[1] Cai Miaohong, Jin Le, He Feng, Wu Lenan, et al. Federated UKF algorithm for mobile location estimationwith TDOA/Doppler measurements [J]. Journal of Southeast University (English Edition), 2009, 25 (3): 294-298. [doi:10.3969/j.issn.1003-7985.2009.03.002]

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Federated UKF algorithm for mobile location estimationwith TDOA/Doppler measurements()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

25

Issue:

2009 3

Page:

294-298

Research Field:

Electromagnetic Field and Microwave Technology

Publishing date:

2009-09-30

Info

Title:

Federated UKF algorithm for mobile location estimationwith TDOA/Doppler measurements

Author(s):

Cai Miaohong;  Jin Le;  He Feng;  Wu Lenan

School of Information Science and Engineering, Southeast University, Nanjing 210096, China

Keywords:

data fusion;  mobile location estimation;  federated filtering;  unscented Kalman filter(UKF)

PACS:

TN957.51

DOI:

10.3969/j.issn.1003-7985.2009.03.002

Abstract:

In order to enhance the location estimation performance of mobile station(MS)tracking and positioning, a new method of mobile location optimal estimation based on the federated filtering structure and the simplified unscented Kalman filter(UKF)is presented.The proposed algorithm uses the Singer mobile statement model as the reference system, and the simplified UKF as the subfilters.The subfilters receive the two groups of independently detected time difference of arrival(TDOA)measurement inputs and Doppler measurement inputs, and produce local estimation outputs to the main estimator.Then the main estimator performs the optimal fusion of the local estimation outputs according to the scalar weighted rule, and a global optimal or suboptimal estimation result is achieved.Finally the main estimator gives feedback and reset information to the subfilters and the reference system for next step estimation.In the simulations, the estimation performance of the proposed algorithm is evaluated and compared with the simplified UKF method with TDOA or Doppler measurement alone.The simulation results demonstrate that the proposed algorithm can effectively reduce the location estimation error and variance of the MS, and has favorable performance in both root mean square error(RMSE)and mean error cumulative distribution function(CDF).

References:

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Memo

Memo:

Biographies: Cai Miaohong(1983—), female, graduate;Wu Lenan(corresponding author), male, doctor, professor, wuln@seu.edu.cn.
Foundation item: The Cultivation Fund of the Key Scientific and Technical Innovation Project of Ministry of Education of China(No.706028).
Citation: Cai Miaohong, Jin Le, He Feng, et al.Federated UKF algorithm for mobile location estimation with TDOA/Doppler measurements[J].Journal of Southeast University(English Edition), 2009, 25(3):294-298.

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