Multiple-model detection of target maneuvers

This paper proposes a multiple-model (MM) hypothesis testing approach for detection of unknown target maneuvers that may have several possible prior distributions. An MM maneuver detector based on sequential hypothesis testing is developed. Simulation results that compare the performance of the proposed MM detector to that of traditional maneuver detectors are presented. They demonstrate that the new sequential MM detector outperforms traditional multiple hypothesis testing based detectors when the prior acceleration distributions are unknown.