New recursive adaptive beamforming algorithms for uniform concentric circular arrays with frequency invariant characteristics

This paper proposes new recursive adaptive beamforming algorithms for uniform concentric circular array (UCCA) that has nearly frequency invariant (FI) characteristics. By using a fixed compensation network, the far field pattern of a UCCA frequency invariant beamformer (FIB) is determined by a set of weights and it is approximately invariant over a wide range of frequencies. New recursive adaptive beamforming algorithms based on the least mean square (LMS) and recursive least square (RLS) algorithms and the Generalized Sidelobe Canceller (GSC) structure are proposed to address the high computational complexity of the sample matrix inversion (SMI) method proposed previously by the authors. Simulation results show that the proposed adaptive FI-UCCA beamformer requires much fewer variable taps than the conventional UCCA for the same steady-state performance, while offering much faster convergence speed.

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