Artificial intelligence applications to constant false alarm rate (CFAR) processing

False alarms are a significant problem in wide area surveillance radar. Many different constant false alarm rate (CFAR) algorithms have been developed to effectively deal with the various types of backgrounds that are encountered. However, any single algorithm is likely to be inadequate in a dynamically changing environment. The approach suggested is to intelligently select the CFAR algorithm or algorithms being executed at any given time, based upon the observed characteristics of the environment. This approach requires sensing the environment, employing the most suitable CFAR algorithm(s), and applying an appropriate multiple algorithm fusion scheme or consensus algorithm to produce a global detection decision.<>