Particle Filter Implementation for Target Tracking

To achieve precise target tracking, accurate observation and measurement of target angles is fundamental. This can be implemented using particle filter algorithms that probabilistically estimate the target's state (position, velocity) through sequential Monte Carlo methods. The system typically involves initialization of particles around the target's predicted location, followed by recursive prediction and update cycles. In the prediction phase, particles are propagated according to the motion model: x_k = f(x_{k-1}) + w_k, where f() represents the system dynamics and w_k is process noise. During the update phase, particle weights are computed based on the likelihood function: w_k^i = p(z_k | x_k^i), where z_k represents the angular measurements from sensors. The resampling step prevents particle degeneration by redistributing particles according to their weights. Implementation considerations include handling environmental factors like weather conditions and terrain through appropriate noise modeling in the measurement covariance matrix. The tracking system can incorporate multiple sensors (e.g., radar, lidar) for robust angle measurements, with data fusion techniques combining observations for improved accuracy. Key MATLAB functions for implementation might include particleFilter() for creating filter objects, predict() for state propagation, and correct() for measurement updates. By continuously adjusting particle distributions based on incoming angle measurements and environmental factors, the system achieves reliable 3D tracking suitable for military, defense, and surveillance applications requiring robustness against nonlinear systems and non-Gaussian noise.