Relevant bibliographies by topics / Covariance-based fusion

Academic literature on the topic 'Covariance-based fusion'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Covariance-based fusion"

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Wang, Kuiwu, Qin Zhang, and Xiaolong Hu. "Multisensor and Multitarget Tracking Based on Generalized Covariance Intersection Rule." Mathematical Problems in Engineering 2022 (August 18, 2022): 1–17. http://dx.doi.org/10.1155/2022/8264359.

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Distributed multitarget tracking (MTT) is suitable for sensors with limited field of view (FoV). Generalized covariance intersection (GCI) fusion is used to solve the MTT problem based on label probability hypothesis density (PHD) filtering in this paper. Because the traditional GCI fusion only has good fusion performance for the targets in the intersection of each sensor’s FoV, and the targets outside the intersection range would be lost, this paper redivides the Gaussian components according to the FoV and distinguishes the Gaussian components of the targets inside and outside the intersection. GCI fusion is sensitive to label inconsistency between different sensors. For label fusion in the intersection region, the best match of labels is found by minimizing label inconsistency index, and then GCI fusion is performed. Finally, the feasibility and effectiveness of the proposed fusion method are verified by simulation, and its robustness is proved. The proposed method is obviously superior to local sensor and traditional GCI algorithm.
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Gao, Yuan, and Zi Li Deng. "Covariance Intersection Fusion Kalman Estimators." Applied Mechanics and Materials 121-126 (October 2011): 750–54. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.750.

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By the CI (Covariance Intersection) fusion algorithm, based on the ARMA innovation model, the two-sensor CI fusion Kalman estimators are presented for the systems with unknown cross-covariance. It is proved that their estimation accuracies are higher than those of the local Kalman estimators, and are lower than those of the optimal fused Kalman estimators. A Monte-Carlo simulation result shows that the actual accuracy of the presented CI fusion Kalman estimator are close to those of the optimal fused Kalman estimators with known cross-covariance.
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Zhao, Kai, Li-Guo Tan, and Shen-Min Song. "Fusion estimation for nonlinear multi-sensor networked systems with packet loss compensation and correlated noises." Sensor Review 39, no. 5 (September 16, 2019): 682–96. http://dx.doi.org/10.1108/sr-11-2018-0311.

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Purpose This paper aims to give the centralized and distributed fusion estimator for nonlinear multi-sensor networked systems with packet loss compensation and correlated noises and give the corresponding square-root cubature Kalman filter. Design/methodology/approach Based on the Gaussian approximation recursive filter framework, the authors derive the centralized fusion filter and using the projection theorem, the authors derive the centralized fusion smoother. Then, based on the fast batch covariance intersection fusion algorithm, the authors give the corresponding results for distributed fusion estimators. Findings Designing the fusion estimators for nonlinear multi-sensor networked systems with packet loss compensation and correlated noises is necessary. It is useful for general nonlinear systems. Originality/value Throughout the whole study, the main highlights of this paper are as follows: packet loss compensation and correlated noises are considered in nonlinear multi-sensor networked systems. There are no relevant conclusions in the existing literature; centralized and distributed fusion estimators are derived based on the above system; for the posterior covariance with compensation factor and correlated noises, a new square-root factor of the error covariance is derived; and the new square-root factor of the error covariance is used to replace the numerical implementation of the covariance in cubature Kalman filter (CKF), which simplified the problem in calculating the posterior covariance in CKF.
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Wang, Dapeng, Hai Zhang, and Baoshuang Ge. "Adaptive Unscented Kalman Filter for Target Tacking with Time-Varying Noise Covariance Based on Multi-Sensor Information Fusion." Sensors 21, no. 17 (August 29, 2021): 5808. http://dx.doi.org/10.3390/s21175808.

