Performance of a non-linear adaptive beamformer algorithm for signal-of-interest extraction /

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2015
Oğuz, Özkan
In this thesis a non-linear adaptive beamforming technique, Adaptive Projections Subgradient Method [1] (APSM) is considered. This method uses projections over convex sets in Reproducing Kernel Hilbert Space. Main advantage of this method is observed if the signal-of-interest is due to digital modulation and when there are more jammers than the number of antennas. The performance of this non-linear beamforming technique is compared with well-known methods including Minimum Variance Distortionless Response [2] (MVDR) Beamformer, Robust Capon Beamformer [3] (RCB), Covariance Matrix Reconstruction [4] (CMR) Beamformer and Recursive Least Squares [5] (RLS) Capon Beamformer. A new beamformer is proposed in order to fill the gap between the non-linear and classical beamformer methods. It is shown that this new beamformer performs better than well-known methods when the number of jammers are larger than the number of antennas like APSM. However, the computational complexity of this method is significantly lower than APSM. The beamformer methods are compared in a variety of scenario in order to outline the advantages and disadvantages clearly.
Citation Formats
Ö. Oğuz, “Performance of a non-linear adaptive beamformer algorithm for signal-of-interest extraction /,” M.S. - Master of Science, Middle East Technical University, 2015.