Improved Sar Target Detection Using Subspace Filtering

We propose a new class of Subspace filter based algorithms for detecting targets in forest clutter environment. The training phase of the proposed SAR target detection algorithms “learns” the clutter characteristics using local or global clutter subspaces. Both off-line and on-the-fly self-training versions of the algorithm are presented. These adaptive approaches utilize the Singular Value Decomposition (SVD) algorithm where small blocks of data in the neighborhood of a sliding test window are processed in real-time to estimate clutter characteristics. The real-time clutter models are then used to nullify clutter in the test window. The proposed approach is shown to be highly effective for removing blob-like impulsive clutter for improved detection performance. An ultra-wideband (UWB) synthetic aperture radar (SAR) simulation technique that employs physical and statistical models is also presented [4, 9]. This joint physics-statistics based technique generates realistic images that have many of the “blob-like” and “spiky” clutter characteristics of UWB radar data in forested regions while avoiding the intensive computations required for the implementation of low-frequency numerical electromagnetic simulation techniques. The SAR image simulation technique allows system designers to test in a variety of operating environments. The proposed subspace filter-based detection algorithms have been applied on the simulated UWB data for performance comparison using ROC curves.