Robust Incremental Kalman Predictor

Abstract

Under certain environmental conditions, when the measurement equation of the system is not verified or calibrated, the use of the measurement equation will often produce unknown system errors, resulting in large filtering errors. Similarly, when the noise variance of the system is uncertain, the performance of the filter will deteriorate, and even cause the filter divergence. The introduction of incremental equation can effectively eliminate the unknown measurement error of the system, so that the state estimation of system under poor observation condition with unknown measurement error can be transformed into the state estimation of incremental system. In this paper, a robust incremental Kalman predictor is proposed for linear discrete systems with unknown measurement error and unknown noise variance. A simulation result shows the effectiveness and feasibility of the proposed algorithm.