基于小波变换的UKF改进算法及其应用

众所周知,UKF滤波的应用需要事先准确知道量测噪声的统计特性。首先简要分析了UKF滤波的基本算法,然后利用小波变换可以实时分离信号和噪声的特性,提出了一种在未知量测噪声条件下的UKF算法,该算法可以实时跟踪量测噪声的变化,即实现了对量测噪声的实时估计,从而解决了在未知量测噪声的条件下UKF滤波问题。最后讨论了该方法在信息融合中的应用,仿真结果证明了方法的有效性和实用性。

Wavelet-Transformation Based Unscented Kalman Filter and Its Application

It is well known that the successful application of UKF depends on whether the prior knowledge of the statistics of the measurement noise is known.In this paper,the UKF algorithm is first analyzed briefly.The feature of wavelet transformation of separating a noise signal into signal part and noise part in real time is combined into UKF.A new method is then proposed that makes the UKF under unknown measurement noise covariance condition valid.This presented method can track the changes of the measurement noise covariance in real time.Finally,the applications of the proposed method for information fusion are discussed.The simulation results verify the effectiveness of the proposed method.