Second-Order Markov Chain Based Multiple-Model Algorithm for Maneuvering Target Tracking

A multiple-model algorithm for maneuvering target tracking is proposed. It is referred to as a second-order Markov chain (SOMC)-based interacting multiple-model (SIMM) algorithm. The target maneuver process is modeled by a SOMC to incorporate more information. SIMM adopts a merging strategy similar to that of the interacting multiple-model (IMM) algorithm, except that the one-step model transition probabilities are updated based on the SOMC. A scheme is proposed to design the transition probabilities of the SOMC for target tracking. The performance of the proposed SIMM algorithm is evaluated via several scenarios for maneuvering target tracking. Simulation results demonstrate the effectiveness of SIMM compared with IMM, the second-order IMM (IMM2) algorithm, and the likely-model set (LMS) algorithm. It is shown that SIMM performs about the same as IMM2 but requires only n filters versus n(2) filters in IMM2 for n models. The effectiveness and efficiency of combining SIMM and LMS for state estimation are also demonstrated in the simulation.