Tracklib library provide a variety of tools, operators and functions to manipulate GPS trajectories

Application of the Kalman filter and Extended Kalman filter for measuring the degree of time varying market efficiency.

Project for Master in Electromechanical Engineering at Bruface (ULB-VUB). Includes code for sending IMU data from Arduino Nano 33 BLE to Python via BLE, and then stream it to a LSL Network. STL files are included for 3D printing a box and clamp to attach to a welding gun.

A library for differentiable robotics.

night sky, kalman, particle

This project aims to explore and compare different Kalman filter architectures and their performance on FPGA platforms. The focus is on two main applications: IMU sensor fusion for quadcopters and prediction in power electronics for microgrid renewable energy systems.

Robot platooning, sensor fusion of odometry and inertial unit and more ...

Code for the paper "Computation-Aware Kalman Filtering and Smoothing"

Kálmán filter based ROS 1 / ROS 2 node (geometry_msgs/pose, sensor_msgs/imu)

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).

Material for the course "Time series analysis with Python"

ROS 2 implementation of robotics algorithms based on the Probabilistic Robotics book

Kalman Filter implementations in C++

Distributed Measurement Operator Trainer for Data Assimilation Applications

R package to accompany Time Series Analysis and Its Applications: With R Examples -and- Time Series: A Data Analysis Approach Using R

Kalman Filter

IoT and ML to assuage the uncertainty in city bus schedules. Track live running status and avail tentative schedule of buses. Minimal IoT setup with a central ML-driven web-backend.

Arbitrage-free Dynamic Generalized Nelson-Siegel model of interest rates following Christensen, Diebold and Rudebusch; and its estimation using the Kalman filter / maximum likelihood.

Replication of the research paper : Catching the curl: Wavelet thresholding improves forward curve modelling

An R implimentation of Square root Kalman Filter using only QR decompositions.