Abstract
Particle probability hypothesis density filtering has become a promising approach for multi-target tracking due to its capability of handling an unknown and time-varying number of targets in a nonlinear, non-Gaussian system. However, its computational complexity linearly increases with the number of obtained observations and the number of particles, which can be very time consuming, particularly when numerous targets and clutter exist in the surveillance region. To address this issue, we present a distributed computation particle probability hypothesis density(PHD) filter for target tracking. It runs several local decomposed particle PHD filters in parallel while processing elements. Each processing element takes responsibility for a portion of particles but all measurements and provides local estimates. A central unit controls particle exchange among the 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. Simulations verify that the proposed method can significantly accelerate and maintain a comparative accuracy compared to the standard particle PHD filter.
Highlights
Multi-target filtering is a class of dynamic state estimation problems in which the object of interest is a finite set that consists of a random number of elements and the values of individual elements [1]
processing elements (PEs) run local decomposed particle probability hypothesis density (PHD) filters independently until part of the particles need to exchange among them; the central unit (CU) associates and fuses the local state estimations submitted by all PEs
This architecture and fusion strategy can make the parallelization of local particle PHD filters possible and provides a comparable filtering accuracy with the sequential particle PHD filter
Summary
Multi-target filtering is a class of dynamic state estimation problems in which the object of interest is a finite set that consists of a random number of elements and the values of individual elements [1]. Considerable work has been devoted to random finite set (RFS)-based approximations, such as the probability hypothesis density (PHD) [5, 6], the cardinalized PHD (CPHD) [7, 8], and the multiple-target multi-Bernoulli (MeMBer) filter [9] These methods avoid the data association problem and provide the set-valued estimations of target states. We exploit the decomposed PHD filter to extract the estimated states and obtain their corresponding measurement labels, which guarantees the association and fusion of states from different PEs. we present two rules for the fusion of local state estimations, considering the influence of clutter on the PHD filter.
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