Particle Filter for Multi-Target Tracking in Undergraduate Thesis Project

A particle filter for multi-target tracking is a probability-based tracking method suitable for detecting and tracking multiple moving targets in complex scenarios. For an undergraduate thesis project, this topic combines fundamental theories of target tracking with practical applications, while allowing for simulation validation using MATLAB.

The Particle Filter is a Bayesian filtering approach based on Monte Carlo sampling, particularly effective for nonlinear and non-Gaussian systems. In multi-target tracking scenarios, each target's state (such as position and velocity) can be estimated using a set of particles. The particle filter continuously optimizes state estimation through prediction, update, and resampling processes, making it suitable for environments where the number of targets changes dynamically.

Implementing a multi-target tracking particle filter in MATLAB typically involves these key algorithmic steps:

Particle initialization: Assign a specified number of particles to each target with initial states (position, velocity). Code implementation would involve creating particle arrays and defining state vectors.

State prediction: Predict particle distribution at the next time step based on motion models (constant velocity or acceleration models). This requires implementing transition equations and adding process noise to simulate motion uncertainty.

Measurement update: Calculate particle weights using sensor data (camera or radar inputs). The weight computation involves likelihood functions that measure how well each particle matches observed data, typically implemented through Gaussian measurement models.

Resampling: Perform particle selection based on weights to prevent particle degeneracy and improve tracking accuracy. Common resampling algorithms like systematic resampling or multinomial resampling need to be coded to redistribute particles effectively.

Data association and target management: In multi-target scenarios, implement data association techniques (nearest neighbor matching or Hungarian algorithm) to assign measurements to tracks. Target initialization and termination logic must handle appearing and disappearing targets.

An undergraduate thesis can explore these core processes through MATLAB simulations, comparing how different parameters (particle count, motion models) affect tracking performance. The project can be enhanced by incorporating real-world applications like traffic monitoring or drone tracking to demonstrate practical significance.