Improved Target Recognition and Target Detection Algorithms Using Hrr Profiles and Sar Images

In this thesis, a new algorithm to improve automatic target recognition techniques on High Range Resolution (HRR) Profiles is presented and also a number of ways are investigated for target detection using Synthetic Aperture Radar (SAR) images. A new 1-D hybrid Automatic Target Recognition (ATR) algorithm is developed for sequential High Range Resolution (HRR) radar signatures. The proposed hybrid algorithm combines Eigen-Template based Matched Filtering (ETMF) and Hidden Markov modeling (HMM) techniques to achieve superior HRR-ATR performance. In the proposed hybrid approach, each HRR test profile is first scored by ETMF that is then followed by independent HMM scoring. The first ETMF scoring step produces a limited number of " most likely " models that are target and aspect dependent. These reduced numbers of models are then used for improved HMM scoring in the second step. Finally, the individual scores of ETMF and HMM are combined using Maximal Ratio Combining to render a classification decision. Classification results are presented for the MSTAR data set via ROC curves. An ultra-wideband (UWB) synthetic aperture radar (SAR) simulation technique that employs physical and statistical models is developed and presented. This joint i v physics/statistics based technique generates 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. Comparative results from three SVD-based subspace filtering approaches on target detection algorithms are reported. These approaches are denoted as " Energy-Normalized SVD " , " Condition-Statistics SVD " , and " Terrain-Filtered SVD ". Approaches towards developing " self-training " algorithms for UWB radar target detection are investigated using the results of this simulation process.