Should actually be defined for _any_ continuous system model! There should be a class between this one and system model tout court, not assuming gaussian uncertainty!
will probably give rise to memory allocation problems if you herit from this class and do not redefine this method.
Member BFL::EKParticleFilter::EKParticleFilter (MCPdf< ColumnVector > *prior, int resampleperiod=0, double resamplethreshold=0, int resamplescheme=DEFAULT_RS)
prior should be of type pdf and not mcpdf. See also notes with implementation
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
This method is not implemented, we can ReSize the std::vector<BFL::Matrix>, but we don't know the dimensions of the matrices self. So this will most certainly result in a segfault. Anyway, why would you need this?
Resampling is not implemented generically enough yet. There's only the possibility to choose between static period resampling and dynamic resampling as proposed by Jun Liu. The correct way of implementing this would be to create a virtual function that has to be implemented by the user, but this creates more hassle for the user (a different particle filter for each scheme).
Resampling is not implemented generically enough yet. There's only the possibility to choose between static period resampling and dynamic resampling as proposed by Jun Liu. The correct way of implementing this would be to create a virtual function that has to be implemented by the user, but this creates more hassle for the user (a different particle filter for each scheme).
Currently supports only systemmodels of the form P(x | x, u), where both u and x are continu or discrete. So it lacks support for mixed systems () and systems with extra parameters. You are welcome to provide an API and implementation for this :-)
Generated on Thu Mar 24 16:57:08 2011 for Bayesian Filtering Library by
1.5.9