Performance of 10- and 20-target MSE classifiers

Novak L.M.; Owirka G.J.; Brower W.S.

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Performance of 10- and 20-target MSE classifiers

机译：10目标和20目标MSE分类器的效果

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MIT Lincoln Laboratory is responsible for developing the ATR (automatic target recognition) system for the DARPA-sponsored SAIP program; the baseline ATR system recognizes 10 GOB (ground order of battle) targets; the enhanced version of SAIP requires the ATR system to recognize 20 GOB targets. This paper presents ATR performance results for 10- and 20-target mean square error (MSE) classifiers using high-resolution SAR (synthetic aperture radar) imagery.

机译：麻省理工学院林肯实验室负责为DARPA赞助的SAIP程序开发ATR（自动目标识别）系统；基线ATR系统可识别10个GOB（战地顺序）目标； SAIP的增强版本要求ATR系统识别20个GOB目标。本文介绍了使用高分辨率SAR（合成孔径雷达）图像对10目标和20目标均方误差（MSE）分类器的ATR性能结果。

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《IEEE Transactions on Aerospace and Electronic Systems》 |2000年第4期|p.1279-1289|共11页
作者
Novak L.M.; Owirka G.J.; Brower W.S.;
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正文语种 eng
中图分类航空;
关键词
image classification; learning (artificial intelligence); mean square error methods; radar computing; radar imaging; radar target recognition; synthetic aperture radar; ATR performance; automatic target recognition system; confusion matrices; extended operating co;

机译：图像分类;学习（人工智能）;均方误差方法;雷达计算;雷达成像;雷达目标识别;合成孔径雷达;ATR性能;自动目标识别系统;混淆矩阵;扩展操作范围;

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Performance of 10- and 20-target MSE classifiers

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