def run_tracker(): sim, tracker = setup() readings, truths, filtered, residuals = filtering(sim, tracker) # plot_results(readings, filtered, residuals) # perform estimation cor = Correlator(residuals[-sample_size:]) correlation = cor.autocorrelation(used_taps) R_mehra = estimate_noise_mehra(correlation, tracker.K, tracker.F, tracker.H) R_approx = estimate_noise_approx(correlation[0], tracker.H, tracker.P) abs_err_approx = R_approx - measurement_var rel_err_approx = abs_err_approx / measurement_var abs_err_mehra = R_mehra - measurement_var rel_err_mehra = abs_err_mehra / measurement_var print("True: %.6f" % measurement_var) print("Filter: %.6f" % tracker.R) print("Estimated (approximation): %.6f" % R_approx) print("Absolute error: %.6f" % abs_err_approx) print("Relative error: %.6f %%" % (rel_err_approx * 100)) print("Estimated (mehra): %.6f" % R_mehra) print("Absolute error: %.6f" % abs_err_mehra) print("Relative error: %.6f %%" % (rel_err_mehra * 100)) print("-" * 15) return (abs_err_mehra, rel_err_mehra, abs_err_approx, rel_err_approx)
def perform_estimation(residuals, residuals_posterior, tracker, H_arr, Ks): cor = Correlator(residuals) correlation = cor.autocorrelation(used_taps) R_mehra = estimate_noise_mehra(correlation, Ks[-1], tracker.F, H_arr[-1]) R_extended = estimate_noise_extended(correlation, Ks, tracker.F, H_arr) R_approx = estimate_noise_approx(correlation[0], H_arr[-1], tracker.P) cor_posterior = Correlator(residuals_posterior) correlation_posterior = cor_posterior.autocorrelation(used_taps) R_approx_posterior = estimate_noise_approx(correlation_posterior[0], H_arr[-1], tracker.P, "posterior") truth = R_proto * measurement_var error_extended = matrix_error(R_extended, truth) error_mehra = matrix_error(R_mehra, truth) error_approx = matrix_error(R_approx, truth) error_approx_posterior = matrix_error(R_approx_posterior, truth) print("Extended estimation:") print("\tEstimated R:", R_extended) print("\tError: %.6f" % error_extended) print("Mehra estimation:") print("\tEstimated R:", R_mehra) print("\tError: %.6f" % error_mehra) print("Approximate estimation:") print("\tEstimated R:", R_approx) print("\tError: %.6f" % error_approx) print("Approximate estimation (posterior):") print("\tEstimated R:", R_approx_posterior) print("\tError: %.6f" % error_approx_posterior) return (error_extended, error_mehra, error_approx, error_approx_posterior)
def perform_estimation(residuals, tracker): cor = Correlator(residuals) C_arr = cor.autocorrelation(used_taps) print("Truth:\n", sim_var) R = estimate_noise_mehra(C_arr, tracker.K, tracker.F, tracker.H) print("Mehra Method:\n", R) R_approx = estimate_noise_approx(C_arr[0], tracker.H, tracker.P) print("Approximated Method:\n", R_approx)
def perform_estimation(residuals, tracker, F_arr, K_arr): residuals = residuals - np.average(residuals, axis=0) cor = Correlator(residuals) C_arr = cor.autocorrelation(used_taps) R = estimate_noise_mehra(C_arr, tracker.K, tracker.F, tracker.H) R_approx = estimate_noise_approx(C_arr[0], tracker.H, tracker.P) R_extended = estimate_noise_extended(C_arr, K_arr, F_arr, tracker.H) return R_approx
def test_mehra_estimate(self): C_arr = np.asarray([[[31.371682426081264]], [[-6.9977524908455147]], [[0.70876720569959328]], [[-7.1734736799032239]], [[6.8883804618764914]], [[-2.6670500807302022]]]) K = np.asarray([[0.32765478], [0.64824051]]) F = np.array([[1, 0.1], [0, 1]]) H = np.array([[1, 0]]) R = estimate_noise_mehra(C_arr, K, F, H) assert R.shape == (1, 1)
