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Robust Tensor-Based DOA and Polarization Estimation in Conformal Polarization Sensitive Array with Bad Data.

Xiaoyu Lan1,2, Lai Jiang3, Shuang Ma1

  • 1School of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China.

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|April 27, 2024
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Summary
This summary is machine-generated.

This study introduces a novel tensor variational sparse Bayesian learning method to accurately estimate direction of arrival (DOA) and polarization parameters, even with partially impaired sensor arrays. The approach effectively handles bad data and recovers lost information for robust signal parameter estimation.

Keywords:
column vector detectionconformal polarization sensitive arraydirection of arrival and polarization parameters estimationtensorvariational sparse Bayesian learning

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Area of Science:

  • Signal Processing
  • Array Signal Processing
  • Electromagnetics

Background:

  • Partially impaired sensor arrays degrade signal parameter estimation accuracy.
  • Bad data in sensor arrays leads to information loss and performance reduction.
  • Accurate direction of arrival (DOA) and polarization estimation is crucial in various applications.

Purpose of the Study:

  • To propose a Tensor Variational Sparse Bayesian Learning (TVSBL) method for joint DOA and polarization estimation.
  • To address challenges posed by partially impaired sensor arrays and bad data.
  • To develop a robust method for signal parameter estimation in realistic scenarios.

Main Methods:

  • Developed a sparse tensor-based received data model for Conformal Polarization Sensitive Arrays (CPSA) incorporating bad data.
  • Proposed a column vector detection method to identify impaired sensors.
  • Employed low-rank matrix completion to recover lost signal information.
  • Utilized Variational Sparse Bayesian Learning (VSBL) and minimum eigenvector methods for parameter estimation.

Main Results:

  • The proposed TVSBL method effectively estimates DOA and polarization parameters with partially impaired sensor arrays.
  • The method demonstrates robustness against bad data and recovers random signal information loss.
  • Simulation results validate the effectiveness and accuracy of the proposed approach.
  • The Cramér-Rao bound for the method is provided.

Conclusions:

  • The developed TVSBL method offers a significant advancement in signal parameter estimation for impaired sensor arrays.
  • The approach provides accurate joint estimation of DOA and polarization parameters.
  • This method enhances the reliability of signal processing systems operating with imperfect sensor data.