Kalman Filter with Wavelet-Based Unknown Measurement Noise Estimation and Its Application for Information Fusion
Jianqiu Zhang · Dianzi xuebao · 2007
It is well known that the successful applications of the Kalman filter are dependent on whether the prior knowledge of the statistical characteristics of the measurement noise is known.In this paper,the effects of the inaccuracy of the measurement noise covariance on the filter performance are first analyzed briefly.The feature of the wavelet transform separating a noise signal into the signal and noise parts in real time is combined into Kalman filter.A new method,making the Kalman filter under unknown measurement noise covariance condition valid,is then proposed.The presented method can track the changes of the measurement noise covariance and estimate the covariance in real time.Finally,the applications of the proposed method for the information fusion are discussed.The simulation results verify the effectiveness of the proposed method.