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Journal articles on the topic 'Covariance-based fusion'

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

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1

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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2

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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3

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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4

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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6

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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7

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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9

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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11

Caballero-Águila, Raquel, Aurora Hermoso-Carazo, and Josefa Linares-Pérez. "Covariance-Based Estimation for Clustered Sensor Networks Subject to Random Deception Attacks." Sensors 19, no. 14 (July 14, 2019): 3112. http://dx.doi.org/10.3390/s19143112.

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In this paper, a cluster-based approach is used to address the distributed fusion estimation problem (filtering and fixed-point smoothing) for discrete-time stochastic signals in the presence of random deception attacks. At each sampling time, measured outputs of the signal are provided by a networked system, whose sensors are grouped into clusters. Each cluster is connected to a local processor which gathers the measured outputs of its sensors and, in turn, the local processors of all clusters are connected with a global fusion center. The proposed cluster-based fusion estimation structure involves two stages. First, every single sensor in a cluster transmits its observations to the corresponding local processor, where least-squares local estimators are designed by an innovation approach. During this transmission, deception attacks to the sensor measurements may be randomly launched by an adversary, with known probabilities of success that may be different at each sensor. In the second stage, the local estimators are sent to the fusion center, where they are combined to generate the proposed fusion estimators. The covariance-based design of the distributed fusion filtering and fixed-point smoothing algorithms does not require full knowledge of the signal evolution model, but only the first and second order moments of the processes involved in the observation model. Simulations are provided to illustrate the theoretical results and analyze the effect of the attack success probability on the estimation performance.
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12

Li, Kailin, Jiansheng Li, Ancheng Wang, Haolong Luo, Xueqiang Li, and Zidi Yang. "A Resilient Method for Visual–Inertial Fusion Based on Covariance Tuning." Sensors 22, no. 24 (December 14, 2022): 9836. http://dx.doi.org/10.3390/s22249836.

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To improve localization and pose precision of visual–inertial simultaneous localization and mapping (viSLAM) in complex scenarios, it is necessary to tune the weights of the visual and inertial inputs during sensor fusion. To this end, we propose a resilient viSLAM algorithm based on covariance tuning. During back-end optimization of the viSLAM process, the unit-weight root-mean-square error (RMSE) of the visual reprojection and IMU preintegration in each optimization is computed to construct a covariance tuning function, producing a new covariance matrix. This is used to perform another round of nonlinear optimization, effectively improving pose and localization precision without closed-loop detection. In the validation experiment, our algorithm outperformed the OKVIS, R-VIO, and VINS-Mono open-source viSLAM frameworks in pose and localization precision on the EuRoc dataset, at all difficulty levels.
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13

Xu, Ke, Xinye Li, Jie Wang, and Yuan Gao. "Robust Sequential Fusion Estimation Based on Adaptive Innovation Event-Triggered Mechanism for Uncertain Networked Systems." Computational Intelligence and Neuroscience 2022 (November 30, 2022): 1–15. http://dx.doi.org/10.1155/2022/8228525.

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In order to reduce the transmission pressure of the networked system and improve its robust performance, an adaptive innovation event-triggered mechanism is designed for the first time, and based on this mechanism, the robust local filtering algorithm for the multi-sensor networked system with uncertain noise variances and correlated noises is presented. To avoid calculating the complex error cross-covariance matrices, applying the sequential fusion idea, the robust sequential covariance intersection (SCI) and sequential inverse covariance intersection (SICI) fusion estimation algorithms are proposed, and their robustness is analyzed. Finally, it is verified in the simulation example that the proposed adaptive innovation event-triggered mechanism can reduce the communication burden, the robust local filtering algorithm is effective for the uncertainty generated by the unknown noise variances, and two robust sequential fusion estimators show good robustness, respectively.
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14

Zhang, Peng, Wen Juan Qi, and Zi Li Deng. "Parallel Covariance Intersection Fusion Optimal Kalman Filter." Applied Mechanics and Materials 475-476 (December 2013): 436–41. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.436.

