Work

Breaking the Gaussian Barrier: Leveraging ReGVAMP to Extend EKF and IEKF

Year: 2024

Type: conference-paper

Abstract: In the non-linear hidden Markov chain (HMC) model, commonly employed in robotics, navigation, signal processing, and control systems, various variants of Kalman filter (KF) like the Extended Kalman Filter (EKF) and Iterated Extended Kalman Filter (IEKF) have been developed for different scenarios, with their limitations and advantages. However, their effectiveness can diminish in the presence of non-Gaussian noise, which is prevalent in many real-world situations. This paper presents a novel approach to tackle non-Gaussian noise leveraging the revisited Generalized Vector Approximate Message Passing (ReGVAMP) within the context of EKF and IEKF. ReGVAMP extends these KF variants to break the Gaussian barrier, thereby enhancing the accuracy of state estimation. A tracking simulation validated the feasibility of our proposed algorithms.

Source: 2018 52nd Asilomar Conference on Signals, Systems, and Computers

DOI: 10.1109/ieeeconf60004.2024.10942737

Authors: Fangqing Xiao, Dirk Slock

Institution: EURECOM

Language: English

Topic: Target Tracking and Data Fusion in Sensor Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Agence Nationale de la Recherche

Award: Reduced Complexity Techniques and Performance Analysis towards 6G Cell-Free Massive MIMO