Data fusion using the expected output membership function

Choi, Jongbae; Dickerson, Julie A.

doi:10.1109/fuzzy.1998.686332

1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98C

DOI: 10.1109/fuzzy.1998.686332

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Data fusion using the expected output membership function

Jongbae Choi

Julie A. Dickerson

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Location estimation with data fusion for wireless location systems

Wann

Lin²

IEEE International Conference on Networking, Sensing and Control, 2004

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Wireless location in wireless communications networks has been studied extensively in recent years, Among the network-based approaches, time of arrival (TOA) md time difference of arrival (TDOA) are hvo time-based techniques used in location estimation. It is known that the accuracy of location estimation may suffer from poor geometric dilution of precision (GDOP) situation and non-line of sight (NLOS) propagation effect. To provide more accurate measures f o r time-based location techniques under the effects of GDOP in wireless communications networks, a data fusion with fuzzy rules ispresented. Based on the GDOP statisticalproperties of the raw location measures, the data fusion integrates data obtained from the TOA and TDOA techniques.Simulation results show that the proposed fuzzy logicbased data fusion effectively improves the overall accuracy of location estimation in wireless location systems.

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Location estimation with data fusion for wireless location systems

Wann

Lin²

IEEE International Conference on Networking, Sensing and Control, 2004

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Data fusion methods for accuracy improvement in wireless location systems

Wann

Lin²

2004 IEEE Wireless Communications and Networking Conference (IEEE Cat. No.04TH8733)

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Wireless location methods in wireless communications networks have received significant attention in recent years. Among the network-based approaches, time of arrival (TOA) and time difference of arrival (TDOA) are two major time-based techniques used for location estimation. It is known that the accuracy of location estimation may suffer from poor geometric dilution of precision (GDOP) situation and non-line of sight (NLOS) propagation effect. To improve the accuracy of timebased location techniques in wireless communications networks, data fusion methods using fuzzy logic and Bayes rules are presented. Based on the statistical properties of the raw location estimates, data fusion methods integrate the data obtained from the TOA and TDOA techniques. Computer simulations are conducted to investigate the wireless location problem affected by GDOP and NLOS. Simulation results show that the overall accuracy of location estimation in wireless location systems can be significantly improved when the LOS reconstruction and the data fusion methods are used.

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Data fusion using expected output membership functions

Choi

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Data fusion using the expected output membership function

Cited by 3 publications

References 9 publications

Location estimation with data fusion for wireless location systems

Location estimation with data fusion for wireless location systems

Data fusion methods for accuracy improvement in wireless location systems

Data fusion using expected output membership functions

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