Convenient Power System Modelling and Analysis based on PYPOWER and pandas

Implementation of several popular Kalman filter nonlinear variants intended for robotics systems and vehicle state estimation, including Extended Kalman Filter, Unscented Kalman Filter, Error State EKF, Invariant EKF, Square Root EKF, Cubature KF.

A powerful 3-phase load flow solver, by Roseau Technologies

State estimation of a Dubin's Car using Extended Kalman Filter

A state estimation package for Lie groups!

Second-order iterated smoothing algorithms for state estimation

Probabilistic Contact State Estimation for Legged Robots in ROS

Data Assimilation with Python: a Package for Experimental Research

State Estimation using Kalman Filters, EKF and solving the Data Association problem

SBG ROS2 Driver: A ROS2-compatible repository providing seamless integration with SBG Systems' Inertial Measurement Unit (IMU) for precise state estimation in autonomous vehicles.

Pure static Lie groups in Numpy, Jax, and C++

Tutorials on data assimilation (DA) and the EnKF

Legged Contact Detection (LCD): A deep learning approach for Contact Estimation of Legged Robots using inertial and F/T measurements

Explore the world of UAV-State-Estimation, a detailed Python repository focusing on 3D state estimation for unmanned aerial vehicles (UAVs) through the use of Kalman Filter methods. This repository uniquely merges theoretical frameworks and hands-on simulations, making it an ideal resource for both drone enthusiasts and experts in drone technology.

Vehicle State Estimation using Error-State Extended Kalman Filter

Variational Filtering via Wasserstein Gradient Flow

Power System Dynamic State Estimation Based on Heterogeneous Computing Acceleration

Physics Informed Deep Learning for Traffic State Estimation: Illustrations with LWR and CTM Models

Open source library for state estimation of a distribution network modeled in OpenDSS

The `KalmanFilter` class implements the Kalman Filter algorithm for estimating the state of linear dynamic systems using noisy measurements. The class accepts system matrices, initial state, and covariance, and provides `predict` and `update` methods for state prediction and refinement based on new observations.