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multisensor data fusion and Time Series to Image Encoding for Hardness Recognition

IEEE SENSORS JOURNAL 2024年第16期24卷 26463-26471页

作者： Kaewrakmuk, Thossapon Srinonchat, Jakkree Rajamangala Univ Technol Thanyaburi Fac Engn Dept Elect & Telecommun Engn Khlong Hok 12110 Pathum Thani Thailand

Robots must possess advanced sensing and recognition skills to effectively manipulate objects in realistic environments. This article presents a unique approach for detecting hardness using a glove sensor. The proposed strategy involves fusing data from many sensors and using time series for image encoding to achieve the desired objective. This technique was established and subsequently refined with a particular objective in focus. The integration of data acquired from five distinct force sensors allows for a thorough comprehension of the characteristics of the material. Simultaneously, using the Gramian angular summation field (GASF), the Gramian angular difference field (GADF), and Markov transition field (MTF) representations facilitates the conversion of time-series data into visual images. This conversion enables the transformation of dynamic measurements into organized formats, enabling the application of 15 deep convolutional neural network (DCNN) models for analysis. Based on the findings of the empirical investigation, it seems that the GADF image encoding method exhibited a remarkably elevated average recognition rate (ARR) total when used in conjunction with DCNN. The conclusions above are derived from the empirical evidence from the conducted investigation. Furthermore, its combination with Vgg-19 yielded a notable ARR of 78.17%.

关键词： Sensors Time series analysis Convolutional neural networks Object recognition Accuracy Tactile sensors Image coding Deep convolutional neural network (DCNN) hardness recognition multisensor data fusion time series to image encoding

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multisensor data fusion-based structural health monitoring for buried metallic pipelines under complicated stress states

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JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING 2022年第6期12卷 1509-1521页

作者： Li, Minghao Feng, Xin Dalian Univ Technol Fac Infrastruct Engn Dalian Peoples R China

During long-term service, buried pipelines are subjected to complex combined loads, including internal and external loads and pipe-soil interactions. In this paper, a multisensor monitoring network is proposed for the structural health monitoring (SHM) and performance assessment of buried metallic pipelines in a complicated stress state. However, in general, longitudinal bending stress and axial thermal stress cannot be directly obtained by separately considering each information source (i.e. measured longitudinal strain and temperature differentials). Therefore, a Bayesian fusion-based estimation method of stress states and a physics-based data fusion method for calculating longitudinal stresses are presented. A field application with construction and operation phases was carried out to investigate the efficiency of this monitoring scheme and data fusion method. The results illustrated that the proposed method successfully estimated and calculated the longitudinal bending stress and axial thermal stress. The distribution of equivalent stress was obtained, and the structural performance of the pipeline was evaluated. Notably, the measured data showed significant longitudinal bending stress caused by overburden loads, uneven pipe trenches and foundation settlements during the construction phase, which are not considered in classical stress calculation models and current design codes. This practical project provides strong evidence for the motivation of this paper.

关键词： Buried pipelines Structural health monitoring multisensor data fusion Complicated stress state

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multisensor data fusion for Life Detection in Cluttered Environments

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IEEE SENSORS JOURNAL 2022年第24期22卷 24559-24566页

作者： Wang, Haipeng Liu, Yibin Jiangsu Coll Engn & Technol Sch Intelligent Mfg Nantong Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan Peoples R China

A multisensor data fusion algorithm is proposed in this study for life detection in cluttered environments. The method consists of four steps: data acquisition, data preprocessing, data fusion, and result sort. First, four kinds of data (RGB image, RGB video, infrared image, and radar wave) are acquired from three different sensors (RGB camera, infrared camera, and life detection radar). Then, four specified algorithms are used on each data for data preprocessing. Third, an ordered weighted average (OWA)-based data fusion is used to fuse the four single results. The result is a sort of four possible results (Alive, Dead, Animal, and None). A series of cluttered environments are designed to verify the effectiveness of the proposed method. The results show that the data fusion method can effectively improve the success rate of life detection in cluttered environments. Specifically, the average accuracies of 97.8%, 96.7%, and 89.7% under different distances, light intensities, and shelter, respectively, are obtained.

