Traffic Flow Prediction Using Kalman Filter
Kalman filter-based traffic flow prediction implementation with detailed MATLAB code examples demonstrating state-space modeling and recursive estimation algorithms
Extended Kalman Filter
The Kalman filter is a highly efficient recursive filter (autoregressive filter) capable of estimating the state of dynamic systems from a series of incomplete and noisy measurements. Named after its inventor Rudolf E. Kalman, this filtering method was originally designed for Gaussian-distributed systems. Later scholars developed various improvements, including the Extended Kalman Filter (EKF) which extends applicability to time-nonlinear dynamic systems through first-order Taylor series linearization.
Stochastic Filtering and Optimal Estimation
Professor Cai Yuanli, Xi'an Jiaotong University, Stochastic Filtering and Optimal Estimation, Nonlinear Filtering, Kalman Filter, Minimum Variance Estimation
Multi-Hypothesis Testing Approach for Multi-Target Tracking with Trajectory Prediction
Implementation of multi-target tracking using the Multi-Hypothesis Testing method, featuring a video surveillance platform integrated with Kalman filtering for target trajectory prediction. Users can customize the codebase according to their specific requirements.
Satellite Positioning Technology Based on Particle Filter and Kalman Filter Algorithms
This graduate thesis project implements satellite positioning technology using Particle Filter (PF) and Kalman Filter (KF) methods. The attachment includes complete MATLAB implementations for wireless channel estimation and equalization, Time Difference of Arrival (TDOA) ranging, and Interacting Multiple Model-Kalman Filter (IMM-KF) algorithms. The code features practical implementations of Bayesian filtering techniques and statistical signal processing, providing valuable resources for developers working on wireless positioning systems. Exclusive contribution to the research community.
MATLAB Implementation of Track Estimation and Data Association Using Kalman Filter and Nearest Neighbor Algorithm
This program utilizes Kalman Filter for track estimation and implements data association through the Nearest Neighbor Algorithm, providing a comprehensive solution for target tracking applications.
Enhanced Kalman Filter Algorithm with Performance Analysis
A comprehensive implementation of an improved Kalman filtering algorithm, featuring comparative performance evaluation against traditional Kalman filter methods
Pedometer Using Acceleration Sensor with Kalman Filter Implementation
A custom-developed pedometer employing Kalman filter algorithm for acceleration sensor data processing, tested with sensor placements at waist and arm positions.
Nine-Dimensional Kalman Filter Tracking Algorithm
A nine-dimensional Kalman filter tracking algorithm that simultaneously estimates position, velocity, and acceleration components along x, y, and z axes.
Kalman Filter for Nonlinear System Identification
Kalman filtering techniques for nonlinear identification, civil engineering health monitoring, and damage detection applications with implementation considerations