This paper presents the performance of a multi-class, template-based, system-oriented High Range Resolution (HRR) Automatic Target Recognition (ATR) algorithm for ground moving targets. The HRR classifier assumes a target aspect estimate derived from the exploitation of moving target indication (MTI) mode target tracking to reduce the template search space. The impact of the MTI tracker target aspect estimate accuracy on the performance and robustness of the HRR ATR is investigated. Next, both individual and hybrid MTIfHRR and Synthetic Aperture Radar (SAR) model-based ATR algorithm results are presented. The hybrid ATR under consideration assumes the coordination of a multimode sensor to provide classification or continuous tracking of targets in a move-stop-move scenario. That is, a high-value moving target is tracked using the GMTI mode and its heading estimated. As the indicated target stops, the last GMTI tracker update is used to aid the SAR mode ATR target acquisition and classification. As the target begins to move again, the MTI-assisted HRR ATR target identification estimates are fused with the previous SAR ATR classification. The hybrid MTI/SAR/HRR ATR decision-level fusion provides a method for robust classification and/or continuous tracking of targets in move-stop-move cycles. Lastly, the baseline HRR ATR performance is compared to a QuickSAR (short dwell or non-square pixel SAR) ATR algorithm for varying cross-range resolutions.
The statistical feature-based (StaF) classifier is presented for robust high range resolution (HRR) radar moving ground target identification. The target features used for classification are the amplitude and location of HRR signature peaks. The peak features are not predetermined using the training data but are extracted on-the-fly from the observed HRR profile and are different for each target observation. A classifier decision is made after statistical evidence is accrued from each feature and across multiple looks. Decision uncertainty is estimated using a beliefbased confidence measure. Classifier decisions are rejected if the decision uncertainty is too high since it is likely that the observed HRR profile is not in the classifier's target database. Robustness is achieved by using only peak features rather than the entire HRR profile (much of which is low-level scatterers buried in noise or simply noise) and by rejecting decisions with high uncertainty.
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