Tracking algorithm compensating acceleration for 3D maneuvering target with PSO-FCM

This paper presents an intelligent tracking method for nonlinear maneuvering target by compartmentalizing external noises of 3D maneuvering target. Proposed method makes the filter recognize the maneuvering target as linear one by separating acceleration properly from the overhaul noises. For achieving that, we use the particle swam optimization-fuzzy c-means (PSO-FCM) clustering as the criteria of methodology. The positional difference between measured point and predicted one is separated into acceleration and noise. Compartmentalized external noises plays the role of acceleration in accordance with assigned position and its quantity. Proposed algorithm makes approximated acceleration to be compensated and approximated noise is filtered by Kalman filter (KF). To depict the real maneuvering target and track the target with unlimited condition, we handle 3D dynamic model. Finally, some examples are provided to show the effectiveness of the proposed algorithm.