Relevant bibliographies by topics / Data fusion algorithms

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data fusion algorithms'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 June 2025

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Journal articles on the topic "Data fusion algorithms"

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ZHU, SHANFENG, QIZHI FANG, and WEIMIN ZHENG. "SOCIAL CHOICE FOR DATA FUSION." International Journal of Information Technology & Decision Making 03, no. 04 (2004): 619–31. http://dx.doi.org/10.1142/s0219622004001288.

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Social choice theory is the study of decision theory on how to aggregate separate preferences into group's rational preference. It has wide applications, especially on the design of voting rules, and brings far-reaching influence on the development of modern political science and welfare economics. With the advent of the information age, social choice theory finds its up-to-date application on designing effective Metasearch engines. Metasearch engines provide effective searching by combining the results of multiple source search engines that make use of diverse models and techniques. In this work, we analyze social choice algorithms in a graph-theoretic approach. In addition to classical social choice algorithms, such as Borda and Condorcet, we study one special type of social choice algorithms, elimination voting, to tackle Metasearch problem. Some new algorithms are proposed and examined in the fusion experiment on TREC data. It shows that these elimination voting algorithms achieve satisfied performance when compared with Borda algorithm.
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Quadri, S. A., and Othman Sidek. "Role of Algorithm Engineering in Data Fusion Algorithms." Journal of Computational Intelligence and Electronic Systems 2, no. 1 (2013): 29–35. http://dx.doi.org/10.1166/jcies.2013.1046.

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LIPOVETSKY, STAN. "DATA FUSION IN SEVERAL ALGORITHMS." Advances in Adaptive Data Analysis 05, no. 03 (2013): 1350014. http://dx.doi.org/10.1142/s1793536913500143.

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Data fusion consists of the process of integrating several datasets with some common variables, and other variables available only in partial datasets. The main problem of data fusion can be described as follows. From one source, having X0 and Y0 datasets (with N0 observations by multiple x and y variables, n and m of those, respectively), and from another source, having X1 data (with N1 observations by the same nx-variables), we need to estimate the missing portion of the Y1 data (of size N1 by m variables) in order to combine all the data into one set. Several algorithms are considered in this work, including estimation of weights proportional to the distances from each ith observation in the X1 "recipients" dataset to all observations in the X0 "donors" dataset. Or we can use a sample balancing technique with the maximum effective base performed by applying ridge-regression for the Gifi system of binaries obtained from the x-variables for the best fit of the "donors" X0 data to the margins defined by each respondent in the "recipients" X1 dataset. Then the weighted regressions of each y in the Y0 dataset by all variables in the X0 are constructed. For each ith observation in the dataset X0, these regressions are used for predicting the y-variables in the Y1 "recipients" dataset. If X and Y are the same n variables from different sources, the dual partial least squares technique and a special regression model with dummies defining each of the three available sets are used for prediction of the Y1 data.
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Zhang, Jie. "Security Technology of Wireless Sensor Internet of Things Based on Data Fusion." International Journal of Online Engineering (iJOE) 13, no. 11 (2017): 25. http://dx.doi.org/10.3991/ijoe.v13i11.7748.

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<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">In order to prove the effect of data fusion technology in the Internet of things, a wireless sensor Internet of things security technology based on data fusion is designed, and the impact of data fusion in the field of communication technology is studied. Therefore, two security fusion algorithms are designed on the basis of analyzing and comparing the advantages and disadvantages of various security fusion algorithms, namely, data security fusion algorithm EDCSDA and approximate fusion algorithm PADSA. By analyzing the probability distribution model of the data collected by the nodes, the disturbance data is superimposed on the original data to hide the effect of the original data. A test bed system for perception layer of the Internet of things is designed and implemented. The test results prove the feasibility of the two algorithms. Meanwhile, it shows that the two algorithms can reduce the transmission overhead of the network while guaranteeing the security. Based on the above finding, it is concluded that data fusion technology is very effective for improving network efficiency and prolonging the network life cycle as one of the key technologies in the perception layer of Internet of things.</span>
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Tan, Yuxiang, Yann Tambouret, and Stefano Monti. "SimFuse: A Novel Fusion Simulator for RNA Sequencing (RNA-Seq) Data." BioMed Research International 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/780519.

