Implementation of Multiple Hypothesis Tracking (MHT) Algorithm

This is a MATLAB package implementing the Multiple Hypothesis Tracking (MHT) algorithm. The package employs Murty's algorithm to solve the bipartite graph matching problem by generating the top-N optimal solutions.

The MHT algorithm is designed for multi-target tracking applications, maintaining multiple tracking hypotheses simultaneously while updating and optimizing them based on observation data. This implementation features a structured approach where the core tracking logic handles hypothesis generation, pruning, and propagation across time steps. Key functions include hypothesis management routines and cost matrix calculations for data association.

Users can efficiently implement MHT tracking by importing the package into MATLAB environment and following provided examples and documentation. The implementation includes modular components for sensor data processing, hypothesis evaluation using probabilistic scoring, and Murty's algorithm integration for optimal assignment selection. Suitable for both academic research and engineering applications, this package provides a robust tool with configurable parameters for different tracking scenarios.