Abstract: The present invention provides a BeiDou-based grid augmentation autonomous driving multi-level warning system comprising a Beidou Satellite Ground-based Augmentation system, user terminals and a Vehicles internet system, wherein the Beidou Satellite Ground-based Augmentation system comprises Beidou grid reference stations, a data processing system and a data broadcast system; the user terminal comprises an in-vehicle receiver and a calculating chips. The present invention can reduce the occurrence of traffic accidents and reduce the loss of life and property.

Abstract: The present invention provides a ground-based augmentation system (GBAS) integrity performance evaluation method based on a pseudorange error distribution model, including: an airborne receiver terminal performing GBAS integrity performance evaluation by acquiring pseudorange error sample data, including the following method steps: a) grouping the pseudorange error sample data; b) building a distribution model having a Gaussian kernel and quadratic Gaussian polynomial tails for each group of pseudorange error samples; c) calculating a weighted sum of the distribution model of each group of pseudorange errors, to obtain an overall pseudorange error distribution model; d) projecting the pseudorange errors to position domain errors; e) calculating a probability that a position domain error is greater than an alarm limit, to obtain an integrity risk probability value; and f) evaluating GBAS integrity performance.

Abstract: The present disclosure provides an integrated navigation integrity monitoring system for unmanned aerial vehicles, comprising: an inertial measurement unit for providing a processor with zero offset values of different levels of inertial measurement units; a receiver for receiving signals from global satellite navigation and providing the processor with an integrity risk of a global satellite navigation system; and the processor for calculating a horizontal protection level of integrated navigation and a vertical protection level of integrated navigation, setting a horizontal alert limit and a vertical alert limit, comparing the horizontal protection level and the vertical protection level obtained by calculation with the corresponding horizontal alert limit and vertical alert limit respectively, and monitoring the integrity of the unmanned aerial vehicle. An inertial navigation system can be achieved without hardware redundancy, and the cost of integrated navigation integrity monitoring can be reduced.

Abstract: The present disclosure provides a method for ARAIM fault detection based on extraction of characteristic value of pseudo-range measurement, comprising: calculating a sum of integrity risks of each of fault modes and a maximum value of the integrity risks of each of the fault modes, calculating a quantity of the fault modes by using a ratio of the sum of integrity risks of each of fault modes to an integrity risk of a largest fault, and using a sample quantity of corresponding pseudo-range measurement values as an effective sample quantity; using a ratio of a time duration T to the effective sample quantity as an effective sampling duration; sampling samples of pseudo-range measurement values that are gathered by a receiver within the effective sampling duration, to obtain an effective pseudo-range measurement set; and by using the effective pseudo-range measurement set, calculating a test statistic, and performing integrity fault detection.

Abstract: A correlation processing method based on observation data from the Deidou satellite includes steps of: a) collecting observation data from the BeiDou Navigation Satellite System; b) capturing data samples according to the observation data, and determining a membership degree of each data sample, and the data samples are classified; c) constructing a target classification function, and selecting the data samples to remove the unavailable data samples; and testing stationarity of each time series; d) building an observation model by using data samples having stationarity in step c), and determining correctness of the observation model and optimizing the observation model if the observation model is not correct; and e) searching a correct observation model obtained in step d) for correlation data, and performing correlation analysis of the observation data. The method of the present invention improved the quality of observation data can be effectively improved.

Abstract: A correlation processing method based on observation data from the Deidou satellite includes steps of: a) collecting observation data from the BeiDou Navigation Satellite System; b) capturing data samples according to the observation data, and determining a membership degree of each data sample, and the data samples are classified; c) constructing a target classification function, and selecting the data samples to remove the unavailable data samples; and testing stationarity of each time series; d) building an observation model by using data samples having stationarity in step c), and determining correctness of the observation model and optimizing the observation model if the observation model is not correct; and e) searching a correct observation model obtained in step d) for correlation data, and performing correlation analysis of the observation data. The method of the present invention improved the quality of observation data can be effectively improved.

Abstract: A Beidou ground-based augmentation system integrity risk monitoring system includes a ground side and an on-board side.

Abstract: An H-ARAIM system of optimizing a horizontal protection level includes constellations, a ground reference station and an aircraft, the ground reference station is used for receiving the satellite coordinate data of the constellations, and processing the received satellite coordinate data into an input data for the calculation of the aircraft horizontal protection level. The aircraft is built-in with a receiver and a data processor, the receiver is used for receiving the input data sent by the ground reference station, and transmitting the input data to the data processor for the data processing as follows: when a difference between a positioning solution of a full visible satellite and a positioning solution of a fault subset is within a threshold of a fault subset monitor system statistical magnitude, the receiver begins to calculate the protection level, which is calculated for protecting the iterative update.

Abstract: A method for evaluating the availability of ARAIM system is provided. A satellite position coordinate and clock offset of each satellite at a current time point and several time points before the current time point are received. Empirical mode decomposition is performed on the position coordinate and the clock offset respectively, to obtain a position coordinate mode component and a clock offset mode component of each satellite at each time point. A position coordinate mode component and a clock offset mode component of each satellite at a time point next to the current time point are obtained respectively. According to the position coordinate mode component and the clock offset mode component of each satellite at the time point next to the current time point, a position coordinate mode component and a clock offset mode component of each satellite at a next time point is obtained, thereby obtaining an evaluation result.