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

机译:评估SAR ATR算法对MSTAR扩展操作条件的性能敏感性

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Abstract: 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.!6
机译:摘要:正如我们在文献中通常看到的那样,在训练条件下或非常接近训练条件下测试SAR自动目标识别(ATR)算法通常会产生接近完美的结果。本文介绍了一系列与ATR算法训练条件相近的实验。设置实验以隔离各个扩展操作条件(EOC),并在这些时间点报告性能。设置了其他实验以隔离EOC的特定组合,并且此处还测量了SAR ATR算法的性能。这里介绍的实验是1997年11月进行的DARPA / AFRL运动和静止目标获取与识别(MSTAR)计划评估的副产品。尽管此处进行的测试属于EOC领域,但这些测试不包括“现实世界”(即您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,包括扩展工作条件概念以及数据;算法;和功绩指标。总而言之,本文着重介绍了基线均方误差ATR算法对各种工况的敏感度,这些工况既接近训练条件,又远离训练条件。6

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