Joint identification and estimation for multi-detection systems

In multi-detection systems including the over-the-horizon radar (OTHR) system, the forward-based receivers (FBR) system, the passive coherent location (PCL) system and the extended target tracking, resolvable multiple detections of one target due to the multi-path propagation will be received. Both identification (including measurement association and propagation mode/transmitting origin identification) and estimation (including path-conditional state estimation and multi-path track fusion) are involved in multi-detection systems. Up to present, all corresponding methods fall in the scope of sequential processing, i.e., dealing with identification and estimation separately, and hence the solutions are not satisfactory especially in the presence of high modeling uncertainty in dynamics and sensors of the multi-detection systems. Different from the traditional sequential processing methods, here the multi-detection joint identification and estimation scheme (MD-JIE) is developed based on the generalized Bayes risk. The likelihood-ratio function and conditional probability density function as the identification cost in the ML sense and the estimation cost in the MMSE sense are derived, respectively. Taking the originality constraints of the multi-detection systems into consideration, the proposed MD-JIE scheme is implemented via online constrained optimization technology. In the PCL base tracking simulation, the effectiveness of the proposed MD-JIE method is verified. Besides, compared with the sequential identification-then-estimation (ITE) method and the estimation-then-identification (ETI) method in the OTHR simulation scenario, the proposed MD-JIE method prevails in the joint performance measure.