Compensation of range-dependent phase error in low-altitude unmanned aerial vehicles synthetic aperture radar by sparse reconstruction

Yuan Zhang; Yang Song; Dan Yang; Hongquan Qu; Yanping Wang; Zhu Han; Yibo Zhang

doi:10.1117/1.JRS.12.045014

10 December 2018 Compensation of range-dependent phase error in low-altitude unmanned aerial vehicles synthetic aperture radar by sparse reconstruction

Yuan Zhang, Yang Song, Dan Yang, Hongquan Qu, Yanping Wang, Zhu Han, Yibo Zhang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 4, 045014 (December 2018). https://doi.org/10.1117/1.JRS.12.045014

Abstract

Unmanned aerial vehicles (UAV) are a useful supplement to traditional synthetic aperture radar (SAR) platforms. In some cases, UAV-based SAR systems have to fly at low altitude. In this case, range-dependent phase errors due to platform motion affect the imaging quality. To solve the problem of motion compensation, an angle-dependent model and a second-order range-dependent model are introduced into autofocusing by previous researchers, but the first one relies too much on the geometric angle while the latter has limited fitting order for solution. We present a higher order range-dependent model, which can approximate analytical solution. Nevertheless, an increase in the fitting order makes the matrix in this model underdetermined. Based on the theoretical proof, this higher order model can be tackled by exploitation of compressive sensing (CS) theory. A CS reconstruction of higher order fitting coefficients is performed in the experiments, and corresponding performance analysis is given. Finally, the range-dependent phase error is compensated under the condition of low altitude.

Citation Download Citation

Yuan Zhang, Yang Song, Dan Yang, Hongquan Qu, Yanping Wang, Zhu Han, and Yibo Zhang "Compensation of range-dependent phase error in low-altitude unmanned aerial vehicles synthetic aperture radar by sparse reconstruction," Journal of Applied Remote Sensing 12(4), 045014 (10 December 2018). https://doi.org/10.1117/1.JRS.12.045014

Received: 12 October 2017; Accepted: 9 November 2018; Published: 10 December 2018

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KEYWORDS

Unmanned aerial vehicles

Synthetic aperture radar

Error analysis

Motion models

Signal to noise ratio

Systems modeling

Data modeling

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