Research on Information Fusion of Two New DLL Discriminator Algorithms

Jian Cao; Cong Yan

doi:10.4028/www.scientific.net/AMM.543-547.1223

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 543-547Research on Information Fusion of Two New DLL...

Research on Information Fusion of Two New DLL Discriminator Algorithms

Abstract:

After information fusion model has been established, the feature-level fusion algorithm based on fuzzy neural network and expert system is proposed, in which the expert system has been embedded into fuzzy neural network so that it could choose the membership function and adjust the network structure. At the same time, for code tracking loop, two new code phase discriminator algorithms based on DLL structure is proposed. Evidence theory has been applied to achieve the decision-making level fusion. The performances of the two algorithms were studied by using theoretical method and experimental method with analog IF signal data and actual IF signal data respectively. Then, the results of feature-level fusion have been taken as the evidences to construct the frame of discernment. The research results show that the process of information fusion has abilities of adapting and self-learning.

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Periodical:

Applied Mechanics and Materials (Volumes 543-547)

Pages:

1223-1226

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.543-547.1223

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Online since:

March 2014

Authors:

Jian Cao*, Cong Yan

Keywords:

Discriminator, DLL, Fusion, Improving

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* - Corresponding Author

References

[1] S. K. Kalyanaraman, High Accuracy GPS Phase Tracking Under Signal Distortion, Ph. D. Thesis of Russ College of Engineering and Technology of Ohio University, Ohio, (2012).

Google Scholar

[2] S. Wang, D. Yang, Z. Liu and D. LUO, Simulation Study on High Dynamic GPS Software Receiver, The international journal for computation and mathematics in electrical and electronic engineering, vol. 28, iss. 6, pp.1386-1401, (2009).

DOI: 10.1108/03321640910991967

Google Scholar

[3] J. Li, X. Ba, S. Anjum and J. Chen, Adaptive PLL Based Carrier Tracking Algorithm for GPS Signals IN High Dynamic Environment, Chinese Journal Of Electron Devices, vol. 30, no. 4, pp.1440-1443, August (2007).

Google Scholar

[4] S. K. Shanmugam, New Enhanced Sensitivity Detection Techniques for GPS L1 C/A and Modernized Signal Acquisition, Ph. D. Thesis of University of Calgary, Calgary, Alberta, (2010).

Google Scholar