Add a new measurement (z) to the Kalman filter. If z is None, nothing
        is changed.

        Parameters
        ----------

        z : np.array
            measurement for this update.

def update(self, z):
        """
        Add a new measurement (z) to the Kalman filter. If z is None, nothing
        is changed.

        Parameters
        ----------

        z : np.array
            measurement for this update.
        """

        # run update on each filter, and save the likelihood
        for i, f in enumerate(self.filters):
            f.update(z)
            self.likelihood[i] = f.likelihood

        # update mode probabilities from total probability * likelihood
        self.mu = self.cbar * self.likelihood
        self.mu /= np.sum(self.mu)  # normalize

        self._compute_mixing_probabilities()

        # compute mixed IMM state and covariance and save posterior estimate
        self._compute_state_estimate()
        self.x_post = self.x.copy()
        self.P_post = self.P.copy()