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Definition
The Kalman filter is a set of mathematical equations that provides an efficient computational (recursive) means to estimate the state of a process, in a way that minimizes the mean of the squared error. The filter is very powerful in several aspects: it supports estimations of past, present, and even future states, and it can do so even when the precise nature of the modeled system is unknown.
Background
In 1960, Rudolf E. Kalman published his famous paper describing a recursive solution to the discrete-data linear filtering problem [1]. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation. The goal of the filter is to produce evolving optimal estimates of a modeled process from noisy measurements of the process.
Theory
The Kalman filter addresses the general...
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References
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Welch, G. (2014). Kalman Filter. In: Ikeuchi, K. (eds) Computer Vision. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-31439-6_716
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DOI: https://doi.org/10.1007/978-0-387-31439-6_716
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