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Information Fusion Based on Artificial Intelligence Method for SINS/GPS Integrated Navigation of Marine Vessel

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Abstract

The approaches of artificial intelligence (AI)-based has widely been utilized to provide higher positioning precision for ship navigation by integrating the GPS with the SINS. To address the restrictions of complicated and dynamic information resulted from ship irregular motion, this paper proposes a novel ensemble learning technique to replace traditional single neural network. The ensemble learning approach is able to construct the SINS/GPS integrated navigation system model according to the SINS attitude, speed information and accelerometer, gyroscope output data. The proposed method is verified using ship-mounted experimental data of various trajectories. Experimental results indicate that the proposed algorithm can significant improvement in positioning precision under the conditions of SINS and specific GPS unavailability.

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References

  1. Zhang C, Guo C, Zhang DH (2018) Ship navigation via GPS/IMU/LOG integration using adaptive fission particle filter. Ocean Eng 156:435–445

    Article  Google Scholar 

  2. Liu Y, Fan X, Lv C, Wu J, Li L (2017) An innovative information fusion method with adaptive Kalman filter for integrated INS/GPS navigation of autonomous vehicles. Mech Syst Signal Process 100:605–616

    Article  Google Scholar 

  3. Chen SY (2012) Kalman filter for robot vision: a survey. IEEE Trans Ind Electron 11(59):4409–4420

    Article  Google Scholar 

  4. Loiola MB, Lopes RR, Romano JMT (2011) Modified Kalman filters for channel estimation in orthogonal space–time coded systems. IEEE Trans Signal Process 60(1):533–538

    Article  MathSciNet  Google Scholar 

  5. Kalivas J (1999) Interrelationships of multivariate regression methods using eigenvector basis sets. J Chemom 13:111–132

    Article  Google Scholar 

  6. Wang N, Er MJ, Han M (2014) Parsimonious extreme learning machine using recursive orthogonal least squares. IEEE Trans Neural Netw Learn Syst 25(10):1828–1841

    Article  Google Scholar 

  7. Wang N, Joo Er M (2015) Self-constructing adaptive robust fuzzy neural tracking control of surface vehicles with uncertainties and unknown disturbances. IEEE Trans Control Syst Technol 23(3):991–1002

    Article  Google Scholar 

  8. Wang N, Er MJ, Han M (2015) Generalized single-hidden layer feedforward networks for regression problems. IEEE Trans Neural Netw Learn Syst 26(6):1161–1176

    Article  MathSciNet  Google Scholar 

  9. Wang N, Joo Er M (2016) Direct adaptive fuzzy tracking control of marine vehicles with fully unknown parametric dynamics and uncertainties. IEEE Trans Control Syst Technol 24(5):1845–1852

    Article  Google Scholar 

  10. Liu H, Wu Y, Sun F, Fang Bin, Guo D (2018) Weakly-paired multi-modal fusion for object recognition. IEEE Trans Autom Sci Eng 15(2):784–795

    Article  Google Scholar 

  11. Bhatt D, Aggarwal P, Devabhaktuni V, Bhattacharya P (2012) A new source difference artificial neural network for enhanced positioning accuracy. Meas Sci Technol 23(10):105101

    Article  Google Scholar 

  12. Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42(1):21–57

    Article  Google Scholar 

  13. El-Shafie A, Najah A, Karim OA (2014) Amplified wavelet-ANFIS-based model for GPS/INS integration to enhance vehicular navigation system. Neural Comput Appl 24(7–8):1905–1916

    Article  Google Scholar 

  14. Zhang T, Xu X (2012) A new method of seamless land navigation for GPS/INS integrated system. Measurement 45(4):691–701

    Article  Google Scholar 

  15. Tan X, Wang J, Jin S, Meng X (2015) GA-SVR and pseudo-position-aided GPS/INS integration during GPS outage. J Navig 68(04):678–696

    Article  Google Scholar 

  16. Yao Y, Xu X, Zhu C, Chan CY (2017) A hybrid fusion algorithm for GPS/INS integration during GPS outages. Measurement 103:42–51

    Article  Google Scholar 

  17. Li J, Song N, Yang G, Li M, Cai Q (2017) Improving positioning accuracy of vehicular navigation system during GPS outages utilizing ensemble learning algorithm. Inf Fusion 35(C):1–10

    Article  Google Scholar 

  18. Yu Z, Zhang Y, Chen C, You J, Wong HS, Dai D et al (2018) Multiobjective semisupervised classifier ensemble. IEEE Trans Cybern 99:1–14

    Google Scholar 

  19. Riccardi A, FernándezNavarro Francisco, Carloni S (2014) Cost-sensitive adaboost algorithm for ordinal regression based on extreme learning machine. IEEE Trans Cybern 44(10):1898–1909

    Article  Google Scholar 

  20. Kantardzic M (2011) Data mining: concepts, models, methods, and algorithms, 2nd edn. Wiley, IEEE Press, Hoboken

    Book  Google Scholar 

  21. Roshany-Yamchi S, Cychowski M, Negenborn RR, Schutter BD, Delaney K, Connell J (2012) Kalman filter-based distributed predictive control of large-scale multi-rate systems: application to power networks. IEEE Trans Control Syst Technol 21(1):27–39

    Article  Google Scholar 

  22. Lisboa PJG (2002) A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Netw 15(1):11–39

    Article  Google Scholar 

  23. Sesmero MP, Alonso-Weber JM, Gutierrez G, Ledezma A, Sanchis A (2015) An ensemble approach of dual base learners for multi-class classification problems. Inf Fusion 24:122–136

    Article  Google Scholar 

  24. Mayr A, Binder H, Gefeller O, Schmid M (2014) The evolution of boosting algorithms-from machine learning to statistical modelling. Methods Inf Med 53(6):419–427

    Article  Google Scholar 

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Funding

This study is supported by Fundamental Research Funds for the Central Universities (Grant Nos. 3132019139, 3132019318) and National Natural Science Foundation of China (Grant Nos. 51579024, 51879027).

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Authors and Affiliations

  1. Marine Electrical and Engineering College, Dalian Maritime University, Dalian, 116026, China

    Chuang Zhang, Chen Guo & Mu-Zhuang Guo

  2. Navigation College, Dalian Maritime University, Dalian, 116026, China

    Chen Guo

Authors
  1. Chuang Zhang
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  2. Chen Guo
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  3. Mu-Zhuang Guo
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Correspondence to Chen Guo.

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Zhang, C., Guo, C. & Guo, MZ. Information Fusion Based on Artificial Intelligence Method for SINS/GPS Integrated Navigation of Marine Vessel. J. Electr. Eng. Technol. 15, 1345–1356 (2020). https://doi.org/10.1007/s42835-020-00378-w

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  • DOI: https://doi.org/10.1007/s42835-020-00378-w

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