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Relevant bibliographies by topics / Data fusion algorithms; Information filters

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Academic literature on the topic 'Data fusion algorithms; Information filters'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Data fusion algorithms; Information filters"

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Jahan, Kausar, and Koteswara Rao Sanagapallea. "Fusion of Angle Measurements from Hull Mounted and Towed Array Sensors." Information 11, no. 9 (2020): 432. http://dx.doi.org/10.3390/info11090432.

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Two sensor arrays, hull-mounted array, and towed array sensors are considered for bearings-only tracking. An algorithm is designed to combine the information obtained as bearing (angle) measurements from both sensor arrays to give a better solution. Using data from two different sensor arrays reduces the problem of observability and the observer need not follow the S-maneuver to attain observability of the process. The performance of the fusion algorithm is comparable to that of theoretical Cramer–Rao lower bound and with that of the algorithm when bearing measurements from a single sensor arr

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HASAN, AHMED M., KHAIRULMIZAM SAMSUDIN, ABDUL RAHMAN RAMLI, and RAJA SYAMSUL AZMIR. "COMPARATIVE STUDY ON WAVELET FILTER AND THRESHOLDING SELECTION FOR GPS/INS DATA FUSION." International Journal of Wavelets, Multiresolution and Information Processing 08, no. 03 (2010): 457–73. http://dx.doi.org/10.1142/s0219691310003572.

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Navigation and guidance of an autonomous vehicle require determination of the position and velocity of the vehicle. Therefore, fusing the Inertial Navigation System (INS) and Global Positioning System (GPS) is important. Various methods have been applied to smooth and predict the INS and GPS errors. Recently, wavelet de-noising methodologies have been applied to improve the accuracy and reliability of the GPS/INS system. In this work, analysis of real data to identify the optimal wavelet filter for each GPS and INS component for high quality error estimation is presented. A comprehensive compa

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Guoyan, Wang, A. V. Fomichev, and Dy Yiran. "Research on Improved Gaussian Smoothing Filters for SLAM Application." Mekhatronika, Avtomatizatsiya, Upravlenie 20, no. 12 (2019): 756–64. http://dx.doi.org/10.17587/mau.20.756-764.

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To address the navigation issues of the planetary rover and construct a map for the unknown environment as well as the surface of the planets in our solar system, the simultaneous localization and mapping can be seen as an alternative method. In terms of the navigation with the laser sensor, the Kalman filter and its improving algorithms, such as EKF and UKF are widely used in the the process of processing information. Nevertheless, these filter algorithms suffer from low accuracy and significant computation expensive. The EKF algorithm has a linearization process, the UKF algorithm is better

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López-Delis, Alberto, Cristiano J. Miosso, João L. A. Carvalho, Adson F. da Rocha, and Geovany A. Borges. "Continuous Estimation Prediction of Knee Joint Angles Using Fusion of Electromyographic and Inertial Sensors for Active Transfemoral Leg Prostheses." Advances in Data Science and Adaptive Analysis 10, no. 02 (2018): 1840008. http://dx.doi.org/10.1142/s2424922x18400089.

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Information extracted from the surface electromyographic (sEMG) signals can allow for the detection of movement intention in transfemoral prostheses. The sEMG can help estimate the angle between the femur and the tibia in the sagittal plane. However, algorithms based exclusively on sEMG information can lead to inaccurate results. Data captured by inertial-sensors can improve this estimate. We propose three myoelectric algorithms that extract data from sEMG and inertial sensors using Kalman-filters. The proposed fusion-based algorithms showed improved performance compared to methods based exclu

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Alshawabkeh, Yahya. "Color and Laser Data as a Complementary Approach for Heritage Documentation." Remote Sensing 12, no. 20 (2020): 3465. http://dx.doi.org/10.3390/rs12203465.

