Time-frequency analysis-based time-windowing algorithm for the inverse synthetic aperture radar imaging of ships

Peng Zhou; Xi Zhang; Weifeng Sun; Yongshou Dai; Yong Wan

doi:10.1117/1.JRS.12.015001

2 January 2018 Time-frequency analysis-based time-windowing algorithm for the inverse synthetic aperture radar imaging of ships

Peng Zhou, Xi Zhang, Weifeng Sun, Yongshou Dai, Yong Wan

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 1, 015001 (January 2018). https://doi.org/10.1117/1.JRS.12.015001

Abstract

An algorithm based on time-frequency analysis is proposed to select an imaging time window for the inverse synthetic aperture radar imaging of ships. An appropriate range bin is selected to perform the time-frequency analysis after radial motion compensation. The selected range bin is that with the maximum mean amplitude among the range bins whose echoes are confirmed to be contributed by a dominant scatter. The criterion for judging whether the echoes of a range bin are contributed by a dominant scatter is key to the proposed algorithm and is therefore described in detail. When the first range bin that satisfies the judgment criterion is found, a sequence composed of the frequencies that have the largest amplitudes in every moment’s time-frequency spectrum corresponding to this range bin is employed to calculate the length and the center moment of the optimal imaging time window. Experiments performed with simulation data and real data show the effectiveness of the proposed algorithm, and comparisons between the proposed algorithm and the image contrast-based algorithm (ICBA) are provided. Similar image contrast and lower entropy are acquired using the proposed algorithm as compared with those values when using the ICBA.

Citation Download Citation

Peng Zhou, Xi Zhang, Weifeng Sun, Yongshou Dai, and Yong Wan "Time-frequency analysis-based time-windowing algorithm for the inverse synthetic aperture radar imaging of ships," Journal of Applied Remote Sensing 12(1), 015001 (2 January 2018). https://doi.org/10.1117/1.JRS.12.015001

Received: 24 July 2017; Accepted: 12 December 2017; Published: 2 January 2018

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