Vehicle Localization Using Sensors Data Fusion Via Integration of Covariance Intersection and Interval Analysis

Lazarus, Samuel; Ashokaraj, I.; Tsourdos, Antonios; Żbikowski, Rafał; Silson, Peter; Aouf, Nabil; White, Brian

doi:10.1109/jsen.2007.901556

Cited by 31 publications

(13 citation statements)

References 11 publications

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“…S.B. Lazarus and I. Ashokaraj [10] proposed a vehicle localization method with sensors data fusion algorithm which combined the extended Kalman filter, interval analysis and covariance intersection. Y. Zhang et al [11] adopted a Kalman filter arithmetic to improve vehicle GPS data accurace.…”

Section: Related Workmentioning

confidence: 99%

An Effective Fuel-Level Data Cleaning and Repairing Method for Vehicle Monitor Platform

Zhu

Duan

et al. 2019

IEEE Trans. Ind. Inf.

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Section: Related Workmentioning

confidence: 99%

An Effective Fuel-Level Data Cleaning and Repairing Method for Vehicle Monitor Platform

Zhu

Duan

et al. 2019

IEEE Trans. Ind. Inf.

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“…(5)(6)(7)(8)(9), in each sample, Kalman filter needs measurement in same sample, but our sensory system has a constant delay in the measurement; hence, we cannot use the original version of the Kalman filter for estimation. Fortunately, we can compensate the delay in the estimation by some modification in the Kalman filter algorithm [23].…”

Section: B Modified Kalman Filtermentioning

confidence: 99%

Delay Compensation of Tilt Sensors Based on MEMS Accelerometer Using Data Fusion Technique

Ghanbari

Yazdanpanah

2015

IEEE Sensors J.

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In this paper, the inaccuracies of the tilt sensor based on MEMS (Micro Electro Mechanical Systems) accelerometer and gyroscope during the process of the angle measurement are considered and a new method is proposed to compensate them, using data fusion of sensors. The tilt sensor has a constant delay and low bandwidth. In the gyroscope, drift phenomena makes some difficulties in the angle measurement. A model for the sensory system is developed and a modified Kalman filter is proposed to compensate the delay of the tilt sensor using the data fusion technique. A complementary filter is designed to overcome the problem of low-bandwidth in the tilt sensor. The overall proposed sensor fusion consists of a complementary filter as a pre-filter and a modified Kalman filter as a post-filter to solve the problems of bandwidth and delay, respectively. The experimental tests are performed to evaluate the effectiveness of the proposed sensor fusion scheme. In these experiments, the accuracy in the tilt measurement is improved from 10 • to 0.9 • and 15 • to 0.6 • in low-frequency tests, and from 18 • to 0.8 • in higher frequency test. Also, the bandwidth of the tilt measurement is enhanced from 0.16 Hz (bandwidth of tilt sensor) to 6 Hz. The results show proper performance of the proposed approach in the line of delay compensation and increment of the bandwidth.

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“…Some sensor fusion algorithms such as Box Particle Filtering (BPF) (Abdallah, Gning, & Bonnifait, 2008; and Covariance Intersection (CI) (Lazarus et al, 2007) are proposed with respect to the propagation from single interval result. However, as shown in Fig.…”

Section: Dynamic Estimationmentioning

confidence: 99%

Cellphone geolocation via magnetic mapping

Cui

Ariyur

2015

Automatica

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Vehicle Localization Using Sensors Data Fusion Via Integration of Covariance Intersection and Interval Analysis

Cited by 31 publications

References 11 publications

An Effective Fuel-Level Data Cleaning and Repairing Method for Vehicle Monitor Platform

An Effective Fuel-Level Data Cleaning and Repairing Method for Vehicle Monitor Platform

Delay Compensation of Tilt Sensors Based on MEMS Accelerometer Using Data Fusion Technique

Cellphone geolocation via magnetic mapping

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