Neural network detection of small moving radar targets in an ocean environment

Small icebergs and pieces of icebergs are virtually undetectable with conventional marine radar systems. The authors describe a detection scheme for such icebergs. The scheme uses the chirplet transform, a wavelet-inspired transform, to generate images of the Doppler-shifted radar returns from icebergs and ocean surfaces. The images are classified using a neural network trained with the backpropagation algorithm, incorporating weight sharing and optimal brain damage paradigms. The network´s architecture is motivated by the known physiology of animal vision. The network design incorporates temporal information. Performance has surpassed the benchmark Fourier-based detection scheme