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Heritage recording has received much attention and benefits from recent developments in the field of range and imaging sensors. While these methods have often been viewed as two different methodologies, data integration can achieve different products, which are not always found in a single technique. Data integration in this paper can be divided into two levels: laser scanner data aided by photogrammetry and photogrammetry aided by scanner data. At the first level, superior radiometric information, mobility and accessibility of imagery can be actively used to add texture information and allow

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Rao, Jin Jun, Tong Yue Gao, Zhen Jiang, and Zhen Bang Gong. "Position and Attitude Information Fusion for Portable Unmanned Aerial Vehicles." Key Engineering Materials 439-440 (June 2010): 155–60. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.155.

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Portable Unmanned Aerial Vehicles (PUAVs) present an enormous application potential, and the real time accurate position and attitude information is the basis of autonomous flight of PUAVs. In order to obtain comprehensive and accurate position and attitude data of PUAVs in flight, focusing on the common sensors configuration of PUAVs, each type of sensor’s characteristic is analyzed, and the data fusion problem of SINS/GPS/Compass combination is presented and studied in this paper. Firstly, the error expressions of MEMS inertia sensors, attitude, velocity and position are researched and deriv

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Guan, Binglei, and Xianfeng Tang. "Multisensor decentralized nonlinear fusion using adaptive cubature information filter." PLOS ONE 15, no. 11 (2020): e0241517. http://dx.doi.org/10.1371/journal.pone.0241517.

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In nonlinear multisensor system, abrupt state changes and unknown variance of measurement noise are very common, which challenges the majority of the previously developed models for precisely known multisensor fusion techniques. In terms of this issue, an adaptive cubature information filter (CIF) is proposed by embedding strong tracking filter (STF) and variational Bayesian (VB) method, and it is extended to multi-sensor fusion under the decentralized fusion framework with feedback. Specifically, the new algorithms use an equivalent description of STF, which avoid the problem of solving Jacob

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Wang, Tao, Xiaoran Wang, and Mingyu Hong. "Gas Leak Location Detection Based on Data Fusion with Time Difference of Arrival and Energy Decay Using an Ultrasonic Sensor Array." Sensors 18, no. 9 (2018): 2985. http://dx.doi.org/10.3390/s18092985.

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Ultrasonic gas leak location technology is based on the detection of ultrasonic waves generated by the ejection of pressured gas from leak holes in sealed containers or pipes. To obtain more accurate leak location information and determine the locations of leak holes in three-dimensional space, this paper proposes an ultrasonic leak location approach based on multi-algorithm data fusion. With the help of a planar ultrasonic sensor array, the eigenvectors of two individual algorithms, i.e., the arrival distance difference, as determined from the time difference of arrival (TDOA) location algori

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Ahrari, A. H., M. Kiavarz, M. Hasanlou, and M. Marofi. "THERMAL AND VISIBLE SATELLITE IMAGE FUSION USING WAVELET IN REMOTE SENSING AND SATELLITE IMAGE PROCESSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 26, 2017): 11–15. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-11-2017.

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Multimodal remote sensing approach is based on merging different data in different portions of electromagnetic radiation that improves the accuracy in satellite image processing and interpretations. Remote Sensing Visible and thermal infrared bands independently contain valuable spatial and spectral information. Visible bands make enough information spatially and thermal makes more different radiometric and spectral information than visible. However low spatial resolution is the most important limitation in thermal infrared bands. Using satellite image fusion, it is possible to merge them as a

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Soundy, Andy W. R., Bradley J. Panckhurst, Phillip Brown, Andrew Martin, Timothy C. A. Molteno, and Daniel Schumayer. "Comparison of Enhanced Noise Model Performance Based on Analysis of Civilian GPS Data." Sensors 20, no. 21 (2020): 6050. http://dx.doi.org/10.3390/s20216050.

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We recorded the time series of location data from stationary, single-frequency (L1) GPS positioning systems at a variety of geographic locations. The empirical autocorrelation function of these data shows significant temporal correlations. The Gaussian white noise model, widely used in sensor-fusion algorithms, does not account for the observed autocorrelations and has an artificially large variance. Noise-model analysis—using Akaike’s Information Criterion—favours alternative models, such as an Ornstein–Uhlenbeck or an autoregressive process. We suggest that incorporating a suitable enhanced

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Dissertations / Theses on the topic "Data fusion algorithms; Information filters"

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Ho, Peter. "Organization in decentralized sensing." Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306873.

