Robust CFAR Radar Detection Using a K-nearest Neighbors Rule

The problem of robust radar detection is addressed from a machine learning inspired perspective. In particular, a novel interpretation of the well-known Kelly's and adaptive matched filter (AMF) detectors is provided in terms of decision region boundaries in a suitable feature space. Then, a new detector based on a feature vector that combines the two detection statistics is obtained by exploiting the k-nearest neighbors (KNN) approach. The resulting receiver possesses the constant false alarm rate (CFAR) property and can achieve the same benchmark performance of Kelly's detector under matched conditions while being almost as robust as the AMF (which instead experiences a loss under matched conditions).