Robust beamforming based on improved generalized linear combination
An adaptive and robust beamforming algorithm was introduced to tackle the performance degradation caused by mismatching guiding vectors and covariance matrix estimation errors in traditional beamforming methods.It first reduced noise through singular spectral decomposition,then constructed a diagonal loading covariance matrix with a function related to signal snapshots,and estimated actual guiding vectors based on null domain integration and subspace projection.These vectors were then combined with the new covariance matrix to form beams.Simulation results proved the algorithm's robust-ness in maintaining a good output signal-to-noise ratio despite estimation errors and limited samples.
robust beamforminggeneralized linear combinationdiagonal loadingsingular spectrum analysissteering vector estimation
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