At the extremes, a high gain close to one will result in a more jumpy estimated trajectory, while low gain close to zero will smooth out noise but decrease the responsiveness.When performing the actual calculations for the filter (as discussed below), the state estimate and covariances are coded into matrices to handle the multiple dimensions involved in a single set of calculations.is a library implementing an Extended Kalman Filter in C . Subclassing allows to define different matrix contents for the filter to work with.Kalman filters are used for some time now, in aeronautics, robot vision and robotics in general.This process is repeated at every time step, with the new estimate and its covariance informing the prediction used in the following iteration.This means that the Kalman filter works recursively and requires only the last "best guess", rather than the entire history, of a system's state to calculate a new state.The relative certainty of the measurements and current state estimate is an important consideration, and it is common to discuss the response of the filter in terms of the Kalman filter's gain.
With a low gain, the filter follows the model predictions more closely.Extensions and generalizations to the method have also been developed, such as the extended Kalman filter and the unscented Kalman filter which work on nonlinear systems.The underlying model is a Bayesian model similar to a hidden Markov model except that the state space of the latent variables is continuous and all latent and observed variables have Gaussian distributions.Your bug reports and comments are important to keep this library alive. didn't like the use of old strstream header, so I had to add some conditional code for the library to compile both on GCC 4. If someone out there tries to compile on one of those compilers, I would be glad to hear about it. Thanks for the bug report that allowed this important new release.