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The performance evaluation of fusion detection algorithms from high-throughput sequencing data crucially relies on the availability of data with known positive and negative cases of gene rearrangements. The use of simulated data circumvents some shortcomings of real data by generation of an unlimited number of true and false positive events, and the consequent robust estimation of accuracy measures, such as precision and recall. Although a few simulated fusion datasets from RNA Sequencing (RNA-Seq) are available, they are of limited sample size. This makes it difficult to systematically evaluate the performance of RNA-Seq based fusion-detection algorithms. Here, we present SimFuse to address this problem. SimFuse utilizes real sequencing data as the fusions’ background to closely approximate the distribution of reads from a real sequencing library and uses a reference genome as the template from which to simulate fusions’ supporting reads. To assess the supporting read-specific performance, SimFuse generates multiple datasets with various numbers of fusion supporting reads. Compared to an extant simulated dataset, SimFuse gives users control over the supporting read features and the sample size of the simulated library, based on which the performance metrics needed for the validation and comparison of alternative fusion-detection algorithms can be rigorously estimated.
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Wu, Jian, Liang Xu, Qi Chen, and Zhihui Ye. "Multi-sensor data fusion path combining fuzzy theory and neural networks." Intelligent Decision Technologies 18, no. 4 (2024): 3365–78. https://doi.org/10.3233/idt-240316.

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In the development of automation and intelligent systems, multi-sensor data fusion technology is crucial. However, due to the uncertainty and incompleteness of sensor data, how to effectively fuse these data has always been a challenge. To solve this problem, the study combines fuzzy theory and neural networks to study the process of multi-sensor data transmission and data fusion. Sensor network clustering algorithms based on whale algorithm optimized fuzzy logic and neural network data fusion algorithms based on sparrow algorithm optimized were designed respectively. The performance test results showed that the first node death time of the data fusion algorithm is delayed to 1122 rounds, which is 391 rounds and 186 rounds later than the comparison algorithm, respectively. In the same round, the remaining energy was always greater than the comparison algorithm, and the difference gradually increased. The results indicate that the proposed multi-sensor data fusion path combining fuzzy theory and neural networks has successfully improved network efficiency and node energy utilization, and extended network lifespan.
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Cui, Haiting, and Shanshan Li. "Controllable Clustering Algorithm for Associated Real-Time Streaming Big Data Based on Multi-Source Data Fusion." Wireless Communications and Mobile Computing 2022 (February 23, 2022): 1–9. http://dx.doi.org/10.1155/2022/5244695.

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Aiming at the problems of poor security and clustering accuracy in current data clustering algorithms, a controllable clustering algorithm for real-time streaming big data based on multi-source data fusion is proposed. The FIR filter structure model is used to suppress network interference, and ant colony algorithm is used to detect the abnormal data in the big data. By optimizing the iteration, the pheromone concentration is placed in the front position as the abnormal data point, and the filter is introduced. The fusion scope of multi-source data fusion is set. Combined with the data similarity function, the multi-source data fusion concept is used to construct the associated real-time streaming big data fusion device, and the data deduplication results are substituted into the fusion device to obtain the data clustering result. The experiments show that the proposed algorithm has high safety factor, good data clustering accuracy, high clustering efficiency, and low energy consumption.
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Abdulhafiz, Waleed A., and Alaa Khamis. "Handling Data Uncertainty and Inconsistency Using Multisensor Data Fusion." Advances in Artificial Intelligence 2013 (November 3, 2013): 1–11. http://dx.doi.org/10.1155/2013/241260.

