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首页> 外文期刊>Aerospace and Electronic Systems, IEEE Transactions on >Multitarget Tracking using Probability Hypothesis Density Smoothing
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Multitarget Tracking using Probability Hypothesis Density Smoothing

机译：使用概率假设密度平滑的多目标跟踪

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摘要

In general, for multitarget problems where the number of targets and their states are time varying, the optimal Bayesian multitarget tracking is computationally demanding. The Probability Hypothesis Density (PHD) filter, which is the first-order moment approximation of the optimal one, is a computationally tractable alternative. By evaluating the PHD, the number of targets as well as their individual states can be extracted. Recent sequential Monte Carlo (SMC) implementations of the PHD filter have paved the way to its application to realistic nonlinear non-Gaussian problems. It is observed that the particle implementation of the PHD filter is dependent on current measurements, especially in the case of low observable target problems (i.e., estimates are sensitive to missed detections and false alarms). In this paper a PHD smoothing algorithm is proposed to improve the capability of PHD-based tracking system. It involves forward multitarget filtering using the standard PHD filter recursion followed by backward smoothing recursion using a novel recursive formula. Smoothing, which produces delayed estimates, results in better estimates for target states and a better estimate for the number of targets. Multiple model PHD (MMPHD) smoothing, which is an extension of the proposed technique to maneuvering targets, is also provided. Simulations are performed with the proposed method on a multitarget scenario. Simulation results confirm improved performance of the proposed algorithm.
机译：通常，对于目标数量及其状态随时间变化的多目标问题，最佳的贝叶斯多目标跟踪在计算上要求很高。概率假设密度（PHD）滤波器是最优变量的一阶矩近似值，它是计算上易于处理的替代方案。通过评估PHD，可以提取目标的数量及其各自的状态。 PHD滤波器的最新顺序蒙特卡罗（SMC）实现为将其应用于实际的非线性非高斯问题铺平了道路。可以看出，PHD滤波器的粒子实现方式取决于当前的测量值，尤其是在可观察到的目标问题较少的情况下（即，估计值对丢失的检测和错误警报很敏感）。本文提出了一种PHD平滑算法，以提高基于PHD的跟踪系统的性能。它涉及使用标准PHD滤波器递归进行的前向多目标滤波，然后使用新颖的递归公式进行后向平滑递归。产生延迟估计的平滑会导致对目标状态的更好估计和对目标数量的更好估计。还提供了多模型PHD（MMPHD）平滑技术，该技术是所提出技术对机动目标的扩展。使用提出的方法对多目标场景进行仿真。仿真结果证实了所提算法的改进性能。

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