A Radar / IR Weighted Fusion Algorithm Based on the Unscented Kalman Filter
Zefeng Xie, Hongfeng Gao, Yafei Ren · 2012
In order to improve the precision of the radar/infrared composite guidance, the nonlinear problem of measurement model in radar/infrared compound guidance information fusion was researched in this paper. A radar and infrared weighted fusion algorithm based on unscented Kalman filter (UKF) was proposed. The algorithm which solved the nonlinear function of the measurement model approximates the probability density distribution of the nonlinear function instead of approximating the linear function used in extended Kalman filter, thus it avoids the filter divergence problem in model linearization. Simulation results show that this algorithm has good convergence properties, high fusion precision, good robustness and good real-time performance, so it meets the need of information fusion of radar/ infrared compound guidance.