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An Adaptive Moving Target Imaging Method for Bistatic Forward-Looking SAR Using Keystone Transform and Optimization

Zhongyu Li1,2, Junjie Wu3, Yulin Huang4

  • 1School of Electronic Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China. zhongyu_li@hotmail.com.

Sensors (Basel, Switzerland)
|January 27, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive imaging method for bistatic forward-looking synthetic aperture radar (BF-SAR) to address challenges in moving target detection. The novel approach effectively corrects range cell migration and balances Doppler parameter variations for clearer moving target imaging.

Keywords:
adaptivebistatic forward-looking SARkeystone transformmoving-target imagingoptimization

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Area of Science:

  • Radar Systems Engineering
  • Signal Processing
  • Remote Sensing

Background:

  • Bistatic forward-looking synthetic aperture radar (BF-SAR) systems are crucial for forward terrain imaging.
  • Existing BF-SAR imaging methods are well-developed for stationary scenes but struggle with moving targets.
  • Moving targets in BF-SAR introduce significant challenges, including unknown range cell migration and nonlinear spatial-varying Doppler parameters.

Purpose of the Study:

  • To develop an adaptive imaging method for BF-SAR systems specifically designed for moving targets.
  • To address the issues of large, unknown range cell migration (RCM) and nonlinear spatial-variances of Doppler parameters.
  • To improve the accuracy and clarity of BF-SAR imagery for dynamic scenes.

Main Methods:

  • Application of keystone transform to correct large and unknown range walk.
  • Establishment of relationships between high-order RCM, nonlinear Doppler parameter variations, and target speed.
  • Utilization of an optimization nonlinear chirp scaling (NLCS) technique for RCM correction and Doppler parameter balancing.
  • Employing a high-order polynomial filter for azimuth data compression.

Main Results:

  • Successfully corrected large and unknown range walk using keystone transform.
  • Accurately corrected unknown high-order RCM and balanced nonlinear spatial-variances of Doppler parameters via NLCS.
  • Achieved effective compression of azimuth data for moving targets.
  • Numerical simulations confirmed the effectiveness of the proposed adaptive imaging method.

Conclusions:

  • The proposed adaptive moving-target imaging method significantly enhances BF-SAR capabilities for dynamic scenes.
  • The technique effectively overcomes the limitations of existing methods when imaging non-cooperative moving targets.
  • This advancement offers improved performance for BF-SAR systems in real-world applications involving moving objects.