扫描以及先前的建议进行比较。仿真结果表明，IPDAF的性能优于采用先前建议的已有粒子滤波器，但采用近似最优建议和适当数量粒子的新型粒子滤波器优于IPDAF。
In this paper, we discuss the derivations, implementations and performance of target tracking algorithms for a single-target single-sensor system estimating the state, covariance and existence probability of a target. Given the target exists, we simulate measurements of the target with a given probability of detection, along with false measurements (clutter) parametrised by a clutter density. We evaluate the performance of the algorithms by computing the area under Receiver Operating Characteristic (ROC) curves against a range of clutter density values. We give particular attention to the effectiveness of correctly inferring the presence or absence of the target. We select the Integrated Probabilistic Data Association Filter (IPDAF) and the Integrated Expected Likelihood Particle Filter (IELPF) algorithms, with the IELPF implementing a near-optimal proposal which uses the current scan of measurements as well as a prior proposal for comparison. Simulation results indicate the performance of the IPDAF exceeds that of the preexisting particle filter implementing a prior proposal, but a novel particle filter using a near-optimal proposal and a modest number of particles outperforms the IPDAF.