为了提高雷达/红外复合制导精度，针对雷达/红外复合制导信息融合中量测模型非线性问题，提出一种基于不敏卡尔曼滤波器（UKF）的分布式雷达红外加权融合算法。该算法在解决量测模型非线性函数问题上，不是对非线性函数进行近似，而是对非线性函数的概率密度分布进行近似，因而避免了扩展卡尔曼滤波的模型线性化误差导致滤波发散的问题。仿真结果表明，该算法收敛性好，融合精度高，鲁棒性好，实时性好，可以满足复合制导中信息融合技术的要求。% In order to improve the precision of the radar/infrared composite guidance, the nonlinear problem of measurement model in radar/infrared composie 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, approximated the probability density distribution of the nonlinear function instead of approximating the linear function used in extended Kalman filter, thus it avoided the filter divergence problem in model linearization. Simulation results showed that this algorithm had good convergence properties, high fusion precision, good robustness and good real-time performance, which could meet the need of information fusion of radar/infrared compound guidance.
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