研究目的
To address the challenge of designing adaptive beamformers with only a single signal-contaminated snapshot, avoiding matrix inversion or eigen-decomposition, and improving performance in scenarios with limited data.
研究成果
The proposed adaptive beamformer effectively handles single-snapshot scenarios by avoiding matrix inversion and eigen-decomposition, outperforming existing methods in output SINR. It is suitable for applications requiring quick responses with large sensor arrays, though performance can be improved with more snapshots.
研究不足
The method relies on sparsity assumptions and requires a known angular sector for the desired signal; performance loss occurs due to inaccurate estimation from a single snapshot, and the regularization parameter γ needs careful tuning.
1：Experimental Design and Method Selection:
The study formulates a sparsity-constrained covariance matrix fitting problem using convex optimization (l1 norm relaxation) to estimate spatial spectrum distribution and reconstruct the interference-plus-noise covariance matrix.
2：Sample Selection and Data Sources:
Simulations use a uniform linear array (ULA) with 10 sensors, generating complex circularly symmetric Gaussian signals for desired signal, interference, and noise.
3：List of Experimental Equipment and Materials:
A ULA with M=10 omnidirectional sensors spaced half-wavelength apart; software tools like CVX for solving convex optimization problems.
4：Experimental Procedures and Operational Workflow:
Steps include computing the sample covariance matrix, solving the optimization problem, reconstructing the covariance matrix, and computing the beamformer weight vector.
5：Data Analysis Methods:
Performance is evaluated using output signal-to-interference-plus-noise ratio (SINR) through Monte-Carlo simulations (500 trials per scenario).
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