TY - JOUR
T1 - A maneuvering multi-sensor information fusion algorithm for enhancing localization reliability in ADAS testing
AU - Sun, Liyang
AU - Xu, Lin
AU - Dong, Xue
AU - Shoukat, Muhammad Usman
AU - Mi, Jia
N1 - Publisher Copyright:
© 2025 Elsevier Inc.
PY - 2025/4
Y1 - 2025/4
N2 - A novel algorithm, the maneuvering first-order generalized pseudo-Bayesian error-state Kalman filter (MGPB1-ESKF), is proposed in this study for localization in advanced driver assistance system (ADAS) testing platform vehicles. The proposed algorithm substantially enhances the reliability and accuracy of multi-sensor localization in ADAS testing. This improvement is particularly significant in challenging non-line-of-sight (NLOS) environments, which typically degrade the performance of global navigation satellite system (GNSS). By adaptively identifying and isolating faulty signal sources, the MGPB1-ESKF provides a novel approach to achieving robust and reliable localization in the presence of transient sensor failures. Rigorous simulations and experimental results demonstrate that the proposed algorithm effectively mitigates the impact of faulty sensors in challenging environments, outperforming conventional multiple model (MM) algorithm and leading to improved localization accuracy.
AB - A novel algorithm, the maneuvering first-order generalized pseudo-Bayesian error-state Kalman filter (MGPB1-ESKF), is proposed in this study for localization in advanced driver assistance system (ADAS) testing platform vehicles. The proposed algorithm substantially enhances the reliability and accuracy of multi-sensor localization in ADAS testing. This improvement is particularly significant in challenging non-line-of-sight (NLOS) environments, which typically degrade the performance of global navigation satellite system (GNSS). By adaptively identifying and isolating faulty signal sources, the MGPB1-ESKF provides a novel approach to achieving robust and reliable localization in the presence of transient sensor failures. Rigorous simulations and experimental results demonstrate that the proposed algorithm effectively mitigates the impact of faulty sensors in challenging environments, outperforming conventional multiple model (MM) algorithm and leading to improved localization accuracy.
KW - Advanced driver assistance system (ADAS)
KW - Error-state Kalman filter (ESKF)
KW - First-order generalized pseudo-Bayesian (GPB1)
KW - Non-line-of-sight (NLOS) environment
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U2 - 10.1016/j.dsp.2025.104991
DO - 10.1016/j.dsp.2025.104991
M3 - Article
AN - SCOPUS:85215845670
SN - 1051-2004
VL - 159
JO - Digital Signal Processing: A Review Journal
JF - Digital Signal Processing: A Review Journal
M1 - 104991
ER -