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A Dynamic-Data-Driven Method for Improving the Performance of Receiver Autonomous Integrity Monitoring
- Source :
- IEEE Access, Vol 9, Pp 55833-55843 (2021)
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- 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 fault-free 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-squares-residuals RAIM, P-RAIM can more easily detect potential faulty satellites in a single constellation.
- Subjects :
- alarm systems
010504 meteorology & atmospheric sciences
General Computer Science
Computer science
Real-time computing
02 engineering and technology
01 natural sciences
Fault detection and isolation
Probabilistic neural network
0203 mechanical engineering
General Materials Science
0105 earth and related environmental sciences
020301 aerospace & aeronautics
Localizer performance with vertical guidance
Receiver autonomous integrity monitoring
General Engineering
Pseudorange
Probabilistic logic
LPV-250
global navigation satellite system
multi-layer neural network
lcsh:Electrical engineering. Electronics. Nuclear engineering
False alarm
Sample collection
lcsh:TK1-9971
Subjects
Details
- ISSN :
- 21693536
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....ab283612790d6531d12229ef49975eb3
- Full Text :
- https://doi.org/10.1109/access.2021.3070658