分布式计算粒子PHD滤波器
Distributed Computation Particle PHD filter
摘要：
粒子概率假设密度滤波由于能够处理非线性、非高斯系统中目标数目未知、时变的情况，已成为一种很有前途的多目标跟踪方法。然而，它的计算复杂度与分配给每个目标的测量和粒子的数量呈线性增长，这可能是非常耗时的，特别是当许多目标和杂波存在于监视区域。针对这个问题，我们提出了一个分布式计算粒子PHD滤波器的目标跟踪。它的框架由几个局部粒子PHD滤波器在每个处理单元和一个中央单元。每个处理元件负责部分粒子但全部测量，并提供局部估计;中央单元控制处理元件之间的粒子交换，并指定融合规则以匹配和融合来自不同局部滤波器的估计。所提出的框架是适合并行实现，并保持跟踪精度。仿真结果表明，该方法可以提供相当的精度，以及一个显着的加速与标准粒子PHD滤波器。
英文摘要：
Particle probability hypothesis density filtering has become a promising means for multi-target tracking due to its capability of handling an unknown and time-varying number of targets in non-linear non-Gaussian system. However, its computational complexity grows linearly with the number of measurements and particles assigned to each target, and this can be very time consuming especially when numerous targets and clutter exist in the surveillance region. Addressing this issue, we present a distributed computation particle PHD filter for target tracking. Its framework consists of several local particle PHD filters at each processing element and a central unit. Each processing element takes responsibility for part particles but full measurements and provides local estimates; central unit controls particle exchange between processing elements and specifies a fusion rule to match and fuse the estimates from different local filters. The proposed framework is suitable for parallel implementation and maintains the tracking accuracy. Simulations verify the proposed method can provide comparative accuracy as well as a significant speedup with the standard particle PHD filter.
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