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Off-Grid DOA Estimation Using Alternating Block Coordinate Descent in Compressed Sensing.

Weijian Si1, Xinggen Qu2, Zhiyu Qu3

  • 1Department of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China. swj0418@263.net.

Sensors (Basel, Switzerland)
|September 8, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces an innovative off-grid direction of arrival (DOA) estimation method using compressed sensing (CS). The technique reconstructs sparse sources and corrects grid mismatches, outperforming existing approaches.

Keywords:
alternating block coordinate descentblock selection criterionblock sparse sourcecompressed sensingoff-grid direction of arrival (DOA) estimation

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Area of Science:

  • Signal Processing
  • Array Signal Processing
  • Compressed Sensing

Background:

  • Direction of Arrival (DOA) estimation is crucial in various applications.
  • Traditional methods struggle with off-grid scenarios and grid mismatches.
  • Compressed Sensing (CS) offers a promising framework for sparse signal reconstruction.

Purpose of the Study:

  • To propose a novel off-grid DOA estimation method with superior performance.
  • To address the challenge of grid mismatch in DOA estimation.
  • To leverage sparse reconstruction techniques for enhanced accuracy.

Main Methods:

  • The DOA estimation problem is formulated as a sparse reconstruction task.
  • A mixed k-l norm minimization is employed to reconstruct sparse sources and estimate grid errors.
  • Alternating convex optimization is used to solve the non-convex problem iteratively.
  • Block sparse sources are exploited for improved off-grid DOA estimation.
  • A block selection criterion is implemented to reduce computational complexity.

Main Results:

  • The proposed method effectively reconstructs sparse sources and estimates grid errors.
  • The use of alternating convex optimization ensures convergence.
  • Exploiting block sparse sources enhances reconstruction properties.
  • Simulation results demonstrate superior performance compared to existing methods.
  • The method is proven to have global convergence.

Conclusions:

  • The novel off-grid DOA estimation method achieves superior performance.
  • The technique effectively handles grid mismatches and sparse source reconstruction.
  • The proposed approach offers a robust solution for DOA estimation in compressed sensing.