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An innovative asynchronous, multi-rate, multi-sensor state vector fusion algorithm for air defence applications

IFAC-PapersOnLine 2016年第1期49卷 337-342页

作者： Shivanand, G. Reddy, K Vamshi Krishna Prasad, D.B.N. Defence Research and Development Laboratory Hyderabad Telanagana India

This paper presents an innovative approach for data fusion of asynchronous, multi-rate, multi-sensor data in real time. This technique is based on state vector data fusion. For air defence applications, the target information from multi sensors is sent to a central control centre to create a complete threat scenario which would be used for taking appropriate decision. This target data needs to be sent in real time, hence there is a need for low bandwidth consuming data fusion algorithms but at the same time there should be minimal degradation in data accuracy. The optimal fusion algorithm requires the entire state covariance information matrix to be transmitted to the data fusion centre, however this requires huge bandwidth for transmission of data. To avoid this generally, only diagonal elements are transmitted at the cost of considerable degradation in performance. The authors present an innovative approach by considering the only covariance of position, velocity and acceleration but by neglecting the cross covariance terms among position, velocity and acceleration. The accuracy of this proposed algorithm is about 95% of that of optimal method but the reduction in bandwidth requirement is of the order of 60%. © 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Sensor data fusion Bandwidth Covariance matrix data fusion Kalman filters Military rockets Bandwidth requirement Covariance information data fusion algorithm Innovative approaches Multi sensor data State vector fusion Target information Transmission of data

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Thermal distortion real-time detection and correction of a high-power Laser beam-splitter mirror based on double Shack-Hartmann wavefront sensors 2

Thermal distortion real-time detection and correction of a h...

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Conference on High-Power, High-Energy, and High-Intensity Laser Technology II

作者： Ning, Yu Sun, Quan Wang, Hongyan Wu, Wuming Du, Shaojun Xu, Xiaojun Natl Univ Def Technol Coll Optoelect Sci & Engn Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781628416343

In a high-power laser system, a beam splitter refers to the mirror which locates at the cross point of the path of high-power beam and the weak light section. Because of the thermo-optic effect and elasto-optic effect, a beam splitter deforms under intense laser radiation. This deformation adds extra phase on the incident waves and deliveries inaccurate information to the wavefront sensor. Consequently, the output laser focuses at finite distance and gets divergent when arrives at the target. To settle the above problem, this paper presents a new method for real-time correction of the thermal distortion of beam splitter, based on algorithm of the data fusion of two Shack-Hartmann wavefront sensors (SH-WFS). Different from the traditional AO system, which contains a wavefront sensor, a corrector and a servo controller, two extra Shack-Hartmann wavefront detectors are adopted in our AO system, to detect the transmitted and reflected aberrations of beam splitter mirror. And these aberrations are real-timely delivered to the wavefront sensor. Based on coordinate conversion and data fusion algorithm, it makes the wavefront sensor of AO can "see" the aberrations of splitter mirror by itself. Thus, the servo system controls the corrector to compensate these aberrations correctly. In this paper, the theoretical model of data fusion algorithm is carried out. A closed-loop AO system, which consists of a typical AO system and two extra Shack-Hartmann wavefront detectors, is set up to validate the data fusion algorithm. Experimental results show that, the distortion of a CaF2 beam splitter can be real-time corrected when the AO closed-loop control is on. The beam quality factor of output laser decreases from 4 to 1.7 times of diffraction limit.

关键词： adaptive optics beam splitter mirror thermal distortion double Shack-Hartmann wavefront sensors data fusion algorithm

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The Environmental Information Monitoring Network Set up based on Service Discovery Mechanism

The Environmental Information Monitoring Network Set up base...

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3rd International Conference on Energy, Environment and Sustainable Development (EESD 2013)

作者： Li Zhaoming Yang Xu Changchun Vocat Inst Technol Network Ctr Changchun 130022 Peoples R China

ISBN: (纸本)9783037859728

The wireless sensor network is widely used in the largely data measure. The research on the energy consumption is the key point in it's practical application recently for the wireless sensor network has the problem of energy restrain. Considering the wireless sensor network applies in the information monitoring system on environmental has the inadequate of the limited energy of nodes and the irregular network energy consumption, this paper analyses the basic structure of the environmental sensor network, and proposes the service discovery mechanism based on the catalogue model. This system can reduces the system's energy consumption, and is able to achieve the purpose of increasing the reliability and security of the sensor network, reducing the data redundancy degree and extending the lifetime of the network according to the experimental simulation.

关键词： wireless sensor network service discovery mechanism improved GAF algorithm data fusion algorithm

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Cooperative Combining of Cumulants-Based Modulation Classification in CR Networks 33

Cooperative Combining of Cumulants-Based Modulation Classifi...

