Ship-traffic control by means of neural networks applied to radar image sequences

In this paper an Automatic Target Recognition (ATR) system for ship-traffic control in the access are of a seaport is presented. The system employs digital image processing techniques applied to X-band real aperture RADAR images. Problems due to target signature variability, aspect-angle dependency, and noise are considered. The estimate of prow-orientation is also done, which provides useful information for drift-angle computation and automatic collision avoidance. First the radar sequence is segmented t locate the ships, then each contour is analysed to compute prow orientations. The same processing in repeated for all the images in the sequence and the resulting data are linked together to give the trajectory of each ship. Supervised Neural Networks have been used to obtain robust segmentation and accurate ship-heading location. An adaptive version of the *-* filter gives accurate trajectory estimates. To validate the system, a simulator has been used to produce image sequences concerning ships of know dimensions, positions, and headings. The errors introduced by the processing system remain below the uncertainty of the sensor and the prow orientation is always recovered with negligible error in the image plane, showing an extremely precise behaviour of the prow-detection algorithm.