Abstract
Abstract We study non-linear signal processing for detection of threshold signals in chaotic backgrounds. The method is based upon synthesis of a polynomial neural network (PNN) for prediction of chaotic dynamics. Using a solution of the Mackey-Glass equation as a chaotic background, we synthesize a non-linear PNN processor which produces a threshold signalto background improvement of over 20 db. 1. Introduction Many natural backgrounds, such as radar clutter, which are thought to be random, may be deterministic chaos. Because of the finite dimensionality of a chaotic background, a non-linear signal processor can be trained as a global predictor. It is well known that many physical sources can produce space-time patterns which follow the dynamics of alow-dimensional attractor [1, 2]. Leung and Haykin have recently foundexperimental evidence that radar clutter from the ocean surface may have a chaotic attractor of dimension 7 [3] . We make the important distinction between irregular behavior, such as 'deterministic' chaos, and randomness.
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