Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Robust minimum variance beamforming
IEEE Transactions on Signal Processing
Robust presteering derivative constraints for broadband antennaarrays
IEEE Transactions on Signal Processing
On robust Capon beamforming and diagonal loading
IEEE Transactions on Signal Processing
A projection approach for robust adaptive beamforming
IEEE Transactions on Signal Processing
Signal waveform estimation in the presence of uncertainties about the steering vector
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Review of user parameter-free robust adaptive beamforming algorithms
Digital Signal Processing
Super-Gaussian loading for robust beamforming
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Wireless Personal Communications: An International Journal
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In this paper we derive a class of new parameter free robust adaptive beamformers using the generalized sidelobe canceler reparameterization of the unit gain constrained minimum variance problem. In this parameterization the minimum variance beamformer is obtained as the solution of a linear least squares (LS) problem. In the case of an inaccurate steering vector and/or few data snapshots this marginally overdetermined system gives an ill fit causing signal cancellation in the standard minimum variance (LS) solution. By regularizing the LS problem using ridge regression techniques we get a whole class of robust adaptive beamformers, none of which requires the choice of a user parameter, as opposed to many existing methods. In this context we also propose a parameter free empirical Bayes-based ridge regression technique which, to the best of our knowledge, is novel. The performance of our approach is illustrated by numerical simulations and compared to other robust adaptive beamformers.