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.