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Baravdish, Ninos. "Information Fusion of Data-Driven Engine Fault Classification from Multiple Algorithms." Thesis, Linköpings universitet, Fordonssystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176508.

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As the automotive industry constantly makes technological progress, higher demands are placed on safety, environmentally friendly and durability. Modern vehicles are headed towards increasingly complex system, in terms of both hardware and software making it important to detect faults in any of the components. Monitoring the engine’s health has traditionally been done using expert knowledge and model-based techniques, where derived models of the system’s nominal state are used to detect any deviations. However, due to increased complexity of the system this approach faces limitations regarding

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Lian, Chunfeng. "Information fusion and decision-making using belief functions : application to therapeutic monitoring of cancer." Thesis, Compiègne, 2017. http://www.theses.fr/2017COMP2333/document.

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La radiothérapie est une des méthodes principales utilisée dans le traitement thérapeutique des tumeurs malignes. Pour améliorer son efficacité, deux problèmes essentiels doivent être soigneusement traités : la prédication fiable des résultats thérapeutiques et la segmentation précise des volumes tumoraux. La tomographie d’émission de positrons au traceur Fluoro- 18-déoxy-glucose (FDG-TEP) peut fournir de manière non invasive des informations significatives sur les activités fonctionnelles des cellules tumorales. Les objectifs de cette thèse sont de proposer: 1) des systèmes fiables pour prédi

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Jesneck, JL, LW Nolte, JA Baker, CE Floyd, and JY Lo. "Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis." Thesis, 2006. http://hdl.handle.net/10161/207.

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As more diagnostic testing options become available to physicians, it becomes more difficult to combine various types of medical information together in order to optimize the overall diagnosis. To improve diagnostic performance, here we introduce an approach to optimize a decision-fusion technique to combine heterogeneous information, such as from different modalities, feature categories, or institutions. For classifier comparison we used two performance metrics: The receiving operator characteristic (ROC) area under the curve [area under the ROC curve (AUC)] and the normalized partial area un

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Books on the topic "Data fusion algorithms; Information filters"

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Braun, Jerome J. Multisensor, multisource information fusion: Architectures, algorithms, and applications 2011 : 27-28 April 2011, Orlando, Florida, United States. SPIE, 2011.

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Braun, Jerome J. Multisensor, multisource information fusion: Architectures, algorithms, and applications 2010 : 7-8 April 2010, Orlando, Florida, United States. SPIE, 2010.

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(Society), SPIE, ed. Multisensor, multisource information fusion: Architectures, algorithms, and applications 2009 : 16-17 April 2009, Orlando, Florida, United States. SPIE, 2009.

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K, Wang R., ed. Frequency domain filtering strategies for hybrid optical information processing. Research Studies Press, 1996.

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V, Dasarathy Belur, Society of Photo-optical Instrumentation Engineers., and Ball Aerospace & Technologies Corporation (USA), eds. Multisensor, multisource information fusion : architectures, algorithms, and applications 2005: 30-31 March, 2005, Orlando, Florida, USA. SPIE, 2005.

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Multisensor, multisource information fusion: Architectures, algorithms, and applications 2004 : 14-15 April 2004, Orlando, Florida, USA. SPIE, 2004.

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V, Dasarathy Belur, and Society of Photo-optical Instrumentation Engineers., eds. Multisensor, multisource information fusion--architectures, algorithms, and applications 2004: 14-15 April 2004, Orlando, Florida, USA. SPIE, 2004.

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V, Dasarathy Belur, and Society of Photo-optical Instrumentation Engineers., eds. Multisensor, multisource information fusion: Architectures, algorithms, and applications 2006 : 19-20 April 2006, Kissimmee, Florida, USA. SPIE, 2006.

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V, Dasarathy Belur, and Society of Photo-optical Instrumentation Engineers., eds. Multisensor, multisource information fusion--architectures, algorithms, and applications 2003: 23-25 April 2003, Orlando, Florida, USA. SPIE, 2003.

