Performance analysis of different autoregressive methods for spectrum estimation along with their real time implementations

Recently Spectrum estimation has become an interesting topic for the researchers. Non-parametric methods generally do not have any knowledge about the process being observed. They also suffer from serious drawbacks like sidelobe leakages and unrealistic windowing methods. The second approach being k...

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Bibliographic Details
Published in2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) pp. 141 - 146
Main Authors Chakraborty, Debashis, Sanyal, Salil Kumar
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2016
Subjects
Autoregerssive
Autoregressive processes
Burg
Computational modeling
Correlation
Covariance
Estimation
Mathematical model
Modified Covariance
PSD
Real-time systems
Spectral analysis
Yule-Walker
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Summary:Recently Spectrum estimation has become an interesting topic for the researchers. Non-parametric methods generally do not have any knowledge about the process being observed. They also suffer from serious drawbacks like sidelobe leakages and unrealistic windowing methods. The second approach being known as parametric method overcomes these shortcomings. In parametric approach initially a suitable model is selected based on apriori knowledge about how the process is generated and then followed by estimating the parameters from the observed data. After calculation of parameters the power spectrum is estimated. In this paper we have studied thoroughly the Autoregressive method of spectrum estimation. We perform both simulation as well as real time implementations on FPGA based radio prototype board known as Wireless Open Access Research Platform (WARP) of RICE University. Various algorithms like Yule-Walker, Burg, Covariance and Modified Covariance have been studied with real time estimation of the statistical parameters by which they are described in AR technique.
DOI:10.1109/ICRCICN.2016.7813646