Resilience Monitoring for Multi-Filter All-Source Navigation Framework With Assurance

REFERENCES

1. ↵
   1. Appleget, A.,
   2. Leishman, R. C., &
   3. Gipson, J. S.

   (2021). Evaluation of sensor-agnostic all-source residual monitoring for navigation. Proc. of the 2021 International Technical Meeting of the Institute of Navigation, 339–353. https://doi.org/10.33012/2021.17837
2. ↵
   1. Brink, K. M.

   (2017). Partial-update Schmidt-Kalman filter. Journal of Guidance, Control, and Dynamics, 40(9), 2214–2228. https://doi.org/10.2514/1.G002808
3. ↵
   1. Call, C.,
   2. Ibis, M.,
   3. McDonald, J., &
   4. Vanderwerf, K.

   (2006). Performance of Honeywell’s Inertial/GPS Hybrid (HIGH) for RNP operations. 2006 IEEE/ION Position, Location, and Navigation Symposium, Coronado, CA. https://doi.org/10.1109/PLANS.2006.1650610
4. ↵
   1. Chen, Z.

   (1991). Local observability and its application to multiple measurement estimation. IEEE Transactions on Industrial Electronics, 38(6), 491–496. https://doi.org/10.1109/41.107106
5. ↵
   1. De Maesschalck, R.,
   2. Jouan-Rimbaud, D., &
   3. Massart, D. L.

   (2000). The Mahalanobis distance. Chemometrics and intelligent laboratory systems, 50(1), 1–18. https://doi.org/10.1016/S0169-7439(99)00047-7

   CrossRefWeb of Science
6. ↵
   1. Hag Elamin, K. A. E. M., &
   2. Taha, M. F. E.

   (2013). On the steady-state error covariance matrix of Kalman filtering with intermittent observations in the presence of correlated noises at the same time. 2013 International Conference on Computer, Electrical and Electronics Engineering (ICCEEE), Khartoum, Sudan. https://doi.org/10.1109/ICCEEE.2013.6633901
7. ↵
   1. Ham, F. M. &
   2. Brown, R. G.

   (1983). Observability, eigenvalues, and Kalman filtering. IEEE Transactions on Aerospace And Electronic Systems, AES-19(2), 269–273. https://doi.org/10.1109/TAES.1983.309446
8. ↵
   1. Hermann, R., &
   2. Krener, A. J.

   (1977). Nonlinear controllability and observability. IEEE Transactions on Automatic Control, 22(5), 728–740. https://doi.org/10.1109/TAC.1977.1101601

   CrossRefWeb of Science
9. ↵
   1. Hong, S.,
   2. Chun, H. -H.,
   3. Kwon, S. -H., &
   4. Lee, M. H.

   (2008). Observability measures and their application to GPS/INS. IEEE Transactions on Vehicular Technology, 57(1), 97–106. https://doi.org/10.1109/TVT.2007.905610
10. ↵
    1. Jurado, J. D., &
    2. Raquet, J. F.

    (2019). Autonomous and resilient management of all-source sensors. Proc. of the ION 2019 Pacific PNT Meeting, Honolulu, HI, 142–159. https://doi.org/10.33012/2019.16800
11. ↵
    1. Jurado, J. D.,
    2. Raquet, J., &
    3. Kabban, C. M. S.

    (2020a). Single-filter finite fault detection and exclusion methodology for real-time validation of plug-and-play sensors. IEEE Transactions on Aerospace and Electronic Systems, 57(1), 66–75. https://doi.org/10.1109/TAES.2020.3010394
12. ↵
    1. Jurado, J.,
    2. Raquet, J.,
    3. Schubert-Kabban, C. M., &
    4. Gipson, J. S.

    (2020b). Residual-based multi-filter methodology for all-source fault detection, exclusion, and performance monitoring. NAVIGATION, 67(3), 493–509. https://doi.org/10.1002/navi.384
13. ↵
    1. Kauffman, K.,
    2. Marietta, D.,
    3. Raquet, J.,
    4. Carson, D.,
    5. Leishman, R. C.,
    6. Canciani, A.,
    7. Schofield, A., &
    8. Caporellie, M.

    (2020). Scorpion: A modular sensor fusion approach for complementary navigation sensors. 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, OR. https://doi.org/10.1109/PLANS46316.2020.9110165
14. ↵
    1. Le Cadre, J. P.

    (1998). Properties of estimability criteria for target motion analysis. IEE Proceedings – Radar, Sonar, and Navigation, 145(2). https://doi.org/10.1049/ip-rsn:19981833
15. ↵
    1. Li, M.,
    2. Wang, D., &
    3. Huang, X.

    (2013a). Study on the observability analysis based on the trace of error covariance matrix for spacecraft autonomous navigation. 10th IEEE International Conference on Control and Automation, Hangzhou, China. https://doi.org/10.1109/ICCA.2013.6565112
16. ↵
    1. Li, W.,
    2. Yang, Z., &
    3. Hu, H.

    (2013b). A new variant of sum-product algorithm for sensor self-localization in wireless networks. 2013 IEEE/CIC International Conference on Communications in China, Xi’an, China. https://doi.org/10.1109/ICCChina.2013.6671189
17. ↵
    1. Maybeck, P. S., &
    2. Siouris, G. M.

    (1980). Stochastic models, estimation, and control, volume I. IEEE Transactions on Systems, Man, and Cybernetics, 10(5), 282–282. https://doi.org/10.1109/TSMC.1980.4308494
18. ↵
    1. Powel, N. D., &
    2. Morgansen, K. A.

    (2020). Empirical observability Gramian for stochastic observability of nonlinear systems. arXiv. https://doi.org/10.48550/arXiv.2006.07451
19. ↵
    1. Quartararo, J. D., &
    2. Langel, S. E.

    (2020). Detecting slowly accumulating faults using a bank of cumulative innovations monitors in Kalman filters. Proc. of the 33rd International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 2020), 2367–2381. https://doi.org/10.33012/2020.17655
20. ↵
    1. Rouaud, M.

    (2017). Probability, statistics and estimation: Propagation of uncertainties. http://www.incertitudes.fr/book.pdf
21. ↵
    1. Roy, P.,
    2. Çela, A., &
    3. Hamam, Y.

    (2009). On the relation of FIM and controllability gramian. 2009 IEEE International Symposium on Industrial Embedded Systems, Lausanne, Switzerland. https://doi.org/10.1109/SIES.2009.5196189
22. ↵
    1. Tang, Y.,
    2. Wu, Y.,
    3. Wu, M.,
    4. Wu, W.,
    5. Hu, X., &
    6. Shen, L.

    (2009). INS/GPS integration: Global observability analysis. IEEE Transactions on Vehicular Technology, 58(3), 1129–1142. https://doi.org/10.1109/TVT.2008.926213