Source Code for Several Particle Filter Algorithms

This article presents source code implementations for several distinct particle filter algorithms. We begin by examining the standard particle filter implementation, which utilizes sequential Monte Carlo methods for state estimation through importance sampling and resampling techniques. The code typically includes core functions for particle initialization, weight calculation based on measurement likelihood, and systematic resampling to mitigate particle degeneracy. Subsequently, we delve into the Extended Kalman Filter-based particle filter implementation, which combines EKF's linearization approach with particle filtering robustness. This hybrid algorithm employs EKF for proposal distribution generation, enhancing estimation accuracy in nonlinear systems. These implementations feature modular code structure with key functions handling state prediction, measurement updates, and covariance propagation. The source codes provide practical examples of handling state-space models and can be adapted for various applications including robotics (for SLAM and localization), image processing (for object tracking), and speech recognition systems. Understanding these implementations will significantly enhance your proficiency in probabilistic filtering and sequential estimation techniques.