Adaptive detection of statistical signals in noise

This paper deals with the forms and properties of a detector operating in an environment in which little statistical information about either the signal or noise field is or can be available. An adaptive detector based on the theory of nonparametric statistics has been designed for this purpose. The detector uses amplitude samples taken from two distinct receivers, only one of which may contain signals from a target. The detector maintains a constant false alarm rate despite any nonstationarity of the noise. The theory of nonparametric statistics suggests that the data from the two sources be ranked in order of amplitude, that a linear weighting (a correlator) be applied to the ranking, and the result be applied to a threshold. In this study, we have considered an adaptive detector of this form. The adaptation mechanism selects the weight (correlating) function on the basis of the past observed data. This class of adaptive detectors is shown to have excellent performance in terms of signal detectability for signals of adequate strength. However, for a given detector design, signals weaker than a certain critical threshold cannot be detected regardless of the amount of data available. Thus, in contrast to the kind of signal suppression effect which characterizes conventional passive detection schemes, the adaptive detector has a sharp minimum detectable signal threshold.