Second-order cone programming with probabilistic regularization for robust adaptive beamforming | Zendy

Xijing Guo | Zendy; Sébastian Miron | Zendy; Yixin Yang | Zendy; Shi-e Yang | Zendy

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Second-order cone programming with probabilistic regularization for robust adaptive beamforming

Author(s) -

Xijing Guo,

Sébastian Miron,

Yixin Yang,

Shi-e Yang

Publication year - 2017

Publication title -

the journal of the acoustical society of america

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.619

H-Index - 187

eISSN - 1520-8524

pISSN - 0001-4966

DOI - 10.1121/1.4976846

Subject(s) - beamforming , robustness (evolution) , second order cone programming , regularization (linguistics) , probabilistic logic , monte carlo method , directivity , computer science , mathematical optimization , algorithm , adaptive beamformer , mathematics , telecommunications , artificial intelligence , statistics , convex optimization , biochemistry , chemistry , geometry , regular polygon , antenna (radio) , gene

International audienceProbabilistic regularization (PR) is introduced to make superdirective array beamforming robust against sensor characteristic mismatches. The objective is to enlarge the directivity while ensuring robustness with high probability. The PR problem is solved via the second-order cone (SOC) programming where the regularization parameter is chosen through a statistical analysis of the system perturbations, based on Monte Carlo simulations. Experiments are carried out on a miniaturized 3-by-3 uniform rectangular array without calibration. The results show that for this particular array, the PR method is robust to sensor mismatches and achieves a higher level of directivity compared with other robust adaptive beamforming approaches

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