Tracking targets with unknown process noise variance using adaptive Kalman filtering

Abstract

A simple algorithm is suggested to estimate, using a Kalman filter, the unknown process noise variance of an otherwise known linear plant. The process noise variance estimator is essentially dead beat, using the difference between the expected prediction error variance, computed in the Kalman filter, and the measured prediction error variance. The estimate is used to adapt the Kalman filter. The use of the adaptive filter is demonstrated in a simulated example in which a wildly manoeuvring target is tracked.<>