Range-doppler based CFAR ship detection with automatic training data selection

Joshi SK, Baumgartner SV, Da Silva ABC, Krieger G (2019)

Publication Type: Journal article

Publication year: 2019


Book Volume: 11

Article Number: 1270

Journal Issue: 11

DOI: 10.3390/rs11111270


Ship detection is an essentialmaritime security requirement. Current state-of-the-art synthetic aperture radar (SAR) based ship detectionmethods employ fully focused images. The time-consuming processing efforts required to generate these images make them generally unsuitable for real time applications. This paper proposes a novel real time oriented ship detection strategy applicable to range-compressed (RC) radar data acquired by an airborne radar sensor during linear, circular and arbitrary flight tracks. A constant false alarm rate (CFAR) detection threshold is computed in the range-Doppler domain using suitable distribution functions. Detection in range-Doppler has the advantage that principally even small ships with a low radar cross section (RCS) can be detected if they are moving fast enough so that the ship signals are shifted to the exo-clutter region. In order to determine a robust threshold, the ocean statistics have to be described accurately. Bright target peaks in the background ocean data bias the statistics and lead to an erroneous threshold. Therefore, an automatic ocean training data extraction procedure is proposed in the paper. It includes (1) a novel target pre-detection module that removes the bright peaks from the data already in time domain, (2) clutter normalization in the Doppler domain using the remaining samples, (3) ocean statistics estimation and (4) threshold computation. Various sea clutter models are investigated and analyzed in the paper for finding the most suitable models for the RC data. The robustness and applicability of the proposed method is validated using real linearly and circularly acquired radar data from DLR's (Deutsches Zentrum für Luft- und Raumfahrt) airborne F-SAR system.

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Joshi, S.K., Baumgartner, S.V., Da Silva, A.B.C., & Krieger, G. (2019). Range-doppler based CFAR ship detection with automatic training data selection. Remote Sensing, 11(11). https://doi.org/10.3390/rs11111270


Joshi, Sushil Kumar, et al. "Range-doppler based CFAR ship detection with automatic training data selection." Remote Sensing 11.11 (2019).

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