Abstract
Kalman filter is regarded as a great discovery in last century. In fact, filtering has a long history and has been widely used in many areas. In this chapter, we briefly introduce the background of filtering, especially Wiener filter and Kalman filter with some historical remarks. Basic formation of Kalman filter, one approach of optimal linear-quadratic estimation, is highlighted for the ease of later in-depth discussions.
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S.L. Lauritzen, Thiele Pioneer in Statistics (Oxford University Press, New York, 2002)
D. Gaylor, E.G. Lightsey, GPS/INS Kalman filter design for spacecraft operating in the proximity of international space station. J. Neurochem. (2006)
R.L. Stratonovich, Conditional markov processes. Theory Probab. Its Appl. 5(2), 172–195 (2006)
O.A. Stepanov, Kalman filtering: past and present. An outlook from Russia. (on the occasion of the 80th birthday of Rudolf Emil Kalman). Gyroscopy Navig. 2(2), 99–110 (2011)
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Ma, H., Yan, L., Xia, Y., Fu, M. (2020). Introduction to Kalman Filtering . In: Kalman Filtering and Information Fusion. Springer, Singapore. https://doi.org/10.1007/978-981-15-0806-6_1
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DOI: https://doi.org/10.1007/978-981-15-0806-6_1
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Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0805-9
Online ISBN: 978-981-15-0806-6
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