Multi-model train state estimation based on multi-sensor parallel fusion filtering

Parallel Kalman filter group integrated particle filter method for the train nonlinear operational status high-precision estimation under non-Gaussian environment

For the problem of multi-mode state estimation in actual train operation, this paper proposes a nonlinear non-gaussian high-precision parallel Kalman filter group (NN-HEKFG) integrated Particle Filter. A multi-model Gaussian decomposition of the probability density function for state equations and measurement equations is performed, and each local state model is represented by a multi-dimensional high-order polynomial to establish the expanded dimensional state model. Then, by updating the mean and variance of the local state expanded dimensional model and in turn solving the particle filtering posterior probability density distribution function, the global estimation results are obtained. In reducing the number of Gaussian terms, a new parameter reduction criterion is established, which can effectively carry out the re-identification of parameters such as weights and means, so as to avoid the problem of parameter explosion. The superiority of NN-HEKFG over particle filters and Gaussian sum filters and its effectiveness for train running state estimation are verified by simulating the multi-model running state of trains.

A cooperative collision-avoidance control methodology for virtual coupling trains

To further improve the line transport capacity, virtual coupling has become a frontier hot topic in the field of rail transit. Specially, the safe and efficient following control strategy based on relative distance braking mode (RDBM) is one of the core technologies. This paper innovatively proposes a cooperative collision-avoidance control methodology, which can enhance the operation efficiency on the premise of ensuring the safety. Firstly, a novel framework for the RDBM based on the predicted trajectory of the preceding train is proposed for the train collision-avoidance control. To reduce the train following distance, a cooperative control model is further proposed and is formulated as a Markov decision process. Then, the Deep-Q-Network (DQN) algorithm is introduced to solve the efficient control problem by learning the safe and efficient control strategy for the following train where the critical elements of the reinforcement learning framework are designed. Finally, experimental simulations are conducted based on the simulated environment to illustrate the effectiveness of the proposed approach. Compared with the absolute distance braking mode (ADBM), the minimum following distance between the adjacent trains can be reduced by 70.23% on average via the proposed approach while the safety can be guaranteed.