JKalman

Java Kalman Filter i

The Kalman Filter was originally designed to represents information about a moving objects. Moving objects are modeled using a discrete time dynamic system. An object has spatio-temporal state x t . The spatio- temporal state is represented by location [x, y, t] and velocity [d x , d y ]. Here [x, y] is referred to as 2D position [x, y] of the object at the time t. Data is supposed to be uncertain – noisy, some states might be missing and other are biased. At the moment, the object state x t is characterized by a state vector x, containing position and velocity vector [x, y, d x , d y ]. In that way, the trajectory is constructed (in 2D). However it cannot be observed directly, because it is encumbered by hidden (Gaussian) noise w. The system produces visible output vector y that is a simple linear observation (x evolves first-order Markov process), encumbered by noise v.

For more information see JKalman.pdf.

Copyright (C) 2007 Petr Chmelar

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