关键词： Sensors data integration Radar Radar imaging Convolutional neural networks Radar detection Cameras data fusion life detection multisensor data fusion ordered weighted average (OWA)

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A Novel Multiobject Tracking Framework Based on multisensor data fusion for Autonomous Driving in Adverse Weather Environments

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IEEE SENSORS JOURNAL 2025年第9期25卷 16068-16079页

作者： Han, Liuyang Wang, Jun Li, Chen Tao, Fazhan Fu, Zhumu Henan Univ Sci & Technol Sch Informat Engn Luoyang 471023 Peoples R China

Multiobject tracking (MOT) is a critical task for autonomous driving (AD), aiming to localize and maintain consistent identities of targets in camera or LiDAR data. However, adverse weather conditions (low-light, rain, fog, and sandstorms) severely degrade sensor data quality, leading to deteriorated detection accuracy (DetA) and unstable tracking performance in existing methods. This study proposes a robust multisensor fusion framework for adverse weather situations to address these challenges. Specifically, a physics-based adverse weather simulation algorithm is introduced to generate the Adverse dataset. Furthermore, a geometrically verified late-fusion detection method is proposed to resolve conflicts between 2-D and 3-D detection results, enhancing detection robustness. In addition, a spatial-appearance progressive association strategy is developed for stable tracking. Experimental results demonstrate the method's superior performance across multiple datasets, particularly under adverse weather conditions. On the KITTI dataset, the method achieves state-of-the-art results in HOTA (80.13%), MOTP (87.69%), and localization accuracy (LocA) (88.84%), outperforming all baseline methods. In addition, it meets the real-time performance requirements for AD deployments.

关键词： Meteorology Three-dimensional displays Sensors Trajectory Target tracking Noise data integration Cameras Point cloud compression Laser radar Adverse weather environments autonomous driving (AD) multiobject tracking (MOT) multisensor data fusion progressive association

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Learning-Based Stance Phase Detection and multisensor data fusion for ZUPT-Aided Pedestrian Dead Reckoning System

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IEEE INTERNET OF THINGS JOURNAL 2024年第4期11卷 5899-5911页

作者： Li, Jie Zhou, Xu Qiu, Sen Mao, Yi Wang, Ziyang Loo, Chu Kiong Liu, Xiaofeng Hohai Univ Key Lab Maritime Intelligent Cyberspace Technol Minist Educ Changzhou 213022 Peoples R China Hohai Univ Sch Artificial Intelligence & Automat Changzhou 213022 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Univ Malaya Fac Comp Sci & Informat Technol Dept Artificial Intelligence Kuala Lumpur 50603 Malaysia

In a closed environment lacking global positioning system (GPS) signals, how to achieve accurate navigation and positioning is a very challenging task. Zero velocity update (ZUPT) is a highly effective foot-mounted inertial pedestrian navigation systems in such environment. However, despite its effectiveness, the limitation of accurate detecting the zero-velocity-interval (ZVI) and heading drift are still the significant challenges of the ZUPT method. To address these issues, a deep learning method for adaptive ZVIs detection is established based solely on inertial sensors by comparing with the optical motion capture system. Additionally, an improved ZUPT-aided extend Kalman filter (EKF) divides the measurement updates of the ZVIs is established for multisensor data fusion, and the heading change with heuristic drift reduction (HDR) is also adopt as measurement, thereby yielding to limit the heading drift. Experimental results demonstrate that our method provides a better estimate of the heading angle, as well as more accurate ZVIs detection, leading to more precise dead-reckoning position estimates than other state-of-the-art methods.

