Small target detection in clutter using recursive nonlinear prediction

Detecting small objects in clutter is usually carried out by using a predictor to suppress the background clutter. The idea is that a predictor which is trained using clutter data usually has small prediction error for the clutter process, but the prediction error will be relatively large if the signal fed into the predictor contains a target. While conventional approaches use a one-step-ahead predictor, we propose using a recursive predictor, which uses the predicted value to continue predicting the future points, to improve this predictive detection scheme. It is shown here that while the recursive prediction error of the clutter process is about the same as that of a one-step ahead predictor, the recursive predictor amplifies the prediction error of the target process. Therefore, the distance between the clutter and target processes is increased and the target detectability is enhanced. In addition, this recursive prediction approach has the same computational complexity as the one-step-ahead predictor since no extra training or modeling procedure is required. Real radar oceanic surveillance data are used to illustrate the effectiveness of the proposed detection method. Results show that the recursive prediction approach outperforms the one-step-ahead predictor in detecting small targets in the presence of strong clutter