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Data provided by sensors is always subjected to some level of uncertainty and inconsistency. Multisensor data fusion algorithms reduce the uncertainty by combining data from several sources. However, if these several sources provide inconsistent data, catastrophic fusion may occur where the performance of multisensor data fusion is significantly lower than the performance of each of the individual sensor. This paper presents an approach to multisensor data fusion in order to decrease data uncertainty with ability to identify and handle inconsistency. The proposed approach relies on combining a modified Bayesian fusion algorithm with Kalman filtering. Three different approaches, namely, prefiltering, postfiltering and pre-postfiltering are described based on how filtering is applied to the sensor data, to the fused data or both. A case study to find the position of a mobile robot by estimating its x and y coordinates using four sensors is presented. The simulations show that combining fusion with filtering helps in handling the problem of uncertainty and inconsistency of the data.
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Gan, Hock, Iosif Mporas, Saeid Safavi, and Reza Sotudeh. "Speaker Identification Using Data-Driven Score Classification." Image Processing & Communications 21, no. 2 (2016): 55–63. http://dx.doi.org/10.1515/ipc-2016-0011.

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Abstract We present a comparative evaluation of different classification algorithms for a fusion engine that is used in a speaker identity selection task. The fusion engine combines the scores from a number of classifiers, which uses the GMM-UBM approach to match speaker identity. The performances of the evaluated classification algorithms were examined in both the text-dependent and text-independent operation modes. The experimental results indicated a significant improvement in terms of speaker identification accuracy, which was approximately 7% and 14.5% for the text-dependent and the text-independent scenarios, respectively. We suggest the use of fusion with a discriminative algorithm such as a Support Vector Machine in a real-world speaker identification application where the text-independent scenario predominates based on the findings.
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Lv, Lihua. "RFID Data Analysis and Evaluation Based on Big Data and Data Clustering." Computational Intelligence and Neuroscience 2022 (March 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/3432688.

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The era people live in is the era of big data, and massive data carry a large amount of information. This study aims to analyze RFID data based on big data and clustering algorithms. In this study, a RFID data extraction technology based on joint Kalman filter fusion is proposed. In the system, the proposed data extraction technology can effectively read RFID tags. The data are recorded, and the KM-KL clustering algorithm is proposed for RFID data, which combines the advantages of the K-means algorithm. The improved KM-KL clustering algorithm can effectively analyze and evaluate RFID data. The experimental results of this study prove that the recognition error rate of the RFID data extraction technology based on the joint Kalman filter fusion is only 2.7%. The improved KM-KL clustering algorithm also has better performance than the traditional algorithm.
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Dissertations / Theses on the topic "Data fusion algorithms"

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Aziz, Ashraf Mamdouh Abdel. "New data fusion algorithms for distributed multi-sensor multi-target environments." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1999. http://handle.dtic.mil/100.2/ADA369780.

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Dissertation (Ph.D. in Electrical Engineering) Naval Postgraduate School, September 1999.<br>"September 1999". Dissertation supervisor(s): Robert Cristi, Murali Tummala. Includes bibliographical references (p. 199-214). Also avaliable online.
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Rivera, velázquez Josué. "Analysis and development of algorithms for data fusion in sensor arrays." Thesis, Montpellier, 2020. http://www.theses.fr/2020MONTS038.

