基于自适应卡尔曼滤波的多传感器信号降噪  

Multi-Sensor Signal Denoising Based on Adaptive Kalman Filter

作　　者：林旭梅 刘帅 石智梁 LIN Xu-mei;LIU Shuai;SHI Zhi-liang(School of Information and Control Engineering,Qingdao University of Technology,Qingdao 266520,China)

机构地区：[1]青岛理工大学信息与控制工程学院,山东青岛266520

出　　处：《计算机仿真》2022年第2期507-511,共5页Computer Simulation

基　　金：国家重点基础研究发展计划项目(2015CB655100)。

摘　　要：针对建筑物混凝土腐蚀检测中传感器单一且常规卡尔曼滤波算法容易出现滤波精度降低的问题,提出了一种多传感器综合检测方法。采用改进的自适应卡尔曼滤波算法,利用最大概似估计准则,将新息方差直接引入卡尔曼滤波器的增益计算,实现估计模型的动态调整,降低了系统噪声和测量噪声的干扰。最后,对自适应卡尔曼滤波和常规卡尔曼滤波算法进行了仿真对比实验,结果表明,自适应卡尔曼滤波算法有效提高了多传感器信号检测的精度和稳定性,性能优于常规卡尔曼滤波算法。Aiming at the problem that the single sensor in the concrete corrosion detection of buildings and the conventional Kalman filter algorithm is prone to decrease the filtering accuracy, a multi-sensor comprehensive detection method is proposed. The improved adaptive Kalman filter algorithm was mainly adopted, and the innovation variance was directly introduced into the gain calculation of the Kalman filter by using the Maximum Likelihood Estimation criterion, so as to realize the dynamic adjustment of the estimation model and reduce the interference of the system noise and measurement noise. Finally, a simulation comparison experiment between the adaptive Kalman filter and the conventional Kalman filter algorithm was performed. The results show that the adaptive Kalman filter algorithm effectively improves the accuracy and stability of multi-sensor signal detection, and the performance is better than the conventional Kalman filter algorithm.

关 键 词：腐蚀检测 多传感器 自适应卡尔曼 滤波精度 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]