Joint optimization of predictive model and transmitted waveform for extended target tracking

An adaptive algorithm for extended target tracking is proposed in the wideband cognitive radar, which is based on minimization of the mean square tracking error. It is carried out by the joint optimization of the predictive model in the receiver and the waveform in the transmitter. First, the autoregressive (AR) model is incorporated into the Kalman filter for target tracking. The degrees of freedom of the AR model can satisfy the polynomial constraint of target motion on the one hand, and are able to reduce the noise on the other hand. Based on the AR model, the influences of the predictive error and the measurement error on the tracking accuracy are decoupled. And then the optimal waveform is obtained by minimizing the Cramér-Rao lower bound (CRLB) for estimating the target range. Simulation demonstrates that the proposed algorithm has a better tracking performance than the traditional one with the fixed predictive model and the fixed waveform.