Impact of fast-converging PEVD algorithms on broadband AoA estimation

Fraser K. Coutts, Keith Thompson, Stephan Weiss, Ian K. Proudler

Research output: Contribution to conferencePaperpeer-review

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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.
Original languageEnglish
Number of pages5
Publication statusAccepted/In press - 6 Sept 2017
EventIEEE Sensor Signal Processing in Defence Conference - London, London, United Kingdom
Duration: 6 Dec 20177 Dec 2017


ConferenceIEEE Sensor Signal Processing in Defence Conference
Abbreviated titleSSPD'17
Country/TerritoryUnited Kingdom
Internet address


  • broadband
  • narrowband
  • angle of arrival


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