Research on robust Kalman filter based on depth-weighted

Abstract

After the analysis of cumulative effect on filter results of gross errors,a new robust filter under the Kalman framework is proposed by improving the weighted mode of the innovation with the depth-weighted algorithm.For the introduction of the calculation of data depth and weighted coefficients,the filter can straightforwardly adjust the contribution of the observations to the filter states without any gross error detections.The depth-weighted step can be viewed as an extension of the optimal criterion(the minimum mean square error,MMSE) in the Kalman filter,By utilizing of the relativity of different observations as well as the relativity between the observations and the states,the new filter can effectively release the disadvantage effect on the filter results of gross errors.Based on the robustness analysis,the feasibility and the efficiency of the new filter are validated by numerical examples finally.