基于M估计的非线性鲁棒检测卡尔曼滤波算法  

Nonlinear robust detection Kalman filter algorithm based on M-estimation

作　　者：李开龙[1] 胡柏青[1] 高敬东[1] 冯国利[1] 

机构地区：[1]海军工程大学电气工程学院,武汉430033

出　　处：《计算机应用》2014年第11期3214-3217,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61304241;61374206)

摘　　要：针对传统鲁棒非线性滤波在观测噪声为非高斯强干扰噪声情况下,滤波性能下降的问题,提出一种利用卡方检测法预判断的非线性鲁棒检测滤波算法。该算法通过卡方检测设置门限,剔除突变野值,利用M估计修正量测更新。仿真实验对比了几种典型非线性滤波方法在不同观测噪声环境下的性能。所提算法在非高斯强干扰噪声情况下,比传统鲁棒滤波算法估计精度平均提高了25.5%;估计方差平均减少了18.3%。实验结果表明:所提算法可以抑制观测量非高斯强干扰噪声的影响,提高滤波精度及稳定性。Aiming at the problem that the traditional nonlinear robust filtering will be severely degraded when the distribution of measurement noise deviates from the assumed Gaussian distribution, a new robust nonlinear Kalman filter based on M-estimation and detection method was proposed. The proposed robust filtering algorithm set a threshold using Chi-square test to delete mutation outliers, and modified the measurement update using M-estimation. Several conventional nonlinear filtering methods were evaluated under different measurement noises in terms of accuracy and stability. Under non-Gaussian noise and strong interference, the proposed algorithm outperforms the traditional robust algorithm with higher estimation accuracy by 25. 5% and lower estimation covariance by 18. 3%. The experimental results show that the proposed filtering algorithm can suppress the influence of non-Gaussian noise and strong interference, and increase the estimation accuracy and stability.

关 键 词：非线性 卡尔曼滤波 观测噪声 M估计 鲁棒 

分 类 号：TN911.72[电子电信—通信与信息系统]