Improving multistatic MIMO radar performance in data-limited scenarios

Abstract

A MIMO Multistatic radar system consists of multiple bistatic MIMO pairs working in potentially different configurations. If a bistatic pair in a Multistatic MIMO radar system employs multiple transmit and receive elements, this increases the dimensionality of the data received over a Coherent Processing Interval (CPI), which in turn increases the training data needed to reliably estimate the covariance matrix. This, coupled with the non-stationarity in the received data resulting from the bistatic geometry further degrades the quality of the covariance matrix estimate used in the adaptive detector. In [1], Bell et al. presented a physics based MIMO clutter model, and showed that lack of training data support renders the MIMO radar unfeasible in that the individual bistatic pairs can outperform the overall MIMO system. For these systems, we need to investigate techniques that perform reasonably well in data limited scenarios. In this paper, we show that the physics based clutter model presented in [1] can be approximated as an AR process of model order 4. This has implications for the amount of data that is needed to reliably estimate the AR parameters. For the purpose of this discussion, we use the optimum AR coefficients for every model order generated using the clairvoyant clutter covariance matrix, and characterize the performance using two metrics: SINR loss, and the probability of detection as a function of SINR.