Maximum-likelihood Estimation for Frequency-hopping Parameters by Cauchy Distribution

JIN Yan; LI Shuguang; JI Hongbing

doi:10.11999/JEIT151029

Volume 38 Issue 7

Jul. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(7): 1696-1702

JIN Yan, LI Shuguang, JI Hongbing. Maximum-likelihood Estimation for Frequency-hopping Parameters by Cauchy Distribution[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1696-1702. doi: 10.11999/JEIT151029

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JIN Yan, LI Shuguang, JI Hongbing. Maximum-likelihood Estimation for Frequency-hopping Parameters by Cauchy Distribution[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1696-1702. doi: 10.11999/JEIT151029

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Maximum-likelihood Estimation for Frequency-hopping Parameters by Cauchy Distribution

doi: 10.11999/JEIT151029

Funds:

The National Natural Science Foundation of China (61201286), The Natural Science Foundation of Shaanxi Province of China (2014JM8304), The Fundamental Research Funds for the Central Universities (K5051202013)

Received Date: 2015-09-10
Rev Recd Date: 2016-01-29
Publish Date: 2016-07-19

Abstract

Abstract

In view that conventional methods for Frequency Hopping (FH) signal parameter estimation suffer from performance degradation in alpha stable noise environment, the Cauchy based maximum likelihood estimation method is introduced in this paper. The FH signal is decomposed into the two-dimensional envelope versus frequency plane, and then a maximum-likelihood function based on Cauchy distribution is established to extract the frequency parameter directly. For the short-time stationarity of FH signals, the maximum-likelihood function is windowed in order to estimate the specific values and sequence of frequency-hopping, after that the hopping timing and the duration can be estimated. Simulation results show that compared with the fractional lower order statistics as well as the Myriad filter based time frequency analysis methods, the proposed method improves the estimation accuracy of FH signal parameters and is robust to the alpha stable distribution noise.
- alpha-stable distribution,
- Maximum-likelihood estimator,
- Frequency Hopping (FH) signals,
- Parameter estimation,
- Two-dimensional plane

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References(19)

References

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