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2-D Joint Sparse Reconstruction and Micro-Motion Parameter Estimation for Ballistic Target Based on Compressive

Jiaqi Wei1, Shuai Shao1, Lei Zhang2

  • 1National Lab of Radar Signal Processing, Xidian University, Xi'an 710071, China.

Sensors (Basel, Switzerland)
|August 9, 2020
PubMed
Summary

This study introduces a new 2-D joint sparse reconstruction and micro-motion parameter estimation (2D-JSR-MPE) algorithm for sparse frequency band (SFB) signals. The method accurately extracts micro-motion curves and enhances robustness for ballistic targets.

Keywords:
ballistic targetmicro-motionsparse frequency band (SFB) signaltwo-dimension (2-D) joint parameter estimationtwo-dimension (2-D) joint sparse reconstruction

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Area of Science:

  • Radar Signal Processing
  • Target Motion Analysis
  • Compressive Sensing

Background:

  • Sparse frequency band (SFB) signals pose challenges for traditional micro-motion extraction.
  • Existing methods struggle with accuracy and robustness in complex signal environments.

Purpose of the Study:

  • To develop a novel algorithm for accurate micro-motion curve extraction from SFB signals.
  • To enhance the robustness and practicality of micro-motion parameter estimation algorithms.

Main Methods:

  • Proposed a two-dimension (2-D) joint sparse reconstruction and micro-motion parameter estimation (2D-JSR-MPE) algorithm based on compressive sensing (CS).
  • Utilized a piecewise 2D-JSR signal model and micro-motion parameter dictionary for segmented SFB echo signals.
  • Employed an improved orthogonal matching pursuit (OMP) algorithm for signal reconstruction and parameter estimation.

Main Results:

  • Accurate reconstruction of high-resolution range profiles (HRRP) was achieved.
  • Precise micro-motion curves were simultaneously extracted with high phase accuracy.
  • The 2-D joint processing effectively mitigated reconstruction errors and improved accuracy.

Conclusions:

  • The proposed 2D-JSR-MPE algorithm demonstrates superior accuracy, robustness, and practicality for SFB signals.
  • The method leverages the anti-jamming properties of SFB signals and 2-D joint processing.
  • Experimental results validate the effectiveness of the algorithm in challenging scenarios.