估计噪声方差与Kalman滤波的传感器动态补偿  

Dynamic Compensating of Sensor Based on Noise Variance Estimation and Kalman Filtering

作　　者：陈战平[1] 

机构地区：[1]南京师范大学计算机科学与技术学院,江苏南京210046

出　　处：《南京师范大学学报（工程技术版）》2011年第3期13-17,共5页Journal of Nanjing Normal University(Engineering and Technology Edition)

摘　　要：传感器动态补偿后的输出噪声被加重且方差未知.为有效地抑制补偿后的噪声干扰,研究了一种在未知观测噪声方差条件下,采用卡尔曼滤波去噪的传感器动态补偿算法.补偿器的参数通过参考模型和系统辨识的方法得到,同时,利用参考模型建立卡尔曼滤波器,消除高频噪声对测量精度的影响.由于补偿器的输出信号可以用一个M阶多项式分段逼近,利用小波消失矩原理对输出信号的噪声进行方差估计,从而解决了在未知观测噪声的条件下卡尔曼滤波失效问题.最后,通过仿真和应用实验,验证了该方法的有效性.After sensor dynamic compensation, the output signal of the noise is increased and the variance is unknown. In order to effectively suppress noise, a dynamic compensation algorithm of adopting Kalman filter de-noising is researched in unknown measurement noise variance. Parameters of the compensator were obtained by reference model and system identification. At the same time, Kalman filter was constructed with reference mode to eliminate high frequency effected measurement precision. On account of the compensator＇ s output signal piecewise approximated by a polynomial with a degree of M, the noise variance can be estimated to utilize vanishing moments of wavelet, and the Kalman filter under the unknown measurement noise variance condition is valid. Simulation experimental results show that the approach is effective.

关 键 词：动态补偿 噪声 方差估计 滤波 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]