A Metacognitive Approach to Adaptive Radar Detection

Detecting objects of interest is one of the core functions of radar systems and doing so in the presence of interference is an ongoing challenge in the domain. Clutter is an especially problematic form of interference that can result in a large number of false alarms. In general, the goal of radar detection systems is to maximize the likelihood of detecting targets while maintaining a constant false alarm rate (CFAR). Adaptive detectors like the generalized likelihood ratio test (GLRT) have been developed to achieve this. However, they are derived assuming that the clutter can be modeled according to a consistent probability distribution. This assumption typically does not hold true in many real-world applications, particularly on airborne or naval systems, which degrades detection performance and eliminates the desired CFAR behavior. In this work, a metacognitive approach to adaptive detection is proposed to achieve CFAR-like behavior over a range of clutter distributions. It is demonstrated that this metacognitive detector maintains CFAR-like behavior when presented with data randomly selected from a range of clutter distribution models (Gaussian, K, and Pareto) and that it matches the performance of the traditional GLRT in Gaussian interference.