Particle Particle Probability Hypothesis Density Filter Research Articles

Hardware architecture and optimisation of FPP particle PHD filter for multi‐target tracking in cyber‐physical systems

To meet the rapidly growing demand of localisation and tracking information of mobile agents in cyber-physical systems, in this paper, we propose a fully pipelined and parallel (FPP) hardware architecture of particle probability hypothesis density (PHD) filter for multi-target tracking on the multi-cores processor hardware platforms, where an improved resampling algorithm is designed. We formulate the demand of minimising the processing delay in this architecture as an optimisation problem, which can be efficiently solved by transforming it to a group of mixed non-linear integer programming problems. Delay analysis shows that the proposed FPP particle PHD filter achieves significant improvement of real-time performance, and simulation results demonstrate that tracking performance remains at the same level as traditional particle PHD filters.

Computation-distributed probability hypothesis density filter

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.

POSE: Design of Hardware-Friendly Particle-Based Observation Selection PHD Filter

Particle probability hypothesis density (PHD) filtering is a promising technology for the multitarget-tracking problem. Traditional particle PHD filter solutions usually have high computational complexity, and the lack of dedicated hardware has seriously limited their usages in real-time industrial applications. The hardware implementation difficulty of the particle PHD filtering in field-programmable gate array (FPGA) platforms lies in that the number of observations for filtering is time varying while the number of parallel processing units in circuit is fixed. To overcome this challenge, we propose a novel particle-based observation selection (POSE) PHD filter algorithm and its hardware implementation in this paper. Specifically, we opportunistically select a fixed number of observations out of a varying number of observations for filtering, where the approximation error is proved to be negligible by adapting the circuit budget to the environment accordingly. To implement the proposed POSE PHD filter, the hardware design issues are addressed in depth. Extensive simulations demonstrate that the POSE PHD filter has a comparable performance with the traditional one while its hardware implementation challenge is overcome. The hardware experiment results of the POSE PHD filter on a Xilinx Virtex-II Pro FPGA platform match the simulation ones well. Furthermore, the execution time of the implemented hardware circuit is evaluated, and the results show that it can achieve a processing rate of 6.892 kHz with a 50-MHz system clock.

An Efficient Data-Driven Particle PHD Filter for Multitarget Tracking

In this paper, we propose an efficient data-driven particle probability hypothesis density (PHD) filter for real-time multitarget tracking of nonlinear/non-Gaussian system in dense clutter environment. In specific, the input measurements are first classified into two sets, namely survival measurements and spontaneous birth measurements, after eliminating clutters by using existing historic state data of targets. Since most clutters do not participate in the complex weight computation of particle PHD filter, better real-time performance can be achieved. The tracking performance is also improved because the survival measurements are used for survival targets and the spontaneous birth measurements are used for spontaneous birth targets, resulting in less interference from each other and from clutters. Extensive simulations validate the improvement of both the real-time performance and tracking performance of the proposed data-driven particle PHD filter in comparison with the traditional particle PHD filter.

Tracking a large number of closely spaced objects based on the particle probability hypothesis density filter via optical sensor

This paper presents a novel approach to tracking a large number of closely spaced objects (CSO) in image sequences that is based on the particle probability hypothesis density (PHD) filter and multiassignment data association. First, the particle PHD filter is adopted to eliminate most of the clutters and to estimate multitarget states. In the particle PHD filter, a noniterative multitarget estimation technique is introduced to reliably estimate multitarget states, and an improved birth particle sampling scheme is present to effectively acquire targets among clutters. Then, an integrated track management method is proposed to realize multitarget track continuity. The core of the track management is the track-to-estimation multiassignment association, which relaxes the traditional one-to-one data association restriction due to the unresolved focal plane CSO measurements. Meanwhile, a unified technique of multiple consecutive misses for track deletion is used jointly to cope with the sensitivity of the PHD filter to the missed detections and to eliminate false alarms further, as well as to initiate tracks of large numbers of CSO. Finally, results of two simulations and one experiment show that the proposed approach is feasible and efficient.