Outlier Rejecting Multirate Model for State Estimation

IntroductionMeasured data is often contaminated by noisein state estimation.Kalman filter is a powerfultool for signal extracting.It is especially efficientin estimating spatially inhomogeneous signal whenthe noise is Gaussian.Due to process noise or non-stationary environment,the measured data is usu-ally corrupted by outliers.The performance is de-graded seriously.Generally,there are two kinds ofapproaches to handle this problem.Outlier can bedetected based on renovation[1],then be replace…     

Outlier Rejecting Muitirate Model for State Estimation

Wavelet transform was introduced to detect and eliminate outliers in time-frequency domain. The outlier rejection and multirate information extraction were initially incorporated by wavelet transform, a new outlier rejecting multirate model for state estimation was proposed. The model is applied to state estimation with interacting multiple model, as the outlier is eliminated and more reasonable multirate information is extracted, the estimation accuracy is greatly enhanced. The simulation results prove that the new model is robust to outliers and the estimation performance is significantly improved.