MAP joint identification and estimation for over-the-horizon radar

Target tracking in over-the-horizon radar (OTHR) is a challenging problem due to the existence of multiple propagation modes between the transmitter, target and receiver. Up to present, all OTHR based target-tracking methods require that the ionospheric parameters should be available via the ionosondes. However, the ionosondes can not be arbitrary deployed, for example, in sea area or hostile zone. Besides, the couple of mode identification and state estimation (estimation errors increase identification risk while identification mistake leads to estimation divergence to the actual value) in OTHR target tracking is another challenge. In this paper, a maximum-a-posterior joint mode identification and state estimation algorithm independent of ionosondes is proposed. Firstly, the joint optimization function is derived based on Maximum a Posteriori Penalty Function method. Through modeling both slant returns of different ray models and ray model sequence modeled as Markov process, mode identification can be performed recursively in Viterbi algorithm. Finally, though defining a quadratic penalty function, state estimation can be solved via extended Kalman filter. The simulation shows that the proposed method can effectively estimate the target state and ionospheric heights without the help of ionosondes.