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For multisensor network systems with unknown cross-covariances, a novel multi-level parallel covariance intersection (PCI) fusion Kalman filter is presented in this paper, which is realized by the multi-level parallel two-sensor covariance intersection (CI) fusers, so it only requires to solve the optimization problems of several one-dimensional nonlinear cost functions in parallel with loss computation burden. It can significantly reduce the computation time and increase data processing rate when the number of sensors is very large. It is proved that the PCI fuser is consistent, and its accuracy is higher than that of each local filter and is lower than that of the optimal Kalman fuser weighted by matrices. The geometric interpretation of accuracy relations based on the covariance ellipses is given. A simulation example for tracking systems verifies the accuracy relations.
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15

Sun, Wei, Peilun Sun, and Jiaji Wu. "An Adaptive Fusion Attitude and Heading Measurement Method of MEMS/GNSS Based on Covariance Matching." Micromachines 13, no. 10 (October 20, 2022): 1787. http://dx.doi.org/10.3390/mi13101787.

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Aimed at the problem of filter divergence caused by unknown noise statistical characteristics or variable noise characteristics in an MEMS/GNSS integrated navigation system in a dynamic environment, on the basis of revealing the parameter adjustment logic of covariance matching adaptive technology, a fusion adaptive filtering scheme combining innovation-based adaptive estimation (IAE) and the adaptive fading Kalman filter (AFKF) is proposed. By setting two system tuning parameters, for the process noise covariance adaptation loop and the measurement noise covariance adaptation loop, covariance matching is sped up and achieves an effective suppression of filter divergence. The vehicle-mounted experimental results show that the mean square error of the combined attitude error obtained based on the fusion filtering method proposed in this paper is better than 0.5°, and the mean square error of the heading error is better than 1.5°. The results can provide technical support for the continuous extraction of low-cost attitude information from mobile platforms.
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16

Fan, Xuefeng, and Fei Liu. "State Fusion of Decentralized Optimal Unbiased FIR Filters." Journal of Electrical and Computer Engineering 2018 (June 3, 2018): 1–11. http://dx.doi.org/10.1155/2018/1505137.

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The paper presents a decentralized fusion strategy based on the optimal unbiased finite impulse response (OUFIR) filter for discrete systems with correlated process and measurement noise. We extend OUFIR filter to apply in the model with control inputs. Taking it as local filters, cross covariance between any two is calculated; then it is expressed to the fast iterative form. Finally based on cross covariance, optimal weights are utilized to fuse local estimates and the overall outcome is obtained. The numerical examples show that the proposed filter exhibits better robustness against temporary modeling uncertainties than the fusion Kalman filter used commonly.
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Huang, Junhao, Wei Wei, and Junshuai Hu. "Longitudinal and Lateral Velocity Estimation based on Covariance Inverse Robust Fusion." IOP Conference Series: Materials Science and Engineering 787 (May 5, 2020): 012008. http://dx.doi.org/10.1088/1757-899x/787/1/012008.

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18

Li, Guchong, Giorgio Battistelli, Wei Yi, and Lingjiang Kong. "Distributed multi-sensor multi-view fusion based on generalized covariance intersection." Signal Processing 166 (January 2020): 107246. http://dx.doi.org/10.1016/j.sigpro.2019.107246.

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19

Wang, Bailu, Wei Yi, Reza Hoseinnezhad, Suqi Li, Lingjiang Kong, and Xiaobo Yang. "Distributed Fusion With Multi-Bernoulli Filter Based on Generalized Covariance Intersection." IEEE Transactions on Signal Processing 65, no. 1 (January 1, 2017): 242–55. http://dx.doi.org/10.1109/tsp.2016.2617825.

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20

Liu, Yanxu, Zhongliang Deng, and Enwen Hu. "Multi-Sensor Fusion Positioning Method Based on Batch Inverse Covariance Intersection and IMM." Applied Sciences 11, no. 11 (May 26, 2021): 4908. http://dx.doi.org/10.3390/app11114908.

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For mass application positioning demands, the current single positioning sensor cannot provide reliable and accurate positioning. Herein, we present batch inverse covariance intersection (BICI) and BICI with interacting multiple model (BICI-IMM) multi-sensor fusion positioning methods, which are based on the batch form of the sequential inverse covariance intersection (SICI) fusion method. Meanwhile, it is proved that the BICI is robust. Compared with SICI, BICI-IMM reduces estimation error variance of the motion model and has less conservativeness. The BICI-IMM algorithm improves the accuracy of local filtering by interacting with multiple models and realizes global fusion estimation based on BICI. The validity of the BICI and BICI-IMM algorithm are demonstrated by two simulations and experiments in the open and semi-open scenes, and its positioning accuracy relations are shown. In addition, it is demonstrated that the BICI-IMM algorithm can improve the positioning accuracy in the actual scenes.
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21

Bao, Bi Zhen, Ping Yang, and Hu Wen Cao. "A Method for Modeling and Simulation of Differential Drive Mobile Robot Localization Based on EKF Multisensor Fusion." Advanced Materials Research 383-390 (November 2011): 2339–45. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.2339.

