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Gaussian mixture probability hypothesis density (GM-PHD) filter is proposed as a closed form solution of PHD filter to estimate the first-order moment of the multi-target posterior density. Recently, different methods such as Competitive GM-PHD (CGM-PHD), Penalized GM-PHD (PGM-PHD) and Collaborative PGM-PHD (CPGM-PHD) are proposed to enhance the performance of GM-PHD filter for tracking closely spaced targets. These methods have no assumption about possible subsequent missed detections which occur in some practical applications. For this reason, the performance of these filters degrades in this condition. In this paper, we propose a novel improvement on GM-PHD filter to track targets in possible subsequent missed detections. In addition to targets weight, we define a probability of confirm (PC) for each target which is adaptively calculated in time. We also propose a new state refinement and state extraction methods based on the defined PC. The experimental results provided for different uncertainties show the effectiveness of the proposed method.

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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.

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