A simple position estimator that improves advanced RAIM performance

Blanch, Juan; Walter, Todd; Enge, Per; Kropp, Victoria

doi:10.1109/taes.2015.140674

Cited by 19 publications

(9 citation statements)

References 4 publications

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“…The use of solution separation statistics for RAIM was introduced early on [1,7,8] and has been shown to be particularly adapted to Advanced RAIM, where the faults are more complex than in RAIM [9][10][11][12][13][14]. It turns out that in addition, the solution separation statistics have optimal properties in several cases.…”

Section: Introductionmentioning

confidence: 99%

Theoretical Results on the Optimal Detection Statistics for Autonomous Integrity Monitoring

2017

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One of the most commonly used detection statistics in autonomous integrity monitoring is the solution separation statistic. In this paper, we show that the solution separation statistic is closely linked to the optimal detection statistics. More precisely, we show that in the case of one threat, even multi-dimensional, the optimal detection statistic is the solution separation, and that in the case of many one-dimensional threats, the optimal statistics can be expressed as a function of the solution separations corresponding to each threat. This is achieved by casting the search of the optimal detection region as a mini-max problem and by using the Neyman-Pearson lemma to limit the search of the detection regions to a class of regions parameterized by a bias. These results allow us to establish a lower bound on the minimum achievable integrity risk.

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Section: Introductionmentioning

confidence: 99%

Theoretical Results on the Optimal Detection Statistics for Autonomous Integrity Monitoring

2017

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“…The problem is then written: (18) In fact, the equation is complex as the process of finding the optimal solution. It is important to notice that we can find a new vector which is approximately the S (0) 3 .…”

Section: Araim Algorithm Of Extremum Approximationmentioning

confidence: 99%

“…Literature [18] improves the algorithm by combining the fault view solution of the largest variance in the vertical direction with the all-in-view solution. A new optimal allin-view solution is obtained by the constraint of precision.…”

Section: Introductionmentioning

confidence: 99%

An Extremum Approximation ARAIM Algorithm Based on GPS and BDS

Sun

et al. 2020

IEEE Access

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Advanced receiver autonomous integrity monitoring (ARAIM) is a new technology that can reduce the vertical protection level (VPL) by optimizing the probability of configuration information to improve availability. Compared with traditional receiver autonomous integrity monitoring (RAIM), the powerful vertical guidance capability of ARAIM highlights its advantages. This paper improves ARAIM performance by reducing the difference between the two most important view solutions affecting the VPL and the all-in-view solution while satisfying the accuracy. First, this paper proposes new constellation configurations suitable for GPS/BDS, which is a nontraditional constellation itself; second, this paper proposes a method called EA-ARAIM for improving ARAIM performance. The results show that for the depleted constellation in the case of visible satellite reduction, the coverage can increase from 59.97% to 76.63% with the assistance of EA-ARAIM. Even for the optimistic constellation, where there is not much room to improve availability, the improved algorithm can increase the coverage to approximately 3%. Finally, compared with the simulation data used in most of the literature, this paper uses real data obtained from MGEX for analysis. INDEX TERMS ARAIM, GPS and BDS, new constellation configurations, EA-ARAIM, coverage.

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“…In particular, the role transition from backup to primary means of navigation has increased the significance of H‐ARAIM continuity as compared to conventional RAIM. Most prior ARAIM work focused on reducing integrity risk [11–13]. Only few investigated continuity, and in most cases, false alarm (FA) was considered the only source of LOC [14, 15].…”

Section: Introductionmentioning

confidence: 99%