关键词： Pedestrians data integration Sensors Hidden Markov models Internet of Things Inertial navigation Hardware Body sensor network deep learning inertial measurement unit multisensor data fusion zero velocity update (ZUPT)

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Feasibility analysis of angles-only navigation algorithm with multisensor data fusion for spacecraft noncooperative rendezvous

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Astrodynamics 2023年第2期7卷 179-196页

作者： Ronghua Du Wenhe Liao Xiang Zhang Micro/Nano Satellite Research Center Nanjing University of Science and TechnologyNanjing 210094China

Relative navigation is crucial for spacecraft noncooperative rendezvous,and angles-only navigation using visible and infrared cameras provides a feasible ***,an angles-only navigation algorithm with multisensor data fusion is proposed to derive the relative motion states between two noncooperative ***,the design model of the proposed algorithm is introduced,including the derivation of the state propagation and measurement ***,models for the sensor and actuator are introduced,and the effects of various factors on the sensors and actuators are *** square-root unscented Kalman filter is used to design the angles-only navigation filtering ***,the Clohessy-Wiltshire terminal guidance algorithm is introducedto obtain the theoretical relative motion trajectories during the rendezvous operations of two noncooperative ***,the effectiveness of the proposed angles-only navigation algorithm is verified using a semi-physical simulation *** results prove that an optical navigation camera combined with average accelerometers and occasional orbital maneuvers is feasible for spacecraft noncooperative rendezvous using angles-only navigation.

关键词： angles-only navigation feasibility analysis multisensor data fusion spacecraft noncooperative rendezvous

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multisensor data fusion approach for sediment assessment of sewers in operation

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 132卷

作者： Li, Chen Chen, Ke Li, Hanlin Luo, Hanbin Huazhong Univ Sci & Technol Natl Ctr Technol Innovat Digital Construct Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan Peoples R China

Urban sewer systems are essential components of urban water infrastructure, but their operations are often affected by sediment. Existing sediment assessment methods generally adopt closed-circuit television (CCTV) or individual types of sensors, but they fail to accurately locate the sediment and quantify the sediment volume. More seriously, these methods become ineffective in an operating sewer. In this study, a sewer sediment assessment approach is developed based on multisensor data fusion (SA-MDF). The raw data are collected by a remotely operated vehicle (ROV) equipped with a rotating sonar device, a gyroscope, an accelerometer, and an odometer. Subsequently, a two-step process of multisensor data fusion is implemented. In the first step, the unscented Kalman filter (UKF) and Rauch-Tung-Striebel (RTS) are applied to fuse the gyroscope, accelerometer, and odometer data, thereby achieving precise localization of the ROV inside the sewer. In the second step, the point cloud data collected by sonar are fused with the sensor data to address the point cloud data offset caused by the sewage flow and robot motion jitter. The laboratory and field experiments demonstrated that the SA-MDF can effectively reduce the ROV location error from 32.37% to 0.7% and the sediment quantification error from 15.25% to 6.38%. As a result, the SA-MDF facilitates accurate sediment assessment of the sewer, which offers valuable support for sewer maintenance decisions.

关键词： Sewer sediment assessment multisensor data fusion Unscented Kalman filter Rauch-Tung-Striebel Point cloud

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multisensor data fusion Based on Modified Belief Entropy in Dempster-Shafer Theory for Smart Environment

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IEEE ACCESS 2021年 9卷 37813-37822页

作者： Ullah, Ihsan Youn, Joosang Han, Youn-Hee Korea Univ Technol & Educ Adv Technol Res Ctr Cheonan 31253 South Korea Dong Eui Univ Dept Ind ICT Engn Busan 47340 South Korea Korea Univ Technol & Educ Dept Comp Sci & Engn Cheonan 31253 South Korea

multisensor data fusion is extensively used to merge data from heterogeneous sensors in a smart environment. However, sensors provide noisy and uncertain information which is a big challenge for researchers. Since uncertainty in the data is a central constraint for data fusion and decision-making systems. Dempster-Shafer's evidence theory is an appropriate method for modeling and fusing uncertain information. In this paper, a novel data fusion scheme is proposed based on the modified belief entropy of the basic probability assignments (BPAs) to quantify the uncertainty in the information and fused them by Dempster-Shafer evidence theory. The proposed DFUDS (data fusion based on measuring uncertainty in Dempster-Shafer) scheme considers the available redundant information in the body of evidence (BoEs). The BoEs obtained from the sensor data are processed by proposed belief entropy, and fuse all pieces of evidence by Dempster's rule of combination to transfer the conflicting data into decision-making results. Extensive computer simulation results show that the proposed scheme outperforms in terms of the degree of uncertainty, evidence, reasoning, and decision accuracy under active contexts of the smart environment.