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A method was proposed based on principle of EKF (extended Kalman Filter) state estimation to improve the localization precision of differential drive mobile robot. The robot position was estimated by Multisensor fusion of Odometer and laser, which kinematics model, the robot sensor perception model and the sensor error model were presented. The models were introduced into the state estimation and covariance matrix update equation of EKF with match convergence condition and nonlinear error correction covariance matrix.The specific iteration equation was acquired.The simulation results demonstrate the approach of modeling and fusion is accurate and validity.
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Kodeli, Ivan. "Comments on the status of modern covariance data based on different fission and fusion reactor studies." EPJ Nuclear Sciences & Technologies 4 (2018): 46. http://dx.doi.org/10.1051/epjn/2018027.

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Both the availability and the quality of covariance data improved over the last years and many recent cross-section evaluations, such as JENDL-4.0, ENDF/B-VII.1, JEFF-3.3, etc. include new covariance data compilations. However, several gaps and inconsistencies still persist. Although most modern nuclear data evaluations are based on similar (or even same) sets of experimental data, and the agreement in the results obtained using different cross-sections is reasonably good, larger discrepancies were observed among the corresponding covariance data. This suggests that the differences in the covariance matrix evaluations reflect more the differences in the (mathematical) approaches used and possibly in the interpretations of the experimental data, rather than the different nuclear experimental data used. Furthermore, “tuning” and adjustments are often used in the process of nuclear data evaluations. In principle, if adjustments or “tunings” are used in the evaluation of cross-section then the covariance matrices should reflect the cross-correlations introduced in this process. However, the presently available cross-section covariance matrices include practically no cross-material correlation terms, although some evidence indicate that tuning is present. Experience in using covariance matrices of different origin (such as JEFF, JENDL, ENDF, TENDL, SCALE, etc.) in sensitivity and uncertainty analysis of vast list of cases ranging from fission to fusion and from criticality, kinetics and shielding to adjustment applications are presented. The status of the available covariance and future needs in the areas including secondary angular and energy distributions is addressed.
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Qi, Wen Juan, Peng Zhang, and Zi Li Deng. "Covariance Intersection Fusion Robust Steady-State Kalman Smoother for Multisensor System with Uncertain Noise Variances." Applied Mechanics and Materials 475-476 (December 2013): 476–81. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.476.

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This paper deals with the problem of designing covariance intersection fusion robust steady-state Kalman smoother for multisensor system with uncertain noise variances. Using the minimax robust estimation principle, the local and covariance intersection (CI) fusion robust steady-state Kalman smoothers are presented based on the worst-case conservative system with the conservative upper bounds of noise variances. Their robustness is proved based on the proposed Lyapunov equation, and the robust accuracy of CI fuser is higher than that of each local robust Kalman smoother. A Monte-Carlo simulation of three sensors tracking system verifies their robustness and robust accuracy relations.
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Peyman, Setoodeh, Khayatian Alireza, and Farjah Ebrahim. "Attitude Estimation By Divided Difference Filter-Based Sensor Fusion." Journal of Navigation 60, no. 1 (December 15, 2006): 119–28. http://dx.doi.org/10.1017/s037346330600405x.

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Strapdown inertial navigation systems (INS) often employ aiding sensors to increase accuracy. Nonlinear filtering algorithms are then needed to fuse the collected data from these aiding sensors with measurements of strapdown rate gyros. Aiding sensors usually have slower dynamics compared to gyros and therefore collect data at lower rates. Thus the system will be unobservable between aiding sensors' sampling instants, and the error covariance, which shows the uncertainty in the estimation, grows during the sampling period. This paper presents a divided difference filter (DDF)-based data fusion algorithm, which utilizes the complementary noise profile of rate gyros and gravimetric inclinometers to extend their limits and achieve more accurate attitude estimates. It is confirmed experimentally that DDF achieves better covariance estimates compared to the extended Kalman filter (EKF) because the uncertainty in the state estimate is taken care of in the DDF polynomial approximation formulation.
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Qi, Wen Juan, Peng Zhang, Gui Huan Nie, and Zi Li Deng. "Covariance Intersection Fusion Robust Time-Varying Kalman Filter for Two-Sensor System with Uncertain Noise Variances." Applied Mechanics and Materials 475-476 (December 2013): 470–75. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.470.