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In this paper, an innovative optimal information fusion methodology based on adaptive and robust unscented Kalman filter (UKF) for multi-sensor nonlinear stochastic systems is proposed. Based on the linear minimum variance criterion, this multi-sensor information fusion method has a two-layer architecture: at the first layer, a new adaptive UKF scheme for the time-varying noise covariance is developed and serves as a local filter to improve the adaptability together with the estimated measurement noise covariance by applying the redundant measurement noise covariance estimation, which is isolated from the state estimation; the second layer is the fusion structure to calculate the optimal matrix weights and gives the final optimal state estimations. Based on the hypothesis testing theory with the Mahalanobis distance, the new adaptive UKF scheme utilizes both the innovation and the residual sequences to adapt the process noise covariance timely. The results of the target tracking simulations indicate that the proposed method is effective under the condition of time-varying process-error and measurement noise covariance.
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Shaukat, Nabil, Muhammad Moinuddin, and Pablo Otero. "Underwater Vehicle Positioning by Correntropy-Based Fuzzy Multi-Sensor Fusion." Sensors 21, no. 18 (September 14, 2021): 6165. http://dx.doi.org/10.3390/s21186165.

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The ability of the underwater vehicle to determine its precise position is vital to completing a mission successfully. Multi-sensor fusion methods for underwater vehicle positioning are commonly based on Kalman filtering, which requires the knowledge of process and measurement noise covariance. As the underwater conditions are continuously changing, incorrect process and measurement noise covariance affect the accuracy of position estimation and sometimes cause divergence. Furthermore, the underwater multi-path effect and nonlinearity cause outliers that have a significant impact on positional accuracy. These non-Gaussian outliers are difficult to handle with conventional Kalman-based methods and their fuzzy variants. To address these issues, this paper presents a new and improved adaptive multi-sensor fusion method by using information-theoretic, learning-based fuzzy rules for Kalman filter covariance adaptation in the presence of outliers. Two novel metrics are proposed by utilizing correntropy Gaussian and Versoria kernels for matching theoretical and actual covariance. Using correntropy-based metrics and fuzzy logic together makes the algorithm robust against outliers in nonlinear dynamic underwater conditions. The performance of the proposed sensor fusion technique is compared and evaluated using Monte-Carlo simulations, and substantial improvements in underwater position estimation are obtained.
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Zhang, Peng. "Multi-Channel ARMA Signal Covariance Intersection Fusion Kalman Smoother." Advanced Materials Research 655-657 (January 2013): 701–4. http://dx.doi.org/10.4028/www.scientific.net/amr.655-657.701.

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For the multi-channel ARMA signal with two sensors, by the classical Kalman filtering method and the covariance intersection (CI) fusion method, a covariance intersection fusion steady-state Kalman signal smoother is presented, which is independent of the unknown cross-covariance. It is proved that its accuracy is higher than that of each local Kalman signal smoother, and is lower than that of the optimal signal fuser weighted by matrices. The geometric interpretation of the above accuracy relations are presented based on the covariance ellipses. A simulation example result shows its effectiveness and correctness.
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Li, Ming Jing, Yu Bing Dong, and Jie Li. "Pixel Level Image Fusion Algorithm Based on PCA." Applied Mechanics and Materials 448-453 (October 2013): 3658–61. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.3658.

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Image fusion algorithm based on principal component analysis (PCA) was proposed in this paper. The algorithm make use of the characteristics that the principal component decomposition can retain the main information of the original data, it get covariance matrix, eigenvalue and eigenvector of covariance matrix from the source image. Then we can get the weighted coefficient and fused image with the help of eigenvalue and eigenvector. Simulation result shows that the better image quality is obtained in the proposed algorithm.
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Peng, Duo, Jingqiang Zhao, and Tongtong Xu. "Intelligent Building Data Fusion Algorithm by Using the Internet of Things Technology." Journal of Physics: Conference Series 2143, no. 1 (December 1, 2021): 012030. http://dx.doi.org/10.1088/1742-6596/2143/1/012030.

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Abstract Analyzed in this paper based on the Internet of things technology for intelligent building data, redundancy of data fusion are pointed out, based on the dynamic Kalman filter algorithm of multi-sensor fusion, first using the theory of fuzzy and covariance matching technique to adjust the noise covariance of traditional algorithm, combined with weighted minimum variance matrix under the optimal information fusion algorithm of data fusion, Finally, the simulation results show that this algorithm can effectively reduce the redundancy of intelligent data and make the estimated value of data fusion more close to the actual value.
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Wang, Kuiwu, Qin Zhang, and Xiaolong Hu. "Improved Distributed Multisensor Fusion Method Based on Generalized Covariance Intersection." Journal of Sensors 2022 (October 28, 2022): 1–22. http://dx.doi.org/10.1155/2022/6348938.

