Abstract
Integration of both global positioning system (GPS) and inertial navigation system (INS) assures a continuous and accurate navigation system. In low-cost low-precision micro-electromechanical system (MEMS)-based INS/GPS integration navigation systems, one of the major concerns is high-level stochastic noise and uncertainties existing in INS sensors and complex model of real noisy data. In such uncertainty-oriented environments, an intelligence structure with extra degrees of freedom which can handle and model a high-level of uncertainties in INS sensors, and an efficient denoising technique as a precursor to the intelligence structure can be efficient solutions. Our approach to these problems is taken in different steps. First, a denoising technique based on empirical mode decomposition (EMD) is used to provide more accurate INS sensor outputs and better generalization ability. Second, an optimized interval type-2 fuzzy neural network is used to model and handle a high-level of uncertainties efficiently and estimate the positioning error of INS sensors when GPS signals are blocked, and still meet both accuracy maximization and complexity minimization. Fast learning and convergence of the algorithm and less computational complexity can be achieved by using an extended Kalman filter in the learning of algorithm and an accurate and simple type-reduction, respectively, which can be utilized in real-time applications with significant performance. The results of EMD-based denoising technique, as a preprocessing phase, verify superior performance in comparison with the discrete wavelet transform denoising method in the signal-to-noise ratio improvement for raw and noisy signals of INS sensors. To verify the effectiveness of our proposed model, we applied challenging conditions consisting of low-cost low-precision inertial sensors based on MEMS technology, long-term outages of GPS satellites, a high-speed experimental test vehicle and noisy real-world data in the real-time flight experiments. The achieved experimental accuracies are compared with the results that we have achieved in other methods, and our proposed method verifies significant improvements.
Similar content being viewed by others
References
Abdolkarimi ES, Abaei G, Mosavi MR (2018) A wavelet-extreme learning machine for low-cost INS/GPS navigation system in high-speed applications. GPS Solut 22(1):1–13
Aggarwal P (2010) MEMS-based integrated navigation. Artech House, Norwood
Chen C, Wu D, Garibaldi JM, John RI, Twycross J, Mendel JM (2020) A comprehensive study of the efficiency of type-reduction algorithms. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2020.2981002
El Shafie A, Hussain A, Eldin AEN (2009) ANFIS-based model for real-time INS/GPS data fusion for vehicular navigation system. In: 2009 international conference on computer technology and development. IEEE, pp 278–282
Eyoh I, John R, De Maere G (2017) Extended Kalman filter-based learning of interval type-2 intuitionistic fuzzy logic system. In: 2017 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 728–733
Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time series analysis. Proc R Soc Lond Ser A Math Phys Eng Sci 454(1971):903–995
Jekeli C (2012) Inertial navigation systems with geodetic applications. Walter de Gruyter, Berlin
Juang CF, Huang RB, Cheng WY (2010) An interval type-2 fuzzy-neural network with support-vector regression for noisy regression problems. IEEE Trans Fuzzy Syst 18(4):686–699
Karnik NN, Mendel JM (1999) Applications of type-2 fuzzy logic systems to forecasting of time-series. Inf Sci 120(1–4):89–111
Karnik NN, Mendel JM (2001) Centroid of a type-2 fuzzy set. Inf Sci 132(1–4):195–220
Karnik NN, Mendel JM, Liang Q (1999) Type-2 fuzzy logic systems. IEEE Trans Fuzzy Syst 7(6):643–658
Kopsinis Y, McLaughlin S (2009) Development of EMD-based denoising methods inspired by wavelet thresholding. IEEE Trans Signal Process 57(4):1351–1362
Li J, Song N, Yang G, Li M, Cai Q (2017a) Improving positioning accuracy of vehicular navigation system during GPS outages utilizing ensemble learning algorithm. Inf Fus 35:1–10
Li J, John R, Coupland S, Kendall G (2017b) On Nie–Tan operator and type-reduction of interval type-2 fuzzy sets. IEEE Trans Fuzzy Syst 26(2):1036–1039
Lin CT, Pal NR, Wu SL, Liu YT, Lin YY (2014) An interval type-2 neural fuzzy system for online system identification and feature elimination. IEEE Trans Neural Netw Learn Syst 26(7):1442–1455
Malleswaran M, Vaidehi V, Saravanaselvan A, Mohankumar M (2013) Performance analysis of various artificial intelligent neural networks for GPS/INS integration. Appl Artif Intell 27(5):367–407
Mendel JM (2017) Uncertain rule-based fuzzy systems. In: Introduction and new directions. Springer, Berlin, p 684
Mendel JM, Liu X (2013) Simplified interval type-2 fuzzy logic systems. IEEE Trans Fuzzy Syst 21(6):1056–1069
Noureldin A, El-Shafie A, El-Sheimy N (2007) Adaptive neuro-fuzzy module for inertial navigation system/global positioning system integration utilizing position and velocity updates with real-time cross-validation. IET Radar Sonar Navig 1(5):388–396
Noureldin A, Karamat TB, Eberts MD, El-Shafie A (2008) Performance enhancement of MEMS-based INS/GPS integration for low-cost navigation applications. IEEE Trans Veh Technol 58(3):1077–1096
Salazar O, Rojas JD, Serrano H (2016) Nie–Tan method and its improved version: a counterexample. Ingeniería 21(2):138–153
Tan WW, Chua TW (2007) Uncertain rule-based fuzzy logic systems: introduction and new directions (Mendel, JM; 2001) [book review]. IEEE Comput Intell Mag 2(1):72–73
Tao Z, Gao S, Huang Y (2019) An integrated Navigation filtering method based on wavelet neural network. J Phys Conf Ser 1302(4):042014
Titterton D, Weston JL, Weston J (2004) Strapdown inertial navigation technology, vol 17. IET, London
Tung SW, Quek C, Guan C (2013) eT2FIS: an evolving type-2 neural fuzzy inference system. Inf Sci 220:124–148
Wang CH, Cheng CS, Lee TT (2004) Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN). IEEE Trans Syst Man Cybernet Part B (Cybernet) 34(3):1462–1477
Xu TL (2013) Seamless INS/GPS integration based on support vector machines. Appl Mech Mater 336:277–280
Zhang Y, Wang L (2019) A hybrid intelligent algorithm DGP-MLP for GNSS/INS integration during GNSS outages. J Navig 72(2):375–388
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is published as part of the special collection “Artificial Intelligence Applications in GNSS”.
Rights and permissions
About this article
Cite this article
Abdolkarimi, E.S., Mosavi, MR. A low-cost integrated MEMS-based INS/GPS vehicle navigation system with challenging conditions based on an optimized IT2FNN in occluded environments. GPS Solut 24, 108 (2020). https://doi.org/10.1007/s10291-020-01023-9
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10291-020-01023-9