class KalmanFilter extends AnyRef
The KalmanFilter class is used to fit state-space models.
x_t = F x_t-1 + G u_t + w_t (State Equation) z_t = H x_t + v_t (Observation/Measurement Equation)
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- new KalmanFilter(x0: VectoD, ff: MatriD, hh: MatriD, qq: MatriD, rr: MatriD, bb: MatriD = null, u: VectoD = null)
- x0
the initial state vector
- ff
the state transition matrix (F)
- hh
the observation matrix (H)
the process noise covariance matrix (Q)
- rr
the observation noise covariance matrix (R)
- bb
the optional control-input matrix (B)
- u
the control vector
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- def predict(): Unit
Predict the state of the process at the next time point
- def solve(dt: Double, u: VectoD = null): VectoD
Iteratively solve for 'x' using predict and update phases.
Iteratively solve for 'x' using predict and update phases.
- dt
the time increment (delta t)
- u
the control vector
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- val traj: MatrixD
- def update(z: VectoD): Unit
Update the state and covariance estimates with the current and possibly noisy measurements
Update the state and covariance estimates with the current and possibly noisy measurements
- z
current measurement/observation of the state
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