\brief Detection represents information about a people detection */

\brief MahalanobisParameters2d Contains variables for bayesian estimation with state dimension = 2. */

\brief MahalanobisParameters4d Contains variables for bayesian estimation with state dimension = 4. */

\brief UDPMessaging provides methods for message passing via UDP */

* Bayesian Filter * * A Bayesian Filter uses Bayes rule to fuse the state probabilities * of a prior and a likelihood function */

Column proxy so S can be sorted indirectly

\brief Conversions provides useful data conversions */

\brief DetectionSource represents information about a source of people detections */

* Extended Kalman Filter * Kalman state representation and linearizable models * * Observe is implemented using an innovation computed from the no

* Functional Filter - Abstract filtering property * Represents only filter predict by a simple functional * (non-stochastic) model * * A similar

General Linear Correlated Additive and Likelihood observe model

General Linear Uncorrelated Additive and Likelihood observe model

General Linearised Correlated Additive and Likelihood observe model

General Linearised Uncorrelated Additive and Likelihood observe model

\brief KalmanFilter provides methods for bayesian estimation with Kalman Filter. */

* Likelihood Filter - Abstract filtering property * Represents only the Bayesian Likelihood of a state observation */