Neural network-based radar detection for an ocean environment

Novel detection schemes are developed using a coherent X-band radar for the detection of small pieces of icebergs. The methods use Wigner-Ville (WV) distribution to perform detection in a joint time-frequency space. Two separate methodologies are presented. The first method extracts classification features from the ambiguity function of the received signal and a neural network is used to perform detection based on these features. The second method uses the method of Principal Components Analysis (PCA) to extract essential information from the time-frequency space for classification. Using real radar data, results are presented and the developed methods are also compared to a conventional Doppler constant false-alarm rate (CFAR) processor.