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Neural Network-Aided Adaptive UKF for Integrated Underwater Navigation

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Standard Standard

Neural Network-Aided Adaptive UKF for Integrated Underwater Navigation. / Wu, M.; Weng, Y.; Arik, S. (Editor) et al.
Neural Information Processing. 2015. ed. Springer, 2015. p. 301-310.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

HarvardHarvard

Wu, M, Weng, Y, Arik, S (ed.), Huang, T (ed.), Lai, WK (ed.) & Liu, Q (ed.) 2015, Neural Network-Aided Adaptive UKF for Integrated Underwater Navigation. in Neural Information Processing. 2015 edn, Springer, pp. 301-310. https://doi.org/10.1007/978-3-319-26535-3_35

APA

Wu, M., Weng, Y., Arik, S. (Ed.), Huang, T. (Ed.), Lai, W. K. (Ed.), & Liu, Q. (Ed.) (2015). Neural Network-Aided Adaptive UKF for Integrated Underwater Navigation. In Neural Information Processing (2015 ed., pp. 301-310). Springer. https://doi.org/10.1007/978-3-319-26535-3_35

CBE

Wu M, Weng Y, Arik S, Huang T, Lai WK, Liu Q, ed. 2015. Neural Network-Aided Adaptive UKF for Integrated Underwater Navigation. In Neural Information Processing. 2015 ed. Springer. pp. 301-310. https://doi.org/10.1007/978-3-319-26535-3_35

MLA

Wu, M. et al. "Neural Network-Aided Adaptive UKF for Integrated Underwater Navigation". Neural Information Processing. 2015 udg., Springer. 2015, 301-310. https://doi.org/10.1007/978-3-319-26535-3_35

VancouverVancouver

Wu M, Weng Y, Arik S, (ed.), Huang T, (ed.), Lai WK, (ed.), Liu Q, (ed.). Neural Network-Aided Adaptive UKF for Integrated Underwater Navigation. In Neural Information Processing. 2015 ed. Springer. 2015. p. 301-310 doi: 10.1007/978-3-319-26535-3_35

Author

Wu, M. ; Weng, Y. ; Arik, S. (Editor) et al. / Neural Network-Aided Adaptive UKF for Integrated Underwater Navigation. Neural Information Processing. 2015. ed. Springer, 2015. pp. 301-310

RIS

TY - CHAP

T1 - Neural Network-Aided Adaptive UKF for Integrated Underwater Navigation

AU - Wu, M.

AU - Weng, Y.

A2 - Arik, S.

A2 - Huang, T.

A2 - Lai, W.K.

A2 - Liu, Q.

PY - 2015/11/10

Y1 - 2015/11/10

N2 - Gravity aided navigation and geomagnetism aided navigation are equally important methods in the field of underwater navigation. However, the former is affected by terrain fluctuations, and the latter is sensitive to time-varying noise. Considering the characteristics that the gravity gradient vector can avoid the influence of time-varying noise and is less sensitive to terrain fluctuations, we propose to integrate the gravity gradient vector and geomagnetic vector together to achieve the merits of each aided navigation method. The gravity gradient vector and geomagnetic vector are used as measurement information from both local neural network-aided adaptive UKF filters, and then an information fusion algorithm based on weighted least squares estimation is used to combine the estimated values from each local filter to form an optimal estimated state value. Finally, the optimal estimated value is used to update the output values from each local neural network –aided adaptive UKF filter. Experimental results prove the feasibility of this integrated navigation method.

AB - Gravity aided navigation and geomagnetism aided navigation are equally important methods in the field of underwater navigation. However, the former is affected by terrain fluctuations, and the latter is sensitive to time-varying noise. Considering the characteristics that the gravity gradient vector can avoid the influence of time-varying noise and is less sensitive to terrain fluctuations, we propose to integrate the gravity gradient vector and geomagnetic vector together to achieve the merits of each aided navigation method. The gravity gradient vector and geomagnetic vector are used as measurement information from both local neural network-aided adaptive UKF filters, and then an information fusion algorithm based on weighted least squares estimation is used to combine the estimated values from each local filter to form an optimal estimated state value. Finally, the optimal estimated value is used to update the output values from each local neural network –aided adaptive UKF filter. Experimental results prove the feasibility of this integrated navigation method.

U2 - 10.1007/978-3-319-26535-3_35

DO - 10.1007/978-3-319-26535-3_35

M3 - Chapter

SN - 978-3-319-26534-6

SP - 301

EP - 310

BT - Neural Information Processing

PB - Springer

ER -