作者:文 胜，刘彩云，栾添添，孙明晓

Authors:WEN Sheng,LIU Caiyun,LUAN Tiantian,SUN Mingxiao
摘要:针对在组合导航系统中应用 Sage-Husa 自适应滤波算法存在的滤波精度低和可靠性差等问题,提出一 种改进的 Sage-Husa 自适应滤波算法 。首先在 Sage-Husa 自适应滤波算法基础上,通过指数渐渐消记忆加权估计方法来估计噪声统计特性,提高算法的自适应能力,后结合强跟踪滤波引入渐消因子在线修正预测均方误差矩阵,使改进后的算法具有应对系统误差干扰等不确定因素的能力 。仿真结果表明,改进后 Sage-Husa 算法具有更高的自适应能力以及在出现模型误差和粗差干扰时能够有效抑制滤波发散,保持良好的滤波性能,应用在组合导航系统中具有更强的稳定性和定位精度。
Abstract:To address the issues of low filtering accuracy and poor reliability encountered when employing the Sage-Husa adaptive filtering algorithm in combined navigation systems, an improved Sage-Husa adaptive filtering algorithm is proposed. Firstly, on the basis of the Sage-Husa adaptive filtering algorithm, the statistical characteristics of noise are estimated by the weighted estimation method of exponential fading memory to improve the adaptive ability of the algorithm. Then, by the introduction of the fading factor in the strong tracking filtering, the prediction mean-square error matrix is corrected online, so that the improved algorithm has the ability to cope with the uncertainties such as systematic error interference. Simulation results demonstrate that the improved Sage-Husa algorithm exhibits greater adaptive ability. It effectively suppresses filter divergence and maintains excellent filtering performance, even when faced with model error and coarse interference. Furthermore, when applied in combined navigation systems, the algorithm showcases improved stability and positioning accuracy.

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