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reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["kalman-filter","kalman-filtering","rust","smoothing"],"created_at":"2024-09-24T15:00:52.535Z","updated_at":"2026-04-08T13:31:25.195Z","avatar_url":"https://github.com/rbagd.png","language":"Rust","funding_links":[],"categories":["Signal Processing","Statistical Modeling"],"sub_categories":[],"readme":"# Kalman filtering and smoothing library written in Rust\n\n[![Build Status](https://travis-ci.org/rbagd/rust-linearkalman.svg?branch=master)](https://travis-ci.org/rbagd/rust-linearkalman)\n[![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](http://www.gnu.org/licenses/gpl-3.0)\n\nAccess documentation for the library [here](http://rbagd.eu/rust-linearkalman/linearkalman/index.html). Library is also referenced in [Cargo][cargo] index.\n\nCurrently, library provides only time-invariant linear Kalman filtering and smoothing technique is known as fixed-interval smoothing (Rauch-Tung-Striebel smoother) which relies on Kalman filter estimates for the entire dataset. `linearkalman` relies on [rulinalg] library to implement linear algebra structures and operations and so input data is expected to be a `std::vec::Vec` of [Vector\u003cf64\u003e] objects, i.e. a vector of vectors.\n\nIn order to use this library, make sure your `Cargo.toml` file contains the following:\n\n```toml\n[dependencies]\nlinearkalman = \"0.1.3\"\n```\n\nLibrary can then be imported using:\n\n```rust\nextern crate linearkalman;\n```\n\n\n## Example\n\nBelow example assumes 3-dimensional measurement data with an underlying 2-dimensional state space model. With the help of a few macros from [rulinalg], a simple attempt to use the library to run Kalman filter and smoother would be as follows.\n\n\n```rust\n#[macro_use]\nextern crate rulinalg;\nextern crate linearkalman;\n\nuse rulinalg::vector::Vector;\nuse linearkalman::KalmanFilter;\n\nfn main() {\n\n  let kalman_filter = KalmanFilter {\n    // Process noise covariance\n    q: matrix![1.0, 0.1;\n               0.1, 1.0],\n    // Measurement noise matrix\n    r: matrix![1.0, 0.2, 0.1;\n               0.2, 0.8, 0.5;\n               0.1, 0.5, 1.2],\n    // Observation matrix\n    h: matrix![1.0, 0.7;\n               0.5, 0.7;\n               0.8, 0.1],\n    // State transition matrix\n    f: matrix![0.6, 0.2;\n               0.1, 0.3],\n    // Initial guess for state mean at time 1\n    x0: vector![1.0, 1.0],\n    // Initial guess for state covariance at time 1\n    p0: matrix![1.0, 0.0;\n                0.0, 1.0],\n  };\n\n  let data: Vec\u003cVector\u003cf64\u003e\u003e = vec![vector![1.04, 2.20, 3.12],\n                                    vector![1.11, 2.33, 3.34],\n                                    vector![1.23, 2.21, 3.45]];\n\n  let run_filter = kalman_filter.filter(\u0026data);\n  let run_smooth = kalman_filter.smooth(\u0026run_filter.0, \u0026run_filter.1);\n\n  // Print filtered and smoothened state variable coordinates\n  println!(\"filtered.1,filtered.2,smoothed.1,smoothed.2\");\n  for k in 0..3 {\n      println!(\"{:.6},{:.6},{:.6},{:.6}\",\n               \u0026run_filter.0[k].x[0], \u0026run_filter.0[k].x[1],\n               \u0026run_smooth[k].x[0], \u0026run_smooth[k].x[1])\n  }\n}\n```\n\n`examples` directory contains code sample which allows to import data from a CSV file and returns filtered and smoothed data to `stdout`.\n\n## License\n\nThis project is licensed under GPL3.\n\n[rulinalg]: https://github.com/AtheMathmo/rulinalg\n[Vector\u003cf64\u003e]: https://athemathmo.github.io/rulinalg/doc/rulinalg/vector/struct.Vector.html\n[cargo]: https://crates.io/crates/linearkalman\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frbagd%2Frust-linearkalman","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frbagd%2Frust-linearkalman","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frbagd%2Frust-linearkalman/lists"}