Automatic Target Recognition in Synthetic Aperture Radar Imagery: A State-of-the-Art Review

El-Darymli, Khalid and Gill, Eric William and McGuire, Peter and Power, Desmond and Moloney, Cecelia (2016) Automatic Target Recognition in Synthetic Aperture Radar Imagery: A State-of-the-Art Review. IEEE Access, 4. pp. 6014-6058. ISSN 2169-3536

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Abstract

The purpose of this paper is to survey and assess the state-of-the-art in automatic target recognition for synthetic aperture radar imagery (SAR-ATR). The aim is not to develop an exhaustive survey of the voluminous literature, but rather to capture in one place the various approaches for implementing the SAR-ATR system. This paper is meant to be as self-contained as possible, and it approaches the SAR-ATR problem from a holistic end-to-end perspective. A brief overview for the breadth of the SAR-ATR challenges is conducted. This is couched in terms of a single-channel SAR, and it is extendable to multi-channel SAR systems. Stages pertinent to the basic SAR-ATR system structure are defined, and the motivations of the requirements and constraints on the system constituents are addressed. For each stage in the SAR-ATR processing chain, a taxonomization methodology for surveying the numerous methods published in the open literature is proposed. Carefully selected works from the literature are presented under the taxa proposed. Novel comparisons, discussions, and comments are pinpointed throughout this paper. A two-fold benchmarking scheme for evaluating existing SAR-ATR systems and motivating new system designs is proposed. The scheme is applied to the works surveyed in this paper. Finally, a discussion is presented in which various interrelated issues, such as standard operating conditions, extended operating conditions, and target-model design, are addressed. This paper is a contribution toward fulfilling an objective of end-to-end SAR-ATR system design.

Item Type: Article
URI: http://research.library.mun.ca/id/eprint/12234
Item ID: 12234
Additional Information: Memorial University Open Access Author's Fund
Keywords: Synthetic aperture radar, Classification, Feature recognition, Target recognition, System analysis and design, Benchmark testing, Radar tracking
Department(s): Engineering and Applied Science, Faculty of
Date: 21 September 2016
Date Type: Publication
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