In the process of location and tracking of unmanned bus, the sampling signal is affected by noise variance, bandwidth and sampling rate, which is prone to signal loss or discontinuity. In addition, the related filtering algorithm lacks asynchronous sampling and smoothing capabilities, leading to location failure. In order to improve the positioning accuracy and supplement missing data, this paper proposes an improved sensor asynchronous sampling fusion smoothing algorithm based on asynchronous extended Kalman filtering and non causal filtering smoothing. First, asynchronous extended Kalman filter is used to exponentially discretize the continuous time stochastic differential equation to process the measured value at any time. After the state value at the next time is predicted and updated, the non causal filter is introduced to smooth the given available initial variance information, so that the noise variance impact is smaller and the estimation performance is better. The algorithm is verified by physical experiments on an unmanned bus. The results show that this multisensor asynchronous fusion smoothing algorithm has a good effect in vehicle driving. Compared with the results of asynchronous Kalman filtering algorithm, it can achieve a positioning accuracy better than 05 m. The data prediction error is significantly reduced, and the positioning accuracy is improved and missing data is supplemented.