We propose a particle-based distributed PHD filter for tracking the states of an unknown, time-varying number of targets. To reduce communication, the local PHD filters at neighboring sensors communicate Gaussian mixture (GM) parameters. In contrast to most existing distributed PHD filters, our filter employs an “arithmetic average” fusion. For particles–GM conversion, we use a method that avoids particle clustering and enables a significance-based pruning of the GM components. For GM–particles conversion, we develop an importance sampling based method that enables a parallelization of filtering and dissemination/fusion operations. The resulting distributed PHD filtering framework is able to integrate both particle-based and GM-based local PHD filters. Simulations demonstrate the excellent performance and small communication and computation requirements of our filter.

我们提出了一种基于粒子的分布式PHD滤波器，用于跟踪未知，时变数量的目标的状态。为了减少通信，相邻传感器处的本地PHD滤波器传递高斯混合（GM）参数。与大多数现有的分布式PHD滤波器相反，我们的滤波器采用“算术平均”融合。对于粒子– GM转换，我们使用一种避免粒子聚类并启用基于重要性的GM组件修剪的方法。对于GM-粒子转换，我们开发了一种基于重要性采样的方法，该方法可实现过滤和传播/融合操作的并行化。最终的分布式PHD过滤框架能够集成基于粒子和基于GM的本地PHD过滤器。