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Signal Space Estimation: Application to Subspace Spectrum Analysis

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Ambient Communications and Computer Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 696))

Abstract

Algorithms like MUSIC are widely used for harmonic analysis of signal when there are limited numbers of data samples and harmonics are closely spaced. These algorithm works based on decomposing autocorrelation matrix of analyzed signal into signal space and noise space, but performance degraded if selected signal space and noise space are not chosen correctly. Our main contribution to this paper is the estimation of signal space and noise space for unknown signals so that subspace harmonic analysis technique can be applied to unknown signal too. Estimation of signal space is based on eigenvalue distribution of correlation matrix of analyzed signal. A threshold is calculated to differentiate between signal space and noise space. Performance of technique is shown through simulation.

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Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology (NIT), Hamirpur, 177005, HP, India

    Rahul Kumar

  2. Department of Electronics and Communication Engineering, Delhi Technological University, New Delhi, 110042, India

    Pappu Kumar Verma

Authors
  1. Rahul Kumar
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  2. Pappu Kumar Verma
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Corresponding author

Correspondence to Pappu Kumar Verma .

Editor information

Editors and Affiliations

  1. University of Murcia, Murcia, Spain

    Gregorio Martinez Perez

  2. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Shailesh Tiwari

  3. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Munesh C. Trivedi

  4. Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology, Allahabad, Uttar Pradesh, India

    Krishn K. Mishra

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© 2018 Springer Nature Singapore Pte Ltd.

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Kumar, R., Verma, P.K. (2018). Signal Space Estimation: Application to Subspace Spectrum Analysis. In: Perez, G., Tiwari, S., Trivedi, M., Mishra, K. (eds) Ambient Communications and Computer Systems. Advances in Intelligent Systems and Computing, vol 696. Springer, Singapore. https://doi.org/10.1007/978-981-10-7386-1_14

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  • DOI: https://doi.org/10.1007/978-981-10-7386-1_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7385-4

  • Online ISBN: 978-981-10-7386-1

  • eBook Packages: EngineeringEngineering (R0)

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