Abdullahi Daniyan2025-04-252025-04-19A. Daniyan, (2025). “Robust Multi-Target Tracking with a Kalman-Gain CPHD Filter: Simulation and Experimental Validation,” World Journal of Advanced Engineering Technology and Sciences, 15(01), 1636-1647https://doi.org/10.30574/wjaets.2025.15.1.0369http://repository.futminna.edu.ng:4000/handle/123456789/989We introduce a novel cardinalized implementation of the Kalman-gain-aided particle probability hypothesis density (KG-SMC-PHD) filter, extending it to form the Kalman-Gain Particle Cardinalized Probability Hypothesis Density (KG- SMC-CPHD) filter. This new approach significantly enhances multi-target tracking by combining the particle-based state correction mechanism with the propagation of both the PHD and target cardinality distribution. Unlike conventional particle filters that require a large number of particles for acceptable performance, our method intelligently corrects selected particles during the weight update stage, resulting in a more accurate posterior with substantially fewer particles. Through comprehensive evaluations on both simulated and experimental datasets, the KG-SMC-CPHD filter demonstrates superior robustness and accuracy, particularly in high-clutter environments and nonlinear target dynamics. Notably, it offers improved cardinality estimation and maintains the computational efficiency and performance advantages of its predecessor, the KG-SMC-PHD filter, making it a powerful tool for advanced multi-target tracking applications.Multi-Target TrackingParticle FilterCardinalized PHDKalman GainSequential Monte CarloPassive RadarArticle