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33rd Annual IEEE Military Communications Conference on Affordable Mission Success - Meeting the Challenge (MILCOM)

作者： Abdelbar, Mahi Tranter, Bill Bose, Tamal Virginia Tech Wireless VT Bradley Dept Elect & Comp Engn Blacksburg VA 24061 USA Univ Arizona Dept Elect & Comp Engn Tucson AZ 85721 USA

ISBN: (纸本)9781479967704

Automatic Modulation Classification (AMC) is a key enabling technology in Cognitive Radio (CR) Networks. The ability of CR transceivers to detect and classify unknown wireless signals has various applications in civilian and military domains. Performance of AMC degrades severely under low Signal-to-Noise Ratio (SNR) and variable channel conditions. Cooperative classification has been presented as a means to overcome the detrimental channel effects by combining the results from physically scattered CR nodes. In this work, Maximum Likelihood (ML) combining of classification features is presented as a data fusion algorithm that provides better classification accuracy compared to hard decision combining algorithms without high network overhead. The performance of a cumulants-based modulation classifier under Additive White Gaussian Noise (AWGN) is analyzed. The enhancement in classification performance when applying ML combining of more than one classifier is presented. Theoretical analysis as well as various simulations are presented for ML combining of CR nodes with equal SNR. In addition, analysis is extended to the case where CR nodes have different SNRs. Theory and simulations show that applying ML combining will result in a better classification accuracy, even when one of the nodes has a much lower SNR.

关键词： AWGN classification cognitive radio cooperative communication higher order statistics maximum likelihood estimation modulation additive white Gaussian noise automatic modulation classification classification features cognitive radio network cooperative classification cooperative combining cumulants based modulation classification data fusion algorithm maximum likelihood combining Accuracy algorithm design and analysis Manganese Maximum likelihood estimation Phase shift keying Signal to noise ratio

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A Three-Level-Module adaptive intrusion detection system

A Three-Level-Module adaptive intrusion detection system

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IEEE International Conference on Networking, Sensing and Control

作者： Zhao, Lin-hui Wang, Yumin Xiao, Jing Dai, Ya-ping Dong, Fang-yan Liu, Hai-le Beijing Inst Technol Sch Informat Sci & Technol Beijing 100081 Peoples R China Qingdao Technol Univ Sch Sci Qingdao 266033 Peoples R China Beijing Union Univ Coll Mechatroni Beijing 100020 Peoples R China Tokyo Inst Technol Dept Comp Intelligence & Syst Sci Midori ku Yokohama Kanagawa 2268502 Japan Artillery Command Acad Langfang 065000 Peoples R China

ISBN: (纸本)9781424410750

Based on the Danger theory, the immune network theory and the decision templates fusion algorithm, a three-level-module adaptive intrusion detection system (TAIDS) is presented in this paper. To consider the effect of danger signals, the results of decision templates algorithm are redefined by adding a kind of suspicion signal. So, the detection templates should be modified online, and a template-adjustable adaptive decision fusion algorithm is proposed. There are two benefits in the TAIDS. First, when it is difficult to distinguish current behaviors depending on familiar features, The TAIDS will discriminate them by means of danger theory, making false alarms reduced and the ability of identifying novel attacks enhanced. Second, the adaptive decision templates algorithm allows detection templates to modify dynamically without periodical updating. Experiments are carried out on KDD-CUP-99 database to verify the performance of this system. The false positive rate is 2.27%, and the accuracies on known attacks and on unknown attacks are respectively 97.67% and 98.75%.

关键词： intrusion detection danger theory data fusion algorithm

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Anode effect prediction method based on local effect detection

Anode effect prediction method based on local effect detecti...

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Chinese Automation Congress (CAC)

作者： Cui, Jiarui Zhang, Shuai Cao, Bin Li, Qing Huang, Ruoyu Yang, Xu Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China Guiyang Aluminum Magnesium Design & Res Inst Co L Guiyang Guizhou Peoples R China

ISBN: (纸本)9781728176871

In view of the situation that the current anode effect prediction methods can not cover all types of anode effects, or the anti-interference ability is poor, or the prediction advance is very small, this paper proposes a method to predict the global anode effect based on the local effect detection, that is, by using the local signal before forming the global anode effect instead of the cell voltage of the whole cell, the local or local anode effect can be detected Anode effect in local anode. Therefore, based on the internal structure of aluminum reduction cell, we divide the area. Then, the potential periodicity of anode current is obtained by analyzing the power spectrum of anode current. Finally, a local effect detection model based on data fusion method is used to predict the occurrence of global effects. The effectiveness of the algorithm is illustrated by the anode current data measured on-line during the actual operation of aluminum electrolysis.

关键词： Local effect power spectrum data fusion algorithm

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Rotational speed monitoring of brushed DC motor via current signal

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MEASUREMENT 2021年 184卷 109890-109890页

作者： Zhang, Qi Wen, Bin He, Yongling Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China

Sensor-less speed estimation of brushed DC motor is preferred for dynamic control and state monitoring. Ripplebased and model-based methods are widely applied for sensor-less speed estimation. This paper firstly offers a ripple-based technique, analyzes their features and performance, then presents a modified Kalman filter to fuse the ripple-base and model-based results. The two source data are fused through modification of the noise covariance matrices of the conventional Kalman filter. Experimental validates the proposed algorithm. Test shows that the proposed method reduces speed estimation to less 3% and owns roughly 8 times of accuracy comparing with conventional ripple-based and model-based methods. The proposed method is suitable for speed monitoring of industrial applications involving brushed DC motor.

