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A subspace spatial smoothing-based sparse reconstruction passive direction-of-arrival estimation method under strong

Chenmu Li1, Liang Xie1, Zhongdi Liu1

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This study introduces a novel passive direction-of-arrival (DOA) estimation method using subspace spatial smoothing and sparse reconstruction. The technique effectively estimates weak targets amidst strong, correlated interference, improving accuracy and efficiency.

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Area of Science:

  • Signal Processing
  • Electromagnetics
  • Array Signal Processing

Background:

  • Passive direction-of-arrival (DOA) estimation is crucial for identifying signal sources without active transmission.
  • Estimating weak targets under strong, correlated interference presents significant challenges in signal processing.
  • Existing methods struggle with closely spaced targets and coherent interference, limiting accuracy.

Purpose of the Study:

  • To develop an advanced passive DOA estimation method for weak targets in complex interference environments.
  • To enhance robustness against strong, correlated, and coherent interference signals.
  • To improve computational efficiency and resolution in DOA estimation.

Main Methods:

  • Subspace spatial smoothing-based sparse reconstruction is proposed.
  • Sample covariance matrix projection into the signal subspace mitigates interference.
  • Modified enhanced spatial smoothing and a grid evolution method are employed.

Main Results:

  • The proposed method achieves higher resolution and accuracy for closely spaced targets under strong interference.
  • Demonstrated robustness against correlated and coherent signals.
  • Significant reduction in computational complexity with maintained estimation accuracy.

Conclusions:

  • The subspace spatial smoothing-based sparse reconstruction method offers superior performance for passive DOA estimation in challenging scenarios.
  • The technique provides a robust and computationally efficient solution for weak target detection with strong interference.
  • This advancement has implications for various applications requiring precise signal source localization.