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Multisensor, multisource information fusion: Architectures, algorithms, and applications 2007 : 11-12 April, 2007, Orlando, Florida, USA. SPIE, 2007.

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Book chapters on the topic "Data fusion algorithms; Information filters"

1

Beltz, Hayley, Timothy Rutledge, Raoul R. Wadhwa, et al. "Ranking Algorithms: Application for Patent Citation Network." In Information Fusion and Data Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-03643-0_21.

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Clark, James J., and Alan L. Yuille. "Data Fusion in Shape From Shading Algorithms." In Data Fusion for Sensory Information Processing Systems. Springer US, 1990. http://dx.doi.org/10.1007/978-1-4757-2076-1_7.

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Clark, James J., and Alan L. Yuille. "Data Fusion Applied to Feature Based Stereo Algorithms." In Data Fusion for Sensory Information Processing Systems. Springer US, 1990. http://dx.doi.org/10.1007/978-1-4757-2076-1_5.

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Chen, Dewang, and Ruijun Cheng. "Multiple GPS Track Information Fusion." In Intelligent Processing Algorithms and Applications for GPS Positioning Data of Qinghai-Tibet Railway. Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-58970-0_7.

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Brighton, Henry, and Chris Mellish. "On the Consistency of Information Filters for Lazy Learning Algorithms." In Principles of Data Mining and Knowledge Discovery. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-540-48247-5_31.

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Li, Chengfan, Jingyuan Yin, Junjuan Zhao, and Lan Liu. "Extraction of Urban Built-Up Land in Remote Sensing Images Based on Multi-sensor Data Fusion Algorithms." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18129-0_39.

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Arasaratnam, Ienkaran, and Kumar Pakki Bharani Chandra. "Cubature Information Filters." In Multisensor Data Fusion. CRC Press, 2017. http://dx.doi.org/10.1201/b18851-12.

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Gao, Wei, Ya Zhang, and Qian Sun. "Nonlinear Information Fusion Algorithm of an Asynchronous Multisensor Based on the Cubature Kalman Filter." In Multisensor Data Fusion. CRC Press, 2017. http://dx.doi.org/10.1201/b18851-14.

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Meng, Tao, Mei-Ling Shyu, and Lin Lin. "Multimodal Information Integration and Fusion for Histology Image Classification." In Multimedia Data Engineering Applications and Processing. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2940-0.ch003.

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Biomedical imaging technology has become an important tool for medical research and clinical practice. A large amount of imaging data is generated and collected every day. Managing and analyzing these data sets require the corresponding development of the computer based algorithms for automatic processing. Histology image classification is one of the important tasks in the bio-image informatics field and has broad applications in phenotype description and disease diagnosis. This study proposes a novel framework of histology image classification. The original images are first divided into several blocks and a set of visual features is extracted for each block. An array of C-RSPM (Collateral Representative Subspace Projection Modeling) models is then built that each model is based on one block from the same location in original images. Finally, the C-Value Enhanced Majority Voting (CEWMV) algorithm is developed to derive the final classification label for each testing image. To evaluate this framework, the authors compare its performance with several well-known classifiers using the benchmark data available from IICBU data repository. The results demonstrate that this framework achieves promising performance and performs significantly better than other classifiers in the comparison.

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Gharbia, Reham, and Aboul Ella Hassanien. "Swarm Intelligence Based on Remote Sensing Image Fusion." In Environmental Information Systems. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7033-2.ch011.

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This chapter presents a remote sensing image fusion based on swarm intelligence. Image fusion is combining multi-sensor images in a single image that has most informative. Remote sensing image fusion is an effective way to extract a large volume of data from multisource images. However, traditional image fusion approaches cannot meet the requirements of applications because they can lose spatial information or distort spectral characteristics. The core of the image fusion is image fusion rules. The main challenge is getting suitable weight of fusion rule. This chapter proposes swarm intelligence to optimize the image fusion rule. Swarm intelligence algorithms are a family of global optimizers inspired by swarm phenomena in nature and have shown better performance. In this chapter, two remote sensing image fusion based on swarm intelligence algorithms, Particle Swarm Optimization (PSO) and flower pollination algorithm are presented to get an adaptive image fusion rule and comparative between them.

