In this article, the problem of receiver autonomous integrity monitoring (RAIM) is transformed into a modeling problem using dynamic data and an artificial neural network. A new RAIM method based on a probabilistic neural network (P-RAIM) is presented to improve integrity monitoring performance. Compared with existing RAIM methods, P-RAIM has a greater ability to meet the monitoring requirements for localizer performance with vertical guidance down to altitudes of 250 feet (LPV-250) in a single global navigation satellite system. First, by projecting the pseudorange error model from the measurement domain into the positioning domain through multiconvolution, patterns including a satellite fault pattern and a faultfree pattern are obtained based on variance inflation theory. Second, the P-RAIM model is proposed as a modified dynamic-data-driven probabilistic neural network with five layers; moreover, unique methods for training sample collection and integrity support are presented. Then, particle swarm optimization is applied to optimize a fitness function based on the false alarm probability and missed detection probability thereby improving the ability of P-RAIM to meet the LPV-250 requirements, including the false alarm probability, missed detection probability, vertical alarm limit and alarm time. Finally, utilizing real satellite data from a receiver located in Beijing to verify the effectiveness and universality of P-RAIM, evaluation experiments show that both the false alarm probability and missed detection probability can be effectively reduced to meet the LPV-250 requirements when the positioning bias is no less than 40 m. Compared with least-squaresresiduals RAIM, P-RAIM can more easily detect potential faulty satellites in a single constellation.INDEX TERMS Receiver autonomous integrity monitoring, LPV-250, global navigation satellite system, multi-layer neural network, alarm systems.

The availability of advanced receiver autonomous integrity monitoring for vertical guidance down to altitudes of 200 ft (LPV-200) is discussed using real satellite orbit/ephemeris data collected at eight international global navigation satellite system service stations across China. Analyses were conducted for the availability of multi-constellation advanced receiver autonomous integrity monitoring and multi-fault advanced receiver autonomous integrity monitoring, and the sensitivity of availability in response to changes in error model parameters (i.e. user range accuracy, user range error, Bias-Nom and Bias-Max) was used to compute the vertical protection level. The results demonstrated that advanced receiver autonomous integrity monitoring availability based on multiple constellations met the requirements of LPV-200 despite multiple-fault detections that reduced the availability of the advanced receiver autonomous integrity monitoring algorithm; the advanced receiver autonomous integrity monitoring availability thresholds of the user range error and Bias-Nom used for accuracy were more relevant to geographic information than the user range accuracy and Bias-Max used for integrity at the eight international global navigation satellite system service stations. Finally, the possibility of using the advanced receiver autonomous integrity monitoring algorithm for a Category III navigation standard is discussed using two sets of predicted errors, revealing that the algorithm could be used in 79% of China.

Utilizing the least squares residuals (LSR) algorithm to detect the faulty satellite, the faulty satellite with a large characteristic slope will bring a high miss detection risk (MDR) and that with a small characteristic slope will bring a high false alert risk (FAR). However, the magnitude of characteristic slopes whether large or small is currently indefinite. In this paper, analyzing the MDR whether exceeding its allowable value or not, we propose the critical value of characteristic slopes to define the magnitude of a characteristic slope. The slope with the value larger than the critical one can be defined as a large slope whereas the slope with a value smaller than the critical one can be defined as a small slope. To reduce the fault detection risk of the LSR algorithm, including the MDR caused by a large slope faulty satellite and the FAR caused by a small slope faulty satellite, a modified LSR algorithm based on the critical value of characteristic slopes is proposed. In the modified algorithm, the most potential faulty satellite is determined via correlation analysis. Then, a subset fault detection methodology will be used to reduce the MDR when the most potential faulty satellite owns a large slope, whereas a threshold amplification fault detection methodology will be used to reduce the FAR when the most potential faulty satellite owns a small slope. The performance evaluation simulations of the modified LSR algorithm show that both the MDR caused by a large slope faulty satellite and the FAR caused by a small slope faulty satellite could be effectively reduced.

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Commonly, the code noise and multipath error is considered to fully obey the Gaussian distribution. While in the cases with different elevation angles and orbit types, the assumption may be inappropriate. Based on an empirical study, by considering both the elevation angle and the orbit type, a new code noise and multipath distribution model is proposed to describe a more accurate code noise and multipath distribution in this paper. Actual code noise and multipath data from 10 observation stations during two months are researched, and the parameters and elevation angle range of code noise and multipath distribution model are determined. The code noise and multipath distribution model is verified to be more accurate than the model presented in the Global Navigation Satellite System Evolutionary Architecture Study report, according to the analysis on the code noise and multipath overbounding, position error overbounding, and the availability of receiver autonomous integrity monitoring. This model provides more accurate prior information for receiver autonomous integrity monitoring, especially its availability.