24 September 2019 Noise suppression method for geosynchronous satellite-based passive inverse synthetic aperture radar imaging under low signal-to-noise ratio conditions
Liang Xu, Yicheng Jiang, Yong Wang, Zhaofa Wang, Zheng Lv, Hui Kuang
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Abstract

In recent years, the geosynchronous satellite-based passive inverse synthetic aperture radar (ISAR) has been widely employed for target detection and automatic target recognition. However, the great distance between the satellite and the receiver causes low signal-to-noise ratio (SNR) of the target signal, which makes the system unable to obtain a well-focused image of the target. To obtain a well-focused ISAR image within a limited coherent processing interval, a method for improving the SNR is required. We propose a two-step noise suppression method to enhance the SNR by eliminating the noise components contained in the received data. The target windowing method is utilized first to reject the noise and clutter interference by windowing the target data from the range-Doppler map. Then, a denoising method is employed to further remove the noise components from the selected target data by threshold techniques. By using the two-step noise suppression, the SNR of the target signal is increased, and consequently a quality-improved ISAR image can be obtained. Simulation results validate the effectiveness of the proposed noise suppression method.

© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2019/$28.00 © 2019 SPIE
Liang Xu, Yicheng Jiang, Yong Wang, Zhaofa Wang, Zheng Lv, and Hui Kuang "Noise suppression method for geosynchronous satellite-based passive inverse synthetic aperture radar imaging under low signal-to-noise ratio conditions," Journal of Applied Remote Sensing 13(3), 036515 (24 September 2019). https://doi.org/10.1117/1.JRS.13.036515
Received: 23 March 2019; Accepted: 26 August 2019; Published: 24 September 2019
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KEYWORDS
Signal to noise ratio

Satellites

Interference (communication)

Target detection

Image quality

Satellite imaging

Denoising

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