Parameter Adaptation for Target Recognition in LADAR (PATROL)

Automatic Target Recognition (ATR) algorithms are extremely sensitive to differences between the operating conditions under which they are trained and the extended operating conditions in which the fielded algorithms operate. For ATR algorithms to robustly recognize targets while retaining low false alarm rates, they must be able to identify the conditions under which they are operating and tune their parameters on the fly. In this proposal, we present a method for tuning the parameters of a model based ATR algorithm using estimates of the current operating conditions. The problem has two components: 1) identifying the current operating conditions and 2) using that information to tune parameters to improve performance. In this project, we will explore the use of a learning technique called Q-learning for parameter adaptation. In Q-learning, we first define a set of valid states describing the world (the operating conditions of interest, such as the level of obscuration). Next, actions (or parameter settings used by the ATR) are defined that are applied when in that state. Parameter settings for each operating condition are learned using an off-line reinforcement learning feedback loop. The result is a lookup table to select the optimal parameter settings for each operation condition.