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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Multitarget Bayes filtering via first-order multitarget moments
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Multitarget Bayes filtering via first-order multitarget moments

机译：通过一阶多目标矩进行多目标贝叶斯滤波

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摘要

The theoretically optimal approach to multisensor-multitarget detection, tracking, and identification is a suitable generalization of the recursive Bayes nonlinear filter. Even in single-target problems, this optimal filter is so computationally challenging that it must usually be approximated. Consequently, multitarget Bayes filtering will never be of practical interest without the development of drastic but principled approximation strategies. In single-target problems, the computationally fastest approximate filtering approach is the constant-gain Kalman filter. This filter propagates a first-order statistical moment - the posterior expectation - in the place of the posterior distribution. The purpose of this paper is to propose an analogous strategy for multitarget systems: propagation of a first-order statistical moment of the multitarget posterior. This moment, the probability hypothesis density (PHD), is the function whose integral in any region of state space is the expected number of targets in that region. We derive recursive Bayes filter equations for the PHD that account for multiple sensors, nonconstant probability of detection, Poisson false alarms, and appearance, spawning, and disappearance of targets. We also show that the PHD is a best-fit approximation of the multitarget posterior in an information-theoretic sense.
机译：理论上最佳的多传感器多目标检测，跟踪和识别方法是递归贝叶斯非线性滤波器的合适概括。即使在单目标问题中，这种最佳滤波器在计算上也具有挑战性，因此通常必须对其进行近似。因此，如果不开发严格但有原则的近似策略，多目标贝叶斯滤波将永远不会具有实际意义。在单目标问题中，计算最快的近似滤波方法是恒定增益卡尔曼滤波器。该过滤器在后验分布的位置传播一阶统计矩-后验期望。本文的目的是为多目标系统提出一种类似的策略：多目标后验的一阶统计矩的传播。此刻，概率假设密度（PHD）是一个函数，其在状态空间任何区域中的积分都是该区域中目标的预期数量。我们推导了PHD的递归贝叶斯滤波器方程，该方程考虑了多个传感器，非恒定检测概率，泊松错误警报以及目标的出现，产生和消失。我们还表明，在信息理论上，PHD是多目标后验的最佳拟合近似。

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