In this paper, 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 which 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 number 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 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.
Approaches towards developing "self-training" algorithms for UWB radar target detection are investigated using the results of this simulation process. These adaptive approaches employ some form of modified singular value decomposition (SVD) algorithm where small blocks of data in the neighborhood of a sliding test window are processed in real-time in an effort to estimate localized clutter characteristics. These real-time local clutter models are then used to cancel clutter in the sliding test window. Comparative results from three SVD-based approaches to adaptive and "self-trained" target detection algorithms are reported. These approaches are denoted as "Energy-Normalized SVD", "Condition-Statistic SVD", and "Terrain-Filtered SVD". The results indicate that the "Terrain-Filtered SVD" approach, where a pre-filter is applied in an effort to eliminate severe clutter discretes that adversely effect performance, appears promising for the purposes of developing "self-training" algorithms for applications that may require localized "on-the-fly" training due to a lack of accurate off-line training data.
A number of aspects of ultra-wideband radar target detection analysis and algorithm development are addressed. The first portion of the paper describes a bi-modal technique for modeling ultra-wideband radar clutter. This technique was developed based on an analysis of ultra-wideband radar phenomenology. Synthetic image samples that were generated by this modeling process are presented. This sample set is characterized by a number of physical parameters. The second portion of this paper describes an approach to developing a class of filters, known as rank-order filters, for ultra-wideband radar target detection applications. The development of a new rank-order filter denoted as a discontinuity filter is presented. Comparative target detection results are presented as a function of data model parameters. The comparative results include discontinuity filter performance versus the performance of median filtering and CFAR filtering.