Nigel H. Parsons

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We present low complexity, quickly converging robust adaptive beamformers that combine robust Capon beamformer (RCB) methods and data-adaptive Krylov subspace dimensionality reduction techniques. We extend a recently proposed reduced-dimension RCB framework, which ensures proper combination of RCBs with any form of dimensionality reduction that can be(More)
Krylov-subspace methods, such as the multistage Wiener filter and conjugate gradient method, are often used for reduced-dimension adaptive beamforming. These techniques do not, however, allow for steering vector mismatch, which is typically present in many applications of interest, including passive sonar. Here, we discuss recently proposed robust methods(More)
When sampling only a coarse set of azimuth and elevation angles for a 2-D array, the resulting angle of arrival errors often lead to target nulling in adaptive beamformers. Methods currently used to alleviate these errors often prevent target nulling at the expense of source localisation. In this paper, we show how the robust Capon beamformer, exploiting a(More)
We present low complexity, quickly converging robust adaptive beamformers that combine robust Capon beamformer (RCB) methods and data-adaptive Krylov subspace dimensionality reduction techniques. We extend a recently proposed reduced-dimension RCB framework, which ensures proper combination of RCBs with any form of dimensionality reduction that can be(More)
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