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Actuellement, la plupart des capteurs sont de nature `` intelligente '', ce qui signifie que les éléments de détection et l'électronique associée sont intégrés sur le même circuit. Parmi ces capteurs de nouvelle génération les systèmes micro-électro-mécaniques (MEMS) utilisent les technologies microélectroniques pour la fabrication par lots de capteurs à des volumes sans précédent et à des prix bas. Si ces composants sur étagère sont satisfaisants pour de nombreuses applications nécessitant un niveau de précision faible à moyen, ils ne peuvent toujours pas répondre pleinement aux besoins de performances de nombreuses applications de haute précision.Cependant, en raison de leur prix décroissant, de leur faible encombrement et de leur faible consommation d'énergie, il est désormais possible de mettre en œuvre des systèmes avec des dizaines ou même des centaines de capteurs. Ces systèmes amènent une solution possible au manque de performances des capteurs individuels et peuvent en outre améliorer la fiabilité et la robustesse de la détection. Les matrices de capteurs sont l'une de ces méthodes de mesures redondantes qui surviennent en réponse aux problèmes susmentionnés. Le développement d'algorithmes de fusion de données pour ces systèmes est un sujet de recherche fréquemment étudié dans la littérature. Néanmoins, il reste encore beaucoup de recherches à faire dans ce domaine de plus en plus important. L'émergence de nouvelles applications aux besoins de plus en plus complexes accroît la nécessité de nouveaux algorithmes avec des propriétés telles que la facilité d'intégration, l'adaptabilité, la robustesse, le faible coût de calcul et la généricité, entre autres.Dans cette thèse, nous présentons un nouvel algorithme pour les systèmes multi-capteurs qui propose une solution viable pour surmonter les contraintes mentionnées précédemment. La proposition est une méthode on-line basée sur une estimation quadratique sans biais de norme minimale (acronyme en Anglais: MINQUE) qui est capable de calculer les variances des capteurs sans connaître les entrées. Cet algorithme est capable de suivre les changements de variances des capteurs causés principalement par les effets du bruit basse fréquence, ainsi que de détecter et de signaler les capteurs affectés par des erreurs permanent ou transitoires. Cette approche est générique, ce qui signifie qu'elle peut être mise en œuvre pour différents types de systèmes de capteurs. De même, cet algorithme peut être implémenté dans des systèmes de réseaux de capteurs.Deux autres contributions de cette thèse peuvent être répertoriées. La première est un modèle de capteur générique pour les simulations de capteurs au niveau système. Cet outil créé dans l'environnement Matlab Simulink permet l'analyse des implémentations d'algorithmes de fusion de données dans des systèmes multi-capteurs. Contrairement aux modèles existant auparavant dans la littérature, ce modèle présente des caractéristiques telles que la généricité et l'inclusion de bruits basse fréquence, ainsi que le paramétrage à travers des graphiques d'analyse spectrale (graphique de Densité Spectrale de Puissance) et des graphiques d'analyse de stabilité dans le temps (graphique de l'écart Allan). La seconde est une étude visant à comparer les performances et la faisabilité de la mise en œuvre de différents algorithmes de fusion de données dans les systèmes multi-capteurs. Cette étude contient une analyse de la complexité de calcul, de la mémoire requise et de l'erreur d'estimation. Les algorithmes analysés sont : la méthode des moindres carrés, le réseau de neurones artificiel, le filtre de Kalman et la pondération aléatoire<br>Currently, most of the sensors are ``smart'' in nature, which means that sensing elements and associated electronics are integrated on the same chip. Among these new generation of sensors, the Micro-Electro-Mechanical-Systems (MEMS) make use of Microelectronics technologies for batch manufacturing of small footprint sensors to unprecedented volumes and at low prices. If those components of the shelf are satisfactory for many consumer and low- to medium-end applications, they still cannot fully meet the performance needs of many high-end applications.However, due to their decreasing price, their small footprint, and their low-power consumption, it is now feasible to implement systems with tens and even hundreds of sensors. Those systems give a possible solution to the lack of performance of individual sensors and additionally they can also improve dependability and robustness of sensing. Sensor array systems are one of these methods of redundant measurements that arise in response to the aforementioned problems. The development of data fusion algorithms for sensor array systems is a research topic frequently studied in the literature. Even so, it still remains a lot of research work to do in this increasingly important area. The emergence of new applications with increasingly complex needs is growing the requirement for new algorithms with features such as integration, adaptability, dependability, low computational cost, and genericity among others.In this thesis we present a new algorithm for sensor array systems that propose a viable solution to overcome constraints mentioned before. The proposal is an on-line method based on the MInimum Norm Quadratic Unbiased Estimation (MINQUE) that is able to compute sensors' variances without the knowledge of the inputs. This algorithm is capable to track changes in sensors' variances caused principally by the low-frequency noise effects, as well as to detect and point out sensors affected by permanent or transitory errors. This approach is generic, which means that it can be implemented for different types of sensor array systems. In addition, this algorithm can be also implemented in sensor network systems.Two more contributions of this thesis can be listed. The first is a generic sensor model for sensor simulations at system level. This tool created inside the Matlab Simulink environment permits the analysis of implementations of data fusion algorithms in multi-sensor systems. Unlike the models previously existing in the literature, this sensor model has characteristics such as genericity and inclusion of low-frequency noises. The second is a study to compare the performance and feasibility in the implementation of different algorithms for data fusion in sensor array systems. This study contains an analysis of computational complexity, memory required, and the error in estimation. The analyzed algorithms are : the method of least squares, an artificial neural network, Kalman filter, and Random weighting
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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 time and knowledge to describe the engine’s states. An alternative approach is therefore data-driven methods which instead are based on historical data measured from different operating points that are used to draw conclusion about engine’s present state. In this thesis a proposed diagnostic framework is presented, consisting of a systematically approach for fault classification of known and unknown faults along with a fault size estimation. The basis for this lies in using principal component analysis to find the fault vector for each fault class and decouple one fault at the time, thus creating different subspaces. Importantly, this work investigates the efficiency of taking multiple classifiers into account in the decision making from a performance perspective. Aggregating multiple classifiers is done solving a quadratic optimization problem. To evaluate the performance, a comparison with a random forest classifier has been made. Evaluation with challenging test data show promising results where the algorithm relates well to the performance of random forest classifier.
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Li, Lingjie Luo Zhi-Quan. "Data fusion and filtering for target tracking and identification /." *McMaster only, 2003.

