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1. Convolutional Beamspace and Sparse Signal Recovery for Linear Arrays

   https://doi.org/10.1109/IEEECONF51394.2020.9443522  

   Chen, Po-Chih ; Vaidyanathan, P. P. ( November 2020 , 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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2. Convolutional Beamspace for Array Signal Processing

   https://doi.org/10.1109/ICASSP40776.2020.9054051  

   Vaidyanathan, P. P. ; Chen, Po-Chih ( May 2020 , 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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3. Sliding-Capon Based Convolutional Beamspace for Linear Arrays

   https://doi.org/10.1109/ICASSP39728.2021.9414022  

   Chen, Po-Chih ; Vaidyanathan, P P ( June 2021 , 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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4. Optimality of the Discrete Fourier Transform for Beamspace Massive MU-MIMO Communication

   https://doi.org/10.1109/ISIT45174.2021.9518255  

   Taner, Sueda ; Studer, Christoph ( July 2021 , IEEE International Symposium on Information Theory)

   null (Ed.)

   Beamspace processing is an emerging technique to reduce baseband complexity in massive multiuser (MU) multipleinput multiple-output (MIMO) communication systems operating at millimeter-wave (mmWave) and terahertz frequencies. The high directionality of wave propagation at such high frequencies ensures that only a small number of transmission paths exist between user equipments and basestation (BS). In order to resolve the sparse nature of wave propagation, beamspace processing traditionally computes a spatial discrete Fourier transform (DFT) across a uniform linear antenna array at the BS where each DFT output is associated with a specific beam. In this paper, we study optimality conditions of the DFT for sparsity-based beamspace processing with idealistic mmWave channel models and realistic channels. To this end, we propose two algorithms that learn unitary beamspace transforms using an l4-norm-based sparsity measure, and we investigate their optimality theoretically and via simulations. 

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5. Improving Surface Current Resolution Using Direction Finding Algorithms for Multiantenna High-Frequency Radars

   https://doi.org/10.1175/JTECH-D-19-0029.1  

   Kirincich, Anthony ; Emery, Brian ; Washburn, Libe ; Flament, Pierre ( October 2019 , Journal of Atmospheric and Oceanic Technology)

   While land-based high-frequency (HF) radars are the only instruments capable of resolving both the temporal and spatial variability of surface currents in the coastal ocean, recent high-resolution views suggest that the coastal ocean is more complex than presently deployed radar systems are able to reveal. This work uses a hybrid system, having elements of both phased arrays and direction finding radars, to improve the azimuthal resolution of HF radars. Data from two radars deployed along the U.S. East Coast and configured as 8-antenna grid arrays were used to evaluate potential direction finding and signal, or emitter, detection methods. Direction finding methods such as maximum likelihood estimation generally performed better than the well-known multiple signal classification (MUSIC) method given identical emitter detection methods. However, accurately estimating the number of emitters present in HF radar observations is a challenge. As MUSIC’s direction-of-arrival (DOA) function permits simple empirical tests that dramatically aid the detection process, MUSIC was found to be the superior method in this study. The 8-antenna arrays were able to provide more accurate estimates of MUSIC’s noise subspace than typical 3-antenna systems, eliminating the need for a series of empirical parameters to control MUSIC’s performance. Code developed for this research has been made available in an online repository.

    

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