Standard Particle Filter Target Tracking Source Code

This source code implements standard particle filtering for target tracking applications. The algorithm operates effectively in one-dimensional environments while handling nonlinear and non-Gaussian system characteristics. Particle filtering represents a Monte Carlo-based state estimation technique that approximates the posterior distribution by maintaining a set of randomly sampled particles in the state space. The implementation follows these key steps: First, particles are initialized and propagated through the system model using prediction equations. Then, each particle's weight is updated based on measurement likelihood calculations, where particles closer to actual observations receive higher weights. The resampling procedure prevents degeneracy by eliminating low-weight particles and duplicating high-probability ones. Key algorithmic components include: - State transition modeling for particle propagation - Likelihood evaluation for weight assignment - Systematic resampling for maintaining particle diversity - Estimation through weighted particle averaging This approach provides superior tracking performance in challenging nonlinear and non-Gaussian scenarios compared to traditional Kalman filter variants, as it doesn't rely on linearity assumptions or Gaussian noise constraints. The code structure demonstrates proper particle management, effective weight normalization, and optimized resampling techniques.