Abstract
The combined navigation system consisting of Global Positioning System (GPS) and Inertial Navigation System in a complementary mode assures an accurate, reliable, and continuous positioning capability in the navigation system. Because of problems such as dealing with a low-cost MEMS-based inertial sensors having a high level of uncertainty and imprecision, stochastic noise, a high-speed vehicle, high noisy real data, and long-term GPS signal outage during the real-time flight test, the advantage is taken for some approaches in different steps: (1) utilizing discrete wavelet transform technique to enhance the signal-to-noise ratio in raw and noisy inertial sensor signals and attenuate high-frequency noise as a preprocessing phase to prepare more accurate data for the proposed model and (2) employing adaptive neural subtractive clustering fuzzy inference system (ANSCFIS) which combines and extracts the best feature of adaptive neuro-fuzzy inference system (ANFIS), and the subtractive clustering algorithm with fewer rules than the ANFIS method, aiming to improve a more efficient, accurate, and especially a faster method which enhances the prediction accuracy and speeds up the positioning system. The achieved accuracies for the proposed model are discussed and compared with the extended Kalman filter (EKF), ANFIS, and ANSCFIS which are implemented and tested experimentally using a high-speed vehicle in three GPS blockages. The proposed model shows considerable improvements in high-speed navigation using low-cost MEMS-based inertial sensors in case of long-term GPS blockage.
Similar content being viewed by others
References
Abdel-Hamid W, Noureldin A, El-Sheimy N (2007) Adaptive fuzzy prediction of low-cost inertial-based positioning errors. IEEE Trans Fuzzy Syst 15(3):519–529
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
Buragohain M, Mahanta C (2006) ANFIS modeling of a nonlinear system based on subtractive clustering and V-fold technique. In: 2006 Annual IEEE India conference, pp 1–6
Cadena C, Neira J (2010) SLAM in O (logn) with the combined Kalman-information filter. Robot Auton Syst 58(11):1207–1219
Chiang KW, Chang HW (2010) Intelligent sensor positioning and orientation through constructive neural network-embedded INS/GPS integration algorithms. Sensors 10(10):9252–9285
El Shafie A, Hussain A, Eldin AEN (2009) ANFIS-based model for real-time INS/GPS data fusion for vehicular navigation system. In: IEEE conference on computer technology and development, vol 2, pp 278–282
El-Sheimy N, Nassar S, Noureldin A (2004) Wavelet de-noising for IMU alignment. IEEE Aerosp Electron Syst Mag 19(10):32–39
Erenturk K (2009) ANFIS-based compensation algorithm for current-transformer saturation effects. IEEE Trans Power Deliv 24(1):195–201
Godha S (2006) Performance evaluation of low cost MEMS-based IMU integrated with GPS for land vehicle navigation application. Master of Science thesis, Department of Geomatics Engineering, University of Calgary, UCGE report, 20239
Gorzalczany MB (2012) Computational intelligence systems and applications: neuro-fuzzy and fuzzy neural synergisms, vol 86. Physica, Heidelberg
Hasan AM, Samsudin K, Ramli AR (2011) Intelligently tuned wavelet parameters for GPS/INS error estimation. Int J Autom Comput 8(4):411–420
Hieu LN, Nguyen VH (2012) Loosely coupled GPS/INS integration with Kalman filtering for land vehicle applications. In IEEE conference on control, automation and information sciences (ICCAIS), pp 90–95
Hussain K, Salleh M, Najib M (2015) Analysis of techniques for ANFIS rule-based minimization and accuracy maximization. ARPN J Eng Appl Sci 10(20):9739–9746
Jekeli C (2012) Inertial navigation systems with geodetic applications. Walter de Gruyter, Berlin
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
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
Navidi N, Landry R Jr, Cheng J, Gingras D (2016) A new technique for integrating MEMS-based low-cost IMU and GPS in vehicular navigation. J Sens 2016:1–16
Noureldin A, Karamat TB, Eberts MD, El-Shafie A (2009) Performance enhancement of MEMS-based INS/GPS integration for low-cost navigation applications. IEEE Trans Veh Technol 58(3):1077–1096
Nourmohammadi H, Keighobadi J (2018) Fuzzy adaptive integration scheme for low-cost SINS/GPS navigation system. Mech Syst Signal Process 99:434–449
Quinchia AG, Falco G, Falletti E, Dovis F, Ferrer C (2013) A comparison between different error modeling of MEMS applied to GPS/INS integrated systems. Sensors 13(8):9549–9588
Rantala J, Koivisto HA (2002) Optimized subtractive clustering for neuro-fuzzy models. In: 3rd WSEAS international conference on fuzzy sets and fuzzy systems, pp 1–6
Saadeddin K, Abdel-Hafez MF, Jaradat MA, Jarrah MA (2013) Optimization of intelligent-based approach for low-cost INS/GPS navigation system. In: International conference on unmanned aircraft systems (ICUAS), Atlanta, GA, USA, pp 668–677
Semeniuk L, Noureldin A (2006) Bridging GPS outages using neural network estimates of INS position and velocity errors. Meas Sci Technol 17(10):2783
Tawafan A, Sulaiman MB, Ibrahim ZB (2012) Adaptive neural subtractive clustering fuzzy inference system for the detection of high impedance fault on distribution power system. IAES Int J Artif Intell 1(2):63–72
Titterton D, Weston JL (2004) Strapdown inertial navigation technology, 2nd edn. Institution of Engineering Technology, Stevenage
Xu Q, Li X, Chan CY (2018) Enhancing localization accuracy of MEMS-INS/GPS/in-vehicle sensors integration during GPS outages. IEEE Trans Instrum Meas 67(8):1966–1978
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.
Rights and permissions
About this article
Cite this article
Abdolkarimi, E.S., Mosavi, M.R. Wavelet-adaptive neural subtractive clustering fuzzy inference system to enhance low-cost and high-speed INS/GPS navigation system. GPS Solut 24, 36 (2020). https://doi.org/10.1007/s10291-020-0951-y
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10291-020-0951-y