Outliers rejection in Kalman filtering — Some new observations

Abstract

A standard outlier-rejection scheme applied in Kalman filtering, based on the acceptance/rejection gate for measurement innovation, is discussed in this paper. The main idea behind this approach is based on assumptions that measurements can be "normal", as described in the measurement model and "abnormal" outliers that are generated by a totally different model. The goal of the acceptance/rejection gate is to accept normal measurements and reject abnormal ones. A concrete and simple case of range estimation in the presence of multipath outliers is thoroughly analyzed. The results are both nontrivial (even surprising) and important for designers of such rejection schemes who may use them as guidance for efficient design. The first observation is that the outlier-rejection scheme may provide worse results than the scheme with no rejection at all. This is because there is a positive, albeit relatively low probability that the system will enter and remain in a mode in which outliers are accepted and normal measurements are rejected. In this case, the estimation errors become very big and have a significant influence on the total standard deviations (even if their probability of occurrence is low). The main and very important conclusion is that outlier-rejection schemes cannot be applied without a proper recovery scheme that prevents the system from remaining "stuck" in normal-measurement rejection mode. In this paper, three different recovery schemes are proposed: a one-sided rejection scheme (only applicable to multipath-type outliers); a Kalman-filter reset scheme; a set of parallel Kalman filters, where the output is provided by the filter with minimal innovation size. The design and performance analysis of each recovery scheme are described. The conclusion is that the performance of the recovery schemes is very close to the case without any outliers at all, up to very high (0.45) multipath-occurrence probability.