Trait KalmanFilterTraitConst

pub trait KalmanFilterTraitConst {
    // Required method
    fn as_raw_KalmanFilter(&self) -> *const c_void;

    // Provided methods
    fn state_pre(&self) -> Mat { ... }
    fn state_post(&self) -> Mat { ... }
    fn transition_matrix(&self) -> Mat { ... }
    fn control_matrix(&self) -> Mat { ... }
    fn measurement_matrix(&self) -> Mat { ... }
    fn process_noise_cov(&self) -> Mat { ... }
    fn measurement_noise_cov(&self) -> Mat { ... }
    fn error_cov_pre(&self) -> Mat { ... }
    fn gain(&self) -> Mat { ... }
    fn error_cov_post(&self) -> Mat { ... }
    fn temp1(&self) -> Mat { ... }
    fn temp2(&self) -> Mat { ... }
    fn temp3(&self) -> Mat { ... }
    fn temp4(&self) -> Mat { ... }
    fn temp5(&self) -> Mat { ... }
}

Constant methods for crate::video::KalmanFilter

Required Methods

fn as_raw_KalmanFilter(&self) -> *const c_void

Provided Methods

fn state_pre(&self) -> Mat

predicted state (x’(k)): x(k)=Ax(k-1)+Bu(k)

fn state_post(&self) -> Mat

corrected state (x(k)): x(k)=x’(k)+K(k)(z(k)-Hx’(k))

fn transition_matrix(&self) -> Mat

state transition matrix (A)

fn control_matrix(&self) -> Mat

control matrix (B) (not used if there is no control)

fn measurement_matrix(&self) -> Mat

measurement matrix (H)

fn process_noise_cov(&self) -> Mat

process noise covariance matrix (Q)

fn measurement_noise_cov(&self) -> Mat

measurement noise covariance matrix (R)

fn error_cov_pre(&self) -> Mat

priori error estimate covariance matrix (P’(k)): P’(k)=A*P(k-1)*At + Q)

fn gain(&self) -> Mat

Kalman gain matrix (K(k)): K(k)=P’(k)Htinv(H*P’(k)*Ht+R)

fn error_cov_post(&self) -> Mat

posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P’(k)

fn temp1(&self) -> Mat

fn temp2(&self) -> Mat

fn temp3(&self) -> Mat

fn temp4(&self) -> Mat

fn temp5(&self) -> Mat

Implementors

impl KalmanFilterTraitConst for BoxedRef<'_, KalmanFilter>

impl KalmanFilterTraitConst for BoxedRefMut<'_, KalmanFilter>

impl KalmanFilterTraitConst for KalmanFilter