def perform_estimation(residuals, tracker):
residuals = residuals - np.average(residuals, axis=0)
cor = Correlator(residuals)
C_arr = cor.autocorrelation(used_taps)
# R_ml = estimate_noise_ukf_ml(C_arr[0], tracker.Pz)
R_scaled = estimate_noise_ukf_scaling(C_arr[0], tracker.Pz, tracker.R)
return R_scaled
def perform_estimation(residuals, tracker, map_estimate):
residuals = residuals - np.average(residuals, axis=0)
cor = Correlator(residuals)
correlation = cor.autocorrelation(used_taps)
R_ml = estimate_noise_ukf_ml(correlation[0], tracker.Pz)
R_scaled = estimate_noise_ukf_scaling(correlation[0], tracker.Pz, tracker.R)
R_map = map_estimate
truth = R_proto * sim_var
error_ml = matrix_error(R_ml, truth)
error_scaled = matrix_error(R_scaled, truth)
error_map = matrix_error(R_map, truth)
# truth_norm = matrix_error(truth, 0)
# print("Truth")
# print(truth)
# print("ML:")
# print("", R_ml)
# print("\tRelative error: %.6f" % (error_ml / truth_norm))
# print("Scaled:")
# print("", R_scaled)
# print("\tRelative error: %.6f" % (error_scaled / truth_norm))
# print("MAP:")
# print("", R_map)
# print("\tRelative error: %.6f" % (error_map / truth_norm))
return error_ml, error_scaled, error_map
def perform_estimation(residuals, tracker,
map_estimate, map_estimate_convergence):
cor = Correlator(residuals)
correlation = cor.autocorrelation(used_taps)
R_ml = estimate_noise_ukf_ml(correlation[0], tracker.Pz)
R_scaled = estimate_noise_ukf_scaling(correlation[0], tracker.Pz, tracker.R)
R_map = map_estimate
R_map_conv = map_estimate_convergence
truth = R_proto * measurement_var
error_ml = matrix_error(R_ml, truth)
error_scaled = matrix_error(R_scaled, truth)
error_map = matrix_error(R_map, truth)
error_map_conv = matrix_error(R_map_conv, truth)
# truth_norm = matrix_error(truth, 0)
# print("Truth")
# print(truth)
# print("ML:")
# print("", R_ml)
# print("\tRelative error: %.6f" % (error_ml / truth_norm))
# print("Scaled:")
# print("", R_scaled)
# print("\tRelative error: %.6f" % (error_scaled / truth_norm))
# print("MAP:")
# print("", R_map)
# print("\tRelative error: %.6f" % (error_map / truth_norm))
return error_ml, error_scaled, error_map, error_map_conv
def test_ukf_estimate_scaling(self):
C = np.asarray([[0.04271917, -0.00366983], [-0.00366983, 0.01893147]])
P_zz = np.asarray([[1.01043425e-02, 3.41826761e-05],
[3.41826761e-05, 1.00712847e-02]])
R_filter = np.asarray([[1, 0.1], [0.1, 1]])
R = estimate_noise_ukf_scaling(C, P_zz, R_filter)
assert R.shape == (2, 2)
def perform_estimation(residuals, tracker):
cor = Correlator(residuals)
correlation = cor.autocorrelation(used_taps)
R = estimate_noise_ukf_scaling(correlation[0], tracker.Pz, tracker.R)
truth = R_proto * measurement_var
error = matrix_error(R, truth)
print("Truth:\n", truth)
print("Estimation:\n", R)
print("Error: %.6f" % error)
print("-" * 15)
return error
def perform_estimation(residuals, tracker):
residuals = residuals - np.average(residuals, axis=0)
cor = Correlator(residuals)
correlation = cor.autocorrelation(used_taps)
# tracker.R = np.array([[1, 1],
# [1, 2]]) * sim_var
R = estimate_noise_ukf_scaling(correlation[0], tracker.Pz, tracker.R)
truth = R_proto * (measurement_var + sim_var)
error = matrix_error(R, truth)
print("Truth:\n", truth)
print("Estimation:\n", R)
print("Error: %.6f" % error)
print("-" * 15)
return error