Low-Complexity Robust Adaptive Beamforming Based on INCM Reconstruction via Subspace Projection

Area of Science:

• Signal Processing
• Array Signal Processing
• Adaptive Systems

Background:

• Adaptive beamforming is susceptible to steering vector (SV) and covariance matrix mismatches.
• These mismatches are particularly problematic when the signal of interest (SOI) is present in training sequences.

Purpose of the Study:

• To develop a low-complexity robust adaptive beamforming (RAB) method.
• To improve robustness against various mismatches in adaptive beamforming systems.

Main Methods:

• Reconstruction of the interference-noise covariance matrix (INCM) using a blocking matrix derived from the minimum mean square error criterion.
• Estimation of the signal of interest steering vector (SOI SV) via iterative mismatch approximation.
• Projection of the blocking matrix onto the signal subspace of the sample covariance matrix (SCM) to obtain the projection matrix.

Main Results:

• The proposed method effectively reconstructs the INCM by incorporating eigenvectors from the projection matrix into the SCM.
• The SOI SV is accurately estimated using iterative mismatch approximation.
• Simulation results demonstrate the method's ability to handle multiple mismatch types.

Conclusions:

• The presented low-complexity RAB method offers high robustness against steering vector and covariance matrix mismatches.
• The method requires only prior knowledge of array geometry and the SOI's angular region.
• This approach provides a practical solution for adaptive beamforming in challenging environments.