Modeling performance and image collection utility for multiple look ATR

We present a performance model for estimating the likelihood function and posterior probability of classes in a multiple-look SAR ATR classifier. We extend performance estimation to performance prediction in order to assess the effects of additional looks at different targets in a scene. This likelihood improvement model depends on a variety of factors including, the resulting look angle diversity and the resolution of the sensor. The performance model parameters are estimated from classification scores and multi-look performance with real data, but could also be developed from simulations in cases where no data exist. Finally, we propose a transformation from the predicted performance to a value for each look that is used to optimize asset tasking. The value transformation is based on the target importance and absolute posterior probability.