A modular neural network for enhancement of cross-polar radar targets

A polarimetric radar navigation system makes use of polarization rotating twist-grid retroreflectors in order to navigate a confined waterway, even in inclement weather or after dark. Despite the polarization diversity offered by such a radar target, depolarization allows significant cross-polar clutter to obscure the reflector return.A novel modular neural network solution integrates an adaptive cross-polar interference canceller, a radial basis function network, and a conventional cell-averaging CFAR processor to successfully demonstrate the enhancement and detection of a polarization target. The modular solution outperforms any one of the aforementioned methods on their own. This is indicated subjectively through the display of the resultant processed images, and objectively by the estimates of target-to-clutter ratio and receiver operating curves.A post-detection processor uses a priori information about the reflector location along the water-land boundary of the waterway. A fuzzy processor combines primary detection information with the output from a vision-based edge detector to effectively remove false alarms.