关键词： Brushed DC motor Sensor-less speed estimation Kalman filter Noise covariance data fusion algorithm

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Design of 2D LiDAR and camera fusion system improved by differential evolutionary PID with nonlinear tracking compensator

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INFRARED PHYSICS & TECHNOLOGY 2021年 116卷 103776-103776页

作者： Xu, Xiaobin Zhao, Minghui Lu, Yonghua Ran, Yingying Tan, Zhiying Luo, Minzhou Hohai Univ Coll Mech & Elect Engn Changzhou 213022 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Peoples R China Changzhou Changgong Elect Technol Co Ltd Changzhou 213001 Jiangsu Peoples R China

An improved 2D LiDAR and camera fusion system is proposed for the 3D reconstruction of unknown environments. It combines the advantages of dense 2D point cloud and rich color image, adopting a differential evolutionary nonlinear tracking PID to control the pitching motion of LiDAR and camera accurately. The quadratic polynomial transition function is used to optimize the pitching trajectory. The environment was scanned by the system and converted into a 3D colored point cloud by the data fusion algorithm. The experimental results show: the proposed PID control algorithm can accurately control the pitching motion with a small average error (0.0267 degrees) and significantly reduce the point cloud inhomogeneity (0.00698);the processing time for converting each 2D point cloud into the 3D point cloud is about 0.6 ms;combined with the data fusion algorithm, the system can obtain the dense colored 3D point cloud;compared with binocular camera, depth camera and 3D LiDAR under the condition of strong light interference, the fusion system outperforms, with the reconstruction object errors of distance, length and width of 0.23%, 0.17% and 0.46% respectively. In conclusion, the system can obtain homogeneous, and dense colored 3D point cloud in real time while ensuring stable refresh frame rate.

关键词： 2D LiDAR Color camera data fusion algorithm Differential evolutionary nonlinear tracking PID

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An Online Calibration Method Based on n-Tuple and Opportunistic Communication for Mine Mass Portable Gas Sensors

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SENSORS 2021年第7期21卷 2451-2451页

作者： Wang, Gang Zhao, Yang Ding, Zeheng Zhao, Xiaohu China Univ Min & Technol IoT Percept Mine Res Ctr Xuzhou 221000 Jiangsu Peoples R China Natl Joint Engn Lab Internet Appl Technol Mines Xuzhou 221000 Jiangsu Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221000 Jiangsu Peoples R China

Due to the increasing deployment of the Internet of Things (IoT) in the mining industry, portable gas monitoring devices have been widely used. Sensor calibration of large-scale portable gas monitoring devices is becoming an urgent problem to be solved. An online sensor calibration algorithm based on n-tuple and opportunistic communication is proposed based on the specific characteristics (i.e., 'single-sensor, multi-position' and 'multi-sensor, single-position') of each portable gas monitoring device employed. In this paper, data collected from portable and fixed sensors were defined as multi-dimensional data points and gas monitoring data pairs, respectively. The cluster-based self-adaptive weighted data fusion algorithm and multi-period single sensor reliability fusion algorithm were proposed and used for overall judging. The overall judgments were broadcast to each wireless access point by network, and the reliability of the calibration information transmission was enhanced by opportunistic communications. The simulation results revealed that efforts required for the calibration of portable sensors were reduced significantly, and their reliability was improved.

关键词： data fusion algorithm gas monitoring data pairs multi-dimensional data points underground coal mine

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Using data monitoring algorithms to physiological indicators in motion based on Internet of Things in smart city

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SUSTAINABLE CITIES AND SOCIETY 2021年 67卷 102727-102727页

作者： Tian, Jian Gao, Lulu Henan Univ Sch Phys Educ Kaifeng 475000 Henan Peoples R China Henan Univ Sch Phys Educ Minsheng Coll Kaifeng 475000 Henan Peoples R China

This article discusses the monitoring of physiological indicators during exercise, combined with the data fusion algorithm of the smart city Internet of Things health. We use the hash value of the tuple key to the corresponding data block of the node, use the data block record to obtain the response of the target node, and output the data tuple. It is used as a measure of the load balance of health data streams to determine whether load migration is needed and to determine the way and amount of migration tasks to make migration decisions. The simulation experiments show that the method has good computational performance and dynamic load balancing. A series of mean arterial pressure and heart rate of patients and non-stationary health data, and a series of blood pressure and heart rate of health individuals in different postures are selected to perform experiments to analyze the transfer function and power spectra in the model, validating that the model can be used to reveal the changes associated with severe systemic response syndrome (SIRS), providing a hypothesis for the decomposition of autoregulation of physiological control under health and disease conditions.

关键词： Internet of Things data fusion algorithm Physiological indicators Monitoring Smart city

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