文章详情

摘要

含有离群值的噪声表现出重尾特性，使得高斯噪声下最优的卡尔曼滤波器性能下降。为解决噪声协方差矩阵未知和时变的状态估计问题，针对离群值影响下产生的非高斯噪声，提出了一种基于统计相似度度量的自适应鲁棒卡尔曼滤波算法（RSSMAKF），算法利用统计相似度度量构建成本函数，同时估计系统状态、过程误差和观测噪声的后验概率密度。为了能够自适应地抑制离群值的影响，提出了一种τ自适应策略并将其嵌入后验概率密度函数的估计过程。仿真结果表明，所提出的RSSMAKF算法与其他鲁棒卡尔曼滤波算法相比，在均方误差准则下的性能更优。

关键词

卡尔曼滤波；离群值；统计相似度度量；非高斯噪声；状态估计

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作者简介

马浪蛟（2001—），男，汉族，江西宜春人，硕士在读，研究方向：自适应信号处理；通信作者：吴君钦（1966—），男，汉族，江西赣州人，教授，硕士，研究方向：通信与信息系统、嵌入式系统。