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In response to the multitarget tracking problem of distributed sensors with a limited detection range, a distributed sensor measurement complementary Gaussian component correlation GCI fusion tracking method is proposed on the basis of the probabilistic hypothesis density filtering tracking theory. First, the sensor sensing range is extended by complementing the measurements. In this case, the multitarget density product is used to classify whether the measurements belong to the intersection region of the detection range. The local intersection region is complemented only once to reduce the computational cost. Secondly, each sensor runs a probabilistic hypothesis density filter separately and floods the filtering posterior with the neighboring sensors so that each sensor obtains the posterior information of the neighboring sensors. Subsequently, Gaussian components are correlated by distance division, and Gaussian components corresponding to the same target are correlated into the same subset. GCI fusion is performed on each correlated subset to complete the fusion state estimation. Simulation experiments show that the proposed method can effectively perform multitarget tracking in a distributed sensor network with a limited sensing range.
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Jin Zefenfen, 金泽芬芬, 侯志强 Hou Zhiqiang, 余旺盛 Yu Wangsheng, and 王. 鑫. Wang Xin. "Multiple Feature Fusion based on Covariance Matrix for Visual Tracking." Acta Optica Sinica 37, no. 9 (2017): 0915005. http://dx.doi.org/10.3788/aos201737.0915005.

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Dissertations / Theses on the topic "Covariance-based fusion"

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Cirujeda, Santolaria Pol. "Covariance-based descriptors for pattern recognition in multiple feature spaces." Doctoral thesis, Universitat Pompeu Fabra, 2015. http://hdl.handle.net/10803/350033.

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En aquesta tesi s’explora l’ús de descriptors basats en la covariància per tal de traslladar la observació de característiques dins de regions d’interès a un determinat espai descriptiu que utilitzi les matrius de covariància de les característiques com a signatures discriminatives de les dades. Aquest espai constitueix la varietat de les matrius simètriques definides positives, amb la seva pròpia mètrica i consideracions analítiques, en la que podem desenvolupar diferents mètodes de machine learning per al reconeixement de patrons. Sigui quin sigui el domini de les característiques, ja siguin observacions visuals en imatges 2D, característiques de forma en núvols de punts 3D, gestos i moviment en seqüències d’imatges de profunditat, o informació de densitat en imatges mèdiques en 3D, l’espai del descriptor de covariància actua com un pas d’unificació en el repte de mantenir un marc de treball comú per a diverses aplicacions.
This dissertation explores the use of covariance-based descriptors in order to translate feature observations within regions of interest to a descriptor space using the feature covariance matrices as discriminative signatures. This space constitutes the particular manifold of symmetric positive definite matrices, with its own metric and analytical considerations, in which we can develop several machine learning algorithms for pattern recognition. Regardless of the feature domain, whether they are 2D image visual cues, 3D unstructured point cloud shape features, gesture and motion measurements from depth image sequences, or 3D tissue information in medical images, the covariance descriptor space acts as a unifying step in the task of keeping a common framework for several applications.
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Book chapters on the topic "Covariance-based fusion"

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Jiang, Chao, Zhiling Wang, Huawei Liang, Shijing Zhang, and Shuhang Tan. "Motion State Estimation Based on Multi-sensor Fusion and Noise Covariance Estimation." In Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021), 706–16. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9492-9_70.

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Awasthi, Peeyush, Ashwin Yadav, Naren Naik, and Mudambi Ramaswamy Ananthasayanam. "A Constant Gain Kalman Filter for Wireless Sensor Network and Maneuvering Target Tracking." In Adaptive Filtering - Recent Advances and Practical Implementation [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.98700.

