The Nonlinear Estimation Toolbox

This is a MATLAB toolbox for nonlinear state estimation developed at the Karlsruhe Institute of Technology (KIT), Germany. It contains state-of-the-art (sample-based) nonlinear Kalman filters and nonlinear estimators such as particle filters. The toolbox is

Reliable: Checks for computed state estimates and proper input arguments, extensive code reuse, and hundreds of unit tests make this toolbox ideal to perform and evaluate state estimation problems with a variety of filters.
Flexible: You can compare a range of filters for a given estimation problem with little effort or simply exchange filters by changing only a few lines of code.
Extensible: Clean interfaces and consequent use of inheritance ease the implementation and development of new estimators and features.

This toolbox is open-source and licensed under the GPLv3.

You can browse the source code online.

News

25.09.2017

Nonlinear Estimation Toolbox 2.0.1 released!

Changes

14.09.2017

Updated the C++ documentation.

01.09.2017

Nonlinear Estimation Toolbox 2.0 released!

This is a major toolbox release for cleaning up its API (more consistent method naming and removal of rather unnecessary functionalities) to allow for a better API understanding and better implementation of new features. This release also includes new estimators.

Updated Getting Started to reflect all API changes.

Also improved documentation for Estimators and Probability Distributions.

General changes

Filters

Renamed getPointEstimate() to getStateMeanAndCov().
In addition to getPointEstimate(), getStateMeanAndCov() now also returns the square root of the system state covariance matrix (the lower Cholesky decomposition) analogous to the getMeanAndCov() methods of probability distributions.
Added setStateMeanAndCov() method for fastly setting the system state without creating a temporary Gaussian distribution, e.g., in order to assign the Gaussian state estimate of a filter to another one. Note that this method does not perform input validation like setState()!
Gaussian filters
- Users can now specify custom state estimate post processing via the {set,get}PredictionPostProcessing() and {set,get}setUpdatePostProcessing() methods, e.g., to implement constrained state estimation. The set post processing methods are then executed after each state prediction and measurement update, respectively.
Added several recursive update filter (RUF) variants
- ERUF: extended recursive update filter (the originally proposed RUF).
- CRUF: the fifth-degree cubature recursive update filter.
- GHRUF: the Gauss-Hermite recursive update filter.
- RURUF: the randomized unscented recursive update filter.
- S2RUF: the smart sampling recursive update filter.
- URUF: the unscented recursive update filter.
Iterative Kalman filters
- Dropped the analytic moment computation models used by Kalman filters and moved the analytic moment computation for the linear cases directly into LinearMeasurementModel and LinearSystemModel by introducing the getAnalyticMoments() methods.
- Users can now specify a custom convergence check via the {set,get}ConvergenceCheck() methods, e.g., based on the Kullback-Leibler divergence (KLD) using the new Utils.getGaussianKLD() method.
- Added getNumIterations().
- Renamed setMeasValidationThreshold() to setMeasGatingThreshold().
- Renamed getMeasValidationThreshold() to getMeasGatingThreshold().
- Removed getLastUpdateData().
- Removed the now obsolete AnalyticKF filter.
- The implementation of a sample-based Kalman filter now handles the special case of equally weighted samples efficiently. That is, there is no need for a specialized implementation, e.g., for the S2KF or the UKF with equally weighted samples. This also holds for the new sample-based recursive update filters.
- RUKF
  
  Renamed setNumIterations() to setNumSamplesFactors.
  
  Renamed getNumIterations() to getNumSamplesFactors.
- S2KF
  
  The used LCD-based Gaussian sampling technique can now be configured completely independently for state prediction and measurement update.
  
  Added getNumSamplesConfigPrediction().
  
  Added getNumSamplesConfigUpdate().
  
  Renamed setNumSamplesByFactor() to setNumSamplesByFactors().
  
  Removed online computation mode including the setOnlineMode() method.
  
  Removed asymmetric LCD-based sampling mode including the setSymmetricMode() method.
- UKF
  
  Renamed setSampleScaling() to setSampleScalings().
  
  Renamed getSampleScaling() to getSampleScalings().
Particle filters
- Changed default name of SIRPF from "SIR-PF" to "SIRPF".
- Changed default name of ASIRPF from "Auxiliary SIR-PF" to "ASIRPF".
- Merged the PF interface into the SIRPF.
PGF
- The used LCD-based Gaussian sampling technique can now be configured completely independently for state prediction and measurement update.
- Added getNumSamplesConfigPrediction().
- Added getNumSamplesConfigUpdate().
- Renamed setNumSamplesByFactor() to setNumSamplesByFactors().
- Renamed getLastUpdateData() to getNumProgSteps().

Distributions

Gaussian sampling techniques

Misc

Interfaces

Examples

Tests

News Archive

First Steps

Installation — How to download and install the Nonlinear Estimation Toolbox

Getting Started — Learn the basic principles of the Nonlinear Estimation Toolbox

Trouble Shooting — Get help if you have trouble with the Nonlinear Estimation Toolbox

Toolbox Components

Estimators — All filters available in the toolbox

Measurement Models — All measurement models available in the toolbox

System Models — All system models available in the toolbox

Probability Distributions — All probability distributions available in the toolbox

Gaussian Sampling Techniques — All Gaussian sampling techniques available in the toolbox

Advanced Topics

Speed Up Your Code with C++ — Make use of MEX Files and the Eigen linear algebra library

Contribute

Don't hesitate to report bugs or suggest toolbox enhancements using the Issue Tracker.

If you directly want to contribute code, e.g., a new estimator, you can make a Pull Request or contact the developers.

Cite the Toolbox

If you use the Nonlinear Estimation Toolbox in your research, please cite it with

@Misc{nonlinearestimationtoolbox,
  Title   = {Nonlinear Estimation Toolbox},
  Author  = {Jannik Steinbring},
  Url     = {https://bitbucket.org/nonlinearestimation/toolbox}
}

Contact

The toolbox is maintained and developed by Jannik Steinbring.

Contributors

Antonio Zea
Christof Chlebek
Florian Faion
Florian Rosenthal
Igor Gilitschenski
Martin Pander

Parts of the toolbox are used by the libDirectional, a MATLAB library for directional statistics and directional estimation.

The Nonlinear Estimation Toolbox

News

Changes

General changes

Filters

Gaussian filters

Added several recursive update filter (RUF) variants

Iterative Kalman filters

RUKF

S2KF

UKF

Particle filters

PGF

Distributions

DiracMixture