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Minimum variance distortionless response (MVDR) focused beamformer based on vector sensor array is widely used in the area of the noise source high-resolution localization and identification. However, when an arbitrary unknown signal steering vector mismatch occurs or the training sample size is small, the performance of MVDR focused beamformer will be severely degraded. In this paper, we develop a new approach, which is based on the worst-case concept, to improve the robustness of the original method. It is shown that the proposed algorithm improves its robustness by imposing the array response constraint on the uncertainty set of the steering vector, and can be reformulated in a convex form as the so called second order cone program (SOCP), and then solved efficiently using the well established optimization tool, Sedumi. Theory analysis and computer simulations show better performance of our robust beamformer as compared with the existing methods: it can achieve a greater dynamic range,sharper focused peak,and lower back-ground noise level.The results in this paper demonstrate our proposed method can be applied in the underwater noise source high-resolution localization and identification. |
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Keywords:vector sensor array; robust; focused beamformer; noise source localization; high-resolution |
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