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Continuous Positioning with Recurrent Auto-Regressive Neural Network for Unmanned Surface Vehicles in GPS Outages

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Abstract

As the vital operation information of unmanned surface vehicles, the positioning data are usually measured with GPS/INS (Global Position System/Inertial Navigation System) which faces measurement loss and calculation failure during GPS outages in a complex environment. A continuous positioning method is proposed based on an improved neural network with the available sensor data. Firstly, the continuous positioning framework is built to synthesize the traditional GPS/INS coupling mode with the novel estimation method of the improved neural network. Secondly, a reconstructed model of the recurrent auto-regressive neural network is proposed with dual-loop structures, which can excavate the time-series features and the nonlinear relation in multiple sensor measurements. Thirdly, the continuous inertial positioning algorithm is designed based on the novel network, in which the alignment of measurement data is studied to form the augmented inputs. Finally, different experiments are designed and conducted to verify the method, including the outage performance, estimation duration, and model comparison. The results show that positioning estimation precision is relatively high, and the estimation duration reaches an acceptable degree. The proposed method is feasible and effective for positioning in GPS outages.

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References

  1. Youn W, Rhudy MB, Cho A et al (2020) Fuzzy adaptive attitude estimation for a fixed-wing UAV with a virtual SSA sensor during a GPS outage. IEEE Sens J 20(3):1456–1472

    Article  Google Scholar 

  2. Bai Y, Wang X, Jin X et al (2020) Adaptive filtering for MEMS gyroscope with dynamic noise model. ISA Trans 101:430–441. https://doi.org/10.1016/j.isatra.2020.01.030

    Article  Google Scholar 

  3. Grewal MS, Andrews AP, Bartone CG et al (2013) Global navigation satellite systems, inertial navigation, and integration, 3rd edn. Wiley, Hoboken

    Google Scholar 

  4. Kaygisiz BH, Erkmen I, Erkmen AM (2004) GPS/INS enhancement for land navigation using neural network. J Navig 57(2):297–310

    Article  Google Scholar 

  5. Kaygisiz BH, Erkmen AM, Erkmen I (2007) Enhancing positioning accuracy of GPS/INS system during GPS outages utilizing artificial neural network. Neural Process Lett 25(3):171–186

    Article  Google Scholar 

  6. Zhang X, Zhang W (2017) Algorithm research of GPS/SINS integrated system based on neural network during GPS outage. J Project Rockets Missiles Guidance 37(3):13–16

    Google Scholar 

  7. Tan X (2014) Research on improved model of inertial navigation aided seamless positioning. Doctor Thesis, China University of Mining and Technology, China

  8. Chen L, Fang J (2014) A hybrid prediction method for bridging GPS outages in high-precision POS application. IEEE Trans Instrum Meas 63(6):1656–1665

    Article  Google Scholar 

  9. Zhu N, Zhao H, Sun C et al (2015) A novel integrated method using EKF and ANN in the vehicle navigation. Appl Electr Techn 41(8):94–96

    Google Scholar 

  10. Xu X, Zhou F, Zhang T et al (2015) Application of neural network by genetic algorithm optimization in SINS/GPS. J Chin Inertial Technol 23(3):322–327

    Google Scholar 

  11. Yao Y, Xu X (2017) A RLS-SVM aided fusion methodology for INS during GPS outages. Sensors 17(3):432

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Sun J, Zhang X, Hou B (2016) Application of ABC-based BP neural network in integrated navigation system. J Telem Track Command 37(5):40–48

    Google Scholar 

  14. Yang Y (2016) Research on the optimization of neural networks method and its application for integrated navigation. Doctor Thesis, Northwestern Polytechnical University, China,

  15. Zhao X (2017) Research and implementation of integrated navigation technology for satellite signal transient outage. Master Thesis, University of Electronic Science and Technology of China, China

  16. Bhatt D, Aggarwal P, Devabhaktuni V et al (2014) A novel hybrid fusion algorithm to bridge the period of GPS outages using low-cost INS. Expert Syst Appl 41(5):2166–2173

    Article  Google Scholar 

  17. Georges HM, Wang D, Xiao Z (2015) GNSS/low-cost MEMS-INS Integration using variational Bayesian adaptive cubature Kalman smoother and ensemble regularized ELM. Math Probl Eng 2015:682907

    Article  MathSciNet  Google Scholar 

  18. Yang Y. Zhong Y. Gao Y (2017) Model predictive filter based neural networks for INS/GPS integrated navigation during GPS outages. In: IEEE international conference on electronics information and emergency communication

  19. Li J, Song N, Yang G et al (2017) Improving positioning accuracy of vehicular navigation system during GPS outages utilizing ensemble learning algorithm. Information Fusion 35(C):1–10

    Article  Google Scholar 

  20. Yan J, Guo C (2018) Convolution neural network aided adaptive filtering for GPS/INS integrated navigation. China Satellite Navigation Conference

  21. Ma Y (2015) Design of vehicle INS/GPS Integrated Navigation System Based on Forest Regression. Master Thesis, Beijing Jiaotong University, China

