Parametric Adaptive Matched Filter for Multistatic MIMO Radar

A fully distributed MIMO radar system can be treated in terms of all bistatic pairs. If a bistatic pair in a distributed MIMO radar system employs multiple transmit and receive elements in a phased array configuration, this increases the dimensionality of the data received over a coherent processing interval, which in turn increases the training data needed to reliably estimate the covariance matrix. This problem is exacerbated by the heterogeneity of training data arising from the bistatic geometry, which further degrades the quality of the covariance matrix estimate used for adaptive detection, resulting in a deleterious impact on space-time adaptive processing performance. To address these issues, we develop a parametric approximation to a physics-based clutter model, which approximates the physical characteristics of a variety of different real world clutter environments, and is especially useful in modeling clutter scenarios encountered in distributed MIMO radar. We show that clutter can be approximated as a multichannel autoregressive process of model order 4. This allows us to reduce the training data requirements and minimize the effects of training data heterogeneity on the detection performance. We demonstrate our results through simulation.