{"id":28927022,"url":"https://github.com/gabrielenava/eigenukf","last_synced_at":"2025-07-11T06:35:13.319Z","repository":{"id":204128118,"uuid":"711170667","full_name":"gabrielenava/EigenUKF","owner":"gabrielenava","description":"Implementation of the Unscented Kalman Filter (UKF) in C++ using Eigen.","archived":false,"fork":false,"pushed_at":"2024-12-27T20:38:11.000Z","size":418,"stargazers_count":3,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-06-22T12:13:37.797Z","etag":null,"topics":["cpp","eigen","estimation","unscented-kalman-filter"],"latest_commit_sha":null,"homepage":"","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/gabrielenava.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-10-28T12:26:57.000Z","updated_at":"2025-03-06T05:40:54.000Z","dependencies_parsed_at":null,"dependency_job_id":"b435c981-20f2-4494-b180-13ccaaca0dad","html_url":"https://github.com/gabrielenava/EigenUKF","commit_stats":null,"previous_names":["gabrielenava/eigen_ukf"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/gabrielenava/EigenUKF","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gabrielenava%2FEigenUKF","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gabrielenava%2FEigenUKF/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gabrielenava%2FEigenUKF/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gabrielenava%2FEigenUKF/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/gabrielenava","download_url":"https://codeload.github.com/gabrielenava/EigenUKF/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gabrielenava%2FEigenUKF/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":264748125,"owners_count":23657971,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cpp","eigen","estimation","unscented-kalman-filter"],"created_at":"2025-06-22T12:12:47.732Z","updated_at":"2025-07-11T06:35:13.313Z","avatar_url":"https://github.com/gabrielenava.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"# EigenUKF\n\n[![CI_UKF](https://github.com/gabrielenava/Eigen_UKF/actions/workflows/ci_ukf.yml/badge.svg)](https://github.com/gabrielenava/Eigen_UKF/actions/workflows/ci_ukf.yml)\n\nA C++ implementation of the Unscented Kalman Filter (UKF) using Eigen. The UKF uses a deterministic sampling technique known as the Unscented Transformation to calculate statistics around the mean. This technique does not require differentiability of the models. \n\nSee also https://groups.seas.harvard.edu/courses/cs281/papers/unscented.pdf for more details.\n\nFor the implementation I took inspiration from https://github.com/CoffeeKumazaki/kalman_filters.\n\n## Installation and usage\n\n**Tested only on Ubuntu 20.04 LTS**\n\n### Dependencies\n\n- [Eigen3](https://eigen.tuxfamily.org/index.php?title=Main_Page) library.\n\n### Compilation\n\nClone the repo on your PC. Then, enter the folder where you downloaded the repo, open a terminal and run:\n\n```\nmkdir build \u0026\u0026 cd build\ncmake .. -DCMAKE_INSTALL_PREFIX=\"/path/to/desired/install/dir\"\nmake install\n```\n\nThe UKF library will be available in the `install/lib` folder. The library is composed of two classes:\n\n- [UnscentedKF](lib/src/UnscentedKF.cpp): implementation of the Unscented Kalman Filter in c++. Provides methods to set up a routine for estimation/filtering of user-defined quantities via UKF. Works alongside with the UKFModel class which provides the prediction and observation models.\n\n- [UKFModel](lib/src/UKFModel.cpp): defines the prediction and observation models for the UnscentedKF class. This is an abstract class: the user must create his own child class derived from UKFModel, and implement with it the prediction and observation models for his specific problem.\n\n### Example\n\nAn example of usage of the library is available in the [example](example) folder. The filter is used to estimate the thrust provided by a jet engine and its rate of change, given the measured thrust data.\n\n### Test\n\nA simple test to verify library integrity is provided in the [test](test) folder. The test uses [Catch2](https://github.com/catchorg/Catch2.git) library and can be run with the `ctest` command from the `build` directory.\n\n## Maintainer\n\nGabriele Nava, @gabrielenava\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgabrielenava%2Feigenukf","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgabrielenava%2Feigenukf","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgabrielenava%2Feigenukf/lists"}