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Informational analysis for compressive sampling in radar imaging.

Jingxiong Zhang1, Ke Yang2

  • 1School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, 430079 Wuhan, China. jxzhang@whu.edu.cn.

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This summary is machine-generated.

This study analyzes information flow in compressed sensing (CS) radar, using information theory to determine sub-Nyquist sampling rates for accurate scene reconstruction. Findings guide efficient CS-radar system design and performance evaluation.

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Area of Science:

  • Signal Processing
  • Information Theory
  • Radar Systems

Background:

  • Compressed Sensing (CS) enables sub-Nyquist sampling by exploiting signal sparsity.
  • Information theory offers a framework for analyzing CS systems, including CS-radar.
  • Research integrating CS, radar, and information theory remains limited.

Purpose of the Study:

  • To bridge the interdisciplinary gap between CS, radar, and information theory.
  • To analyze information flow from sparse scenes to measurements in CS-radar.
  • To determine necessary sub-Nyquist sampling rates for scene reconstruction within specified distortion thresholds.

Main Methods:

  • Information-theoretic analysis of information flow in CS-radar.
  • Determination of sub-Nyquist sampling rates based on scene sparsity and SNR.
  • Simulated studies to validate information-theoretic analysis.

Main Results:

  • Quantified information flow from sparse scenes to CS-radar measurements.
  • Established sub-Nyquist sampling rate criteria for scene reconstruction.
  • Validated information-theoretic analysis through simulations.

Conclusions:

  • The proposed information-theoretic approach is valuable for CS-radar analysis.
  • Provides a framework for optimizing CS-radar performance.
  • Enables efficient data acquisition while maintaining accuracy.