Impact of fast-converging PEVD algorithms on broadband AoA estimation
Coutts, Fraser K. and Thompson, Keith and Weiss, Stephan and Proudler, Ian K. (2017) Impact of fast-converging PEVD algorithms on broadband AoA estimation. In: IEEE Sensor Signal Processing in Defence Conference, 2017-12-06 - 2017-12-07, London. (In Press)
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Abstract
Polynomial matrix eigenvalue decomposition (PEVD) algorithms have been shown to enable a solution to the broadband angle of arrival (AoA) estimation problem. A parahermitian cross-spectral density (CSD) matrix can be generated from samples gathered by multiple array elements. The application of the PEVD to this CSD matrix leads to a paraunitary matrix which can be used within the spatio-spectral polynomial multiple signal classification (SSP-MUSIC) AoA estimation algorithm. Here, we demonstrate that the recent low-complexity divide-and-conquer sequential matrix diagonalisation (DC-SMD) algorithm, when paired with SSP-MUSIC, is able to provide superior AoA estimation versus traditional PEVD methods for the same algorithm execution time. We also provide results that quantify the performance trade-offs that DC-SMD offers for various algorithm parameters, and show that algorithm convergence speed can be increased at the expense of increased decomposition error and poorer AoA estimation performance.
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Item type: Conference or Workshop Item(Paper) ID code: 61842 Dates: DateEvent6 September 2017Published6 September 2017AcceptedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset ManagementDepositing user: Pure Administrator Date deposited: 21 Sep 2017 16:27 Last modified: 26 Mar 2024 01:38 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/61842