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Conference papers on the topic "Data fusion algorithms; Information filters"

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Roussel, Stephane, Hemanth Porumamilla, Charles Birdsong, Peter Schuster, and Christopher Clark. "Enhanced Vehicle Identification Utilizing Sensor Fusion and Statistical Algorithms." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-12012.

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Several studies in the area of vehicle detection and identification involve the use of probabilistic analysis and sensor fusion. While several sensors utilized for identifying vehicle presence and proximity have been researched, their effectiveness in identifying vehicle types has remained inadequate. This study presents the utilization of an ultrasonic sensor coupled with a magnetic sensor and the development of statistical algorithms to overcome this limitation. Mathematical models of both the ultrasonic and magnetic sensors were constructed to first understand the intrinsic characteristics

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Lloyd, George M. "A Kalman Filter Framework for High-Dimensional Sensor Fusion Using Stochastic Non-Linear Networks." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-37834.

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The textbook Kalman Filter (LKF) seeks to estimate the state of a linear system based on having two things in hand: a.) a reasonable state-space model of the underlying process and its noise components; b.) imperfect (noisy) measurements obtained from the process via one or more sensors. The LKF approach results in a predictor-corrector algorithm which can be applied recursively to correct predictions from the state model so as to yield posterior estimates of the current process state, as new sensor data are made available. The LKF can be shown to be optimal in a Gaussian setting and is eminen

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Zhang, Yi, Xiaojing Shen, Zhiguo Wang, and Yunmin Zhu. "Random MHT data association algorithm based on random coefficient Kalman filter." In 2017 20th International Conference on Information Fusion (Fusion). IEEE, 2017. http://dx.doi.org/10.23919/icif.2017.8009766.

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Sungra, Anshul, and Brian Fabien. "Evaluation of Control Algorithms on Mobile Robots for Collision Avoidance." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23500.

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Abstract This paper describes the implementation of various algorithms to control the distance between a lead vehicle and a following (ego) vehicle. The ego robot equipped with a monocular camera and a rotating laser sensor (LDS). The monocular camera used for object detection using the Aggregate Channel Features (ACF) detection algorithm. The width of the bounding box generated by the detection algorithm had used to determine the distance between the lead and the following vehicles. Since this research focused on longitudinal autonomy, the data from the rotating laser sensor downsampled from

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Zhang, Chaokun, and Huiying Wang. "Decentralized Multi-sensor Data Fusion Algorithm Using Information Filter." In 2010 International Conference on Measuring Technology and Mechatronics Automation (ICMTMA 2010). IEEE, 2010. http://dx.doi.org/10.1109/icmtma.2010.506.

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Cherchar, Ammar, Messaoud Thameri, and Adel Belouchrani. "A new multi-sensor fusion algorithm based on the Information Filter framework." In 2017 Seminar on Detection Systems Architectures and Technologies (DAT). IEEE, 2017. http://dx.doi.org/10.1109/dat.2017.7889154.

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Clark, J. M. C. "Projection filters and matched moment filters in tracking." In IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications. IEE, 2008. http://dx.doi.org/10.1049/ic:20080065.

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Atherton, D. P. "Data fusion for several Kalman filters tracking a single target." In Target Tracking 2004: Algorithms and Applications. IEE, 2004. http://dx.doi.org/10.1049/ic:20040053.

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Gong, Ting. "Expression Recognition Method of Fusion Gabor Filter and 2DPCA Algorithm." In 2020 International Conference on Computer Information and Big Data Applications (CIBDA). IEEE, 2020. http://dx.doi.org/10.1109/cibda50819.2020.00121.

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Easthope, P. F. "Tracking Simulated UAV Swarms Using Particle Filters." In IET Conference on Data Fusion & Target Tracking 2014: Algorithms and Applications. Institution of Engineering and Technology, 2014. http://dx.doi.org/10.1049/cp.2014.0524.

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