Adaptive target tracking with serial Kalman filters

An adaptive target tracking scheme using two Kalman filters in series is presented. The first filter is a priori designed to cope with all possible target maneuvers, The second filter is controlled adaptively to suppress measurement noise, A pseudosystem concept is applied that have an influence on the Kalman filter algorithms of both filters. This includes the decomposition of the first filter state covariance into a system noise part and a measurement noise part and the inclusion of the covariance between the state estimates of the two filters in the second filter gain and covariance algorithms. The covariance between the state estimates is recursively calculated and accounts for the serially correlated measurement noise of the second filter. The qualitative merits of the method are discussed, Results from computer simulations are included to demonstrate performance obtained.