Cognitive radar for target tracking in multipath scenarios

In this paper, we propose a cognitive radar system for target tracking in the presence of multipath reflections. We exploit the inherent spatial diversity offered by the multipath environment by constructing a new measurement vector, which we refer to as a virtual measurement vector. We employ broadband Orthogonal Frequency Division Multiplexing (OFDM) signalling at the transmitter and implement adaptive waveform design by minimizing the posterior Cramér Rao bound (PCRB) on the target state estimates to find the optimal weights to be transmitted on each subcarrier bin of the OFDM signal. We demonstrate with numerical simulations that the mean square error in the case of a cognitive radar is significantly lower than the mean square error in the case of a standard radar.