In this paper, an asynchronous information fusion issue is investigated for indoor positioning of an unmanned ground vehicle equipped with an ultra-wideband (UWB) positioning system and a camera. A local UWB estimator with a fixed sampling period is developed based on a delta operator Kalman filter. A local camera estimator is designed using a distinct fixed sampling period under information loss. To address the asynchronous sampling challenge among different sensors, a delta operator approach is introduced into a matrix-weighted fusion algorithm to obtain a comprehensive fusion estimator. Rigorous convergence analysis for both local estimators and the fusion estimator is conducted for the indoor positioning system. The effectiveness of the proposed asynchronous information fusion method is validated through experimental testing on an Ackermann vehicle.
AsharifMRMoshiriBHoseinNezhadR (2002) Sensor fusion by pseudo information measure: A mobile robot application. ISA Transactions41(3): 283–301.
2.
GerwenJVVGeebelenKWanJ, et al. (2022) Indoor drone positioning: Accuracy and cost trade-off for sensor fusion. IEEE Transactions on Vehicular Technology71(1): 961–974.
3.
Girbes-JuanVArmestoLHernandez-FerrandizD, et al. (2021) Asynchronous sensor fusion of GPS, IMU and can-based odometry for heavy-duty vehicles. IEEE Transactions on Vehicular Technology70(9): 8617–8626.
4.
HaoXXiaYYangH, et al. (2023) Asynchronous information fusion in intelligent driving systems for target tracking using cameras and radars. IEEE Transactions on Industrial Electronics70(3): 2708–2717.
5.
HuangZYeGYangP, et al. (2025) Application of multi-sensor fusion localization algorithm based on recurrent neural networks. Scientific Reports15: 8195.
6.
JeonWZemoucheARajamaniR (2019) Tracking of vehicle motion on highways and urban roads using a nonlinear observer. IEEE/ASME Transactions on Mechatronics24(2): 644–655.
7.
JinYFengNLiY, et al. (2023) State estimation for high-speed train based on multirate asynchronous sensors fusion with missing measurements. IEEE Sensors Journal23(5): 4486–4499.
8.
KwakJJeonHSongS (2025) Efficient multi-sensor fusion for cooperative autonomous vehicles leveraging C-ITS infrastructure and machine learning. Sensors25(7): 1975.
LiLNiuMXiaY, et al. (2019) Event-triggered distributed fusion estimation with random transmission delays. Information Sciences475: 67–81.
11.
NguyenTHNguyenTMXieL (2021) Range-focused fusion of camera-IMU-UWB for accurate and drift-reduced localization. IEEE Robotics and Automation Letters6(2): 1678–1685.
12.
PouloseAHanD (2020) UWB indoor localization using deep learning LSTM networks. Applied Sciences10(18): 6290.
13.
RanTYuanLZhangJ (2021) Scene perception based visual navigation of mobile robot in indoor environment. ISA Transactions109: 389–400.
14.
RezaeiMTerauchiMKletteR (2015) Robust vehicle detection and distance estimation under challenging lighting conditions. IEEE Transactions on Intelligent Transportation Systems16(5): 2723–2743.
15.
Sadeghzadeh-NokhodberizNPoshtanJWagnerA, et al. (2014) Distributed observers for pose estimation in the presence of inertial sensory soft faults. ISA Transactions53(4): 1307–1319.
SiLWangZLiJ, et al. (2023) A novel positioning method of anti-punching drilling robot based on the fusion of multi-IMUs and visual image. ISA Transactions137: 730–746.
18.
SinopoliBSchenatoLFranceschettiM, et al. (2004) Kalman filtering with intermittent observations. IEEE Transactions on Automatic Control49(9): 1453–1464.
19.
SongHLaiMHongZ, et al. (2025) Event-based probability-guaranteed set-membership secure fusion estimation for energy-constrained multi-sensor systems with asynchronous samplings. IEEE Transactions on Automation Science and Engineering22: 13983–13994.
20.
SongRChenXFangY, et al. (2020) Integrated navigation of GPS/INS based on fusion of recursive maximum likelihood IMM and square-root cubature Kalman filter. ISA Transactions105: 387–395.
21.
WuYLiYLiW, et al. (2020) Robust LiDAR-based localization scheme for unmanned ground vehicle via multisensor fusion. IEEE Transactions on Neural Networks and Learning Systems32(12): 5633–5643.
22.
YangHXiaYShiP, et al. (2011) A novel delta operator Kalman filter design and convergence analysis. IEEE Transactions on Circuits and Systems I: Regular Papers58(10): 2458–2468.
23.
YangHXiaYShiP, et al. (2012) Analysis and Synthesis of Delta Operator Systems. Berlin; Heidelberg: Springer-Verlag.
24.
YinZJiangXYangZ, et al. (2019) WUB-IP: A high-precision UWB positioning scheme for indoor multiuser applications. IEEE Systems Journal13(1): 279–288.
25.
YouYWuC (2021) Hybrid indoor positioning system for pedestrians with swinging arms based on smartphone IMU and RSSI of BLE. IEEE Transactions on Instrumentation and Measurement70: 9510615.
26.
YuBLiTGeS, et al. (2021) Robust CPHD fusion for distributed multitarget tracking using asynchronous sensors. IEEE Sensors Journal22(1): 1030–1040.
27.
YuanLYangBWeiQ (2019) Human indoor location for binary infrared sensor tracking system: On improved credit and dynamic pruning algorithm. ISA Transactions94: 370–378.
28.
ZhangLWuXGaoR, et al. (2023) A multi-sensor fusion positioning approach for indoor mobile robot using factor graph. Measurement216: 112926.
ZhouYLawCLGuanY, et al. (2011) Indoor elliptical localization based on asynchronous UWB range measurement. IEEE Transactions on Instrumentation and Measurement60(1): 248–257.
31.
ZhouZ (2019) Optimal batch distributed asynchronous multisensor fusion with feedback. IEEE Transactions on Aerospace and Electronic Systems55(1): 46–56.
32.
ZhuJKiaSS (2019) Bias compensation for UWB ranging for pedestrian geolocation applications. IEEE Sensors Letters3(9): 7001704.
33.
ZihajehzadehSParkEJ (2017) A novel biomechanical model-aided IMU/UWB fusion for magnetometer-free lower body motion capture. IEEE Transactions on Systems, Man, and Cybernetics: Systems47(6): 927–938.
