Enhancement of Cooperative Cross-Polar Radar Targets

Abstract

<p>A polarimetric radar navigation (PRAN) system makes use of a specially modified marine radar and 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. The objective of the thesis is to successfully demonstrate the enhancement and detection of a cooperative cross-polar target.</p> <p>A field experiment is designed in Hamilton Bay, and 28 scans of real-time non-coherent HH-pol and HV-pol radar video recorded in a digital format from atop the Canadian Centre for Inland Waters, in Burlington, Ontario. The two reflectors are located at sites in the Dofasco area and the La Salle Park area. A conventional cell-averaging CFAR processor is initially used to give a benchmark against which to compare joint signal processing methods. A dimensionless normalized target-to-clutter ratio (NTCR) is introduced to quantify performance, along with standard sub-images to subjectively show the effect of the processing.</p> <p>An adaptive cross-polar interference canceller is designed which processes the dual-polarization channels jointly, reducing the nonstationary clutter variance and enhancing the target. An analog implementation of the processor was granted Canadian and U.S. patents.</p> <p>In another approach, mutual information based unsupervised learning of linear and nonlinear networks is investigated. The RBF network is shown to greatly enhance cross-polar reflector response in the non-Gaussian statistical environment.</p> <p>Next, a modular solution integrates all three methods to produce superior reflector enhancement in average and peak clutter.</p> <p>Finally, a novel post-detection processor is demonstrated that successfully 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.</p>