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Bougiouklis, Theodoros C. "Traffic management algorithms in wireless sensor networks." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Sep%5FBougiouklis.pdf.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, September 2006.<br>Thesis Advisor(s): Weillian Su. "September 2006." Includes bibliographical references (p. 79-80). Also available in print.
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Ayodeji, Akiwowo. "Developing integrated data fusion algorithms for a portable cargo screening detection system." Thesis, Loughborough University, 2012. https://dspace.lboro.ac.uk/2134/9901.

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Towards having a one size fits all solution to cocaine detection at borders; this thesis proposes a systematic cocaine detection methodology that can use raw data output from a fibre optic sensor to produce a set of unique features whose decisions can be combined to lead to reliable output. This multidisciplinary research makes use of real data sourced from cocaine analyte detecting fibre optic sensor developed by one of the collaborators - City University, London. This research advocates a two-step approach: For the first step, the raw sensor data are collected and stored. Level one fusion i.e. analyses, pre-processing and feature extraction is performed at this stage. In step two, using experimentally pre-determined thresholds, each feature decides on detection of cocaine or otherwise with a corresponding posterior probability. High level sensor fusion is then performed on this output locally to combine these decisions and their probabilities at time intervals. Output from every time interval is stored in the database and used as prior data for the next time interval. The final output is a decision on detection of cocaine. The key contributions of this thesis includes investigating the use of data fusion techniques as a solution for overcoming challenges in the real time detection of cocaine using fibre optic sensor technology together with an innovative user interface design. A generalizable sensor fusion architecture is suggested and implemented using the Bayesian and Dempster-Shafer techniques. The results from implemented experiments show great promise with this architecture especially in overcoming sensor limitations. A 5-fold cross validation system using a 12 13 - 1 Neural Network was used in validating the feature selection process. This validation step yielded 89.5% and 10.5% true positive and false alarm rates with 0.8 correlation coefficient. Using the Bayesian Technique, it is possible to achieve 100% detection whilst the Dempster Shafer technique achieves a 95% detection using the same features as inputs to the DF system.
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SAVARESE, FRANCESCO. "Data Fusion Methods and Algorithms in the Context of Autonomous Systems - A path planning algorithms analysis and optimization exploiting fused data." Doctoral thesis, Politecnico di Torino, 2019. http://hdl.handle.net/11583/2752655.

