Inertial Sensing Meets Artificial Intelligence: Opportunity or Challenge?

作者: Zhouzheng Gao , Xiaoji Niu , Yuan Zhuang , You Li , Ruizhi Chen

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摘要: The inertial navigation system (INS) has been widely used to provide self-contained and continuous motion estimation in intelligent transportation systems. Recently, the emergence …

参考文章(56)
Carl Edward Rasmussen, Gaussian processes in machine learning Lecture Notes in Computer Science. pp. 63- 71 ,(2003) , 10.1007/978-3-540-28650-9_4
Kai-Wei Chiang, Yun-Wen Huang, Xiaoji Niu, Rapid and accurate INS alignment for land applications Survey Review. ,vol. 42, pp. 279- 291 ,(2010) , 10.1179/003962610X12747001420348
Qilong Han, Shuang Liang, Hongli Zhang, Mobile cloud sensing, big data, and 5G networks make an intelligent and smart world IEEE Network. ,vol. 29, pp. 40- 45 ,(2015) , 10.1109/MNET.2015.7064901
Marta C. González, César A. Hidalgo, Albert-László Barabási, Understanding individual human mobility patterns Nature. ,vol. 453, pp. 779- 782 ,(2008) , 10.1038/NATURE06958
Dah-Jing Jwo, Chih-Hsun Chuang, Jing-Yu Yang, Yu-He Lu, Neural network assisted ultra-tightly coupled GPS/INS integration for seamless navigation international conference on its telecommunications. pp. 385- 390 ,(2012) , 10.1109/ITST.2012.6425204
Mohammad Abdel Kareem Jaradat, Mamoun F. Abdel-Hafez, Enhanced, Delay Dependent, Intelligent Fusion for INS/GPS Navigation System IEEE Sensors Journal. ,vol. 14, pp. 1545- 1554 ,(2014) , 10.1109/JSEN.2014.2298896
Chun-Fei Hsu, Adaptive fuzzy wavelet neural controller design for chaos synchronization Expert Systems With Applications. ,vol. 38, pp. 10475- 10483 ,(2011) , 10.1016/J.ESWA.2011.02.092
Zha Feng, Hu Bai-Qing, Prediction of Gyro Motor's State Based on Grey Model and BP Neural Network international conference on intelligent computation technology and automation. ,vol. 3, pp. 87- 90 ,(2009) , 10.1109/ICICTA.2009.489
Srujana Adusumilli, Deepak Bhatt, Hong Wang, Prabir Bhattacharya, Vijay Devabhaktuni, A low-cost INS/GPS integration methodology based on random forest regression Expert Systems With Applications. ,vol. 40, pp. 4653- 4659 ,(2013) , 10.1016/J.ESWA.2013.02.002