Urban terrain tracking in high clutter with waveform-agility

We consider the problem of tracking a maneuvering target in urban terrain with high clutter. Although multipath has been previously exploited to improve target tracking in complex urban environments, when the clutter is high, multipath returns can suffer from large losses in signal-to-noise ratio (SNR), reducing probability of detection (PD). Maneuvering, a common motion in urban terrain, can also affect PD as different multipaths occur at different times. We propose a waveform-agile probabilistic data association target tracker that allows for multiple interactive motion models. The new adaptive tracker computes the different PD values for the validated measurements of line-of-sight (LOS) and non-LOS (NLOS) returns from the target, while selecting the transmit waveform that minimizes the mean-squared error (MSE) at each time step. The proposed approach is demonstrated using simulations of a realistic high clutter urban environment.