A multi-sensor data fusion and tracking algorithm based on fuzzy logic for the large-scale maneuvering target

In this work a novel multi-sensor data fusion algorithm for tracking the large-scale maneuvering target is proposed. This algorithm is based on a hybrid structure integrating the least mean variance fusion algorithm and fuzzy adaptive Kalman filtering algorithm. Since the multi-sensor data fusion algorithm can achieve improved accuracy and reliability, the least mean variance fusion algorithm is introduced which is simply and does not increase the computational burden. To overcome the defects of the current statistical model on non-maneuvering target tracking, fuzzy adaptive Kalman filtering algorithm with maneuvering detection is used for large-scale maneuvering target which extracts feature data from Kalman filtering processes to estimate the magnitude and time of maneuvering. The simulation results show that the tracking system with active and passive radar has higher precision than those with a single sensor for large-scale maneuvering target.