基于高阶统计量滤波器的磁导航信号的野点识别  

Outlier Recognition of Magnetic Navigation Signal Based onHigh Order Statistics Filter

作　　者：黄卓群 杨光永[1] 程满 于元滐 徐天奇[1] HUANG Zhuoqun;YANG Guangyong;CHEN Man;YU Yuanjie;XU Tianqi(College of Electronics and Information,Yunnan Minzu University,Kunming 650500)

机构地区：[1]云南民族大学电气信息工程学院,昆明650500

出　　处：《计算机与数字工程》2023年第3期731-735,共5页Computer & Digital Engineering

基　　金：国家自然科学基金项目(编号:61761049,61261022);云南省专业学位研究生教学案例库及研究生优质课程项目资助。

摘　　要：在轮式搬运机器人(AGV)工作的过程中,采集磁导航信号时,由于杂质产生的干扰,会使采集到的信号中包含野点分量。野点作为一种非平稳分布的随机过程,会对AGV的行驶产生较大的影响。为了提高信号的信噪比和收敛速度,使AGV平稳运行,采用四阶累积量均方误差准则的高阶统计量自适应滤波器算法。仿真结果说明,相比于传统的FastICA方法,高阶统计量自适应滤波器算法拥有较高的信噪比和较好的滤波效果。In the process of AGV working,when collecting the magnetic navigation signal,due to the interference of impuri⁃ties,the collected signal contains the wild point component.As a non-stationary random process,outliers have great influence on AGV.In order to improve the signal-to-noise ratio(SNR)and convergence speed of the signal and make AGV run smoothly,a high-order statistics adaptive filter algorithm based on fourth-order cumulant mean square error criterion is adopted.The simulation results show that compared with the traditional FastICA method,the high-order statistics adaptive filter algorithm has higher sig⁃nal-to-noise ratio and better filtering effect.

关 键 词：轮式搬运机器人 野点 四阶累积量 高阶统计量 FASTICA 

分 类 号：TN912[电子电信—通信与信息系统]