A maneuvering multi-sensor information fusion algorithm for enhancing localization reliability in ADAS testing

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Digital Signal Processing Pub Date : 2025-04-01 Epub Date: 2025-01-13 DOI:10.1016/j.dsp.2025.104991

Liyang Sun , Lin Xu , Xue Dong , Muhammad Usman Shoukat , Jia Mi

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Abstract

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.

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一种提高ADAS测试中定位可靠性的机动多传感器信息融合算法

针对先进驾驶辅助系统（ADAS）测试平台车辆定位问题，提出了机动一阶广义伪贝叶斯误差状态卡尔曼滤波（MGPB1-ESKF）算法。该算法大大提高了ADAS测试中多传感器定位的可靠性和准确性。这种改进在具有挑战性的非视距（NLOS）环境中尤为重要，因为非视距（NLOS）环境通常会降低全球导航卫星系统（GNSS）的性能。通过自适应识别和隔离故障信号源，MGPB1-ESKF提供了一种在瞬态传感器故障存在时实现鲁棒可靠定位的新方法。严格的仿真和实验结果表明，该算法有效地减轻了传感器故障在挑战性环境中的影响，优于传统的多模型（MM）算法，从而提高了定位精度。

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来源期刊

Digital Signal Processing 工程技术-工程：电子与电气

CiteScore

5.30

自引率

17.20%

发文量

435

审稿时长

66 days

期刊介绍： Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,