# Particle Filters

The following C++ classes are the base for different PF implementations all across MRPT:

- mrpt::bayes::CParticleFilterCapable: This virtual class defines the interface that any particles based PDF class must implement in order to be executed by a CParticleFilter.
- mrpt::bayes::CParticleFilter: The class that executes iterations on a
*CParticleFilterCapable*object. - The container model mrpt::bayes::CParticleFilterData is used by some classes which are not really intended to be processed with a PF. It just represent a C++ template for sets of weighted samples a any given type, and common memory-keeping tasks.

Both the specific *particle filter algorithm* to run and the *resampling scheme* to use can be independently selected in the options structuremrpt::bayes::CParticleFilter::TParticleFilterOptions:

**PF algorithms**– See also the**description of the algorithms**.*pfStandardProposal*: Standard proposal distribution + weights according to likelihood function.*pfAuxiliaryPFStandard*: An auxiliary PF using the standard proposal distribution.*pfOptimalProposal*: Use the**exact**optimal proposal distribution (where available!, usually this will perform approximations)*pfAuxiliaryPFOptimal*: Use the optimal proposal and a auxiliary particle filter (see paper).- In addition,
*adaptiveSampleSize*can select whether to use a dynamic number of samples, or not. Take into account that only a subset of all the possible combinations of algorithms may be implemented for each problem.

**Resampling algorithms**– See also the**description of the algorithms**.*prMultinomial*(Default): Uses standard select with replacement (draws M random uniform numbers)*prResidual*: The residual or “remainder” method.*prStratified*: The stratified resampling, where a uniform sample is drawn for each of M subdivisions of the range (0,1].*prSystematic*: A single uniform sample is drawn in the range (0,1/M].

## An illustrative implementation: 2D tracking

The following video shows the example samples/bayesianTracking running. Follow the link to check out the source code to get all the details of the implementation. The same code demonstrates Kalman filtering.