High-accuracy railway track surveying is essential for railway construction and maintenance. The traditional approaches based on total station equipment are not efficient enough since high precision surveying frequently needs static measurements. This paper proposes a new filtering and smoothing algorithm based on the IMU/odometer and landmarks integration for the railway track surveying. In order to overcome the difficulty of estimating too many error parameters with too few landmark observations, a new model with completely observable error states is established by combining error terms of the system. Based on covariance analysis, the analytical relationship between the railway track surveying accuracy requirements and equivalent gyro drifts including bias instability and random walk noise are established. Experiment results show that the accuracy of the new filtering and smoothing algorithm for railway track surveying can reach 1 mm (1σ) when using a Ring Laser Gyroscope (RLG)-based Inertial Measurement Unit (IMU) with gyro bias instability of 0.03°/h and random walk noise of 0.005°/h while control points of the track control network (CPIII) position observations are provided by the optical total station in about every 60 m interval. The proposed approach can satisfy at the same time the demands of high accuracy and work efficiency for railway track surveying.
Proxy re-encryption (PRE) is a cryptographic primitive that allows a proxy to turn an Alice's ciphertext into a Bob's ciphertext on the same plaintext. At present, there are many different PRE schemes that have been proposed with different properties. However, all of them are based on the logarithm assumption and the large integer factorization assumption except for a bidirectional PRE scheme over lattices. In this paper, we construct the first multi-use unidirectional PRE scheme based on lattices. In addition, the generation of the PRE key does not interact with two users, and the scheme can resist collusion attacks. Moreover, it is proved chosen plaintext attack secure in the standard model based on the Learning With Errors assumption. Finally, an identity-based PRE is obtained from the basic construction.
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