Proc. SPIE. 6568, Algorithms for Synthetic Aperture Radar Imagery XIV
KEYWORDS: Target detection, Radar, Defense and security, Principal component analysis, Detection and tracking algorithms, Data modeling, Digital filtering, Clouds, Feature extraction, Radar signal processing
In this paper, we present a Multi-Frequency Space-Time Orthogonal (MF-STOP) adaptive filtering approach for detection and discrimination of targets based on a two stage orthogonal projection whereby target parameters can be extracted in the presence of heavy clutter and noise. The proposed technique detects targets within heavy clutter tracked by a radar system. After targets are detected, motion information is extracted that can be used to discriminate threats such as reentry vehicles from other targets. Target detection is generated in stage one by a combination of Windowed Short Time Fast Fourier Transform (WSTFFT) processing and Principal Component Analysis (PCA). Target discrimination is done in a second stage via Partial Least Squares (PLS) using a training filter constructed from the stage one detection. The target is discriminated explicitly by metric criteria such as size or precession. These discriminate features do not have to be known a priori.