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Robust Adaptive Beamforming with Sensor Position Errors Using Weighted Subspace Fitting-Based Covariance Matrix

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Summary
This summary is machine-generated.

Sensor position errors degrade adaptive beamformer performance. This study introduces a robust algorithm to reconstruct the interference-plus-noise covariance matrix (INCM), significantly improving beamformer accuracy despite sensor displacement.

Keywords:
covariance matrix reconstructionrobust adaptive beamformingsensor position errorsweighted subspace fitting (WSF)

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Area of Science:

  • Signal Processing
  • Array Signal Processing
  • Adaptive Beamforming

Background:

  • Adaptive beamformers using interference-plus-noise covariance matrix (INCM) are sensitive to sensor position errors.
  • Sensor displacement can severely degrade the performance of these beamformers.

Purpose of the Study:

  • To develop a robust INCM reconstruction algorithm for linear arrays that compensates for unknown sensor position errors.
  • To enhance the performance of INCM-based adaptive beamformers in the presence of sensor displacement.

Main Methods:

  • A weighted subspace fitting (WSF) approach is proposed for INCM reconstruction.
  • The algorithm estimates signal directions to construct a set of possible mismatched steering vectors (SVs).
  • Iterative optimization is used to reconstruct the INCM and estimate the desired signal's SV.

Main Results:

  • The proposed algorithm demonstrates robustness against SV mismatches caused by sensor position errors.
  • Even with position errors up to half the sensor spacing, the output signal-to-interference-plus-noise ratio (SINR) degradation is minimal (4 dB).
  • Experimental data shows superior interference suppression compared to other beamformers.

Conclusions:

  • The developed WSF-based INCM reconstruction algorithm effectively mitigates performance degradation due to sensor position errors in linear array beamformers.
  • The method offers a significant improvement in robustness and interference suppression capabilities.