Robust adaptive divided difference filter based on forgetting factors and its applications

Abstract

This paper proposes robust and adaptive divided difference filters (RADDFs) based on forgetting factors which are robust to dynamic systems with biases or uncertainties. The RADDFs are founded on the principle of covariance matching. The robustness of RADDFs is reflected in that it amplifies the innovation covariance to compensate the effect of dynamic biases or uncertainties. The forgetting factor is adjusted adaptively. Then, the scalar forgetting factor is further extended to multiple forgetting factors. The proposed RADDFs are illustrated by Mars entry navigation system with atmospheric density uncertainty, lift over drag ratio uncertainty, and ballistic coefficient uncertainty. To validate the filter performance by multiple forgetting factors, a typical tight coupling nonlinear system with abrupt biases is used.