Modern CFAR techniques in heterogeneous radar clutter scenarios

This talk will provide an overview of adaptive radar signal processing starting from the early work of Howells, Applebaum and Widrow on adaptive arrays. The sample matrix inverter (SMI) method and its variants will be discussed in some detail. The focus of the discussion will be on the constant false alarm rate (CFAR) characteristics. These methods are based on the formation and inversion of a sample covariance matrix. Problems encountered in covariance estimation on account of heterogeneous training data will be discussed from a phenomenological, systems, and statistical perspective. The resulting impact on adaptive processing performance will be addressed. Statistical and ad hoc techniques for characterizing heterogeneous training data will be discussed. Intelligent training data selection schemes will be presented and analyzed. The performance of candidate adaptive processing methods employing intelligent training data selection will be presented using simulated and measured data. This course is suited for both the practicing engineer seeking practical aspects of basic and advanced adaptive radar techniques as well as for scientists and engineers from academia and government interested in a tutorial exposition emphasizing theoretical aspects of adaptive radar.