Optimal filtering in the presence of faulty measurement biases

The problem of fault tolerant estimation with application to a system subjected to faulty measurement biases is addressed. A new estimation approach, called fault adaptive filtering, is proposed. The proposed estimation scheme consists of a single minimum variance state estimator, which takes into account fault probabilities, combined with a fault estimator, which provides estimates of these probabilities. Unlike the multiple model approach this method does not require a large number of parallel filters. On the other hand, unlike separate estimation and fault detection techniques, the fault adaptive filter is able to utilize partial information about the existence of faults. A numerical example of spacecraft attitude determination is presented, which demonstrates the performance of the proposed approach.