There are many problems in the speed measurement and positioning of urban rail transit trains, such as fewer available sensors, more lines with small radius curves and large slopes, frequent changes in operating conditions, and higher real-time and accuracy requirements. In this article, a speed measurement and positioning method based on an innovation-based adaptive Kalman filter is proposed, taking the unmanned metro as the research object. Firstly, based on the prior traction or braking target level constraint, the train is regarded as a one-dimensional rigid uniform mass model and taken into account the dynamic behavior of the train passing through the equivalent grade change point. A train motion model with modified maneuver acceleration is formulated. Then, based on the innovation-based adaptive Kalman filter, the statistical noise affected by the change of operating and line conditions is estimated and modified in real-time. Finally, taking the real train data of 3 typical conditions as an example, the speed measurement and positioning are carried out based on 16 sets of motor axle speed information, comparing its six accuracy evaluation indicators with that of the average axle speed method and conventional Kalman filter algorithm without adaptive noise estimation. The results show that this method can effectively modify the progressive data drift caused by wheel-rail creep and reduce the high-frequency noise in the high-speed area. The root mean square of speed error is 0. 349 0 km·h -1 , and the braking position error is 0. 491 3 m. Under the condition that the axle speed in the high-speed zone has a random loss of 1. 5% , the root mean square of the speed error can be stabilized at about 0. 371 7 km·h -1 , and the braking position error can be stabilized at about 0. 042 0 m, which has strong robustness to the loss of axle speed in the high-speed zone. Under the condition of train sliding, the root mean square of speed error is 0. 360 1 km·h -1 , and the braking position error is 0. 310 5 m, which has strong robustness to train slipping or sliding. The research results can provide a theoretical basis and engineering reference for the accurate speed measurement and positioning of unmanned metros.