This is an open source Kalman filter C++ library based on Eigen3 library for matrix operations. The library has generic template based classes for most of Kalman filter variants including: (1) Kalman Filter, (2) Extended Kalman Filter, (3) Unscented Kalman Filter, and (4) Square-root UKF..

A generic library for linear and non-linear Gaussian smoothing problems. The code leverages JAX and implements several linearization algorithms, both in a sequential and parallel fashion, as well as efficient gradient rules for computing gradients of required quantities (such as the pseudo-loglikelihood of the system).

State estimation and filtering algorithms in Go

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.

State estimation, smoothing and parameter estimation using Kalman and particle filters.

Kalman Filter implementations in C++

Harness the power of GPU acceleration for fusing visual odometry and IMU data with an advanced Unscented Kalman Filter (UKF) implementation. Developed in C++ and utilizing CUDA, cuBLAS, and cuSOLVER, this system offers unparalleled real-time performance in state and covariance estimation for robotics and autonomous system applications.

Experiments for online learning and data assimilation for time series data.

Implementation of the Unscented Kalman Filter (UKF) in C++ using Eigen.

Unscented kalman filter

An implementation of an Unscented Kalman Filter (UKF) for parameter identification on MATLAB.

A software package designed for estimating historical ship tracks utilising either Kalman filters or Gaussian processes.

Self-position estimation by eskf by measuring gnss and imu

Fused noisy LiDAR and Radar sensor measurements to estimate the states of multiple cars on highway using Unscented Kalman Filter(UKF).

Multi Object Tracking Sensor Fusion 3D Interactive Simulation/Game in PCL

Underwater Object Tracking using SONAR and Unscented Kalman Filter is a simulation aimed at modeling an underwater object tracking scenario using SONAR and the Unscented Kalman Filter (UKF). The project utilizes the Phased Array Toolbox in MATLAB to implement the SONAR equations in real-time.

Unscented Kalman Filter and Normalized Innovation Squared evaluation

Contains my assignments and labs for Udacity's Self-Driving Car Engineer nanodegree

UKF for orientation tracking on inertial data

A small collection of Kalman Filters on Lie groups