关键词： data integration Entropy Measurement uncertainty Sensor fusion Intelligent sensors Evidence theory Wireless sensor networks multisensor data fusion Dempster-Shaffer theory uncertainty measurements belief entropy wireless sensor network

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multisensor data fusion in IoT Environments in Dempster-Shafer Theory Setting: An Improved Evidence Distance-Based Approach

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SENSORS 2023年第11期23卷 5141-5141页

作者： Hamda, Nour El Imane Hadjali, Allel Lagha, Mohand Blida 1 Univ Aeronaut & Spatial Studies Inst ASL Blida 09000 Algeria Natl Engn Sch Mech & Aerotech LIAS F-86961 Futuroscope France

In IoT environments, voluminous amounts of data are produced every single second. Due to multiple factors, these data are prone to various imperfections, they could be uncertain, conflicting, or even incorrect leading to wrong decisions. multisensor data fusion has proved to be powerful for managing data coming from heterogeneous sources and moving towards effective decision-making. Dempster-Shafer (D-S) theory is a robust and flexible mathematical tool for modeling and merging uncertain, imprecise, and incomplete data, and is widely used in multisensor data fusion applications such as decision-making, fault diagnosis, pattern recognition, etc. However, the combination of contradictory data has always been challenging in D-S theory, unreasonable results may arise when dealing with highly conflicting sources. In this paper, an improved evidence combination approach is proposed to represent and manage both conflict and uncertainty in IoT environments in order to improve decision-making accuracy. It mainly relies on an improved evidence distance based on Hellinger distance and Deng entropy. To demonstrate the effectiveness of the proposed method, a benchmark example for target recognition and two real application cases in fault diagnosis and IoT decision-making have been provided. fusion results were compared with several similar methods, and simulation analyses have shown the superiority of the proposed method in terms of conflict management, convergence speed, fusion results reliability, and decision accuracy. In fact, our approach achieved remarkable accuracy rates of 99.32% in target recognition example, 96.14% in fault diagnosis problem, and 99.54% in IoT decision-making application.

关键词： multisensor data fusion IoT Dempster-Shafer theory uncertainty evidence distance belief entropy

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A Fast Anomaly Diagnosis Approach Based on Modified CNN and multisensor data fusion

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2022年第12期69卷 13636-13646页

作者： Gong, Wenfeng Wang, Yuanzhe Zhang, Meiling Mihankhah, Ehsan Chen, Hui Wang, Danwei Wuhan Univ Technol Sch Naval Architecture Ocean & Energy Power Engn Wuhan 430063 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Guilin Univ Elect & Technol Sch Ocean Informat Engn Beihai Campus Beihai 536000 Peoples R China

In this article, a modified convolutional neural network (CNN) algorithm, namely 1D-GAPCNN-SVM, is proposed to address the early anomaly diagnosis problem. Considering the fact that traditional 2D-CNN based approaches contain too many model parameters and are not suitable for fast diagnosis applications using multisensor 1-D time-series measurements, 1D-CNN is introduced to deal with this problem. To reduce the number of parameters, a 1-D global average pooling layer is designed to substitute the fully connected layer with two or three layers. In order to further improve the diagnosis accuracy, a nonlinear multiclass support vector machine (SVM) is adopted to replace the traditional Softmax classifier as the final discriminator. Raw multisensor 1-D time-series data are directly fed into the diagnosis model, then the diagnosis result can be automatically generated. Two experiments, which are rolling bearing and GPS anomaly detection, have been conducted to demonstrate the effectiveness and the superior performance of the proposed method compared to the conventional SVM, K-nearest neighbor, deep neural network (DNN), and traditional 2D-CNN.

关键词： 1-D convolutional neural network (1D-CNN) 1-D global average pooling (1D-GAP) anomaly diagnosis GPS cyberattack multisensor data fusion rolling bearing support vector machine (SVM)

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