Abstract
As stated in the previous chapter the accuracy of an INS is affected by the errors in the inertial sensors, initialization and computational algorithms. The situation is worse for the low cost MEMS sensors where the INS output can drift rapidly and render them essentially unusable as standalone sensors for navigation applications owing to severe stochastic errors.
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References
Biezad DJ (1999) Integrated navigation and guidance systems. The American Institute of Aeronautics and Astronautics, Reston. ISBN:15634729109781563472916
Brown RG, Hwang PYC (1997) Introduction to random signals and applied kalman filtering : with MATLAB exercises and solutions, 3rd edn. Wiley, New York
Bucy RS, Joseph PD (2005) Filtering for stochastic processes with applications to guidance. American Mathematical Society
Fagin SL (1964) Recursive linear regression theory and error analysis of optimal systems. IEEE Int Conv Rec 2:216–240
Gelb (editor) A (1974) Applied optimal estimation. M.I.T. Press, Cambridge, Mass
Grewal MS, Andrews AP (2001) Kalman filtering: theory and practice using MATLAB, 2nd edn. Wiley, New York
Grewal MS, Andrews AP (2008) Kalman filtering : theory and practice using MATLAB®. Wiley, Oxford
Maybeck PS (1979) Stochastic models, estimation, and control, vol 1. Academic, New York
Maybeck PS (1982a) Stochastic models, estimation, and control, vol 2. New York, Academic
Maybeck PS (1982b) Stochastic models, estimation, and control, vol 3. New York, Academic
Minkler G, Minkler J (1993) Theory and application of kalman filtering. Magellan Book Co., Palm Bay, FL
Schlee FH, Standish CJ, Toda NF (1967) Divergence in the kalman filter. AIAA J 5(6):1114–1120
Schmidt SF (1968) Case study of kalman filtering in the C-5 aircraft navigation system. Joint Autom, Control Conference, Ann Arbor, MI
Simon D (2006) Optimal state estimation : Kalman, H infinity, and nonlinear approaches. Wiley, Hoboken
Sorenson HW, Sacks JE (1971) Recursive fading memory filtering. Inf Sci 3(2):101–119
Welch G, Bishop G (2001) SIGGRAPH course 8: an introduction to the Kalman filter. University of North Carolina at Chapel Hill, Chapel Hill
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Noureldin, A., Karamat, T.B., Georgy, J. (2013). Kalman Filter . In: Fundamentals of Inertial Navigation, Satellite-based Positioning and their Integration. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30466-8_7
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DOI: https://doi.org/10.1007/978-3-642-30466-8_7
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