plt.show(block=False)
plt.pause(plotpause)
# %% setup and track
# Model parameters EKF
sigma_a = 3 # From EKF exc 3
sigma_z = 10 # From EKF exc 3
# PDA relevant
PD = 0.9
clutter_intensity = 10e-6 # TODO # Basically estimated from poisson clutter model, makes no sense to have fixed one
gate_size = 3
CV = dynamicmodels.WhitenoiseAccelleration(sigma_a)
measurement_model = measurementmodels.CartesianPosition(sigma_z)
ekf_filter = ekf.EKF(CV, measurement_model)
tracker = pda.PDA(ekf_filter, clutter_intensity, PD, gate_size)
# initialize
z_0 = Z[0]
x_bar_init = np.array([*z_0[0], 0, 0])
P_bar_init = np.zeros((4, 4))
P_bar_init[[0, 1], [0, 1]] = 2 * sigma_z**2
P_bar_init[[2, 3], [2, 3]] = 5**2
init_state = tracker.init_filter_state({"mean": x_bar_init, "cov": P_bar_init})
# allocate
PI = np.array([[PI11, (1 - PI11)], [(1 - PI22), PI22]])
assert np.allclose(np.sum(PI, axis=1), 1), "rows of PI must sum to 1"
mean_init = np.array([0, 0, 0, 0, 0])
cov_init = np.diag([1000, 1000, 30, 30, 0.1])**2 # THIS WILL NOT BE GOOD
mode_probabilities_init = np.array([p10, (1 - p10)])
mode_states_init = GaussParams(mean_init, cov_init)
init_imm_state = MixtureParameters(mode_probabilities_init,
[mode_states_init] * 2)
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))
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)
NEESvel = np.zeros(K)