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This paper investigates the problem of designing covariance intersection fusion robust time-varying Kalman filter for two-sensor time-varying system with uncertain noise variances. Using the minimax robust estimation principle, the local and covariance intersection (CI) fusion robust time-varying Kalman filters are presented based on the worst-case conservative system with the conservative upper bounds of noise variances. Their robustness is proved based on the proposed Lyapunov equation, and the robust accuracy of time-varying CI fuser is higher than that of each local robust time-varying Kalman filter. A two-sensor tracking system simulation verifies the robustness and robust accuracy relations.
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Liu, Jianhua, Peng Geng, and Hongtao Ma. "Multifocus image fusion based on coefficient significance of redundant discrete wavelet transform." Industrial Robot: the international journal of robotics research and application 46, no. 3 (May 20, 2019): 377–83. http://dx.doi.org/10.1108/ir-11-2018-0229.

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Purpose This study aims to obtain the more precise decision map to fuse the source images by Coefficient significance method. In the area of multifocus image fusion, the better decision map is very important the fusion results. In the processing of distinguishing the well-focus part with blur part in an image, the edge between the parts is more difficult to be processed. Coefficient significance is very effective in generating the better decision map to fuse the multifocus images. Design/methodology/approach The energy of Laplacian is used in the approximation coefficients of redundant discrete wavelet transform. On the other side, the coefficient significance based on statistic property of covariance is proposed to merge the detail coefficient. Findings Due to the shift-variance of the redundant discrete wavelet and the effectiveness of fusion rule, the presented fusion method is superior to the region energy in harmonic cosine wavelet domain, pixel significance with the cross bilateral filter and multiscale geometry analysis method of Ripplet transform. Originality/value In redundant discrete wavelet domain, the coefficient significance based on statistic property of covariance is proposed to merge the detail coefficient of source images.
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Du, Jun, Mei Sun, Liang Hua, Jia Sheng Ge, and Ju Ping Gu. "Weighted Multi-Sensor Data Fusion Based on Fuzzy Kalman Filter for Seam Tracking of the Welding Robots." Advanced Materials Research 542-543 (June 2012): 800–805. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.800.

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In order to resolve the problem of seam tracking of the welding robots with unknown noise characteristics, a Weighted Multi-Sensor Data Fusion (MSDF) algorithm based on the fuzzy Kalman filter algorithm is proposed. Firstly, each Fuzzy Kalman Filter (FKF) uses a fuzzy inference system based on a covariance matching technique to adjust the weight coefficient of measurement noise covariance matrix, so it makes measurement noise close to the true noise level. Secondly, a membership function in fuzzy set is used to measure the mutual support degree matrix of each FKF and corresponding weight coefficients are allocated by this matrix’s maximum modulus eigenvectors, hence, the final expression of data fusion is obtained. Finally, simulation results show that MSDF in seam tracking has both high precision and strong ability of stableness.
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Ma, Hui, and Xian Fei Liu. "Asynchronous Multi-Sensor Fusion Algorithm Based on the Steady-State Kalman Filter." Applied Mechanics and Materials 490-491 (January 2014): 781–88. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.781.

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The paper studies an asynchronous multi-sensor fusion problem based a kind of asynchronous multi-sensor dynamic system. Firstly, this paper presents a centralized fusion algorithm based on the Kalman filter without ignoring the correlation between process noise and augmented measurement noise. It is optimal in minimum mean square error. Then using the steady-state Kalman filter to estimate and fuse. Secondly, in the condition that the local sensor estimation error is associated, a distributed fusion algorithm is given by utilizing S.L. Sun optimal information fusion criterion in minimum error covariance matrix trace at fusion center. In distributed algorithm, the value transmitting to the fusion center is determined by the local sensor estimation based on the steady-state Kalman filter and one step predictive value. Since both optimal fusion algorithm standards are different, so the fusion precision will vary. Finally the effectiveness of the algorithm is verified by computer simulation.
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Fariña, Bibiana, Jonay Toledo, Jose Ignacio Estevez, and Leopoldo Acosta. "Improving Robot Localization Using Doppler-Based Variable Sensor Covariance Calculation." Sensors 20, no. 8 (April 17, 2020): 2287. http://dx.doi.org/10.3390/s20082287.

