Improved Coarray Interpolation Algorithms with Additional Orthogonal Constraint for Cyclostationary Signals
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College of Automation, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China
2
School of Electrical Engineering & Automation, Harbin Institute of Technology, No. 92 Xidazhi Street, Harbin 150006, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(1), 219; https://doi.org/10.3390/s18010219
Received: 12 December 2017
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Revised: 10 January 2018
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Accepted: 11 January 2018
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Published: 14 January 2018
(This article belongs to the Special Issue Recent Advances in Array Signal Processing and Its Applications in IoT Security)
Many modulated signals exhibit a cyclostationarity property, which can be exploited in direction-of-arrival (DOA) estimation to effectively eliminate interference and noise. In this paper, our aim is to integrate the cyclostationarity with the spatial domain and enable the algorithm to estimate more sources than sensors. However, DOA estimation with a sparse array is performed in the coarray domain and the holes within the coarray limit the usage of the complete coarray information. In order to use the complete coarray information to increase the degrees-of-freedom (DOFs), sparsity-aware-based methods and the difference coarray interpolation methods have been proposed. In this paper, the coarray interpolation technique is further explored with cyclostationary signals. Besides the difference coarray model and its corresponding Toeplitz completion formulation, we build up a sum coarray model and formulate a Hankel completion problem. In order to further improve the performance of the structured matrix completion, we define the spatial spectrum sampling operations and the derivative (conjugate) correlation subspaces, which can be exploited to construct orthogonal constraints for the autocorrelation vectors in the coarray interpolation problem. Prior knowledge of the source interval can also be incorporated into the problem. Simulation results demonstrate that the additional constraints contribute to a remarkable performance improvement.
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Keywords:
coarray interpolation; cyclostationarity; (conjugate) correlation subspaces; coprime array; orthogonal constraint; Toeplitz completion; Hankel completion
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MDPI and ACS Style
Song, J.; Shen, F. Improved Coarray Interpolation Algorithms with Additional Orthogonal Constraint for Cyclostationary Signals. Sensors 2018, 18, 219. https://doi.org/10.3390/s18010219
AMA Style
Song J, Shen F. Improved Coarray Interpolation Algorithms with Additional Orthogonal Constraint for Cyclostationary Signals. Sensors. 2018; 18(1):219. https://doi.org/10.3390/s18010219
Chicago/Turabian StyleSong, Jinyang, and Feng Shen. 2018. "Improved Coarray Interpolation Algorithms with Additional Orthogonal Constraint for Cyclostationary Signals" Sensors 18, no. 1: 219. https://doi.org/10.3390/s18010219
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