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| Filter (Pdf< StateVar > *prior) |
| Constructor. More...
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| Filter (const Filter< StateVar, MeasVar > &filt) |
| copy constructor More...
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virtual | ~Filter () |
| destructor
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virtual void | Reset (Pdf< StateVar > *prior) |
| Reset Filter.
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virtual bool | Update (SystemModel< StateVar > *const sysmodel, const StateVar &u, MeasurementModel< MeasVar, StateVar > *const measmodel, const MeasVar &z, const StateVar &s) |
| Full Update (system with inputs/sensing params) More...
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virtual bool | Update (SystemModel< StateVar > *const sysmodel, MeasurementModel< MeasVar, StateVar > *const measmodel, const MeasVar &z, const StateVar &s) |
| Full Update (system without inputs, with sensing params) More...
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virtual bool | Update (SystemModel< StateVar > *const sysmodel, MeasurementModel< MeasVar, StateVar > *const measmodel, const MeasVar &z) |
| Full Update (system without inputs/sensing params) More...
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virtual bool | Update (SystemModel< StateVar > *const sysmodel, const StateVar &u, MeasurementModel< MeasVar, StateVar > *const measmodel, const MeasVar &z) |
| Full Update (system with inputs, without sensing params) More...
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virtual bool | Update (SystemModel< StateVar > *const sysmodel, const StateVar &u) |
| System Update (system with inputs) More...
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virtual bool | Update (SystemModel< StateVar > *const sysmodel) |
| System Update (system without inputs) More...
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virtual bool | Update (MeasurementModel< MeasVar, StateVar > *const measmodel, const MeasVar &z, const StateVar &s) |
| Measurement Update (system with "sensing params") More...
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virtual bool | Update (MeasurementModel< MeasVar, StateVar > *const measmodel, const MeasVar &z) |
| Measurement Update (system without "sensing params") More...
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virtual Pdf< StateVar > * | PostGet () |
| Get Posterior density. More...
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int | TimeStepGet () const |
| Get current time. More...
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template<typename StateVar, typename MeasVar>
class BFL::Filter< StateVar, MeasVar >
Abstract class representing an interface for Bayesian Filters.
This is the Abstract interface class that defines the interface of Bayesian filters. These filters are all related to i) a System Model, ii) a Measurement Model and iii) a Prior density reflecting the subjective belief of the person applying the filter BEFORE getting sensor or any other form of information about the modeled system.
This class is the base class for particle filters, kalman filters, ...
This class is a template class with 2 templates. In this way it allows filtering for "semi-discrete" models, eg. models with a fixed number of states (discrete states) but with continuous observations, as needed in Automatic Speech Recognition.
- See also
- Pdf SystemModel MeasurementModel ConditionalPdf
- Bug:
- For now, due to a "bug" (= non-existence of a feature :-) in the ConditionalPdf class, STATES AND INPUTS MUST BE OF THE SAME TYPE (both discrete, or both continuous! This means that you can use this class for the following model types:
- States, inputs and measurements continuous (most frequently used?)
- States and inputs continous, Measurements discrete
- States and inputs discrete, Measurements continous
- States, inputs and measurements discrete
StateVar represents the nature of the states and inputs MeasVar represents the nature of the measurements
BEWARE: The order of the template arguments is reversed with respect to the notation used in "measurementmodel.h"
Definition at line 77 of file filter.h.