def __getInnovation__(self, prediction, measurements, probabilities):
y_u = 0
y_i = []
for nm, measurement in enumerate(measurements):
y_u_i = np.array(
KalmanFilter.__getInnovation__(self, prediction, measurement))
y_u += probabilities[nm] * y_u_i
y_i.append(y_u_i)
return np.matrix(y_u).T, np.matrix(y_i).T
