Comparative Analysis on Diverse Heuristic-Based Joint Probabilistic Data Association for Multi-target Tracking in a Cluttered Environment

Abstract

The target tracking using the passive multi-static radar system produces various detections via distinct signal propagation paths. Trackers solve the uncertainties that arise from the measurement path as well as the measurement origin. The existing multi-target tracking algorithms suffer from high computational loads, because they require the entire probable joint measurement-to-track assignments. This paper proposes to develop a comparative analysis on diverse heuristic algorithms for implementing the optimized JDPA model for tracking multiple targets using multi-static passive radar system in the presence of clutter. Here, the nature-inspired algorithms like Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) are used for analyzing the optimized JDPA model for tracking multiple targets in multi-static passive scenario. This paper further, aims to tune the position and velocity of the tracker towards the target using two heuristic algorithms, and intends to analyze the effect of those algorithms on improving the performance of multiple target tracking. The key objective of the proposed model is to minimize the Mean Absolute Error (MAE) between the estimated trajectory of the track and the true target state. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.