Decreased Probability of Error in Template-Matching Classification Using Aspect-Diverse Bistatic SAR

We extend the concept of monostatic aspect diversity for improved automatic target recognition (ATR) to a bistatic synthetic aperture radar (SAR) platform. We derive the probability of error with respect to the number of aspects used for a simple two-target template-matching classification system. The validity of the error prediction is confirmed using simulated bistatic SAR images. Our results demonstrate the ATR benefits of supplementing monostatic SAR images with one or more bistatic SAR images.