A Hybrid Intelligent Algorithm DGP-MLP for GNSS/INS Integration during GNSS Outages

The performance of Global Navigation Satellite System (GNSS) and Micro-Electro-Mechanical System (MEMS)-based Inertial Navigation System (INS) integrated navigation is reduced during GNSS outages. To bridge the period during GNSS outages, a novel hybrid intelligent algorithm incorporating a Discrete Grey Predictor (DGP) and a Multilayer Perceptron (MLP) neural network (DGP-MLP) is proposed. The DGP-MLP is used to provide a pseudo-GNSS position to correct the INS errors during GNSS outages; the DGP uses the GNSS position information of the latest few moments to predict the position of future moments; in the process of DGP-MLP, the MLP is used to modify the prediction errors of DGP, and the MLP is improved by adding momentum terms and adaptively adjusting the learning rate and momentum factor. To evaluate the effectiveness of the proposed methodology, four GNSS outages in different cases over a real field test data were employed. The experimental results demonstrate that the proposed methodology can significantly improve positioning accuracy during GNSS outages.

[1]  Qimin Xu,et al.  A Reliable Fusion Positioning Strategy for Land Vehicles in GPS-Denied Environments Based on Low-Cost Sensors , 2017, IEEE Transactions on Industrial Electronics.

[2]  M. E. Cannon,et al.  GPS/MEMS INS integrated system for navigation in urban areas , 2007 .

[3]  Mamoun F. Abdel-Hafez,et al.  Enhanced, Delay Dependent, Intelligent Fusion for INS/GPS Navigation System , 2014, IEEE Sensors Journal.

[4]  Shuanggen Jin,et al.  GA-SVR and Pseudo-position-aided GPS/INS Integration during GPS Outage , 2015 .

[5]  Liang Li,et al.  An innovative information fusion method with adaptive Kalman filter for integrated INS/GPS navigation of autonomous vehicles , 2018 .

[6]  Zhou Jun,et al.  Hard fault isolation of GPS navigation system based on gray prediction for agricultural robot. , 2010 .

[7]  Aboelmagd Noureldin,et al.  Sensor Integration for Satellite-Based Vehicular Navigation Using Neural Networks , 2007, IEEE Transactions on Neural Networks.

[8]  Xiaosu Xu,et al.  A hybrid fusion algorithm for GPS/INS integration during GPS outages , 2017 .

[9]  Sifeng Liu,et al.  Discrete grey forecasting model and its optimization , 2009 .

[10]  Prabir Bhattacharya,et al.  A novel hybrid fusion algorithm to bridge the period of GPS outages using low-cost INS , 2014, Expert Syst. Appl..

[11]  Abdelouahed Abounada,et al.  Fusion of GPS/INS/Odometer measurements for land vehicle navigation with GPS outage , 2016, 2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech).

[12]  Hossam S. Hassanein,et al.  Integrated cooperative localization for Vehicular networks with partial GPS access in Urban Canyons , 2017, Veh. Commun..

[13]  Fabio Dovis,et al.  A Comparison between Different Error Modeling of MEMS Applied to GPS/INS Integrated Systems , 2013, Sensors.

[14]  A. D. Sarma,et al.  A NEW TECHNIQUE BASED ON GREY MODEL FOR FORECASTING OF IONOSPHERIC GPS SIGNAL DELAY USING GAGAN DATA , 2017 .

[15]  Yanming Feng,et al.  Analysis of a robust Kalman filter in loosely coupled GPS/INS navigation system , 2016 .

[16]  J. Deng,et al.  Introduction to Grey system theory , 1989 .

[17]  Zhu Xiao,et al.  A novel hybrid approach based-SRG model for vehicle position prediction in multi-GPS outage conditions , 2018, Inf. Fusion.

[18]  Abdul Rahman Ramli,et al.  Optimizing of ANFIS for estimating INS error during GPS outages , 2011 .

[19]  Mamoun F. Abdel-Hafez,et al.  Constrained low-cost GPS/INS filter with encoder bias estimation for ground vehicles׳ applications , 2015 .

[20]  Deng Ju-Long,et al.  Control problems of grey systems , 1982 .

[21]  Xiyuan Chen,et al.  Performance analysis of improved iterated cubature Kalman filter and its application to GNSS/INS. , 2017, ISA transactions.

[22]  Bin Li,et al.  A novel fusion methodology to bridge GPS outages for land vehicle positioning , 2015 .

[23]  John Weston,et al.  Strapdown Inertial Navigation Technology , 1997 .

[24]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[25]  Yan Lu,et al.  Performance Test Results of an Integrated GPS/MEMS Inertial Navigation Package , 2004 .

[26]  Okyay Kaynak,et al.  Grey system theory-based models in time series prediction , 2010, Expert Syst. Appl..

[27]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[28]  Dong-Hwan Hwang,et al.  Low-cost and high performance ultra-tightly coupled GPS/INS integrated navigation method , 2017 .