Improved Neural Network Enhanced Navigation System of Adaptive Unsented Kalman Filter

Guangwu CHEN; Jianhao CHENG; Juhua YANG; Hao LIU; Linjing ZHANG

doi:10.11999/JEIT181171

Volume 41 Issue 7

Jul. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(7): 1766-1773

Guangwu CHEN, Jianhao CHENG, Juhua YANG, Hao LIU, Linjing ZHANG. Improved Neural Network Enhanced Navigation System of Adaptive Unsented Kalman Filter[J]. Journal of Electronics & Information Technology, 2019, 41(7): 1766-1773. doi: 10.11999/JEIT181171

Citation:

Guangwu CHEN, Jianhao CHENG, Juhua YANG, Hao LIU, Linjing ZHANG. Improved Neural Network Enhanced Navigation System of Adaptive Unsented Kalman Filter[J]. Journal of Electronics & Information Technology, 2019, 41(7): 1766-1773. doi: 10.11999/JEIT181171

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Improved Neural Network Enhanced Navigation System of Adaptive Unsented Kalman Filter

doi: 10.11999/JEIT181171

Guangwu CHEN^{1, 2},
Jianhao CHENG^{1, 2
,
,},
Juhua YANG³,
Hao LIU^{1, 2},
Linjing ZHANG^{1, 2}

1.
Automatic Control Institute, Lanzhou Jiaotong University, Lanzhou 730070, China
2.
Gansu Provincial Key Laboratory of Traffic Information Engineering and Control, Lanzhou 730070, China
3.
School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China

Funds: The National Natural Science Foundation of China (61863024), The Gansu Province Basic Research Innovation Group Program (1606RJIA327), The Gansu Province Higher Education Research Project (2018C-11), The Gansu Province Natural Science Foundation (18JR3RA107), The Gansu Province Science and Technology Plan Funding (18CX3ZA004)

Received Date: 2018-12-19
Rev Recd Date: 2019-04-22

Available Online: 2019-05-22

Publish Date: 2019-07-01

Abstract

Abstract

In order to solve the problem of speed and position error divergence in the integrated navigation system based on MicroElectro Mechanical Systems (MEMS) inertial device and GPS system combined positioning, an improved Adaptive Unsecnted Kalman Filter (AUKF) enhanced by the Radial Basis Function(RBF) neural network based on Artificial Bee Colony(ABC) algorithm is proposed. When the GPS signal is out of lock, the trained network outputs predictied information to perform error correction on the Strapdown Inertial Navigation System(SINS). Finally, the performance of the method is verified by vehicle-mounted semi-physical simulation experiments. The experimental results show that the proposed method has a significant inhibitory effect on the error divergence of the strapdown inertial navigation system in the case of loss of lock.
- Intergrated navigation,
- Radial basis neural network,
- Unscented Kalman Filter(UKF),
- GPS break down

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References(20)

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