  22. Noureldin A, El-Shafie A, Bayoumi M (2011) GPS/INS integration utilizing dynamic neural networks for vehicular navigation. Information Fusion 12(1):48–57

    Article  Google Scholar 

  23. Sharaf R, Noureldin A, Osman A et al (2005) Online INS/GPS integration with a radial basis function neural network. IEEE Aerosp Electron Syst Mag 20(3):8–14

    Article  Google Scholar 

  24. Adusumilli S, Bhatt D, Wang H et al (2015) A novel hybrid approach utilizing principal component regression and random forest regression to bridge the period of GPS outages. Neurocomputing 166(C):185–192

    Article  Google Scholar 

  25. Malleswaran M, Vaidehi V, Sivasankari N (2014) A novel approach to the integration of GPS and INS using recurrent neural networks with evolutionary optimization techniques. Aerosp Sci Technol 32(1):169–179

    Article  Google Scholar 

  26. Qin H, Cong L, Sun X (2012) Accuracy improvement of GPS/MEMS-INS integrated navigation system during GPS signal outage for land vehicle navigation. J Syst Eng Electron 23(2):256–264

    Article  Google Scholar 

  27. Buscema M (1998) Back propagation neural networks. Int J Adhes 33(2):233–270

    Google Scholar 

  28. Primasiwi C, Sarno R, Sungkono KR et al (2019) Stock composite prediction using nonlinear autoregression with exogenous input (NARX)[C]. In: 2019 12th international conference on information & communication technology and system (ICTS)

  29. Bai Y, Jin X, Wang X et al (2019) Compound autoregressive network for prediction of multivariate time series. Complexity 2019:9107167. https://doi.org/10.1155/2019/9107167

    Article  Google Scholar 

  30. Shi Z, Bai Y, Jin X et al (2021) Parallel deep prediction with covariance intersection fusion on non-stationary time series. Knowl-Based Syst 211:106523. https://doi.org/10.1016/j.knosys.2020.106523

    Article  Google Scholar 

  31. Jin X, Yang N, Wang X et al (2020) Deep hybrid model based on EMD with classification by frequency characteristics for long-term air quality prediction. Mathematics 8:214

    Article  Google Scholar 

  32. Manickam I, Ramachandran R, Rajchakit G et al (2020) Novel Lagrange sense exponential stability criteria for time-delayed stochastic Cohen-Grossberg neural networks with Markovian jump parameters: A graph-theoretic approach. Nonlinear Anal: Model Control 25(5):726–744

    MathSciNet  MATH  Google Scholar 

  33. Zhang J, Huang C (2020) Dynamics analysis on a class of delayed neural networks involving inertial terms. Adv Differ Equ 2020:120

    Article  MathSciNet  Google Scholar 

  34. Cao Q, Long X (2020) New convergence on inertial neural networks with time-varying delays and continuously distributed delays. AIMS Math 5(6):5955–5968

    Article  MathSciNet  Google Scholar 

  35. Zhang H, Qian C (2020) Convergence analysis on inertial proportional delayed neural networks. Adv Differ Equ 2020:277

    Article  MathSciNet  Google Scholar 

  36. Zhang X, Hu H (2020) Convergence in a system of critical neutral functional differential equations. Appl Math Lett 107:106385

    Article  MathSciNet  Google Scholar 

  37. Huang C, Yang L, Cao J (2020) Asymptotic behavior for a class of population dynamics. AIMS Math 5(4):3378–3390

    Article  MathSciNet  Google Scholar 

  38. Yan L, Wen Y, Teo KL et al (2020) Construction of regional logistics weighted network model and its robust optimization: evidence from China. Complexity 2020:2109423

    Google Scholar 

Download references

Funding

This research was funded in part by the National Natural Science Foundation of China No. 61903008, Beijing Excellent Talent Training Support Project for Young Top-Notch Team No. 2018000026833TD01, and Beijing Talents Project No. 2020A28.

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Authors and Affiliations

  1. School of Artificial Intelligence, Beijing Technology and Business University, Beijing, China

    Yu-ting Bai, Zhi-yao Zhao, Xiao-yi Wang & Xue-bo Jin

  2. State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing, China

    Yu-ting Bai, Zhi-yao Zhao, Xiao-yi Wang & Xue-bo Jin

  3. Beijing Laboratory for Intelligent Environmental Protection, Beijing Technology and Business University, Beijing, China

    Yu-ting Bai, Zhi-yao Zhao, Xiao-yi Wang & Xue-bo Jin

  4. School of Automation, Beijing Institute of Technology, Beijing, China

    Bai-hai Zhang

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  1. Yu-ting Bai
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  2. Zhi-yao Zhao
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  3. Xiao-yi Wang
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  4. Xue-bo Jin
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Correspondence to Zhi-yao Zhao or Xiao-yi Wang.

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Bai, Yt., Zhao, Zy., Wang, Xy. et al. Continuous Positioning with Recurrent Auto-Regressive Neural Network for Unmanned Surface Vehicles in GPS Outages. Neural Process Lett 54, 1413–1434 (2022). https://doi.org/10.1007/s11063-021-10688-3

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  • DOI: https://doi.org/10.1007/s11063-021-10688-3

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