A fuzzy adaptive fusion algorithm for radar/infrared based on wavelet analysis

In order to improve tracking ability, a fuzzy adaptive fusion algorithm based on wavelet analysis for radar/infrared is proposed, which combines the merits of fuzzy logic and wavelet analysis. Fuzzy adaptive fusion algorithm is a powerful tool to make the actual value of the covariance of the residual consistent with its theoretical value. To overcome the defect of the dependence on the knowledge of the process and measurement noise statistics of Kalman filter, wavelet analysis is introduced, which needs no prior knowledge of the process and measurement noise. And fuzzy inference system is applied for its simplicity of the approach and its capability of processing imprecise information. The simulation experiments on the novel adaptive fusion algorithm have been performed. The computational results show that the proposed algorithm can effectively strengthen the system robustness and improve the tracking precision. It is obvious that the algorithm has significant advantages over the traditional Kalman filter algorithms in tracking application.