Novel N ‐scan GM‐PHD‐based approach for multi‐target tracking

Abstract

The GM-PHD-based filter has been proposed as an alternative of the PHD filter to estimate the first-order moment of the multi-target posterior density. The GM-PHD filter utilises a weighted summation of Gaussian components to estimate the target states. This filter and its recent variants perform state extraction of the targets based on the target weights. However, due to different uncertainties such as noisy observation, miss-detection, clutter or occlusion, the weight of a target is decreased and the estimation of the target is lost in some steps. In this study, the authors develop a simple and effective N-scan approach which employs the weight history of targets to improve the performance of the GM-PHD-based methods. They propose to assign a label, a weight history and a binary confidence indicator to each Gaussian component and propagate them in time. Then, they explain a novel N-scan state extraction algorithm to estimate the target states based on their histories in the N last steps. To study the efficiency of the proposed N-scan approach, it is applied on the GM-PHD filter as well as its several recent variants. The experimental results provided for various uncertainties show the effectiveness of the method.

Distributed multi‐sensor fusion using generalised multi‐Bernoulli densities

“…have been of significant interest due to their superior performance in terms of accuracy of cardinality and state estimation of multiple targets with their trajectories. Vo–Vo filter [2, 5] provides a closed‐form solution to the optimal Bayesian filter, and has shown to outperform the well‐known probability hypothesis density (PHD) filter [6, 7] and its cardinalised version, CPHD filter [8], and the multi‐Berboulli (MB) filter [9] in challenging multi‐target tracking scenarios.…”