ROBUST ADAPTIVE BEAMFORMING FOR A CLASS OF GAUSSIAN STEERING VECTOR MISMATCH

Compared with the worst-case optimization-based ap- proach, the probability-constrained approach is a more flexible one to robust adaptive beamforming. In this paper, a precise relationship between the two approaches is built in the case of Gaussian steering vector mismatch, which shows that the probability-constrained beam- former design can be interpreted in terms of the worst-case beamformer design. Numerical simulations demonstrate that the precise version of the probability-constrained beamformer is more robust to the steering vector mismatch than the other popular robust adaptive beamformers.

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