A Novel Adaptive Tracking Algorithm for Maneuvering Targets Based on Information Fusion by Neural Network

Abstract

The current statistical model and adaptive filtering (CSMAF) algorithm is one of the most effective methods for tracking the maneuvering targets. However, it is still worthy to investigate the characteristics of the CSMAF algorithm, which has a higher precision in tracking the maneuvering targets with larger accelerations while it has a lower precision in tracking the maneuvering targets with smaller acceleration. In this paper a novel adaptive tracking algorithm for maneuvering targets is proposed. To overcome the disadvantage of the CSMAF algorithm, a simple multi-layer feedforward neural network (NN) is used. By introducing NN, two sources of information of the filter are fused while its output adjusts the covariance process noise. Simulation results show that the proposed scheme can improve the precision of the CSMAF algorithm significantly. Moreover, it exhibits much better performance in estimating the position, velocity and acceleration of a target in a wide range of maneuvers.