A BDS/SINS integrated positioning approach for trains in complicated operation scenes

Abstract

The traditional train positioning methods suffer from inadequate accuracy and high maintenance costs, rendering them unsuitable for the development requirements of lightweight and intelligent train positioning technology. To address these restraints, the BeiDou navigation satellite system/strapdown inertial navigation system (BDS/SINS) integrated train positioning system based on an adaptive unscented Kalman filter (AUKF) is proposed. Firstly, the combined denoising algorithm (CDA) and Lagrange interpolation algorithm are introduced to preprocess the original data, effectively eliminating the influence of noise signals and abnormal measurements on the train positioning system. Secondly, the innovation theory is incorporated into the unscented Kalman filter (UKF) to derive the AUKF, which accomplishes an adaptive update of the measurement noise covariance. Finally, the positioning performance of the proposed AUKF is contrasted with that of conventional algorithms in various operation scenes. Simulation results demonstrate that the average value of error calculated by AUKF is less than 1.5 m, and the success rate of positioning touches 95.0%. Compared to Kalman filter (KF) and UKF, AUKF exhibits superior accuracy and stability in train positioning. Consequently, the proposed AUKF is well-suited for providing precise positioning services in variable operating environments for trains.

Keywords

train integrated positioning / BeiDou navigation satellite system (BDS) / strapdown inertial navigation system (SINS) / Lagrange interpolation algorithm / combined denoising algorithm (CDA) / adaptive unscented Kalman filter (AUKF)

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