High-speed target inverse synthetic aperture radar imaging via parametric sparse representation

Chunhui Chen; Qun Zhang; Fufei Gu; Ying Luo

doi:10.1117/1.JRS.11.035011

17 August 2017 High-speed target inverse synthetic aperture radar imaging via parametric sparse representation

Chunhui Chen, Qun Zhang, Fufei Gu, Ying Luo

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 3, 035011 (August 2017). https://doi.org/10.1117/1.JRS.11.035011

Abstract

The inverse synthetic aperture radar (ISAR) imaging of high-speed targets is affected significantly by the phase modulation induced by the high-speed motion. To improve the imaging quality and efficiently suppress the influence of high-speed motion, a method of ISAR imaging via parametric sparse representation is proposed for high-speed targets. First, the echo is dynamically represented as a sparse signal via a flexible parametric sensing matrix according to the target high-speed motion. Subsequently, the sensing matrix is optimized through adaptive computation, during which the target velocity estimation is also achieved. Finally, the ISAR image of high-speed targets can be reconstructed with sparse sampling data. Compared to the existing method based on compressed sensing, the proposed method produces comparative imaging quality with less computational complexity and better robustness. Simulations are performed to validate the effectiveness of the method.

Citation Download Citation

Chunhui Chen, Qun Zhang, Fufei Gu, and Ying Luo "High-speed target inverse synthetic aperture radar imaging via parametric sparse representation," Journal of Applied Remote Sensing 11(3), 035011 (17 August 2017). https://doi.org/10.1117/1.JRS.11.035011

Received: 25 February 2017; Accepted: 18 July 2017; Published: 17 August 2017

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