def __init__(self,
n_particles,
state_kappa,
observation_kappa,
outlier_prob=0,
*args,
**kwargs):
"""
Localizes source using Von Mises particle filter
x_t ~ VM(x_{t-1}, kappa_v)
y_t ~ VM(x_t, kappa_w)
:param n_particles: number of particles to use
:param state_kappa: concentration parameter for state von mises distribution
:param observation_kappa: concentration parameter for observation von mises
distribution
:param outlier_prob: Probability that a given observation comes from a
background uniform outlier von mises distribution. If
this is omitted, it will be set to 0, and the normal
particle filtering algorithm will be used
All other parameters will be passed to TrackingLocalizer in the form of *args
and **kwargs
"""
TrackingLocalizer.__init__(self, *args, **kwargs)
self._grid_size = self._n_theta * self._n_phi
#self._process_search_space(search_space)
self._setup_particle_filters(n_particles, state_kappa,
observation_kappa, outlier_prob)
def __init__(self, mic_positions, search_space, mic_forward, mic_above,
trans_mat, state_cov, emission_mat, emission_cov, dft_len=512,
sample_rate=44100, n_theta=20, n_phi=1):
"""
Localizes source using Kalman Filter:
x_t = A*x_{t-1} + u, u ~ N(0,Q)
y_t = B*x_t + v, v ~ N(0,R)
The state vectors x_t have 6 components: 3 coordinates and 3 velocities.
So x = [c_1, c_2, c_3, v_1, v_2, v_3], where c_i is associated with v_i.
Note that since x is expected to be on a source plane, c_3 and v_3 should
be zero. However they are included for freedom and because this will make
linear transforms among 6 dimensional spaces more easy
:param mic_forward: length 3 vector indicating direction mic points forward
This should be the positive y direction from the mic's
point of view
:param mic_up: length 3 vector indicating direction mic points up. This
should be the positive z direction from the mics point of
view
:param trans_mat: Transition matrix A (should be 6x6 matrix).
:param state_cov: State noise covariance matrix Q
:param emission_mat: Emission matrix B
:param emission_cov: Emission covariance matrix R
"""
TrackingLocalizer.__init__(self, mic_positions, search_space, dft_len,
sample_rate, n_theta, n_phi)
self._grid_size = self._n_theta * self._n_phi
self._process_search_space(search_space)
self._setup_posterior_grid()
self._setup_state_model(mic_forward, mic_above, trans_mat, state_cov,
emission_mat, emission_cov)
def __init__(self, mic_positions, search_space, source_cov, dft_len=512, sample_rate=44100,
n_theta=20, n_phi=1):
"""
"""
TrackingLocalizer.__init__(self, mic_positions, search_space, dft_len,
sample_rate, n_theta, n_phi)
self._grid_size = self._n_theta * self._n_phi
self._process_search_space(search_space)
self._setup_posterior_grid()
self._setup_state_model(source_cov)
self._setup_structures()
def __init__(self, n_particles, state_kappa, *args, **kwargs):
"""
Localizes source using Von Mises particle filter
x_t ~ VM(x_{t-1}, kappa_v)
y_t ~ SRPLikelihood(x_t)
:param n_particles: number of particles to use
:param state_kappa: concentration parameter for state von mises distribution
All other parameters will be passed to TrackingLocalizer in the form of *args
and **kwargs
"""
TrackingLocalizer.__init__(self, *args, **kwargs)
self._grid_size = self._n_theta * self._n_phi
self._setup_particle_filters(n_particles, state_kappa)
def __init__(self, n_particles, state_kappa, *args, **kwargs):
"""
Localizes source using Von Mises particle filter
x_t ~ VM(x_{t-1}, kappa_v)
y_t ~ SRPLikelihood(x_t)
:param n_particles: number of particles to use
:param state_kappa: concentration parameter for state von mises distribution
All other parameters will be passed to TrackingLocalizer in the form of *args
and **kwargs
"""
TrackingLocalizer.__init__(self, *args, **kwargs)
self._grid_size = self._n_theta * self._n_phi
self._setup_particle_filters(n_particles, state_kappa)
def __init__(self,
mic_positions,
search_space,
source_cov,
dft_len=512,
sample_rate=44100,
n_theta=20,
n_phi=1):
"""
"""
TrackingLocalizer.__init__(self, mic_positions, search_space, dft_len,
sample_rate, n_theta, n_phi)
self._grid_size = self._n_theta * self._n_phi
self._process_search_space(search_space)
self._setup_posterior_grid()
self._setup_state_model(source_cov)
self._setup_structures()
def __init__(self,
mic_positions,
search_space,
mic_forward,
mic_above,
trans_mat,
state_cov,
emission_mat,
emission_cov,
dft_len=512,
sample_rate=44100,
n_theta=20,
n_phi=1):
"""
Localizes source using Kalman Filter:
x_t = A*x_{t-1} + u, u ~ N(0,Q)
y_t = B*x_t + v, v ~ N(0,R)
The state vectors x_t have 6 components: 3 coordinates and 3 velocities.
So x = [c_1, c_2, c_3, v_1, v_2, v_3], where c_i is associated with v_i.
Note that since x is expected to be on a source plane, c_3 and v_3 should
be zero. However they are included for freedom and because this will make
linear transforms among 6 dimensional spaces more easy
:param mic_forward: length 3 vector indicating direction mic points forward
This should be the positive y direction from the mic's
point of view
:param mic_up: length 3 vector indicating direction mic points up. This
should be the positive z direction from the mics point of
view
:param trans_mat: Transition matrix A (should be 6x6 matrix).
:param state_cov: State noise covariance matrix Q
:param emission_mat: Emission matrix B
:param emission_cov: Emission covariance matrix R
"""
TrackingLocalizer.__init__(self, mic_positions, search_space, dft_len,
sample_rate, n_theta, n_phi)
self._grid_size = self._n_theta * self._n_phi
self._process_search_space(search_space)
self._setup_posterior_grid()
self._setup_state_model(mic_forward, mic_above, trans_mat, state_cov,
emission_mat, emission_cov)
def __init__(self, n_particles, state_kappa, observation_kappa, outlier_prob=0,
*args, **kwargs):
"""
Localizes source using Von Mises particle filter
x_t ~ VM(x_{t-1}, kappa_v)
y_t ~ VM(x_t, kappa_w)
:param n_particles: number of particles to use
:param state_kappa: concentration parameter for state von mises distribution
:param observation_kappa: concentration parameter for observation von mises
distribution
:param outlier_prob: Probability that a given observation comes from a
background uniform outlier von mises distribution. If
this is omitted, it will be set to 0, and the normal
particle filtering algorithm will be used
All other parameters will be passed to TrackingLocalizer in the form of *args
and **kwargs
"""
TrackingLocalizer.__init__(self, *args, **kwargs)
self._grid_size = self._n_theta * self._n_phi
#self._process_search_space(search_space)
self._setup_particle_filters(n_particles, state_kappa, observation_kappa, outlier_prob)
