Research on Data Fusion of Adaptive Weighted Multi-source Sensor

Li, Donghui; Shen, Cong; Xiao-peng, Dai; Zhu, Xinghui; Luo, Jian; Li, Xueting; Chen, Haiwen; Liang, Zhiyao

doi:10.32604/cmc.2019.06354

Cited by 21 publications

(9 citation statements)

References 15 publications

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“…In this study, the scale factors, bias, and installation errors are focused to be calibrated. Bias is the sensor output observed even in the absence of an applied physical input [23,24]. This term often varies slowly with time and hence is also called drift or g-independent bias for the gyroscope [25].…”

Section: Sensor Error Modelmentioning

confidence: 99%

Design of Automatic Batch Calibration and Correction System for IMU

Ajmi¹,

Mahmood²,

Jamaludin³

et al. 2022

Computers, Materials &Amp; Continua

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Thanks to its light weight, low power consumption, and low price, the inertial measurement units (IMUs) have been widely used in civil and military applications such as autopilot, robotics, and tactical weapons. The calibration is an essential procedure before the IMU is put in use, which is generally used to estimate the error parameters such as the bias, installation error, scale factor of the IMU. Currently, the manual one-by-one calibration is still the mostly used manner, which is low in efficiency, time-consuming, and easy to introduce mis-operation. Aiming at this issue, this paper designs an automatic batch calibration method for a set of IMUs. The designed automatic calibration master controller can control the turntable and the data acquisition system at the same time. Each data acquisition front-end can complete data acquisition of eight IMUs one time. And various scenarios of experimental tests have been carried out to validate the proposed design, such as the multi-position tests, the rate tests and swaying tests. The results illustrate the reliability of each function module and the feasibility automatic batch calibration. Compared with the traditional calibration method, the proposed design can reduce errors caused by the manual calibration and greatly improve the efficiency of IMU calibration.

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Section: Sensor Error Modelmentioning

confidence: 99%

Design of Automatic Batch Calibration and Correction System for IMU

Ajmi¹,

Mahmood²,

Jamaludin³

et al. 2022

Computers, Materials &Amp; Continua

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“…GEP Representation. In recent years, there have been many studies based on evolutionary algorithms and multisource data such as data fusion of adaptive weighted multisource sensor [10], the research on evolutionary algorithm for symbolic network [11], the application of the genetic algorithm in multiobjective multicast routing [12], and multiplicity problems in genetic association studies [13]. Zhi and Liu [14] proposed a new GA algorithm for mechanical design optimization problems.…”

Section: Algorithm 1: Single-output Circuitmentioning

confidence: 99%

Hardware Trojan Detection Based on Ordered Mixed Feature GEP

Zhang

Zhou

Gao

et al. 2021

Security and Communication Networks

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In the hardware Trojan detection field, destructive reverse engineering and bypass detection are both important methods. This paper proposed an evolutionary algorithm called Ordered Mixed Feature GEP (OMF-GEP), trying to restore the circuit structure only by using the bypass information. This algorithm was developed from the basic GEP through three sets of experiments at different stages. To solve the problem, this paper transformed the GEP by introducing mixed features, ordered genes, and superchromosomes. And the experiment results show that the algorithm is effective.

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“…We assume that the sensor measurements are independent and follow a normal distribution with standard deviation γ j k (measurement accuracy) [56,57]. According to the extreme value theory of multivariate function [54,55,58], we can obtain the weights corresponding to the minimum mean square error as below.…”

Section: Data Fusion For Trajectory Informationmentioning

confidence: 99%

Real-Time Inter-Vehicle Data Fusion Based on a New Metric for Evidence Distance in Autonomous Vehicle Systems

2020

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Safety is a major concern for autonomous vehicle driving. The autonomous vehicles relying solely on ego-vehicle sensors have limitations in dealing with collisions. The risk can be reduced by communicating with other vehicles sharing sensed information. In this paper, we study a real-time multisource data fusion scheme based on Dempster–Shafer theory of evidence (DS) through cooperative vehicle-to-vehicle (V2V) communications. The classical DS can produce erroneous outputs when confidences are highly conflicting. In particular, a false negative error on object detection may lead to serious accidents. We propose a novel metric to measure distance between confidences on the collected data taking the asymmetry in the importance of sensed events into account. Based on the proposed distance, we consider the weighted DS combination in order to fuse multiple confidences. The proposed scheme is simple, and enables the vehicles to combine confidences in real-time. From real-world experiments, we show that it is feasible to lower the false negative rate by the proposed data fusion scheme, and to avoid accidents using the proposed collision warning system.

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Research on Data Fusion of Adaptive Weighted Multi-source Sensor

Cited by 21 publications

References 15 publications

Design of Automatic Batch Calibration and Correction System for IMU

Design of Automatic Batch Calibration and Correction System for IMU

Hardware Trojan Detection Based on Ordered Mixed Feature GEP

Real-Time Inter-Vehicle Data Fusion Based on a New Metric for Evidence Distance in Autonomous Vehicle Systems

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