Experimental and Analytical Studies on Improved Feedforward ML Estimation Based on LS-SVR
Joint Authors
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-11-27
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Maximum likelihood (ML) algorithm is the most common and effective parameter estimation method.
However, when dealing with small sample and low signal-to-noise ratio (SNR), threshold effects are resulted and estimation performance degrades greatly.
It is proved that support vector machine (SVM) is suitable for small sample.
Consequently, we employ the linear relationship between least squares support vector regression (LS-SVR)’s inputs and outputs and regard LS-SVR process as a time-varying linear filter to increase input SNR of received signals and decrease the threshold value of mean square error (MSE) curve.
Furthermore, it is verified that by taking single-tone sinusoidal frequency estimation, for example, and integrating data analysis and experimental validation, if LS-SVR’s parameters are set appropriately, not only can the LS-SVR process ensure the single-tone sinusoid and additive white Gaussian noise (AWGN) channel characteristics of original signals well, but it can also improves the frequency estimation performance.
During experimental simulations, LS-SVR process is applied to two common and representative single-tone sinusoidal ML frequency estimation algorithms, the DFT-based frequency-domain periodogram (FDP) and phase-based Kay ones.
And the threshold values of their MSE curves are decreased by 0.3 dB and 1.2 dB, respectively, which obviously exhibit the advantage of the proposed algorithm.
American Psychological Association (APA)
Liu, Xueqian& Yu, Hongyi. 2013. Experimental and Analytical Studies on Improved Feedforward ML Estimation Based on LS-SVR. Discrete Dynamics in Nature and Society،Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-453277
Modern Language Association (MLA)
Liu, Xueqian& Yu, Hongyi. Experimental and Analytical Studies on Improved Feedforward ML Estimation Based on LS-SVR. Discrete Dynamics in Nature and Society No. 2013 (2013), pp.1-10.
https://search.emarefa.net/detail/BIM-453277
American Medical Association (AMA)
Liu, Xueqian& Yu, Hongyi. Experimental and Analytical Studies on Improved Feedforward ML Estimation Based on LS-SVR. Discrete Dynamics in Nature and Society. 2013. Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-453277
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-453277