mode_states_init = GaussParams(mean_init, cov_init)
init_imm_state = MixtureParameters(mode_probabilities_init,
[mode_states_init] * 2)
if run_three_models:
mode_probabilities_init = np.array([p10, p20, p30])
init_imm_state = MixtureParameters(mode_probabilities_init,
[mode_states_init] * 3)
assert np.allclose(np.sum(mode_probabilities_init),
1), "initial mode probabilities must sum to 1"
# make model
measurement_model = measurementmodels.CartesianPosition(sigma_z, state_dim=5)
dynamic_models: List[dynamicmodels.DynamicModel] = []
dynamic_models.append(dynamicmodels.WhitenoiseAccelleration(sigma_a_CV, n=5))
dynamic_models.append(dynamicmodels.ConstantTurnrate(sigma_a_CT, sigma_omega))
ekf_filters = []
ekf_filters.append(ekf.EKF(dynamic_models[0], measurement_model))
ekf_filters.append(ekf.EKF(dynamic_models[1], measurement_model))
if run_three_models:
dynamic_models.append(
dynamicmodels.WhitenoiseAccelleration(sigma_a_CV_high, n=5))
ekf_filters.append(ekf.EKF(dynamic_models[2], measurement_model))
imm_filter = imm.IMM(ekf_filters, PI)
tracker = pda.PDA(imm_filter, clutter_intensity, PD, gate_size)
# init_imm_pda_state = tracker.init_filter_state(init__immstate)
NEES = np.zeros(K)
NEESpos = np.zeros(K)
ax1.scatter(*Z_plot_data.T, color="C1")
ax1.plot(*Xgt.T[:2], color="C0", linewidth=1.5)
ax1.set_title("True trajectory and the nearby measurements")
plt.show(block=False)
sigma_z = 20
sigma_omega = 0.1
PD = 0.65
clutter_intensity = 4 / (4000 * 4000)
gate_size = 5
useTurnRateModel = True
if useTurnRateModel:
sigma_a = 4 # works really well with sigma_omega = 0.1
dynamic_model = dynamicmodels.ConstantTurnrate(sigma_a, sigma_omega)
else: # constant velocity model
sigma_a = 2
dynamic_model = dynamicmodels.WhitenoiseAccelleration(sigma_a, n=5)
measurement_model = measurementmodels.CartesianPosition(sigma_z, state_dim=5)
ekf_filter = ekf.EKF(dynamic_model, measurement_model)
tracker = pda.PDA(ekf_filter, clutter_intensity, PD, gate_size)
# allocate
NEES = np.zeros(K)
NEESpos = np.zeros(K)
NEESvel = np.zeros(K)
# initialize
sigma_a_CT = 0.07 sigma_omega = 0.0016 * np.pi # initial values init_mean = np.array([0, 0, 2, 0, 0]) init_cov = np.diag([25, 25, 3, 3, 0.0005]) ** 2 init_state_CV = GaussParams(init_mean[:4], init_cov[:4, :4]) # get rid of turn rate init_state_CT = GaussParams(init_mean, init_cov) # same init otherwise init_states = [init_state_CV, init_state_CT] # create models measurement_model_CV = measurementmodels.CartesianPosition(sigma_z) measurement_model_CT = measurementmodels.CartesianPosition(sigma_z, state_dim=5) CV = dynamicmodels.WhitenoiseAccelleration(sigma_a_CV) CT = dynamicmodels.ConstantTurnrate(sigma_a_CT, sigma_omega) # create filters filters = [] filters.append(ekf.EKF(CV, measurement_model_CV)) filters.append(ekf.EKF(CT, measurement_model_CT)) # allocate pred = [] upd = [] NIS = np.empty((2, K)) NEES_pred = np.empty((2, K)) NEES_upd = np.empty((2, K)) err_pred = np.empty((2, 2, K)) # (filters, vel/pos, time) err_upd = np.empty((2, 2, K)) # (filters, vel/pos, time)