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Elbakary, Mohamed Ibrahim. "Novel Pixel-Level and Subpixel-Level Registration Algorithms for Multi-Modal Imagery Data." Diss., Tucson, Arizona : University of Arizona, 2005. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu%5Fetd%5F1293%5F1%5Fm.pdf&type=application/pdf.

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Trailović, Lidija. "Ranking and optimization of target tracking algorithms." online access from Digital Dissertation Consortium access full-text, 2002. http://libweb.cityu.edu.hk/cgi-bin/er/db/ddcdiss.pl?3074810.

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Gnanapandithan, Nithya. "Data detection and fusion in decentralized sensor networks." Thesis, Manhattan, Kan. : Kansas State University, 2005. http://hdl.handle.net/2097/132.

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Books on the topic "Data fusion algorithms"

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Antony, Richard T. Principles of data fusion automation. Artech House, 1995.

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Abdelgawad, Ahmed, and Magdy Bayoumi. Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1350-9.

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Abdelgawad, Ahmed. Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks. Springer US, 2012.

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Klein, Lawrence A. Sensor and data fusion concepts and applications. SPIE Optical Engineering Press, 1993.

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Klein, Lawrence A. Sensor and data fusion concepts and applications. 2nd ed. SPIE, 1999.

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V, Dasarathy Belur, and Society of Photo-Optical Instrumentation Engineers., eds. Sensor fusion--architectures, algorithms, and applications: 24-25 April 1997, Orlando, Florida. SPIE, 1997.

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V, Dasarathy Belur, and Society of Photo-optical Instrumentation Engineers., eds. Sensor fusion--architectures, algorithms, and applications VI: 3-5 April 2002, Orlando, [Florida] USA. SPIE, 2002.

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V, Dasarathy Belur, and Society of Photo-optical Instrumentation Engineers., eds. Sensor fusion--architectures, algorithms, and applications II: 16-17 April 1998, Orlando, Florida. SPIE, 1998.

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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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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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Book chapters on the topic "Data fusion algorithms"

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Abdelgawad, Ahmed, and Magdy Bayoumi. "Proposed Centralized Data Fusion Algorithms." In Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1350-9_3.

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Abdelgawad, Ahmed, and Magdy Bayoumi. "Data Fusion in WSN." In Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1350-9_2.

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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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Reddy, Dugimpudi Abhishek, Deepak Yadav, Nishi Yadav, and Devendra Kumar Singh. "Impact on Security Using Fusion of Algorithms." In Innovative Data Communication Technologies and Application. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38040-3_65.

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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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Harris, Chris, Xia Hong, and Qiang Gan. "An introduction to modelling and learning algorithms." In Adaptive Modelling, Estimation and Fusion from Data. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-18242-6_1.

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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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Lei, Rujie, Min Xiang, Peng Wang, Ruiheng Ma, and Kun Yu. "Data Correction-Based Data Fusion Method for Internet of Things Terminals." In Smart Communications, Intelligent Algorithms and Interactive Methods. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5164-9_24.

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Cardille, Jeffrey A., Rylan Boothman, Mary Villamor, Elijah Perez, Eidan Willis, and Flavie Pelletier. "Data Fusion: Merging Classification Streams." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_20.

