Method for monitoring autonomous integrity of GNSS receiver

Abstract

The invention discloses a method for monitoring autonomous integrity of a GNSS receiver, relates to a receiver autonomous integrity monitoring technology, and belongs to the technical field of measurement and test. The method adopts a CKF-based and innovation extrapolation method to achieve monitoring of the autonomous integrity of the GNSS receiver, and innovation generated in an innovation extrapolation process and a variance-covariance matrix thereof construct test statistic of each extrapolation moment, micro slowly-changing pseudo-range deviation can be tested; a machine learning algorithm is adopted to judge and classify the test statistic of the each extrapolation moment, and dynamically updates a fault database used for generating a machine learning rule at the same time; on-line learning of judgment rules of unmodeled fault types has the advantages of being less in calculation amount, proper in precision, suitable for nonlinear systems and capable of monitoring the micro slowly-changing pseudo-range deviation with high confidence degree.

Description

technical field [0001] The invention discloses a method for monitoring the autonomous integrity of a GNSS receiver, relates to receiver autonomous integrity monitoring technology, and belongs to the technical field of measurement and testing. Background technique [0002] Research on RAIM (Receiver Autonomous Integrity Monitoring, Receiver Autonomous Integrity Monitoring) began in the mid-1980s. At the American Society of Navigation in 1986, Y.C.Lee proposed the distance comparison method, that is, only the observations of the current epoch are used when making integrity decisions each time, and whether there is a fault is judged based on the weighted sum of the five satellite distance errors , this method can only be used to detect faults but not to determine faulty satellites. In the following year, R.M.Kalafus formally proposed the concept of RAIM and demonstrated the minimum number of satellites required for fault detection and fault isolation. In 1988, BradfordW.Parki...

AI Technical Summary

Problems solved by technology

[0009] The purpose of the present invention is to address the deficiencies of the above-mentioned background technology and provide a method for autonomous integrity monitoring of GNSS receivers. By combining the volumetric Kalman filter algorithm and the information extrapolation method, online learning of fault types can be achieved with less calculation and Appropriate accuracy can better monitor small slowly changing pseudo-range deviations, realize the detection of slow unmodeled faults, and solve the technical problem that integrity monitoring cannot meet the fault detection requirements because it is difficult to extract the characteristics of each satellite fault

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