def predict_steadystate(x, F=1, u=0, B=1):
    """
    Predict next state (prior) using the Kalman filter state propagation
    equations. This steady state form only computes x, assuming that the
    covariance is constant.
    Parameters
    ----------
    x : numpy.array
        State estimate vector
    P : numpy.array
        Covariance matrix
    F : numpy.array()
        State Transition matrix
    u : numpy.array, Optional, default 0.
        Control vector. If non-zero, it is multiplied by B
        to create the control input into the system.
    B : numpy.array, optional, default 0.
        Control transition matrix.
    Returns
    -------
    x : numpy.array
        Prior state estimate vector
    """

    if np.isscalar(F):
        F = np.array(F)
    x = dot(F, x) + dot(B, u)

    return x

    Predict next state (prior) using the Kalman filter state propagation
    equations. This steady state form only computes x, assuming that the
    covariance is constant.
    Parameters
    ----------
    x : numpy.array
        State estimate vector
    P : numpy.array
        Covariance matrix
    F : numpy.array()
        State Transition matrix
    u : numpy.array, Optional, default 0.
        Control vector. If non-zero, it is multiplied by B
        to create the control input into the system.
    B : numpy.array, optional, default 0.
        Control transition matrix.
    Returns
    -------
    x : numpy.array
        Prior state estimate vector
    
trackers/ocsort_tracker/kalmanfilter.py