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Creators/Authors contains: "Chen, Po-Chih"

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  1. ; (, Proc. IEEE Int. Conf. Acoust. Speech, and Signal Proc)
    null (Ed.)
    A new method to design the filter for convolutional beamspace (CBS), called Capon-CBS, is proposed. The idea is to design the filter to be a sliding Capon beamformer. Such design takes input statistics into account, so it can do a better job of suppressing the sources that fall in the stopband. Capon-CBS can offer higher probability of resolution and smaller mean square error for DOA estimation, as demonstrated in the simulations. Moreover, like traditional CBS, Capon-CBS also has the advantage of low computational complexity 
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  2. ; (, Proc. Asil. Conf. Sig., Sys., and Comp)
    null (Ed.)
    The convolutional beamspace (CBS) method for DOA estimation using dictionary-based sparse signal recovery is introduced. Beamspace methods enjoy lower computational complexity, increased parallelism of subband processing, and improved DOA resolution. But unlike classical beamspace methods, CBS allows root-MUSIC and ESPRIT to be performed directly for ULAs without additional preparation since the Vandermonde structure for ULAs are preserved in the CBS output. Due to the same reason, it is shown in this paper that sparse signal representation problems can also be directly formulated on the CBS output. Significant reduction in computational complexity and higher probability of resolution are obtained by using CBS. It is also shown how the regularization parameter involved in the method should be chosen 
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  3. ; (, Proc. IEEE Int. Conf. Acoust. Speech, and Signal Proc)
    null (Ed.)
    A new type of beamspace for array processing is introduced called convolutional beamspace. It enjoys the advantages of traditional beamspace such as lower computational complexity, increased parallelism of subband processing, and improved resolution threshold for DOA estimation. But unlike traditional beamspace methods, it allows root-MUSIC and ESPRIT to be performed directly for ULAs without any overhead of preparation, as the Vandermonde structure and the shift-invariance are preserved under the transformation. The method produces more accurate DOA estimates than traditional beamspace methods, and for correlated sources it produces better estimates than element-space methods. 
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  4. ; (, Proc. Asil. Conf. Sig., Sys., and Comp.)
    In conventional ESPRIT, a single translational invariance in a sensor array is used to obtain high-resolution direction-of-arrival (DOA) estimation. However, when the invariance is greater than the classical sensor spacing =2, spatial frequency ambiguity may occur. In this paper, we propose to use multiple setwise coprime invariances to resolve this ambiguity. While special cases of this were known in the literature, our algorithm is more general in that we consider any number of invariances, and that it can perfectly recover any number of DOAs (limited only in terms of number of sensors) if infinite snapshots are available. We also demonstrate through simulation that our algorithm works well in a practical setting where only finite snapshots are available. 
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