MATLAB Implementation of Particle Filter for Target Tracking

Particle Filter (PF) is a nonlinear, non-Gaussian state estimation method based on Monte Carlo sampling, widely applied in target tracking applications. Implementing particle filter target tracking in MATLAB typically involves the following key steps: Initialization of particle set: Generate a set of random particles based on the target's initial state (such as position and velocity). Each particle represents a potential state hypothesis of the target and is assigned an initial weight. In MATLAB implementation, this can be achieved using functions like randn or rand to create normally distributed or uniformly distributed particles around the initial state. Prediction phase: Use motion models (such as constant velocity model or constant acceleration model) to predict the state of each particle. This phase incorporates process noise to simulate uncertainties in target movement. The implementation typically involves matrix operations for state transition and adding Gaussian noise using functions like normrnd. Weight update: Calculate the likelihood probability of each particle using sensor measurement data (such as target detection boxes or feature points). Typically, an observation model (like Gaussian distribution) is used to measure the matching degree between particles and actual measurement data, and particle weights are updated accordingly. This step often involves probability density function calculations using normpdf or custom likelihood functions. Resampling: To prevent particle degeneration (where a few particles dominate most weights), resampling algorithms (such as systematic resampling or residual resampling) are employed to eliminate low-weight particles and replicate high-weight particles, thereby maintaining particle diversity. MATLAB implementations commonly use functions like datasample or custom resampling algorithms with cumulative sum operations using cumsum. State estimation: Calculate the optimal estimated state of the target based on the resampled particle set, typically using weighted average or maximum a posteriori probability as the output result. This can be implemented using mean or weighted mean calculations with the particle weights. The advantage of particle filters lies in their ability to handle nonlinear dynamic systems, but their performance depends on the number of particles and the accuracy of motion/observation models. In MATLAB, tracking robustness can be further improved by optimizing resampling strategies or combining other filtering methods (such as Kalman filters) through hybrid filtering approaches.