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This paper describes a localization module for an autonomous wheelchair. This module includes a combination of various sensors such as odometers, laser scanners, IMU and Doppler speed sensors. Every sensor used in the module features variable covariance estimation in order to yield a final accurate localization. The main problem of a localization module composed of different sensors is the accuracy estimation of each sensor. Average static values are normally used, but these can lead to failure in some situations. In this paper, all the sensors have a variable covariance estimation that depends on the data quality. A Doppler speed sensor is used to estimate the covariance of the encoder odometric localization. Lidar is also used as a scan matching localization algorithm, comparing the difference between two consecutive scans to obtain the change in position. Matching quality gives the accuracy of the scan matcher localization. This structure yields a better position than a traditional odometric static covariance method. This is tested in a real prototype and compared to a standard fusion technique.
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Li, Jiali, Shengjing Tang, and Jie Guo. "Noise-Adaption Extended Kalman Filter Based on Deep Deterministic Policy Gradient for Maneuvering Targets." Sensors 22, no. 14 (July 19, 2022): 5389. http://dx.doi.org/10.3390/s22145389.

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Although there have been numerous studies on maneuvering target tracking, few studies have focused on the distinction between unknown maneuvers and inaccurate measurements, leading to low accuracy, poor robustness, or even divergence. To this end, a noise-adaption extended Kalman filter is proposed to track maneuvering targets with multiple synchronous sensors. This filter avoids the simultaneous adjustment of the process model and measurement model without distinction. Instead, the maneuver detection based on the Dempster-Shafer evidence theory is constructed to achieve the reliable distinction between unknown maneuvers and inaccurate measurements by fusing multi-sensor information, which effectively improves the robustness of the filter. Moreover, the adaptive estimation of the process noise covariance is modeled by a Markovian decision process with a proper reward function. Deep deterministic policy gradient is designed to obtain the optimal process noise covariance by taking the innovation as the state and the compensation factor as the action. Furthermore, the recursive estimation of the measurement noise covariance is applied to modify a priori measurement noise covariance of the corresponding sensor. Finally, the fusion algorithm is developed for the global estimation. Simulation experiments are carried out in two scenarios, and simulation results illustrate the feasibility and superiority of the proposed algorithm.
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31

Wu, Ming, Lin Lin Li, and Wei Zhen Hua. "Cooperative Multi-Robot Object Tracking in Unknown Environment Using Covariance Intersection." Advanced Materials Research 631-632 (January 2013): 1101–5. http://dx.doi.org/10.4028/www.scientific.net/amr.631-632.1101.

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This work presents a approach for multiple cooperating mobile robots for moving object tracking in unknown environment. Each robot in the team uses the full covariance extend Kalman filter based algorithm to simultaneously localize the robot and target while building a landmark feature map of the surrounding environment. Meanwhile, in local robot system the covariance intersection based data fusion method is used to fuse information sent by the other robot teammates, those information may contains the location of target and the location of robot itself from other teammate’s point of view. The method is distributed, and let the multi-robot system have the ability of robustness. The results of simulation validate a higher accuracy of our method compared with non-fusion single robot solution.
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32

Eliav, Rei, and Itzik Klein. "INS/Partial DVL Measurements Fusion with Correlated Process and Measurement Noise." Proceedings 4, no. 1 (November 14, 2018): 34. http://dx.doi.org/10.3390/ecsa-5-05727.

