This paper presents a novel Autonomous Integrity Monitoring and Assurance (AIMA) scheme for integrity assurance of the GNSS-based train integrated positioning system. In this scheme, integrity assurance strategies are combined with a three-stage hierarchical architecture, considering the coupling effects among sensor collection, sensor fusion and matching decision level in train integrated positioning. In sensor collecting stage, the AIMA scheme deals with sensor faults and failures with a PCA-based fault detection, diagnosis and isolation approach. In multi-sensor fusion stage, a novel cubature point H ∞ filter is presented to enhance the fault tolerance capability, and a hybrid approach is applied to indicating and monitoring the protection level of position estimation, concerning both the estimating covariance and measurement slopes. In map matching stage, hypothesis testing with specific test statistic is carried out to determine effectiveness of positioning results. Position calculation will be invalid with an alarm triggered if the specific integrity criterion is not satisfied in any stage. Since independent solutions are applied in AIMA, integrity assurance is closely coupled with information processing in train integrated positioning. Numerical results of the three cases correspond to the hierarchical architecture with field data and simulations are presented to illustrate features and applicability of the proposed AIMA scheme and specific solutions. This paper presents a novel Autonomous Integrity Monitoring and Assurance (AIMA) scheme for integrity assurance of the GNSS-based train integrated positioning system. In this scheme, integrity assurance strategies are combined with a three-stage hierarchical architecture. collection, sensor fusion and matching decision level in train integrated positioning. In sensor collecting stage, the AIMA scheme deals with sensor faults and failures with a PCA-based fault detection, diagnosis and isolation approach. In multi-sensor fusion stage, a novel cubature point H ∞ filter is presented to enhance the fault tolerance capability, and a hybrid approach is applied to indicating and monitoring the protection level of position estimation, monitoring both concerning estimating covariance and measurement slopes. is carried out to determine effectiveness of positioning results culation will be invalid with an alarm triggered if the specific integrity criterion is not satisfied in any stage. Since independent solutions are applied in AIMA, integrity assurance is closely coupled with information processing in train integrated positioning. Numerical results of the three cases correspond to the hierarchical architecture with field data and simulations are presented to illustrate features and applicability of the proposed AIMA scheme and specific solutions.