pf_base.h File Reference

All non Rao-Blackwellized particle filters without covariates inherit from this. More...

#include <map>
#include <string>
#include <vector>
#include <Eigen/Dense>

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Classes
class	pf::bases::pf_base< float_t, dimobs, dimstate >
class	pf::bases::pf_withcov_base< float_t, dimobs, dimstate, dimcov >
class	pf::bases::rbpf_base< float_t, dim_s_state, dim_ns_state, dimobs >
class	pf::bases::ForwardMod< dimx, dimy, float_t >
class	pf::bases::GenForwardMod< dimx, dimy, float_t >
class	pf::bases::FutureSimulator< dimx, dimy, float_t, nparts >
class	pf::bases::GenFutureSimulator< dimx, dimy, float_t, nparts >
class	pf::bases::cf_filter< dimstate, dimobs, float_t >
	Abstract Base Class for all closed-form filters. More...
class	pf::bases::pf_base_crn< float_t, dimobs, dimstate, dimu, dimur, numparts >

Detailed Description

All non Rao-Blackwellized particle filters without covariates inherit from this.

inherit from this if, in addition to filtering, you want to simulate future trajectories from the current filtering distribution. This simulates two future trajectories for each particle you have–one for the state path, and one for the observation path. Unlike the above class, this one

inherit from this if, in addition to filtering, you want to simulate future trajectories from the current filtering distribution. This simulates two future trajectories for each particle you have–one for the state path, and one for the observation path.

inherit from this if you want to simulate from a homogeneous forward/generative model. This class is more general than the above because it can simulate future states using past observations.

inherit from this if you want to simulate from a homogeneous forward/generative model.

All Rao-Blackwellized particle filters inherit from this.

All non Rao-Blackwellized particle filters with covariates inherit from this.

Author

t

Template Parameters

float_t	(e.g. double, float, etc.)
dimobs	the dimension of each observation
dimstate	the dimension of each state

Author

t

Template Parameters

float_t	(e.g. double, float, etc.)
dimobs	the dimension of each observation
dimstate	the dimension of each state
dimcov

Author

t

Template Parameters

float_t	(e.g. double, float, etc.)
dim_s_state	the dimension of the state vector that's sampled
dim_ns_state	the dimension of the state vector that isn't sampled
dimobs	the dimension of each observation vector