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AbstractAs the ability to rapidly produce classifications of satellite images grows, it will be increasingly important to have algorithms that can shift through them to separate the signal from inevitable classification noise. The purpose of this chapter is to explore how to update classification time series by blending information from multiple classifications made from a wide variety of data sources. In this lab, we will explore how to update the classification time series of the Roosevelt River found in Fortin et al. (Remote Sens Environ 238, 2020). That time series began with the 1972 launch of Landsat 1, blending evidence from 10 sensors and more than 140 images to show the evolution of the area until 2016. How has it changed since 2016? What new tools and data streams might we tap to understand the land surface through time?
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Segarra, David, Jessica Caballeros, Wilbert G. Aguilar, Albert Samà, and Daniel Rodríguez-Martín. "Orientation Estimation Using Filter-Based Inertial Data Fusion for Posture Recognition." In Algorithms for Sensor Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14094-6_15.

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Conference papers on the topic "Data fusion algorithms"

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Flynn, Conor, Christopher Ebersole, and Edmund Zelnio. "The comparability of model fusion to measured data in confuser rejection." In Algorithms for Synthetic Aperture Radar Imagery XXXII, edited by Edmund Zelnio and Frederick D. Garber. SPIE, 2025. https://doi.org/10.1117/12.3054007.

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Handke, Sebastian Thomas, Christian Steffes, and Wolfgang Koch. "Comparing Illuminator Selection Algorithms for Mobile Communication Based Passive Radar." In 2024 Sensor Data Fusion: Trends, Solutions, Applications (SDF). IEEE, 2024. https://doi.org/10.1109/sdf63218.2024.10773772.

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Lin, Honghong, Ziquan Liu, Wenxu Chen, et al. "Infrared Heating Anomaly Detection Method Based on Multi-Modal Data Fusion." In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025. https://doi.org/10.1109/icaace65325.2025.11019516.

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xu, xiaoxiang, FANZHANG LI, and LI ZHANG. "Fusion of data dimensionality reduction algorithms based on category representation theory." In Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), edited by Hui Yuan and Lu Leng. SPIE, 2025. https://doi.org/10.1117/12.3055752.

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Guo, Jiadi. "Multimodal data fusion algorithm for power marketing inspection based on knowledge graph." In International Conference on Algorithms, High Performance Computing and Artificial Intelligence, edited by Pavel Loskot and Liang Hu. SPIE, 2024. http://dx.doi.org/10.1117/12.3051850.

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Yu, Bo, Xiaogang Dong, Zhihua Chen, et al. "Research on knowledge fusion method of heterogeneous data in aerospace control software." In International Conference on Algorithms, High Performance Computing and Artificial Intelligence, edited by Pavel Loskot and Liang Hu. SPIE, 2024. http://dx.doi.org/10.1117/12.3051996.

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Ruidi, Ma, Hu Wei, Zhao Chuanting, et al. "Wave Height Inversion Algorithm Based on Multimodal Data Fusion and Attention Mechanism." In 2024 7th International Conference on Algorithms, Computing and Artificial Intelligence (ACAI). IEEE, 2024. https://doi.org/10.1109/acai63924.2024.10899453.

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Bather, J. "Tracking and data fusion." In IEE International Seminar Target Tracking: Algorithms and Applications. IEE, 2001. http://dx.doi.org/10.1049/ic:20010234.

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Julier, S. J. "Fusion without independence." In IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications. IEE, 2008. http://dx.doi.org/10.1049/ic:20080050.

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Kurz, Aidan G., Arthur C. Depoian, and Colleen P. Bailey. "Multiband remote 3D-ViT data fusion." In Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX, edited by David W. Messinger and Miguel Velez-Reyes. SPIE, 2024. http://dx.doi.org/10.1117/12.3013935.

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Reports on the topic "Data fusion algorithms"

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DVore, Ronald A. New Theory and Algorithms for Scalable Data Fusion. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada587535.

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Wainwright, Martin. New Theory and Algorithms for Scalable Data Fusion. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada588861.

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Yocky, D. A., M. D. Chadwick, S. P. Goudy, and D. K. Johnson. Multisensor data fusion algorithm development. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/172138.

