Multichannel adaptive signal detection: basic theory and literature review

Multichannel adaptive signal detection uses test and training data jointly to form an adaptive detector to determine whether a target exists. The resulting adaptive detectors typically possess constant false alarm rate (CFAR) properties; thus, no additional CFAR processing is required. In addition, a filtering process is also not required because the filtering function is embedded in the adaptive detector. Adaptive detection typically exhibits better detection performance than the filtering-then-CFAR detection technique. It has been approximately 35 years since the first multichannel adaptive detector was proposed by Kelly in 1986. However, there are few overview articles on this topic. Thus, in this study, we present a tutorial overview of multichannel adaptive signal detection with an emphasis on the Gaussian background. We discuss the main design criteria for adaptive detectors, investigate the relationship between adaptive detection and filtering-then-CFAR detection techniques, investigate the relationship between adaptive detectors and adaptive filters, summarize typical adaptive detectors, present numerical examples, provide a comprehensive literature review, and discuss potential future research tracks.

Automated summary

Multichannel adaptive signal detection uses test and training data jointly to form an adaptive detector to determine the presence or absence of a target. These adaptive detectors possess constant false alarm rate properties and do not require additional processing. Compared to the filtering-then-CFAR technique, adaptive detection typically exhibits better performance. However, there are few overview articles on this topic, hence this study provides a tutorial overview specifically focusing on Gaussian background and covers various aspects.