Beispiel #1
0
def default_phd_parameters():
    state_update_fn = fn_params()

    # Clutter function
    clutter_fn_handle = uniform_clutter
    clutter_fn_parameters = PARAMETERS()
    clutter_fn_parameters.intensity = 2
    clutter_fn_parameters.range = [[-1, 1], [-1, 1]]
    clutter_fn = fn_params(clutter_fn_handle, clutter_fn_parameters)

    # Birth function
    birth_fn_handle = measurement_birth
    birth_fn_parameters = PARAMETERS()
    birth_fn_parameters.intensity = 0.01
    birth_fn = fn_params(birth_fn_handle, birth_fn_parameters)

    # Survival/detection probability
    ps_fn_handle = constant_survival
    ps_fn_parameters = PARAMETERS()
    ps_fn_parameters.ps = 1
    ps_fn = fn_params(ps_fn_handle, ps_fn_parameters)
    pd_fn_handle = constant_detection
    pd_fn_parameters = PARAMETERS()
    pd_fn_parameters.pd = 0.98
    pd_fn = fn_params(pd_fn_handle, pd_fn_parameters)

    # Use default estimator
    estimate_fn = fn_params()

    return (state_update_fn, clutter_fn, birth_fn, ps_fn, pd_fn, estimate_fn)
Beispiel #2
0
def default_constant_position_model(dims=3):
    markov_predict_fn_handle = markov_predict
    markov_predict_fn_parameters = PARAMETERS()
    markov_predict_fn_parameters.F = np.eye(dims)
    markov_predict_fn_parameters.Q = np.eye(dims)
    markov_predict_fn = fn_params(markov_predict_fn_handle, markov_predict_fn_parameters)

    obs_fn_handle = None
    obs_fn_parameters = PARAMETERS()
    obs_fn_parameters.H = np.eye(dims)
    obs_fn_parameters.R = np.eye(dims)
    obs_fn = fn_params(obs_fn_handle, obs_fn_parameters)

    # Likelihood function - not used here
    likelihood_fn = fn_params()

    return (markov_predict_fn, obs_fn, likelihood_fn)