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Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

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The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, spectral characteristics in the VIS/NIR (0.4-1.0 micron) will be used in conjunction with thermal data to provide accurate and robust detection of fruit in the tree canopy. Hyper-spectral image pairs will be combined to provide automatic stereo matching for accurate 3D position. Secondly, VIS/NIR/FIR (0.4-15.0 micron) spectral sensor technology will be evaluated for potential in-field on-the-tree grading of surface defect, maturity and size for selective fruit harvest. Thirdly, new adaptive Lyapunov-basedHBVS (homography-based visual servo) methods to compensate for camera uncertainty, distortion effects, and provide range to target from a single camera will be developed, simulated, and implemented on a camera testbed to prove concept. HBVS methods coupled with imagespace navigation will be implemented to provide robust target tracking. And finally, harvesting test will be conducted on the developed technologies using the University of Florida harvesting manipulator test bed. During the course of the project it was determined that the second objective was overly ambitious for the project period and effort was directed toward the other objectives. The results reflect the synergistic efforts of the three principals. The USA team has focused on citrus based approaches while the Israeli counterpart has focused on apples. The USA team has improved visual servo control through the use of a statistical-based range estimate and homography. The results have been promising as long as the target is visible. In addition, the USA team has developed improved fruit detection algorithms that are robust under light variation and can localize fruit centers for partially occluded fruit. Additionally, algorithms have been developed to fuse thermal and visible spectrum image prior to segmentation in order to evaluate the potential improvements in fruit detection. Lastly, the USA team has developed a multispectral detection approach which demonstrated fruit detection levels above 90% of non-occluded fruit. The Israel team has focused on image registration and statistical based fruit detection with post-segmentation fusion. The results of all programs have shown significant progress with increased levels of fruit detection over prior art.
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Chou, Fu-Mao. An Algorithm-Level Test Bed for Level-One Data Fusion Research (CASE-ATTI). Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada387790.

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Chou, K. C., and A. S. Willsky. Multiscale Riccati Equations and a Two-Sweep Algorithm for the Optimal Fusion of Multiresolution Data. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada459314.

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Brown, Douglas, and Genetha Anne Gray. Implementation of a data fusion algorithm for RODS, a real-time outbreak and disease surveillance system. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/876344.

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Guo, Ge, Jiageng Liu, and Guangheng Liu. Multi-sensor Fusion-based Vehicle Localization. SAE International, 2024. http://dx.doi.org/10.4271/epr2024023.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Multi-sensor fusion (MSF) is believed to be a promising tool for vehicular localization in urban environments. Due to the differences in principles and performance of various onboard vehicle sensors, MSF inevitably suffers from heterogeneous sources and vulnerability to cyber-attacks. Therefore, an essential requirement of MSF is the capability of providing a consumer-grade solution that operates in real-time, is accurate, and immune to abnormal conditions with guaranteed performance and quality of service for location-based applications. In other words, an MSF algorithm depends heavily on data synchronization, cost, an accurate process model, a priori knowledge of covariance matrices, integrity assessments, and security against cyber-attacks.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;&lt;b&gt;Multi-sensor Fusion-based Vehicle Localization&lt;/b&gt; addresses trending technologies in MSF-based vehicle localization and outlines some insights into the unsettled issues and their potential solutions. The discussions and outlook are presented as a collection of key topics, including multi-sensor measurement data processing, sensory selection, filtering, integrity assessment, and cybersecurity.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;&lt;a href="https://www.sae.org/publications/edge-research-reports" target="_blank"&gt;Click here to access the full SAE EDGE&lt;/a&gt;&lt;sup&gt;TM&lt;/sup&gt;&lt;a href="https://www.sae.org/publications/edge-research-reports" target="_blank"&gt; Research Report portfolio.&lt;/a&gt;&lt;/div&gt;&lt;/div&gt;
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