{"id":25517827,"url":"https://github.com/s4k10503/kalmanfilterimplementations","last_synced_at":"2026-05-07T01:31:15.724Z","repository":{"id":187083399,"uuid":"666090629","full_name":"s4k10503/KalmanFilterImplementations","owner":"s4k10503","description":"An open-source collection of Kalman Filter implementations in various programming languages.","archived":false,"fork":false,"pushed_at":"2023-10-14T05:53:09.000Z","size":8,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-07-26T11:41:55.141Z","etag":null,"topics":["cpp","csharp","python3"],"latest_commit_sha":null,"homepage":"","language":"C#","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/s4k10503.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","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,"zenodo":null}},"created_at":"2023-07-13T17:29:26.000Z","updated_at":"2023-10-04T13:32:31.000Z","dependencies_parsed_at":"2025-05-21T04:11:41.732Z","dependency_job_id":"69dba969-96bc-4e68-9c92-8af0fc3f18e2","html_url":"https://github.com/s4k10503/KalmanFilterImplementations","commit_stats":null,"previous_names":["s4k10503/kalmanfilterimplementations"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/s4k10503/KalmanFilterImplementations","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/s4k10503%2FKalmanFilterImplementations","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/s4k10503%2FKalmanFilterImplementations/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/s4k10503%2FKalmanFilterImplementations/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/s4k10503%2FKalmanFilterImplementations/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/s4k10503","download_url":"https://codeload.github.com/s4k10503/KalmanFilterImplementations/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/s4k10503%2FKalmanFilterImplementations/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32719387,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-07T00:29:05.620Z","status":"ssl_error","status_checked_at":"2026-05-07T00:28:57.074Z","response_time":117,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while 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":["cpp","csharp","python3"],"created_at":"2025-02-19T15:38:03.092Z","updated_at":"2026-05-07T01:31:15.708Z","avatar_url":"https://github.com/s4k10503.png","language":"C#","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# Multi-Language Kalman Filter Implementations\n\nThis repository contains implementations of a Kalman filter in multiple programming languages. Currently, the following languages are supported:\n\n- C#\n- Python\n- C++\n\nEach implementation can be found in the respective directory under `src/`.\n\n## C# Implementation\n\nThe C# implementation can be found in the `src/csharp` directory. This implementation uses the MathNet.Numerics library.\n\n### Dependencies\n\nThe C# implementation uses the following libraries:\n\n- [MathNet.Numerics](https://github.com/mathnet/mathnet-numerics) (MIT License)\n\n### Usage\n\n```csharp\nusing MathNet.Numerics.LinearAlgebra;\nusing MathNet.Numerics.Distributions;\n\n// Initialize model parameters\nMatrix\u003cfloat\u003e F = ...; // State transition matrix\nMatrix\u003cfloat\u003e H = ...; // Observation matrix\nMatrix\u003cfloat\u003e B = ...; // Control matrix\nMatrix\u003cfloat\u003e x = ...; // Initial state estimate\nMatrix\u003cfloat\u003e P = ...; // Initial error covariance estimate\nMatrix\u003cfloat\u003e Q = ...; // Estimated error in process\nMatrix\u003cfloat\u003e R = ...; // Estimated error in measurements\n\n// Create an instance of the KalmanFilter class\nvar kf = new KalmanFilter(F, H, B, x, P, Q, R);\n\n// Control vector\nMatrix\u003cfloat\u003e u = ...;\n\n// Perform the prediction step\nkf.Predict(u);\n\n// Measurement vector\nMatrix\u003cfloat\u003e z = ...;\n\n// Perform the update step\nkf.Update(z);\n\n// Get the current state estimate\nvar currentState = kf.GetCurrentState();\n```\n\nPlease replace the ... with appropriate data.  \n\n## Python Implementation\n\nThe Python implementation can be found in the `src/python` directory.\n\n### Dependencies\n\nThe Python implementation uses the following libraries:\n\n- [numpy](https://numpy.org/) (BSD License)\n- [scipy](https://www.scipy.org/) (BSD License)\n\n### Usage\n\n```python\nimport numpy as np\nfrom kalman_filter import KalmanFilter\n\n# Initialize the Kalman Filter\nF = np.array([[...]])  # State transition matrix\nH = np.array([[...]])  # Observation matrix\nB = np.array([[...]])  # Control matrix\nx = np.array([[...]])  # Initial state estimate\nP = np.array([[...]])  # Initial error covariance estimate\nQ = np.array([[...]])  # Estimated error in process\nR = np.array([[...]])  # Estimated error in measurement\n\nkf = KalmanFilter(F, H, B, x, P, Q, R)\n\n# Update the state transition matrix\ndt = ...  # Time step\nkf.update_state_transition_matrix(dt)\n\n# Predict the next state\nu = np.array([[...]])  # Control input\nkf.predict(u)\n\n# Update the state with the observation\nz = np.array([[...]])  # Observation\nkf.update(z)\n\n# Get the current state estimate\nx = kf.get_current_state()\n```\n\nPlease replace the ... with appropriate data.\n\n## C++ Implementation\n\nThe C++ implementation can be found in the `src/cpp` directory.\n### Dependencies\n\nThe C++ implementation uses the following libraries:\n\n- [Eigen](http://eigen.tuxfamily.org/index.php?title=Main_Page) (MPL2 License)\n- [Boost](https://www.boost.org/) (Boost Software License)\n\n### Usage\n\n```cpp\n#include \"KalmanFilter.h\"\n#include \u003cEigen/Dense\u003e\n#include \u003cboost/math/distributions/chi_squared.hpp\u003e\n\n// Initialize model parameters\nEigen::MatrixXd F = ...; // State transition matrix\nEigen::MatrixXd B = ...; // Control matrix\nEigen::MatrixXd H = ...; // Observation matrix\nEigen::MatrixXd x = ...; // Initial state estimate\nEigen::MatrixXd P = ...; // Initial error covariance estimate\nEigen::MatrixXd Q = ...; // Estimated error in process\nEigen::MatrixXd R = ...; // Estimated error in measurements\n\n// Create an instance of the KalmanFilter class\nKalmanFilter kf(F, B, H, x, P, Q, R);\n\n// Time step\ndouble dt = ...;\n\n// Update the state transition matrix\nkf.update_state_transition_matrix(dt);\n\n// Control vector\nEigen::MatrixXd u = ...;\n\n// Perform the prediction step\nkf.predict(u);\n\n// Measurement vector\nEigen::MatrixXd z = ...;\n\n// Perform the update step\nkf.update(z);\n\n// Get the current state estimate\nEigen::MatrixXd currentState = kf.get_current_state();\n```\n\nPlease replace the ... with appropriate data.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fs4k10503%2Fkalmanfilterimplementations","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fs4k10503%2Fkalmanfilterimplementations","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fs4k10503%2Fkalmanfilterimplementations/lists"}