def perform_estimation(residuals, tracker, F_arr, K_arr): cor = Correlator(residuals) C_arr = cor.autocorrelation(used_taps) print("Simulated:\n", R_proto * sim_var) R = estimate_noise_mehra(C_arr, tracker.K, tracker.F, tracker.H) print("Mehra:\n", R) R_approx = estimate_noise_approx(C_arr[0], tracker.H, tracker.P) print("Approximation:\n", R_approx) R_extended = estimate_noise_extended(C_arr, K_arr, F_arr, tracker.H) print("Extended:\n", R_extended)
def perform_estimation(residuals, tracker, H, F_arr, K_arr): cor = Correlator(residuals) correlation = cor.autocorrelation(used_taps) R_extended = estimate_noise_extended(correlation, K_arr, F_arr, H) R_mehra = estimate_noise_mehra(correlation, K_arr[-1], F_arr[-1], H) R_approx = estimate_noise_approx(correlation[0], H, tracker.P) truth = R_proto * measurement_var error_extended = matrix_error(R_extended, truth) error_mehra = matrix_error(R_mehra, truth) error_approx = matrix_error(R_approx, truth) return (error_extended, error_mehra, error_approx)
def perform_estimation(residuals, tracker, F_arr, Ks): residuals = residuals - np.average(residuals, axis=0) cor = Correlator(residuals) C_arr = cor.autocorrelation(used_taps) truth = R_proto * (sim_var + measurement_var) R = estimate_noise_mehra(C_arr, tracker.K, tracker.F, tracker.H) error_mehra = matrix_error(R, truth) R_approx = estimate_noise_approx(C_arr[0], tracker.H, tracker.P) error_approx = matrix_error(R_approx, truth) R_extended = estimate_noise_extended(C_arr, Ks, F_arr, tracker.H) error_extended = matrix_error(R_extended, truth) return (error_mehra, error_approx, error_extended)
def perform_estimation(residuals, tracker, lags): cor = Correlator(residuals[-sample_size:]) correlation = cor.autocorrelation(lags) R_mehra = estimate_noise_mehra(correlation, tracker.K, tracker.F, tracker.H) R_approx = estimate_noise_approx(correlation[0], tracker.H, tracker.P, "posterior") truth = R_proto * measurement_var error_mehra = matrix_error(R_mehra, truth) error_approx = matrix_error(R_approx, truth) print("Truth:\n", truth) print("Estimation Mehra:\n", R_mehra) print("Error Mehra:\n", error_mehra) print("Estimation Mohamed:\n", R_approx) print("Error Mohamed:\n", error_approx) print("-" * 15) return (error_mehra, error_approx)
def perform_estimation(residuals, tracker, F_arr, K_arr): cor = Correlator(residuals) correlation = cor.autocorrelation(used_taps) R_extended = estimate_noise_extended(correlation, K_arr, F_arr, tracker.H) R_mehra = estimate_noise_mehra(correlation, K_arr[-1], F_arr[-1], tracker.H) R_approx = estimate_noise_approx(correlation[0], tracker.H, tracker.P) truth = R_proto * measurement_var print("Truth:\n", truth) print("Extended Estimation:\n", R_extended) print("Error:\n", matrix_error(R_extended, truth)) print("Mehra estimation:\n", R_mehra) print("Error:\n", matrix_error(R_mehra, truth)) print("Approximated estimation:\n", R_approx) print("Error:\n", matrix_error(R_approx, truth)) print("-" * 15) error = matrix_error(R_extended, truth) return error
def perform_estimation(residuals, tracker, F_arr, K_arr): residuals = residuals - np.average(residuals, axis=0) cor = Correlator(residuals) C_arr = cor.autocorrelation(used_taps) truth = R_proto * sim_var matrix_size = matrix_error(truth, 0) print("Truth:\n", truth) R = estimate_noise_mehra(C_arr, tracker.K, tracker.F, tracker.H) error_R = matrix_error(R, truth) print("Mehra:\n", R) print("\t Relative error: %.6f" % (error_R / matrix_size)) R_extended = estimate_noise_extended(C_arr, K_arr, F_arr, tracker.H) error_R_extended = matrix_error(R_extended, truth) print("Extended:\n", R_extended) print("\t Relative error: %.6f" % (error_R_extended / matrix_size)) R_approx = estimate_noise_approx(C_arr[0], tracker.H, tracker.P) error_R_approx = matrix_error(R_approx, truth) print("Approximation:\n", R_approx) print("\t Relative error: %.6f" % (error_R_approx / matrix_size))