Abstract
The main challenge of low-cost strap-down inertial navigation systems (SINSs) is time-growing positioning error due to erroneous measurements of micro-electro mechanical system (MEMS)-based inertial sensors. The global navigation satellite system (GNSS) provides drift-free positioning data that can be appropriately utilized to prevent the cumulative error of stand-alone SINS. The primary aim of this research is to enhance the positioning accuracy, performance, and reliability of low-cost SINS/GNSS-integrated navigation system. To attain this, we propose an applied data fusion algorithm for indirect centralized (IC) integrated SINS/GNSS. The proposed data fusion algorithm is based on fuzzy-adaptive constrained estimation filter. Velocity and altitude constraints are embedded in the integration scheme of the proposed SINS/GNSS system to preserve system reliability during abrupt GNSS outage. In an innovative way, the state constraints of altitude are defined based on the measurements of air-data sensors. The state estimation is effectively optimized since the respective states are projected on a constraint surface. Furthermore, a fuzzy type-2 inference system is developed for adaptively changing the covariance matrix of the estimation algorithm. Inertial measurements are used as the input of the fuzzy inference system. Accordingly, the state estimation algorithm is adaptively modified based on the vehicle’s maneuvering during the navigation trajectory. The proposed SINS/GNSS system is experimentally assessed through several vehicular tests. The results indicate that not only does the proposed algorithm improve the navigation accuracy, it will also enhance the reliability of the integrated navigation system during GNSS outage.
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References
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(8):1905–1916
Faruqi FA, Turner KJ (2000) Extended Kalman filter synthesis for integrated global positioning/inertial navigation systems. Appl Math Comput 115(2):213–227
Fletcher R (2013) Practical methods of optimization. Wiley, Hoboken
Hu G, Gao S, Zhong Y (2015) A derivative UKF for tightly coupled INS/GPS integrated navigation. ISA transactions 56:135–144
Lemoine FG et al (1997) The development of the NASA GSFC and NIMA joint geopotential model. The International Association of Geodesy Symposia, IAG Symposia, Berlin
Milanchian H, Keighobadi J, Nourmohammadi H (2015) Magnetic calibration of three-axis strapdown magnetometers for applications in MEMS attitude-heading reference systems. AUT J Model Simul 47(1):55–65
Musavi N, Keighobadi J (2015) Adaptive fuzzy neuro-observer applied to low-cost INS/GPS. Appl Soft Comput 29:82–94
Nourmohammadi H, Keighobadi J (2017) Decentralized INS/GNSS system with MEMS-grade inertial sensors using QR-factorized CKF. IEEE Sens J 17(11):3278–3287
Nourmohammadi H, Keighobadi J (2018a) Integration scheme for SINS/GPS system based on vertical channel decomposition and in-motion alignment. AUT J Model Simul 50(1):13–22
Nourmohammadi H, Keighobadi J (2018b) Design and experimental evaluation of indirect centralized and direct decentralized integration scheme for low-cost INS/GNSS system. GPS Solut 22(3):1–18
Nourmohammadi H, Keighobadi J (2018c) Fuzzy adaptive integration scheme for low-cost SINS/GPS navigation system. Mech Syst Signal Process 99:434–449
Rafatnia S, Nourmohammadi H, Keighobadi J, Badamchizadeh MA (2018) In-move aligned SINS/GNSS system using recurrent wavelet neural network (RWNN)-based integration scheme. Mechatronics 54:155–165
Simon D (2006) Optimal state estimation: Kalman, H infinity, and nonlinear approaches. Wiley, Hoboken
Simon D (2010) Kalman filtering with state constraints: a survey of linear and nonlinear algorithms. IET Control Theory Appl 4(8):1303–1318
Titterton S, Weston JL (2018) Strapdown inertial navigation technology. The Institution of Engineering and technology, IET, London
van der Merwe R, Wan E, Julier S (2004) Sigma-point Kalman filters for nonlinear estimation and sensor-fusion: applications to integrated navigation. In: AIAA guidance navigation, and control conference and exhibit, Providence, Rhode Island, 16–19 August 2004. https://doi.org/10.2514/6.2004-5120
Wang W, Liu ZY, Wie RR (2006) Quadratic extended Kalman filter approach for GPS/INS integration. Aerosp Sci Technol 10(8):709–713
Wang D, Lv H, Wu J (2017) Augmented cubature Kalman filter for nonlinear RTK/MIMU integrated navigation with non-adaptive noise. Measurement 97:111–125
Yao Y, Xu X, Zhu C, Chan CY (2017) A hybrid fusion algorithm for GPS/INS integration during GPS outages. Measurement 103:42–51
Zhao Y (2016) Performance evaluation of cubature Kalman filter in a GPS/IMU tightly coupled navigation system. Signal Process 119:67–79
Zhao L, Qiu H, Feng Y (2016) Analysis of a robust Kalman filter in loosely coupled GPS/INS navigation system. Measurement 80:138–147
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Rafatnia, S., Nourmohammadi, H. & Keighobadi, J. Fuzzy-adaptive constrained data fusion algorithm for indirect centralized integrated SINS/GNSS navigation system. GPS Solut 23, 62 (2019). https://doi.org/10.1007/s10291-019-0845-z
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DOI: https://doi.org/10.1007/s10291-019-0845-z