A multi-layer neural network classifier for radar clutter

A multilayer neural network classifier has been successfully implemented on a Warp systolic computer for distinguishing several major categories of radar returns: target (aircraft), weather, birds, and ground. The experimental results show that the neural network method is better than the traditional statistical method, which gives an average rate of 81.8% for classifying target, weather, and birds, in the same SNR range. The design of this neural classifier also suggests that the preprocessing and postprocessing procedures based on some prior information about the input data are very important for enhancing the classification performance.