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One of the well-known approaches to target tracking is the Kalman filter. The problem of applying the Kalman Filter in practice is that in the presence of unknown noise statistics, accurate results cannot be obtained. Hence the tuning of the noise covariances is of paramount importance in order to employ the filter. The difficulty involved with the tuning attracts the applicability of the concept of Constant Gain Kalman Filter (CGKF). It has been generally observed that after an initial transient the Kalman Filter gain and the State Error Covariance P settles down to steady state values. This encourages one to consider working directly with steady state or constant Kalman gain, rather than with error covariances in order to obtain efficient tracking. Since there are no covariances in CGKF, only the state equations need to be propagated and updated at a measurement, thus enormously reducing the computational load. The current work first applies the CGKF concept to heterogeneous sensor based wireless sensor network (WSN) target tracking problem. The paper considers the Standard EKF and CGKF for tracking various manoeuvring targets using nonlinear state and measurement models. Based on the numerical studies it is clearly seen that the CGKF out performs the Standard EKF. To the best of our knowledge, such a comprehensive study of the CGKF has not been carried out in its application to diverse target tracking scenarios and data fusion aspects.
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Conference papers on the topic "Covariance-based fusion"

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Vanli, O. Arda, and Clark N. Taylor. "Covariance Estimation for Factor Graph Based Bayesian Estimation." In 2020 IEEE 23rd International Conference on Information Fusion (FUSION). IEEE, 2020. http://dx.doi.org/10.23919/fusion45008.2020.9190223.

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Hu, Baiqing, Lubin Chang, and Fangjun Qin. "Robust Gaussian filtering based on M-estimate with adaptive measurement noise covariance." In 2017 20th International Conference on Information Fusion (Fusion). IEEE, 2017. http://dx.doi.org/10.23919/icif.2017.8009738.

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Tang, Qi, Zhansheng Duan, Donglin Zhang, and X. Rong Li. "Estimation Fusion Based on Simplified Model for Cross-Covariance of Local Estimation Errors." In 2022 25th International Conference on Information Fusion (FUSION). IEEE, 2022. http://dx.doi.org/10.23919/fusion49751.2022.9841394.

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He, Nanjun, Leyuan Fang, Shutao Li, and Antonio J. Plara. "Covariance Matrix Based Feature Fusion for Scene Classification." In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. http://dx.doi.org/10.1109/igarss.2018.8517914.

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Reinhardt, Marc, Sanjeev Kulkarni, and Uwe D. Hanebeck. "Generalized covariance intersection based on noise decomposition." In 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI). IEEE, 2014. http://dx.doi.org/10.1109/mfi.2014.6997718.

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Lai, Gan, Suqi Li, Wei Yi, Giorgio Battistelli, Luigi Chisci, and Lingjiang Kong. "Computationally Efficient CPHD Fusion based on Generalized Covariance Intersection." In 2019 IEEE Radar Conference (RadarConf19). IEEE, 2019. http://dx.doi.org/10.1109/radar.2019.8835504.

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Hui-dong, Guo, Zhang Xin-hua, Xu Lin-zhou, Song Yuan, Xu Ce, and Tang Shao-bo. "Asynchronous Multisensor Data Fusion Based on Minimum Trace of Error Covariance." In 2006 9th International Conference on Information Fusion. IEEE, 2006. http://dx.doi.org/10.1109/icif.2006.301673.

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Bailu Wang, Wei Yi, Suqi Li, Lingjiang Kong, and Xiaobo Yang. "Distributed fusion with multi-Bernoulli filter based on generalized Covariance Intersection." In 2015 IEEE International Radar Conference (RadarCon). IEEE, 2015. http://dx.doi.org/10.1109/radar.2015.7131133.

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Jiang, Xiangang, Panpan Zhang, and Zizhu Fan. "Flame detection based on temporal-spatial block covariance matrix fusion features." In 2015 8th International Congress on Image and Signal Processing (CISP). IEEE, 2015. http://dx.doi.org/10.1109/cisp.2015.7407904.

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Briese, Danilo, Holger Kunze, and Georg Rose. "UWB localization using adaptive covariance Kalman Filter based on sensor fusion." In 2017 IEEE 17th International Conference on Ubiquitous Wireless Broadband (ICUWB). IEEE, 2017. http://dx.doi.org/10.1109/icuwb.2017.8250968.

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