Localization and Tracking Robot
This program computes localization and tracking for robotic systems. A complete MATLAB simulation is achievable using the same codebase, featuring algorithms for position estimation and motion tracking.
PN Code Acquisition and Tracking Using Matched Filter Implementation
MATLAB-based implementation of PN code acquisition and tracking using matched filters, featuring code examples and algorithm explanations to aid learners in understanding spread spectrum communication techniques
Particle Filter Algorithm for Motion Tracking
Particle filter implementation for tracking moving objects, featuring probabilistic state estimation using sequential Monte Carlo methods.
Kalman Filter-Based Video Sequence Frame Tracking Program
This implementation features video sequence frame tracking using Kalman filters and CONDENSATION algorithm operators, dynamically reading frame sequences for robust estimation and tracking. The program demonstrates excellent performance with practical code implementations of state prediction and measurement update mechanisms.
Motion Vehicle Detection and Tracking Using Threshold Segmentation
Detection and tracking of moving vehicles through threshold segmentation, implementing image segmentation in video sequences by adjusting grayscale threshold values and labeling the resulting objects.
Motion Object Detection and Tracking
Motion Detection for Object Detection and Tracking with Algorithm Implementation Details
MATLAB Implementation of GPS Software Receiver with Signal Processing Algorithms
GPS software receiver implementation including signal acquisition, tracking loop, pseudorange computation, and navigation solution calculation using MATLAB signal processing techniques.
Kalman Filter-Based Channel Estimation
Channel estimation utilizing Kalman filtering principles for OFDM channel tracking and state prediction, with implementation insights on recursive estimation algorithms and system modeling.
Dynamic Object Recognition, Tracking, and Detection in Videos
Implementation of dynamic object recognition, tracking, and detection in videos using MATLAB with computer vision and machine learning algorithms
Probability Hypothesis Density (PHD) Filter for Multi-Target Tracking
An implementation-focused overview of the Probability Hypothesis Density filter for multi-target tracking applications, including key algorithmic concepts and practical applications.