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A Priori-Based Subarray Selection Algorithm for DOA Estimation.

Linghao Zeng1, Guanghua Zhang1, Chongzhao Han1

  • 1Ministry of Education Key Laboratory for Intelligent Networks and Network Security, School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

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Summary
This summary is machine-generated.

This study introduces a novel method for direction-of-arrival (DOA) estimation using nonuniform arrays to overcome mutual coupling. The approach leverages prior DOA information to enhance estimation accuracy and array performance.

Keywords:
DOA estimationlow-rank matrix approximationsparse array synthesis

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Area of Science:

  • Signal Processing
  • Array Signal Processing
  • Electromagnetics

Background:

  • Direction-of-Arrival (DOA) estimation with large, dense arrays is crucial for high resolution.
  • Mutual coupling in such arrays degrades DOA estimation performance.
  • Nonuniform arrays offer a potential solution to mitigate mutual coupling effects.

Purpose of the Study:

  • To develop a DOA estimation method that mitigates mutual coupling using prior DOA information.
  • To improve the performance and accuracy of DOA estimation in the presence of mutual coupling.
  • To propose an algorithm for selecting array elements based on low-rank matrix approximation.

Main Methods:

  • Utilizing a priori DOA information to assign weights based on prior probability distributions.
  • Applying low-rank matrix approximation theory to obtain an optimal approximate matrix.
  • Developing an algorithm to select array elements using right singular vectors of the approximate matrix.
  • Analyzing the impact of different weighting schemes and introducing a flexible mixed weight.

Main Results:

  • The proposed method effectively reduces the impact of mutual coupling on DOA estimation.
  • The weighting strategy leads the sensing matrix towards a low-rank structure, enhancing approximation.
  • Numerical simulations confirm the superior performance of the developed algorithm compared to existing methods.
  • The mixed weight provides flexibility for various practical applications.

Conclusions:

  • The novel DOA estimation approach effectively mitigates mutual coupling using prior information and low-rank approximation.
  • The proposed algorithm offers improved accuracy and robustness for DOA estimation.
  • The method provides a flexible and effective solution for array signal processing applications.