Shortening of paraunitary matrices obtained by polynomial eigenvalue decomposition algorithms

Jamie Corr, Keith Thompson, Stephan Weiss, Ian K. Proudler, John G. McWhirter

Research output: Contribution to conferencePaper

10 Citations (Scopus)

Abstract

This paper extends the analysis of the recently introduced row-shift corrected truncation method for paraunitary matrices to those produced by the state-of-the-art sequential matrix diagonalisation (SMD) family of polynomial eigenvalue decomposition (PEVD) algorithms. The row-shift corrected truncation method utilises the ambiguity in the paraunitary matrices to reduce their order. The results presented in this paper compare the effect a simple change in PEVD method can have on the performance of the paraunitary truncation. In the case of the SMD algorithm the benefits of the new approach are reduced compared to what has been seen before however there is still a reduction in both reconstruction error and paraunitary matrix order.

Conference

Conference5th Conference of the Sensor Signal Processing for Defence
Abbreviated titleSSPD 2015
CountryUnited Kingdom
CityEdinburgh
Period9/07/1510/07/15
Internet address

Fingerprint

Polynomials
Decomposition

Keywords

  • sequential matrix diagonalisation algorithms
  • polynomial eigenvalue decomposition
  • broadband array processing

Cite this

Corr, J., Thompson, K., Weiss, S., Proudler, I. K., & McWhirter, J. G. (2015). Shortening of paraunitary matrices obtained by polynomial eigenvalue decomposition algorithms. 1-5. Paper presented at 5th Conference of the Sensor Signal Processing for Defence , Edinburgh, United Kingdom. https://doi.org/10.1109/SSPD.2015.7288523
Corr, Jamie ; Thompson, Keith ; Weiss, Stephan ; Proudler, Ian K. ; McWhirter, John G. . / Shortening of paraunitary matrices obtained by polynomial eigenvalue decomposition algorithms. Paper presented at 5th Conference of the Sensor Signal Processing for Defence , Edinburgh, United Kingdom.5 p.
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Corr, J, Thompson, K, Weiss, S, Proudler, IK & McWhirter, JG 2015, 'Shortening of paraunitary matrices obtained by polynomial eigenvalue decomposition algorithms' Paper presented at 5th Conference of the Sensor Signal Processing for Defence , Edinburgh, United Kingdom, 9/07/15 - 10/07/15, pp. 1-5. https://doi.org/10.1109/SSPD.2015.7288523

Shortening of paraunitary matrices obtained by polynomial eigenvalue decomposition algorithms. / Corr, Jamie; Thompson, Keith; Weiss, Stephan; Proudler, Ian K.; McWhirter, John G. .

2015. 1-5 Paper presented at 5th Conference of the Sensor Signal Processing for Defence , Edinburgh, United Kingdom.

Research output: Contribution to conferencePaper

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AU - Thompson, Keith

AU - Weiss, Stephan

AU - Proudler, Ian K.

AU - McWhirter, John G.

N1 - (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

PY - 2015/9/11

Y1 - 2015/9/11

N2 - This paper extends the analysis of the recently introduced row-shift corrected truncation method for paraunitary matrices to those produced by the state-of-the-art sequential matrix diagonalisation (SMD) family of polynomial eigenvalue decomposition (PEVD) algorithms. The row-shift corrected truncation method utilises the ambiguity in the paraunitary matrices to reduce their order. The results presented in this paper compare the effect a simple change in PEVD method can have on the performance of the paraunitary truncation. In the case of the SMD algorithm the benefits of the new approach are reduced compared to what has been seen before however there is still a reduction in both reconstruction error and paraunitary matrix order.

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Corr J, Thompson K, Weiss S, Proudler IK, McWhirter JG. Shortening of paraunitary matrices obtained by polynomial eigenvalue decomposition algorithms. 2015. Paper presented at 5th Conference of the Sensor Signal Processing for Defence , Edinburgh, United Kingdom. https://doi.org/10.1109/SSPD.2015.7288523