基于无气味卡尔曼滤波的雷达/红外加权融合算法

Zefeng Xie, Hongfeng Gao, Yafei Ren
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引用次数: 0

摘要

为了提高雷达/红外复合制导的精度,研究了雷达/红外复合制导信息融合中测量模型的非线性问题。提出了一种基于无气味卡尔曼滤波(UKF)的雷达与红外加权融合算法。求解测量模型非线性函数的算法近似非线性函数的概率密度分布,而不是近似扩展卡尔曼滤波中使用的线性函数,从而避免了模型线性化中的滤波散度问题。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,能够满足雷达/红外复合制导信息融合的需要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Radar / IR Weighted Fusion Algorithm Based on the Unscented Kalman Filter
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.
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