Having relevant sensor data available during the early phases of ATR algorithm development and evaluation projects is paramount. The source of this data primarily comes from either being synthetically-generated or from measured collections. These collections, in turn, can either be highly-controlled or operational-like exercises. This paper presents a broad overview on the types of data being housed within the Automatic Target Recognition Division of the Air Force Research Laboratory (AFRL/SNA) and that are available to the ATR developer.
A strong and growing interest in systems that adapt to changing circumstances was evident in panel discussions at the "Algorithms for SAR Imagery" Conference of the AeroSense Symposium in April 2003, with DARPA, Air Force, industry and academia participation. As a result, Conference Co-Chair Mr. Ed Zelnio suggested producing a dynamic model to create problem sets suitable for adaptive system research and development. Such a problem set provides a framework for the overall problem, including organization of operating conditions, performance measures and specific test cases. It is hoped that this AdaptSAPS framework will help provide the community with a more concrete base for discussing adaptation in SAR imagery exploitation. AdaptSAPS Version 1.0 was produced by the AFRL COMPASE and SDMS organizations and posted on 5 August 2003. AdaptSAPS consists of over a dozen MatLab programs that allow the user to create "missions" with SAR data of varying complexities and then present that test data one image at a time, first as unexploited imagery and then later with the exploitation results that an ATR could use for adaptation in an operational environment. AdaptSAPS keeps track of performance results and reports performance measures. This paper describes AdaptSAPS - its application process and possible improvements as a problem set.
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