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In most autonomous underwater vehicles (AUVs), the navigation system is based on an inertial navigation system (INS) aided by a Doppler velocity log (DVL). In several INSs, only the velocity vector, provided by the DVL, can be used as input for assistance, thus limiting the integration approach to a loosely coupled one. In situations of partial DVL measurements (such as failure to maintain bottom lock) the DVL cannot provide the AUV velocity vector, and as a result, the navigation solution is based only on the standalone INS solution and will drift in time. To circumvent that problem, the extended loosely coupled (ELC) approach was recently proposed. ELC combines the partial DVL measurements and additional information, such as the pervious navigation solution, to form a calculated velocity measurement to aid the INS. When doing so, the assumption made in the extended Kalman filter (EKF) derivation of zero correlated process and measurement noise covariance does not hold. In this paper, we elaborate the ELC approach by taking into account the cross-covariance matrix of the correlated process (INS) and measurement (Partial DVL) noises. At first, this covariance matrix is evaluated based on the specific assumption used in the ELC approach and then implemented in the EKF algorithm. Using a 6DOF AUV simulation, results show that the proposed methodology improves the performance of the ELC integration approach.
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33

Wu, Xiao-Hang, and Shen-Min Song. "Covariance intersection-based fusion algorithm for asynchronous multirate multisensor system with cross-correlation." IET Science, Measurement & Technology 11, no. 7 (October 1, 2017): 878–85. http://dx.doi.org/10.1049/iet-smt.2016.0524.

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34

Guo, Qing, Siyue Chen, Henry Leung, and Shutian Liu. "Covariance intersection based image fusion technique with application to pansharpening in remote sensing." Information Sciences 180, no. 18 (September 2010): 3434–43. http://dx.doi.org/10.1016/j.ins.2010.05.010.

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35

Cesar Bolzani de Campos Ferreira, Julio, and Jacques Waldmann. "Covariance intersection-based sensor fusion for sounding rocket tracking and impact area prediction." Control Engineering Practice 15, no. 4 (April 2007): 389–409. http://dx.doi.org/10.1016/j.conengprac.2006.07.002.

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36

Jiang, Chen, Wenkai Liu, Hui Li, and Haijun Xu. "Modified Adaptive Fusion Scheme for Kalman Filter Based on the Hypothesis Test." Journal of Sensors 2022 (January 29, 2022): 1–12. http://dx.doi.org/10.1155/2022/4064339.

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In the literature, the fading factor was constructed to overcome the shortage of model uncertainties in the Kalman filter. However, the a priori covariance matrix might be inflated abnormally by the fading factor once the measurement is unreliable. Thus, the fading factor may become invalid, and this problem is rarely discussed and tested. In this paper, squares of the Mahalanobis distance are introduced as the judging index, and the fading factor or the covariance inflation factor is adopted conditionally according to the hypothesis testing result. Therefore, an adaptive filtering scheme based on the Mahalanobis distance is put forward for the systems with model uncertainties. The proposed algorithm is implemented with the actual data collected by the integration of the global navigation satellite system (GNSS) and the inertial navigation system and INS (inertial navigation system) integrated systems (INS). For the systems with model uncertainties, experimental results demonstrate that the influences of both the outlying measurements and model errors are controlled effectively with the proposed scheme.
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37

Zhang, Hongmei, Huaqing Zhang, Guangyan Xu, Hao Liu, and Xin Liu. "Attitude anti-interference federal filtering algorithm for MEMS-SINS/GPS/magnetometer/SV integrated navigation system." Measurement and Control 53, no. 1-2 (January 2020): 46–60. http://dx.doi.org/10.1177/0020294019882965.

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The attitude measurement systems composed of magnetic, angular rate and gravity sensors in the navigation system of an unmanned aerial vehicle are vulnerable to disturbed magnetic field observations. In this paper, an anti-interference integrated navigation federal Kalman filter is designed with fusing the information of microelectromechanical-system-based strapdown inertial navigation system, gravity field, geomagnetic field and solar vector (direction of sun). The filter works in the mode of fusion reset or no reset according to environmental conditions or manual setting. In addition, the characteristic is discovered that disturbed magnetic field observations can interfere with the state estimation covariance matrixes of quaternions, which in turn affect the fusion update of estimated quaternions in no reset mode of the federal filter. In order to eliminate the effect of magnetic field observations on the attitude information of pitch and roll angles, a method of correction update for a yaw angle component in quaternions using the magnetometer is designed, and an Euler angle information fusion method is developed by constructing a pseudo-covariance matrix of Euler angels in fusion update of the filter. Thus, when there exist disturbed magnetic field observations and the filter is in no reset mode, the estimated pitch and roll angles of the main filter after fusion update are not affected, and the yaw angle estimation errors of the main filter decrease relative to the estimation errors of the sub-filter which is corrected by a magnetometer. The effectiveness of the algorithm is illustrated by a numerical simulation and a semi-physical simulation.
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38

Fangfang, Peng, and Sun Shuli. "Distributed Fusion Estimation for Multisensor Multirate Systems with Stochastic Observation Multiplicative Noises." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/373270.

