Performance comparison of the two-stage Kalman filtering techniques for target tracking

The two-stage filtering methods, such as the wellknown augmented state Kalman estimator (AUSKE) and the optimal two-stage Kalman estimator (OTSKE), suffer from some major drawbacks. These drawbacks stem from assuming constant acceleration and assuming the input term is observable from the measurement equation. In addition, these methodologies are usually computationally expensive. The innovative optimal partitioned state Kalman estimator (OPSKE) developed to overcome these drawbacks of traditional methodologies. In this paper, we compare performance of the OPSKE with the OTSKE and the AUSKE in the maneuvering target tracking (MTT) problem. We provide some analytic results to demonstrate the computational advantages of the OPSKE.