Particle Filter Source Code Based on Extended Kalman Filter Implementation

In this article, we present the source code implementation of a particle filter based on the extended Kalman filter, which is an algorithm for data processing and state estimation applicable to various fields including robotics, autonomous vehicles, and aerospace engineering. The extended Kalman filter represents a common filtering approach for estimating and predicting nonlinear systems, typically implemented through linearization of nonlinear state transition and measurement models using Jacobian matrices. Particle filters, on the other hand, are more suitable for high-dimensional problems and can provide more accurate estimation results through sequential Monte Carlo methods that represent probability distributions using weighted particles.

By combining these two filtering techniques, we obtain the iterated extended Kalman particle filter, which employs importance sampling where the proposal distribution is generated using the extended Kalman filter iteration. This hybrid approach provides superior estimation accuracy when handling high-dimensional, nonlinear systems. The primary objective of this article is to present the source code implementation of the iterated extended Kalman particle filter and discuss its principles and applications in detail. We will examine the algorithm's implementation methodology, including the resampling process and weight update mechanisms, along with application cases across different domains and future research directions. Through studying this article, readers will understand the algorithm's advantages, limitations, and applicable conditions, as well as how to apply it to practical problems, thereby gaining better comprehension and application capabilities for the iterated extended Kalman particle filter algorithm.