An Adaptive Maneuvering Target Tracking Algorithm Based on Fuzzy Inference

An adaptive maneuvering target tracking algorithm based on fuzzy inference is proposed to deal with the low adaptive capacity of variable structure interacting multi-model algorithms for target maneuver uncertainty and measurement uncertainty in maneuvering target tracking. A two-stage maneuvering discrimination model based on fuzzy inference is designed, which uses the probability of models and residual weighted norm of the main model to infer the reliability of the main model and the possibility of maneuvering discrimination. The two-stage maneuvering discriminant is introduced into the framework of expected-model augmentation based on likely model-set (EMA-LMS). A kind of fuzzy inference-based EMA-LMS algorithm is proposed to adjust the parameter and strategy of model-set adaption online. This algorithm generates an expected model that is closer to the real motion model and makes better choices for model selection. The simulation results show that the proposed algorithm can strengthen the adaptive capacity for the uncertainty of target maneuver and measurement, and improve accuracy.