Refined GM-PHD tracker for tracking targets in possible subsequent missed detections

•RGM-PHD tracker is proposed to track targets in the presence of randomness in detection.•We aim at improving the efficiency of GM-PHD filter when miss-deletion is unpredictable.•Probability of confirm is introduced to measure the eligibility of a target for confirmation.•State refinement and novel state extraction are proposed to improve the tracking performance.•Tracking performance is studied in terms of different uncertainties via Monte Carlo simulation.

Gaussian mixture probability hypothesis density (GM-PHD) filter is proposed as an alternative of PHD filter to estimate the first-order moment of the multi-target posterior density. Theoretically, the GM-PHD filter handles missed detection in its filtering recursion. However, in practice, the performance of this filter might be degraded in subsequent miss-detection, when all factors that affect miss-detection cannot be appropriately incorporated in the filtering recursions. In this paper, we propose a heuristic method called Refined GM-PHD (RGM-PHD) tracker which aims at improving the performance of GM-PHD filter in subsequent missed detections. To accomplish this purpose, we define a survival model and introduce probability of confirm for each Gaussian component and each track which are adaptively calculated in time. Accordingly, we propose a state refinement step and a novel state extraction step to improve the tracking performance of the filter. Comprehensive Monte Carlo simulations in terms of various probabilities of detection, false alarm rates and subsequent miss-detection rates are performed to investigate the effectiveness of the method.