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Evaluation of SAR ATR algorithm performance sensitivity to MSTAR extended operating conditions

机译:SAR ATR算法对MSTAR扩展操作条件的性能敏感性的评估

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Testing a SAR Automatic Target Recognition (ATR) algorithm at or very near its training conditions often yields near perfect results as we commonly see in the literature. This paper describes a series of experiments near and not so near to ATR algorithm training conditions. Experiments are setup to isolate individual Extended Operating Conditions (EOCs) and performance is reported at these points. Additional experiments are setup to isolate specific combinations of EOCs and the SAR ATR algorithm's performance is measured here also. The experiments presented here are a by-product of a DARPA/AFRL Moving and Stationary Target Acquisition and Recognition (MSTAR) program evaluation conducted in November of 1997. Although the tests conducted here are in the domain of EOCs, these tests do not encompass the `real world' (i.e., what you might see on the battlefield) problem. In addition to performance results this paper describes an evaluation methodology including the Extended Operating Condition concept, as well as, data; algorithm; and figures of merit. In summary, this paper highlights the sensitivity that a baseline Mean Squared Error ATR algorithm has to various operating conditions both near and varying degrees away from the training conditions.
机译:在其训练条件下或近附近测试SAR自动目标识别(ATR)算法通常在文献中通常看到的完美结果附近。本文介绍了一系列的实验,靠近ATR算法训练条件。设定实验以隔离各个扩展操作条件(EoC),在这些点报告性能。设置了另外的实验以隔离EoC的特定组合,并且还测量SAR ATR算法的性能。这里提出的实验是1997年11月进行的DARPA / AFRL移动和静止目标采集和识别(MSTAR)计划评估的副产品。虽然这里进行的测试是在EOC的领域,但这些测试不包括在内“真实世界”(即,你在战场上可能看到的)问题。除了性能结果外,本文还介绍了一种评估方法,包括扩展的操作条件概念,以及数据;算法;和数字的数字。总之,本文突出了基线均方误差ATR算法对远离训练条件的各种操作条件的敏感性。

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