Deep Unfolded Annealed Stein Particle Filter for Vehicle Tracking

Abstract

This paper focuses on highly precise localization and tracking of vehicles in race circuits, where centimeter-level accuracy is required for safety and for enabling complex maneuvering. Recently, the Annealed Stein Particle Filter (ASPF) has been proposed as a promising Bayesian tracking tool for tracking, showing its superior performances against conventional Bayesian filtering methods, such as the Extended Kalman Filter (EKF) and the Particle Filter (PF). Despite its excellent performances, the ASPF entails large computational complexity, making it unsuitable for highly dynamic vehicular scenarios. To address this shortcoming, we propose a Deep Unfolded ASPF (DU-ASPF), a novel Bayesian tracking algorithm integrating the deep unfolding paradigm where the ASPF operations are rearranged into a sequential structure with learnable weights. Experimental results using raw Ultra-Wide Band (UWB) measurements show that the DU-ASPF is able to substantially speed up the tracking process while maintaining the ASPF accuracy.