Abdullahi DaniyanYu GongSangarapillai LambotharanPengming FengJonathon Chambers2025-04-252017-04-052017-10-012019-08-13Daniyan, A., Gong, Y., Lambotharan, S., Feng, P., & Chambers, J. (2017). Kalman-gain aided particle PHD filter for multitarget tracking. IEEE Transactions on Aerospace and Electronic Systems, 53(5), 2251-2265.DOI: 10.1109/TAES.2017.2690530http://repository.futminna.edu.ng:4000/handle/123456789/980We propose an efficient sequential Monte Carlo probability hypothesis density (PHD) filter which employs the Kalman-gain approach during weight update to correct predicted particle states by minimizing the mean square error between the estimated measurement and the actual measurement received at a given time in order to arrive at a more accurate posterior. This technique identifies and selects those particles belonging to a particular target from a given PHD for state correction during weight computation. Besides the improved tracking accuracy, fewer particles are required in the proposed approach. Simulation results confirm the improved tracking performance when evaluated with different measures.Bayesian trackingKalman gainmultitarget tracking (MTT)particle filterprobability hypothesis density (PHD) filtersequential Monte Carlo (SMC)Article