Improving HRR ATR performance at low-SNR by multilook adaptive weighting

Multi-look adaptive weighting (MAW) is an adaptive beamforming method of improving target high range resolution (HRR) signatures for automatic target recognition (ATR) systems. The primary goal in developing MAW is the concern with improving the probability of correct classification (Pcc) and reducing the probability of false classification (Pfc) in ATR systems. An additional objective, driven by operational considerations, is to reduce the radar resources required to achieve a desired Pcc and Pfc. We have shown in previous HRR ATR studies on ground military targets that a significant classifier performance gain can be obtained if speckle noise and scintillation in HRR profiles are reduced through noncoherent averaging of multiple independent coherent processing intervals (CPIs) that are separated by small changes in azimuth angles. Given the advantage of using multi-look CPIs on HRR ATR performance, we have designed MAW specifically to take advantage of the multiple independent CPIs in forming the HRR profiles. From a radar resource perspective, for an HRR ATR system to be operationally useful, the system must operate at a signal-to-noise ratio (SNR) in the range of 20-25 dB. In this paper, we discuss the theoretical foundation underlying MAW and present corresponding MAW-processed HRR ATR results at 20-25db SNR compared against other image processing techniques such as weighted fast Fourier transform (FFT) and high definition vector imaging (HDVI). These results are based upon HRR profiles formed from synthetic aperture radar (SAR) images of targets taken from the high quality Moving and Stationary Target Acquisition and Recognition (MSTAR) data set. We also discuss the impact these image processing techniques have on the HRR ATR performance in terms of radar resources.