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Off-Grid DOA Estimation Using Sparse Bayesian Learning for MIMO Radar under Impulsive Noise.

Jitong Ma1,2, Jiacheng Zhang3,2, Zhengyan Yang4

  • 1College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China.

Sensors (Basel, Switzerland)
|August 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an off-grid Direction of Arrival (DOA) estimation method for monostatic multiple-input multiple-output (MIMO) radar. The novel approach enhances accuracy and robustness in impulsive noise environments.

Keywords:
DOA estimationimpulsive noisemonostatic MIMO radarsparse Bayesian learning

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Area of Science:

  • Array signal processing
  • Radar systems engineering
  • Statistical signal processing

Background:

  • Direction of Arrival (DOA) estimation is crucial for applications like autonomous driving and navigation.
  • Accurate DOA estimation for multiple-input multiple-output (MIMO) radar is challenging in impulsive noise.
  • Existing methods struggle with non-circular signals in noisy environments.

Purpose of the Study:

  • To propose an off-grid DOA estimation method for monostatic MIMO radar.
  • To address the challenge of estimating DOA for non-circular signals in impulsive noise.
  • To improve the accuracy and robustness of DOA estimation in adverse noise conditions.

Main Methods:

  • Constructed a virtual array output leveraging non-circular signal properties and array structure.
  • Developed a real-valued sparse representation for the signal model.
  • Applied an off-grid sparse Bayesian learning (SBL) framework to a novel off-grid sparse model.
  • Utilized sparse reconstruction to achieve off-grid DOA estimation.

Main Results:

  • The proposed method demonstrated high accuracy in DOA estimation even under impulsive noise.
  • Simulations confirmed significant performance improvements in accuracy and robustness compared to existing methods.
  • The off-grid SBL framework effectively handled non-circular signals in impulsive noise environments.

Conclusions:

  • The developed off-grid DOA estimation method offers superior performance for monostatic MIMO radar.
  • The approach provides a robust solution for DOA estimation in challenging impulsive noise conditions.
  • This work advances array signal processing techniques for radar applications.