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Purpose This paper aims to propose a novel adaptive Kalman filter (APRKF) designed to accommodate the operational environment of maglev pipeline trains, enhancing positioning accuracy by mitigating the impact of noise and sensor errors. Design/methodology/approach This approach is built upon the Kalman filtering algorithm, using a limited-memory weighted method to derive adaptive factors, while establishing judgment thresholds to constrain the range of their variation. Findings A comparative analysis with the traditional Kalman filter and the Sage-Husa adaptive filter reveals that APRKF offers superior speed estimation performance while effectively mitigating error accumulation caused by speed integration. The results indicate that APRKF excels in both speed and position estimation, demonstrating its effectiveness in improving estimation accuracy. Originality/value This study proposes an innovative method for the velocity and position estimation system of maglev tube-track trains.