Path Integral Particle Filtering for Hybrid Systems via Saltation Matrices
Abstract
State estimation for hybrid systems that undergo intermittent contact with their environments, such as extraplanetary robots and satellites undergoing docking operations, is difficult due to the discrete uncertainty propagation during contact. To handle this propagation, this paper presents an optimal-control-based particle filtering method that leverages saltation matrices to map out uncertainty propagation during contact events. By exploiting a path integral filtering framework that exploits the duality between smoothing and optimal control, the resulting state estimation algorithm is robust to outlier effects, flexible to non-Gaussian noise distributions, and handles challenging contact dynamics in hybrid systems. To evaluate the validity and consistency of the proposed approach, this paper tests it against strong baselines on the stochastic dynamics generated by a bouncing ball and spring loaded inverted pendulum.
Fields of Study & Subjects
Target Tracking and Data Fusion in Sensor Networks
Control Systems and Identification
Robotics and Sensor-Based Localization
Publication details
- Journal / source
- arXiv (Cornell University)
- Publication date
- 1 March 2026
- Language
- EN
- Document type
- Preprint