Practical AGC for Quasi Invariant Target Signature

This Phase I SBIR program is aimed at developing and demonstrating an automated gain control method that reduces the large dynamic range changes that can be observed in target and background signatures, while maximizing the preservation of the information content (such as target features) needed for ATR processing. The ability to reduce the variability in the observed signature will lead to overall performance improvements in ATR performance independent of the specific ATR process applied and reduced process complexity. Our proposed approach belongs to the class of locally adaptive enhancements techniques and is based on the Peli&Lim algorithm.. The local processing affords scale adaptation, and a better utilization of the available dynamic range than global techniques. In the Phase I program, we will identify the current methods that perform AGC and select a subset as a baseline for performance evaluation. We will define the corresponding metric(s) that will allow us to assess performance without the need to test it against a large variety of ATR processes. Using a limited set of FLIR imagery, we will evaluate the baseline and the proposed methods, quantify computational and hardware requirements and recommend the best method for real time implementation in the Phase II program. Anticipated benefits of the proposed research include dramatically improved performance and reduced computational complexity of target acquisition, recognition and tracking for smart missiles. The improvement in ATR and tracking performance will improve the kill probability of a missile, while the improvement in the target acquisition stage will increase the survivability and reduce the vulnerability of the missile fire control target acquisition system