Abstract
Existing robust Capon beamformers achieve robustness against steering vector errors at a high cost in terms of computational complexity. Computationally efficient robust Capon beamforming approach based on the reduced-rank technique is proposed in this paper. The proposed method projects the received data snapshots onto a lower dimensional subspace consisting of the matched filters of the multistage Wiener filter (MSWF). The subsequent adaptive beamforming will then be performed within this subspace. The combination of the benefit of the robust adaptive beamforming and the reduced-rank technique improves the performance on combating steering vector errors and lowering the computational complexity.
Highlights
The Capon beamformer chooses the weight vector by minimizing the array output power subject to a look direction constraint [1, 2]
The basic idea behind the robust Capon beamforming (RCB) approach of [3] is to estimate the desired steering vector in an uncertainty set by maximizing the array output power
Based on the uncertainty set of the steering vector, the RCB approach can be formulated as follows [3]: main aHRx−x1 a subject to a − a (θ1)2 ≤ β, (8)
Summary
The Capon beamformer chooses the weight vector by minimizing the array output power subject to a look direction constraint [1, 2]. The knowledge of the steering vector corresponding to the SOI may be imprecise because of some factors, such as DOA error, array calibration error, local scattering, near-far spatial signature mismatch, and finite sample effect [3,4,5,6,7,8,9,10] Whenever this happens, the output SINR of the SCB degrades dramatically [6]. The basic idea behind the robust Capon beamforming (RCB) approach of [3] is to estimate the desired steering vector in an uncertainty set by maximizing the array output power. We propose to employ the matched filters of the MSWF as the projection matrix for reduced-rank processing.
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