Research on train positioning method based on LSTM network aided unscented particle filter

Aiming at the situation that the accuracy of GPS/SINS train positioning system is reduced due to the difficulty of obtaining GPS signals in poor visibility areas during the actual operation of the train, a long short term memory network (LSTM) aided unscented particle filter (UPF) positioning method is proposed. When the GPS signal is available, UPF1 is used for train positioning, and the position and speed information output by UPF1 is used to train LSTM1. When the GPS signal is unavailable, the neural network supervisory control idea is introduced, and the trained LSTM1 is used instead of the GPS signal. With SINS output information as the input of the feedback controller UPF2, the neural network controller LSTM2 is built using the input and output of UPF2. The output of the system is jointly determined by the output of UPF2 and LSTM2, but as LSTM2 continues to approach the system model, it will replace UPF2 to decide the final output the result. The simulation results prove that the LSTM network aided UPF method can meet the requirements of train positioning.