Analysing the performance of divide-and-conquer sequential matrix diagonalisation for large broadband sensor arrays

Coutts, Fraser K. and Thompson, Keith and Weiss, Stephan and Proudler, Ian K. (2017) Analysing the performance of divide-and-conquer sequential matrix diagonalisation for large broadband sensor arrays. In: IEEE International Workshop on Signal Processing Systems, 2017-10-03 - 2017-10-05.

[img]
Preview
Text (Coutts-etal-SPS-2017-performance-of-divide-and-conquer-sequential-matrix-diagonalisation-for-large-broadband-sensor-arrays)
Coutts_etal_SPS_2017_performance_of_divide_and_conquer_sequential_matrix_diagonalisation_for_large_broadband_sensor_arrays.pdf
Accepted Author Manuscript

Download (170kB)| Preview

    Abstract

    A number of algorithms capable of iteratively calculating a polynomial matrix eigenvalue decomposition (PEVD) have been introduced. The PEVD is an extension of the ordinary EVD to polynomial matrices and will diagonalise a parahermitian matrix using paraunitary operations. Inspired by recent work towards a low complexity divide-and-conquer PEVD algorithm, this paper analyses the performance of this algorithm - named divide-and-conquer sequential matrix diagonalisation (DC-SMD) - for applications involving broadband sensor arrays of various dimensionalities. We demonstrate that by using the DC-SMD algorithm instead of a traditional alternative, PEVD complexity and execution time can be significantly reduced. This reduction is shown to be especially impactful for broadband multichannel problems involving large arrays.