Bayesian hypothesis testing for key comparisons

Wübbeler, Gerd; Bodnar, Olha; Elster, Clemens

doi:10.1088/0026-1394/53/4/1131

Cited by 7 publications

(9 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, the problem of satellite fault detection has been transformed into one of identifying a statistical model of random variables. In accordance with the principle of Bayesian hypothesis testing [30], when P(s j = σ j ) P(s j = kσ j ) > 1, we consider s j = σ j to be more credible, indicating that b j is normal. Otherwise, if P(s j = kσ j ) > P(s j = σ j ), b x is identified as containing a fault.…”

Section: Methodology a Classification Model Based On Variance Expansionmentioning

confidence: 95%

See 1 more Smart Citation

A Dynamic-Data-Driven Method for Improving the Performance of Receiver Autonomous Integrity Monitoring

Zheng

Wang³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Section: Methodology a Classification Model Based On Variance Expansionmentioning

confidence: 95%

“…a 1j e j )de 1 de n−1 (30) When e i ∼ N (0, σ 2 i ), according to (30), we can derive b x ∼ N (0, σ 2…”

Section: Appendix Derivation Of the Position Error Distributionmentioning

confidence: 99%

A Dynamic-Data-Driven Method for Improving the Performance of Receiver Autonomous Integrity Monitoring

Zheng

Wang³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…But the numbers of UMTS measurements will be equal the list of dates of measurements in according to the Technical Protocol [8]. Polynomial regression was selected to track the drift behavior of the TS (Table 2).…”

Section: Non-linear Driftmentioning

confidence: 99%

Features of evaluation drift effect during Key Comparison COOMET.EM-K5

Karpenko¹

2017

Ukrainian Metrological Journal

View full text Add to dashboard Cite

Results of pilot laboratory and participating laboratories which are measuring the same travelling standard during Key Comparison must be most similar. However, this may not always be possible, because the travelling standard which is sent in turn to all participating laboratories is not always perfectly stable. So that it is necessary to take into account the drift effect of the travelling standard. The article describes some problems concerning the evaluation drift effect of travelling standard and its further influence on evaluation key comparison reference value during Key Comparison. The procedure for evaluation drift effect when its model can be linear or not linear was presented. Comparative analysis of the proposed procedures for evaluation drift effect is applied to the Key Comparison of Power COOMET.EM-K5 was done.

show abstract

“…It is therefore vital that the DoE accurately characterises the metrological equivalence of participants. This assessment can be understood as a statistical hypothesis test, with the null hypothesis being that a participant has carried out an adequate uncertainty analysis with no unrecognised effects that would bias the measurements [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…Also, the likelihood of incorrectly accepting a participant should be low because the detection of unacknowledged laboratory bias is a primary reason for carrying out comparisons. There has been a lot of discussion about how to determine DoEs and, since the seminal paper by Cox [4], many variants have been proposed to deal with inconsistent data sets [5][6][7] or to offer statistically distinct approaches or models [3,[8][9][10][11]. This proliferation of options demonstrates two things: first, that a single analysis method is unlikely to be suitable for all comparisons; and second, that there are few tools available to compare one method with another in any given comparison scenario.…”

Section: Introductionmentioning

confidence: 99%

The Statistical Power and Confidence of Some Key Comparison Analysis Methods to Correctly Identify Participant Bias

et al. 2021

View full text Add to dashboard Cite

The validity of calibration and measurement capability (CMC) claims by national metrology institutes is supported by the results of international measurement comparisons. Many methods of comparison analysis are described in the literature and some have been recommended by CIPM Consultative Committees. However, the power of various methods to correctly identify biased results is not well understood. In this work, the statistical power and confidence of some methods of interest to the CIPM Consultative Committees were assessed using synthetic data sets with known properties. Our results show that the common mean model with largest consistent subset delivers the highest statistical power under conditions likely to prevail in mature technical fields, where most participants are in agreement and CMC claims can reasonably be supported by the results of the comparison. Our approach to testing methods is easily applicable to other comparison scenarios or analysis methods and will help the metrology community to choose appropriate analysis methods for comparisons in mature technical fields.

show abstract

Bayesian hypothesis testing for key comparisons

Cited by 7 publications

References 34 publications

A Dynamic-Data-Driven Method for Improving the Performance of Receiver Autonomous Integrity Monitoring

A Dynamic-Data-Driven Method for Improving the Performance of Receiver Autonomous Integrity Monitoring

Features of evaluation drift effect during Key Comparison COOMET.EM-K5

The Statistical Power and Confidence of Some Key Comparison Analysis Methods to Correctly Identify Participant Bias

Contact Info

Product

Resources

About