Source Code for Simulation Study of Kalman Filter Application in Target Tracking
Source code implementation for simulating Kalman Filter applications in target tracking systems, featuring noise estimation and trajectory prediction algorithms
MATLAB Implementation of Particle Filter Algorithm
A MATLAB-based particle filtering program designed for target tracking applications, featuring customizable parameters and modular code structure.
Simulation Study of Kalman Filter Application in Target Tracking
The application context of target tracking involves radar data processing, where radar systems detect targets and record their positional data. The measured target position data (referred to as plots) are processed to automatically form trajectories and predict target positions at subsequent time steps. Implementation typically involves data association algorithms and state estimation techniques to handle measurement uncertainties and target dynamics.
Meanshift Algorithm for Target Tracking Implementation
Implementation of target tracking using the Meanshift algorithm, which extracts the first frame from video sequences, defines target regions, performs tracking, and dynamically displays tracking results with color histogram matching and iterative convergence
MeanShift Target Tracking
MeanShift, also known as mean shift, is widely applied in clustering, image smoothing, segmentation, and tracking. The shifted mean vector defines a family of kernel functions where the contribution of each sample's shift to the mean shift vector varies based on its distance from the shifted point. By incorporating a weight coefficient that assigns different importance to sample points, MeanSignificantly broadens its application scope. Target tracking using MeanShift is now a mature technique. Fundamentally, the MeanShift algorithm operates as a kernel density estimation method, often implemented through iterative gradient ascent to locate probability density maxima.
Extended Kalman Filter Algorithm for Target Tracking
The main function file for target tracking using Extended Kalman Filter is kal_demo.m. The main function file for approximate grid filtering is bayes_demo.m. The grid partitioning method for approximate grid filtering uses the target's previous position as the center point, with each grid cell size of 1 unit and total coverage area of 5x5 units. The improved algorithm's main function file is trackiing_demo.m.
MATLAB Implementation of Object Tracking Algorithm Using Self-Learning Subspace
Object tracking algorithm leveraging self-learning subspace with particle filtering, featuring high-speed performance and robustness to noise through probabilistic state estimation.
Optical Flow Method for Target Tracking and Segmentation
Implementation of target tracking and segmentation using optical flow method, designed for high-precision simulation and modeling applications
Simple and Understandable Color Histogram Quantization and Conversion Implementation
A relatively straightforward color histogram quantization and conversion program suitable for applications like object tracking, designed to assist those needing to understand this domain with clear code implementation examples.
Kalman Filter Implementation for Target Tracking
Application of Kalman Filter in target tracking, implemented using MATLAB with detailed code examples and algorithm explanations.