A low-complexity Nyström-Based algorithm for array subspace estimation

Cheng Qian, Lei Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Conventional subspace estimation methods rely on the eigenvalue decomposition (EVD) of sample covariance matrix (SCM). For a large array, the EVD-based algorithms inevitably lead to heavy computational load due to the calculation of SCM and its EVD. To circumvent this problem, a Nyström-Based algorithm for subspace estimation is proposed in this paper. In particular, we construct a rank-κ EVD method to find the signal subspace without the computation of SCM and its EVD, leading to computational simplicity. Statistical analysis and simulation results show that the devised algorithm for signal subspace estimation is computationally simple.

Original languageEnglish (US)
Title of host publicationProceedings of the 2012 2nd International Conference on Instrumentation and Measurement, Computer, Communication and Control, IMCCC 2012
Pages112-114
Number of pages3
DOIs
StatePublished - Dec 1 2012
Event2012 2nd International Conference on Instrumentation and Measurement, Computer, Communication and Control, IMCCC 2012 - Harbin, Heilongjiang, China
Duration: Dec 8 2012Dec 10 2012

Publication series

NameProceedings of the 2012 2nd International Conference on Instrumentation and Measurement, Computer, Communication and Control, IMCCC 2012

Other

Other2012 2nd International Conference on Instrumentation and Measurement, Computer, Communication and Control, IMCCC 2012
CountryChina
CityHarbin, Heilongjiang
Period12/8/1212/10/12

Keywords

  • Eigenvalue decomposition
  • Low-complexity
  • Sample covariance matrix
  • Signal subspace

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