def considerAnalysis(self, Xbar_0, Pbar_0, t_0, C_ref, cbar, Pbar_cc): """ Before computing the kalman solution, call this method. :param Xref_0: [1-dimensional numpy array] Initial guess of the state. :param xbar_0: [1-dimensional numpy array] Deviation from the initial guess (usually 0). :param Pbar_0: [2-dimensional numpy array] A-priori covariance. :param t_0: [double] Initial time. :param C_ref: :param cbar: :param Pbar_cc :return: """ ckfProc.configureFilter(self, Xbar_0, Pbar_0, t_0) q = C_ref.size n = Xbar_0.size self._S_i_1 = np.zeros((n,q)) self._xhatc_i_1 = np.zeros(n) self._P_c_i_1 = np.copy(Pbar_0) self._P_xc_i_1 = np.zeros((n,q)) self._Xhat_c_i_1 = np.zeros(n) self._Pbar_cc = np.copy(Pbar_cc) self._Cref = C_ref # Parameters reference self._cbar = cbar # Parameters' deviation self._posfit_consider_residual = None self._theta_i_1 = np.zeros((n,q)) self._theta_i_1_0 = np.zeros((n,q)) self._zeroMat = np.zeros((n,q)) return
def configureFilter(self, Xbar_0, Pbar_0, t_0): """ Before computing the kalman solution, call this method. :param Xref_0: [1-dimensional numpy array] Initial guess of the state. :param xbar_0: [1-dimensional numpy array] Deviation from the initial guess (usually 0). :param Pbar_0: [2-dimensional numpy array] A-priori covariance. :param t_0: [double] Initial time. :param joseph_flag: [boolean] Set to true to propagate the covariance using Joseph Formulation. :return: """ ckfProc.configureFilter(self, Xbar_0, Pbar_0, t_0) self._ckf_counter = 0 self._start_using_EKF_at_obs = 0 self._lastObsTime = t_0 return