Measurement-Marks Based GM-PHD Merging and Extraction Method for Close Proximity Multi-Target Tracking

In scenarios involving unknown data associations, Gaussian Mixture Probability Hypothesis Density (GM-PHD) filtering, rooted in Random Finite Set (RFS) theory, presents a significant advantage over traditional data association methods. However, the performance of the GM-PHD filter can degrade substantially when targets are in close proximity, as multiple measurements may be associated with a single target. To address this limitation, we propose an improved Gaussian component merging and extraction method for GM-PHD filters based on measurement marks. This approach assigns a unique mark to each instantaneous measurement and subsequently merges Gaussian components with the same mark through a filtering process. Employing this approach ensures comprehensive collaboration of the weights, means, and covariances of the corresponding target components, thereby facilitating the efficient merging and extraction of similar target intensities. Importantly, this method effectively prevents incorrect fusion of genuine target components. Simulation results indicate that the proposed algorithm achieves a lower localization error and a more accurate estimation of target counts compared to the standard GM-PHD filter, particularly in scenarios involving closely spaced targets with varying clutter means and detection probabilities.