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Sparsity Adaptive Matching Pursuit Detection Algorithm Based on Compressed Sensing for Radar Signals.

Yanbo Wei1, Zhizhong Lu2, Gannan Yuan3

  • 1College of Automation, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China. weiyanbo@hrbeu.edu.cn.

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|May 16, 2017
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Summary

This study introduces a novel sparsity adaptive matching pursuit (SAMP) algorithm for radar image signal detection. The new method effectively detects targets without prior knowledge of signal sparsity, matching the performance of existing algorithms.

Keywords:
compressed sensingradar signalsparsity adaptivetarget detection

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Area of Science:

  • Radar imaging and signal processing
  • Compressed sensing theory applications
  • Target detection algorithms

Background:

  • Compressed sensing (CS) based signal detection requires prior knowledge of target signal sparsity.
  • In practical scenarios, signal sparsity is often unknown beforehand.
  • This limitation hinders the effective application of CS in radar imaging.

Purpose of the Study:

  • To propose a novel detection algorithm that overcomes the limitation of unknown signal sparsity in CS theory.
  • To investigate the application of compressed sensing and target geometric characteristics in radar imaging.
  • To develop a method for adaptive signal detection in radar images.

Main Methods:

  • A sparsity adaptive matching pursuit (SAMP) detection algorithm is proposed.
  • The algorithm updates the support set and incrementally increases sparsity to approximate the original signal.
  • Utilizes geometric characteristics of targets in radar images.

Main Results:

  • The proposed SAMP algorithm successfully performs signal detection without prior sparsity knowledge.
  • Experimental results using 2010 Pingtan coastal radar data validate the algorithm's effectiveness.
  • The detection performance of SAMP is comparable to established Matching Pursuit (MP) and Orthogonal Matching Pursuit (OMP) algorithms.

Conclusions:

  • The novel SAMP algorithm provides an effective solution for radar signal detection when sparsity is unknown.
  • The method demonstrates adaptive detection capabilities, broadening the applicability of CS in radar imaging.
  • The findings suggest SAMP as a viable alternative to existing CS-based detection techniques.