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Sensors 2014, 14(7), 11308-11350; doi:10.3390/s140711308

A Compact Methodology to Understand, Evaluate, and Predict the Performance of Automatic Target Recognition

1
School of Electrical Science and Engineering, National University of Defense Technology, 137 Yanwachi Street, Changsha 410073, China
2
State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE), Luoyang 471003, China
*
Author to whom correspondence should be addressed.
Received: 5 December 2013 / Revised: 23 May 2014 / Accepted: 9 June 2014 / Published: 25 June 2014
(This article belongs to the Section Physical Sensors)

Abstract

This paper offers a compacted mechanism to carry out the performance evaluation work for an automatic target recognition (ATR) system: (a) a standard description of the ATR system’s output is suggested, a quantity to indicate the operating condition is presented based on the principle of feature extraction in pattern recognition, and a series of indexes to assess the output in different aspects are developed with the application of statistics; (b) performance of the ATR system is interpreted by a quality factor based on knowledge of engineering mathematics; (c) through a novel utility called “context-probability” estimation proposed based on probability, performance prediction for an ATR system is realized. The simulation result shows that the performance of an ATR system can be accounted for and forecasted by the above-mentioned measures. Compared to existing technologies, the novel method can offer more objective performance conclusions for an ATR system. These conclusions may be helpful in knowing the practical capability of the tested ATR system. At the same time, the generalization performance of the proposed method is good.
Keywords: automatic target recognition; performance evaluation; performance prediction automatic target recognition; performance evaluation; performance prediction
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Li, Y.; Li, X.; Wang, H.; Chen, Y.; Zhuang, Z.; Cheng, Y.; Deng, B.; Wang, L.; Zeng, Y.; Gao, L. A Compact Methodology to Understand, Evaluate, and Predict the Performance of Automatic Target Recognition. Sensors 2014, 14, 11308-11350.

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