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Content Provider | IEEE Xplore Digital Library |
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Author | Yong Wang Yixin Yang Yuanliang Ma Xiaoyu Wang |
Copyright Year | 2012 |
Description | Author affiliation: College of Marine Engineering, Northwestern Polytechnical University, Xi'an, China, 710072 (Yong Wang; Yixin Yang; Yuanliang Ma; Xiaoyu Wang) |
Abstract | Beamformer with low sidelobe is important in many applications, but its performance will degrade in the presence of errors in sensor array characteristics, such as position, gain and phase errors. White noise gain constraint is always used to improve the robustness, but it is not clear how to choose the related parameters according to the given level of errors. The robust method based on worst-case performance optimization can use the a priori knowledge of the error levels, but not sufficiently. The bounds of the manifold vector uncertainty set cannot be easily obtained. In this paper, a feasible way is used to derive the bounds considering the errors in sensor array characteristics together, and then a robust minimum sidelobe beamforming method based on worst-case performance optimization in mode space for circular arrays is presented. This problem can be transformed to convex optimization and solved by second-order cone programming. Simulation results show the computation load of this method is effectively reduced and the results are more reliable. |
Starting Page | 300 |
Ending Page | 304 |
File Size | 1125352 |
Page Count | 5 |
File Format | |
ISBN | 9781467321921 |
e-ISBN | 9781467321938 |
DOI | 10.1109/ICSPCC.2012.6335704 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2012-08-12 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Manifolds Uncertainty Beamforming Array signal processing Circular arrays Worst-case performance optimization Robustness Vectors Second-order cone programming Optimization Sensor arrays |
Content Type | Text |
Resource Type | Article |
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