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This paper studies the fusion estimation problem of a class of multisensor multirate systems with observation multiplicative noises. The dynamic system is sampled uniformly. Sampling period of each sensor is uniform and the integer multiple of the state update period. Moreover, different sensors have the different sampling rates and observations of sensors are subject to the stochastic uncertainties of multiplicative noises. At first, local filters at the observation sampling points are obtained based on the observations of each sensor. Further, local estimators at the state update points are obtained by predictions of local filters at the observation sampling points. They have the reduced computational cost and a good real-time property. Then, the cross-covariance matrices between any two local estimators are derived at the state update points. At last, using the matrix weighted optimal fusion estimation algorithm in the linear minimum variance sense, the distributed optimal fusion estimator is obtained based on the local estimators and the cross-covariance matrices. An example shows the effectiveness of the proposed algorithms.
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39

Liang, Wenkai, Yan Wu, Ming Li, Yice Cao, and Xin Hu. "High-Resolution SAR Image Classification Using Multi-Scale Deep Feature Fusion and Covariance Pooling Manifold Network." Remote Sensing 13, no. 2 (January 19, 2021): 328. http://dx.doi.org/10.3390/rs13020328.

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The classification of high-resolution (HR) synthetic aperture radar (SAR) images is of great importance for SAR scene interpretation and application. However, the presence of intricate spatial structural patterns and complex statistical nature makes SAR image classification a challenging task, especially in the case of limited labeled SAR data. This paper proposes a novel HR SAR image classification method, using a multi-scale deep feature fusion network and covariance pooling manifold network (MFFN-CPMN). MFFN-CPMN combines the advantages of local spatial features and global statistical properties and considers the multi-feature information fusion of SAR images in representation learning. First, we propose a Gabor-filtering-based multi-scale feature fusion network (MFFN) to capture the spatial pattern and get the discriminative features of SAR images. The MFFN belongs to a deep convolutional neural network (CNN). To make full use of a large amount of unlabeled data, the weights of each layer of MFFN are optimized by unsupervised denoising dual-sparse encoder. Moreover, the feature fusion strategy in MFFN can effectively exploit the complementary information between different levels and different scales. Second, we utilize a covariance pooling manifold network to extract further the global second-order statistics of SAR images over the fusional feature maps. Finally, the obtained covariance descriptor is more distinct for various land covers. Experimental results on four HR SAR images demonstrate the effectiveness of the proposed method and achieve promising results over other related algorithms.
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Li, Xin Yu, and Dong Yi Chen. "Sensor Fusion Based on Strong Tracking Filter for Augmented Reality Registration." Key Engineering Materials 467-469 (February 2011): 108–13. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.108.

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Accurate tracking for Augmented Reality applications is a challenging task. Multi-sensors hybrid tracking generally provide more stable than the effect of the single visual tracking. This paper presents a new tightly-coupled hybrid tracking approach combining vision-based systems with inertial sensor. Based on multi-frequency sampling theory in the measurement data synchronization, a strong tracking filter (STF) is used to smooth sensor data and estimate position and orientation. Through adding time-varying fading factor to adaptively adjust the prediction error covariance of filter, this method improves the performance of tracking for fast moving targets. Experimental results show the efficiency and robustness of this proposed approach.
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41

Liu, Guohua, Juan Guan, Haiying Liu, and Chenlin Wang. "Multirobot Collaborative Navigation Algorithms Based on Odometer/Vision Information Fusion." Mathematical Problems in Engineering 2020 (August 27, 2020): 1–16. http://dx.doi.org/10.1155/2020/5819409.

