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Yixin Yang

Researcher at Northwestern Polytechnical University

Publications -  124
Citations -  724

Yixin Yang is an academic researcher from Northwestern Polytechnical University. The author has contributed to research in topics: Beamforming & Sonar. The author has an hindex of 9, co-authored 112 publications receiving 403 citations. Previous affiliations of Yixin Yang include Fourth Military Medical University.

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Robust high-order superdirectivity of circular sensor arraysa)

TL;DR: It is shown that the circular arrays possess good potential for directivity improvement, and the sensitivity function used as a robustness measurement can be accurately decomposed into a series of closed-form sensitivity functions of eigenbeams, similar to the optimal beampattern and its corresponding directivity factor.
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A Complexity-Based Approach for the Detection of Weak Signals in Ocean Ambient Noise

TL;DR: The simulation results indicate that the complexity is sensitive to change in the information in the ambient noise and the change in SNR, a finding that enables the detection of weak ship signals in strong background ambient noise.
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Experimental verification of effect of horizontal inhomogeneity of evaporation duct on electromagnetic wave propagation

TL;DR: In this article, the authors investigated the effects of horizontal inhomogeneity of evaporation duct on electromagnetic wave propagation, both in numerical simulation and experimental observation methods, and showed that path loss is significantly higher than that in the homogeneous case when the EDH at the receiver is lower than that at the transmitter.
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Adaptive Beamforming With Sensor Position Errors Using Covariance Matrix Construction Based on Subspace Bases Transition

TL;DR: Simulations and experimental results show that the proposed beamformer outperforms other tested beamformers in the presence of sensor position errors, and also obtains the bases transition matrix between the estimated angle-related bases and the orthogonal bases.
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Localized Multiple Kernel Learning With Dynamical Clustering and Matrix Regularization

TL;DR: To enable the learner to discover and benefit from the underlying local coherence and diversity of the samples, the clustering procedure is incorporated into the canonical support vector machine-based LMKL framework and how the cluster structure is gradually revealed and the matrix-regularized kernel weights are obtained.