Integrity monitoring is crucial in applications closely related to the safety of human life and property, such as aviation, maritime navigation, autonomous driving, and rail transportation. Receiver autonomous integrity monitoring (RAIM) has attracted significant attention due to its comprehensive monitoring coverage and fast alerting capability. The paper provides a comprehensive review of RAIM algorithms for global navigation satellite system (GNSS) positioning applications. The parameters related to integrity assessment and typical fault detection and exclusion methods are reviewed, and RAIM is categorized into three types of methods: error probability distribution model-based, set representation-based, and machine learning-based. The latest state-of-the-art research, along with the strengths and shortcomings of each type of method, is presented for each type. The opportunities for the future development of RAIM are analyzed in the light of current challenges and existing results, aiming to promote further research and provide effective assurance for GNSS integrity.

This paper introduces the time-frequency analyzed long short-term memory (TF-LSTM) neural network method for jamming signal recognition over the Global Navigation Satellite System (GNSS) receiver. The method introduces the long short-term memory (LSTM) neural network into the recognition algorithm and combines the time-frequency (TF) analysis for signal preprocessing. Five kinds of navigation jamming signals including white Gaussian noise (WGN), pulse jamming, sweep jamming, audio jamming, and spread spectrum jamming are used as input for training and recognition. Since the signal parameters and quantity are unknown in the actual scenario, this work builds a data set containing multiple kinds and parameters jamming to train the TF-LSTM. The performance of this method is evaluated by simulations and experiments. The method has higher recognition accuracy and better robustness than the existing methods, such as LSTM and the convolutional neural network (CNN).