Classical bearings-only target-motion analysis (TMA) is restricted to sources with constant motion parameters (usually position and velocity). However, most interesting sources have maneuvering abilities, thus degrading the performance of classical TMA. In the passive sonar context a long-time source-observer encounter is realistic, so the source maneuver possibilities may be important in regard to the source and array baseline. This advocates for the consideration and modeling of the whole source trajectory including source maneuver uncertainty. With that aim, a convenient framework is the hidden Markov model (HMM). A basic idea consists of a two-levels discretization of the state-space. The probabilities of position transition are deduced from the probabilities of velocity transitions which, themselves, are directly related to the source maneuvering capability. The source state sequence estimation is achieved by means of classical dynamic programming (DP). This approach does not require any prior information relative to the source maneuvers. However, the probabilistic nature of the source trajectory confers a major role to the optimization of the observer maneuvers. This problem is then solved by using the general framework of the Markov decision process (MDP).
展开▼
