The DIA-method for navigational integrity

• Autorzy: Teunissen P. J. G.

Identyfikatory

DOI

10.12716/1001.17.01.16

• Języki publikacji: EN

Abstrakty

EN

In this contribution we present a review of the DIA-method to ensure navigational integrity. The DIA-method rigorously combines parameter estimation and statistical testing for the Detection, Identification and Adaptation of multivariate and multiple model misspecifications. We describe the statistical properties of the so-obtained DIA-estimator together with its probability density function. Numerical examples are given to highlight various aspects of the navigational DIA-estimator.

Słowa kluczowe

EN

detection; identification; adaptation; integrity risk; DIA-estimator; probability distribution; estimation; Statistical testing

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2023
• Tom: Vol. 17 No. 1
• Strony: 157--161
• Opis fizyczny: Bibliogr. 11 poz., rys.

Twórcy

autor

Teunissen P. J. G.

• Delft University of Technology, Delft, Netherlands
• Curtin University, Perth, Australia

Bibliografia

• [1] Teunissen PJG (2017) Distributional theory for the DIA method. Journal of Geodesy, Springer, 1–22, DOI 10.1007/s00190‐017‐1045‐7.
• [2] Teunissen PJG (1990) An integrity and quality control procedure for use in multi‐sensor integration. In: Proc. of ION GPS‐1990, ION, pp 513–522.
• [3] Amiri Simkooei A (2001) Comparison of reliability and geometrical strength criteria in geodetic networks. Journal of Geodesy 75(4):227–233.
• [4] Baarda W (1967) Statistical concepts in geodesy. Netherlands Geodetic Commission, Publ. on geodesy, New series 2(4).
• [5] Teunissen PJG (2000) Testing theory: an introduction. Delft University Press, Series on Mathematical Geodesy and Positioning.
• [6] Zaminpardaz S, Teunissen PJG (2022): GNSS Detection and Estimation. Encyclopedia of Geodesy, Springer, MG Sideris (Ed.), 1‐9.
• [7] Koch KR (1999): Parameter estimation and hypothesis testing in linear models. Springer Science & Business Media.
• [8] Hoover W (1984): Algorithms for confidence circles and ellipses (NOAA Technical Report NOS 107 C&GS 3). Rockville, MD: National Oceanic and Atmospheri Administration.
• [9] Hofmann‐Wellenhof B, Legat K, Wieser M (2003): Navigation: Principles of Positioning and Guidance. Springer.
• [10] Hyndman RJ (1996): Computing and graphing highest density regions. The American Statistician 50(2): 120‐126.
• [11] Teunissen PJG (2007): Least‐squares prediction in linear models with integer unknowns. Journal of Geodesy 81: 565‐579.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).