The integrated Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) system has been widely used in vehicular positioning and navigation. However, the complex unstructured environments would lead ...
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The integrated Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) system has been widely used in vehicular positioning and navigation. However, the complex unstructured environments would lead to positioning degradation due to the GNSS outage. This paper proposed a Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) assisted 21-dimensional GNSS/INS integrated navigation system using a recomputed method based on the Bias and Scale factor (BS-RM) error of the Inertial Measurement Unit (IMU). When GNSS is available, the obtained accurate GNSS/INS integrated navigation information is used as the input of the proposed BS-RM model to calculate the precise theoretical bias and scale factor error, which are trained as the target values of CNN-LSTM. When GNSS is unavailable, the trained CNN-LSTM is utilized to predict the accurate bias and scale factor. The consistent system positioning could be obtained with the suppressed INS error divergence by applying the IMU dead reckoning. To verify the performance of the proposed BS-RM model, three GNSS signal failure segments at different periods were randomly selected to evaluate the system. In addition, two GNSS failure segments were selected in the straight and curved roads respectively to further evaluate the performance of the CNN-LSTM assisted 21-dimensional GNSS/INS navigation system based on BS-RM. Compared with the traditional model of predicting position and velocity, the horizontal Distance Root Mean Square Error (DRMS) of BS-RM in the straight and curve tracks is improved by 83.85% and 88.06%, respectively, which confirms the improvement and consistent accuracy capability of the proposed method.
Global navigation satellite system (GNSS)/inertial navigation system (INS) integration is widely used for train positioning, but railways tunnels and mountains can interfere GNSS signals and will lead to performance d...
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Global navigation satellite system (GNSS)/inertial navigation system (INS) integration is widely used for train positioning, but railways tunnels and mountains can interfere GNSS signals and will lead to performance degradation when the system is operated in the standalone INS mode. This article proposes a long short-term memory (LSTM)-assisted GNSS/INS integration system using recomputed inertial measurement unit (IMU) error to suppress the error divergence of an INS in the case of GNSS solution nonavailability. The IMU error recomputation method (RM) is first proposed, where the GNSS/INS-derived position, velocity, and attitude information is utilized when is GNSS available. The train's attitude computed using the GNSS dual-antenna moving baseline method is used as the heading constraint for GNSS/INS integration so as to provide accurate attitude information. The recomputed IMU sensor error is then used for model training, and the system switches to LSTM-assisted INS mode when GNSS solutions are unavailable. The system predicts the IMU sensor error using the train motion state, and corrects the IMU measurements to suppress the accumulating IMU sensor error. The proposed system was evaluated through a train experiment on the Shuozhou-Huanghua railway. The IMU-RM was evaluated on four time slots of varying lengths, and the proposed LSTM-assisted GNSS/INS integration system using IMU-RM was evaluated in two "difficult" GNSS signal areas of curved and straight railtrack segments, and were simulated. Results showed significant improvement in horizontal position accuracy compared to conventional methods, with suppression of INS sensor error divergence by 79% and 63% for curved and straight segments, respectively.
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