A C++, object-oriented, Extended Kalman Filter library.
Numerical method used to track a time-varying signal in the presence of noise.
A sequential linear least-squares algorithm which uses an initial estimate and incremental data to find an optimal solution. A variant of this algorithm is used as the mathematical foundation of the BaBar track fit. Code implementing the track f it is in package KalmanTrack. Reference Link (http://www.slac.stanford.edu/BFROOT/www/Computing/Offline/Reconstruction/Tracking/trackDocu.html)
A filtering algorithm for a time series of imprecise linear or non-linear data that incorporates a statistical model in analyzing error.
a mathematical technique to extract a meaningful signal from a noisy environment
a method for providing an optimal estimate of variables in the presence of noise by generating recursion formulas
an estimator of the state of a dynamic system given measurements that are related to the state
a recursive estimator which updates its estimates periodically, on the basis of system observations
a recursive optimal estimator that can extract data from inaccurate or uncertain observations
a stochastic, recursive estimator, which estimates the state of a system based on the knowledge of the system input, the measurement of the system output, and a model of the relation between input and output
Consider a stochastic system evolving in time. The true state of the system is characterized by an unobserved vector of variables. Instead of the true state we observe at each time a vector of quantities, dependent of the true state and disturbed by a measurement error. The Kalman filter is a recursive procedure for computing the optimal estimator of the state vector, based on observations available up to the current time.
a process for estimating the value of parameters in the presence of noise and time delays.
A mathematical tool that sorts out information and weights the relative contributions of measurements compared with its assumed model of the process; a recursive estimator that produces the minimum covariance estimate of the state vector in a least squares sense.
The Kalman filter is an efficient recursive filter which estimates the state of a dynamic system from a series of incomplete and noisy measurements.
