ARAIM Fault Detection via Pseudo-Range Characteristic Extraction
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Solution Overview
Problem
The existing ARAIM fault detection methods face challenges in real-time capability and unavailability under constellation faults, particularly due to the large amount of calculations required for pseudo-range measurement processing in airborne receivers, which affects the integrity and reliability of satellite navigation services.
Innovation Solution
A method for ARAIM fault detection based on the extraction of characteristic values from pseudo-range measurements, involving the calculation of integrity risks, effective sampling durations, and singular value decomposition to reduce data processing and enable fault detection during constellation faults.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the solution separation algorithm is used for ARAIM fault detection, then fault detection capability is improved, but the calculation amount increases significantly affecting real-time capability
Solution Approach 1:
The patent extracts only the necessary pseudo-range measurement values from the total collected measurements based on effective sampling duration calculation. By identifying and removing redundant measurements, the system reduces the data processing burden while maintaining the integrity of fault detection, thus resolving the contradiction between comprehensive fault detection and real-time processing capability.
Solution Approach 2:
The patent segments the pseudo-range measurement data processing into distinct phases: collecting all pseudo-range measurements, calculating effective sampling duration based on integrity risk, selecting subset of measurements for processing, and performing fault detection. This segmentation allows the system to maintain thorough fault detection capability while processing only the necessary data in real-time.
2Measurement precision
If all collected pseudo-range measurement values are processed, then measurement completeness is improved, but data processing burden increases
Solution Approach 1:
The patent applies partial action by processing only a subset of collected pseudo-range measurement values rather than all measurements. The effective sampling duration is calculated to determine the minimum necessary processing window, allowing the system to achieve sufficient measurement completeness for fault detection without the excessive burden of processing every available measurement.
3Speed
If the effective sampling duration is reduced, then processing speed is improved, but the accuracy of fault detection may be affected
Solution Approach 1:
The patent changes the parameter of sampling duration from a fixed or maximum value to a dynamically calculated effective sampling duration based on integrity risk requirements. This parameter optimization ensures that the processing window is long enough to maintain fault detection accuracy while short enough to achieve real-time processing speeds, resolving the contradiction between speed and accuracy.
Data Source
AI summary
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.