The neural network method for radar weak target detection

Abstract

Because of the statistical nature nature of many types of clutter, a radar target detector must set a fairly high threshold in order to order to maintain a reasonable false-alarm rate. However, weak targets are usually missed for the above threshold detector. This paper presents an effective detector, which can be considered as a two-dimensional feature matching filter for radar signals. The feature extraction is performed by Hopfield neural networks and the feature integration is finished by a multilayer perceptron. In order to overcome the local optimum problem, a novel modification which is called energy comparing method is introduced into the Hopfield model dynamic equation to find the global optimum. By testing with the real radar return data in a low signal-to-clutter ratio, the detector presented in this paper has more advantages than the conventional threshold detector.<>