Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 21, Issue 5, Pages 2039-2049Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2019.2911692
Keywords
Clutter; Microsoft Windows; Parameter estimation; Detectors; Marine vehicles; Adaptation models; Synthetic aperture radar; Synthetic aperture radar (SAR); marine surveillance; Gaussian clutter; outliers-robust CFAR detection (OR-CFAR); truncated-maximum-likelihood-estimator
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Funding
- National Natural Science Foundation of China [61701157, 51704089]
- Natural Science Foundation of Anhui Province [1808085QF206, 1808085QF190]
- China Postdoctoral Science Foundation [2018M640581]
- Open Research Fund of National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University
- Open Research Fund of Key Laboratory of Radar Imaging and Microwave Photonics, Nanjing University of Aeronautics and Astronautics
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This paper proposes an outliers-robust constant false-alarm rate (OR-CFAR) detector of Gaussian clutter based on the truncated-maximum-likelihood estimator (TMLE) in SAR imagery. The proposed method aims at elevating the detection performance in multiple-target environment, where the sea clutter samples are often contaminated by the interfering target pixels, the azimuth ambiguities, and the breakwater. As a consequence, the parameters used for statistical modeling are over-estimated, resulting in a degradation of the CFAR detection rate. Inspired by the traditional two-parameter CFAR (TP-CFAR) detector of Gaussian clutter, OR-CFAR designs an adaptive threshold-based clutter truncation method to eliminate the high-intensity outliers from the clutter samples in the local reference window, and the probability density function (PDF) of the sea clutter can be accurately modeled through the newly raised TMLE. Furthermore, the optimal truncation depth used for clutter truncation and PDF modeling is evaluated and selected properly to get the best detection results. The OR-CFAR greatly enhances the CFAR detection rate in multiple-target environment, and it is computationally simple and efficient, which has a great application value. The Chinese Gaofen-3 SAR data are used for experiments to show the better detection performance of OR-CFAR.
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