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Collaborative navigation is the key technology for multimobile robot system. In order to improve the performance of collaborative navigation system, the collaborative navigation algorithms based on odometer/vision multisource information fusion are presented in this paper. Firstly, the multisource information fusion collaborative navigation system model is established, including mobile robot model, odometry measurement model, lidar relative measurement model, UWB relative measurement model, and the SLAM model based on lidar measurement. Secondly, the frameworks of centralized and decentralized collaborative navigation based on odometer/vision fusion are given, and the SLAM algorithms based on vision are presented. Then, the centralized and decentralized odometer/vision collaborative navigation algorithms are derived, including the time update, single node measurement update, relative measurement update between nodes, and covariance cross filtering algorithm. Finally, different simulation experiments are designed to verify the effectiveness of the algorithms. Two kinds of multirobot collaborative navigation experimental scenes, which are relative measurement aided odometer and odometer/SLAM fusion, are designed, respectively. The advantages and disadvantages of centralized versus decentralized collaborative navigation algorithms in different experimental scenes are analyzed.
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42

Qi, Wen Juan, Peng Zhang, Zi Li Deng, and Yuan Gao. "Covariance Intersection Fusion Smoothers for Multichannel ARMA Signal with Colored Measurement Noises." Applied Mechanics and Materials 373-375 (August 2013): 716–22. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.716.

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For multichannel autoregressive moving average (ARMA) signal with colored measurement noises, based on classical Kalman filtering theory, a covariance intersection (CI) fusion smoother without cross-covariances is presented by the augmented state space model. It has the advantage that the computation of cross-covariances is avoid, so it can significantly reduce the computational burden, and it can solve the fusion problem for multi-sensor systems with unknown cross-covariances. Under the unbiased linear minimum variance (ULMV) criterion, three optimal weighted fusion smoothers with matrix weights, scalar weights and diagonal weights are also presented respectively. The accuracy comparison of the CI fuser with the other three weighted fusers is given. It is shown that its accuracy is higher than that of each local smoother, and is lower than or close to that of the optimal fuser weighted by matrices. So the presented fusion smoother is better in performance.
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43

Hu, Zhentao, Tianxiang Chen, Quanbo Ge, and Hebin Wang. "Observable Degree Analysis for Multi-Sensor Fusion System." Sensors 18, no. 12 (November 30, 2018): 4197. http://dx.doi.org/10.3390/s18124197.

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Multi-sensor fusion system has many advantages, such as reduce error and improve filtering accuracy. The observability of the system state is an important index to test the convergence accuracy and speed of the designed Kalman filter. In this paper, we evaluate different multi-sensor fusion systems from the perspective of observability. To adjust and optimize the filter performance before filtering, in this paper, we derive the expression form of estimation error covariance of three different fusion methods and discussed both observable degree of fusion center and local filter of fusion step. Based on the ODAEPM, we obtained their discriminant matrix of observable degree and the relationship among different fusion methods is given by mathematical proof. To confirm mathematical conclusion, the simulation analysis is done for multi-sensor CV model. The result demonstrates our theory and verifies the advantage of information fusion system.
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44

Lou, Tai-shan, Nan-hua Chen, Xiao-qian Wang, Zhen-dong He, and Jie Liu. "Reliable Distributed Integrated Navigation Based on CI during Mars Entry." International Journal of Aerospace Engineering 2019 (May 2, 2019): 1–11. http://dx.doi.org/10.1155/2019/1802659.

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A reliable distributed covariance intersection (CI) fusion integrated navigation algorithm with information feedback during the Mars atmospheric entry is proposed to meet robust, reliable, and high-precision Mars atmospheric entry navigation strategy. A distributed integrated navigation scheme includes four independent subsystems consisting of an IMU and one radio beacon, but each subsystem is weakly observable under limited Mars entry measurements. The scalar weights are fast obtained by maximizing the information contribution of the corresponding estimate. The distributed framework based upon the CI fusion algorithm is designed by using dynamic information distribution coefficients and information feedback strategy. This distributed fusion approach could meet the lower computation cost and robust and reliable capacity and especially is beneficial to the weakly observable or unobservable subsystems during the Mars entry navigation scheme. Numerical simulations show that the proposed distributed CI fusion integrated navigation algorithm can provide consistent navigation accuracy for the Mars entry vehicle and improve the entry navigation robustness under the weakly observable navigation subsystems.
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45

Caballero-Águila, R., A. Hermoso-Carazo, and J. Linares-Pérez. "Covariance-based fusion filtering for networked systems with random transmission delays and non-consecutive losses." International Journal of General Systems 46, no. 7 (June 21, 2017): 752–71. http://dx.doi.org/10.1080/03081079.2017.1341501.

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46

Jwo, Dah-Jing, and Tsu-Pin Weng. "An Adaptive Sensor Fusion Method with Applications in Integrated Navigation." Journal of Navigation 61, no. 4 (October 2008): 705–21. http://dx.doi.org/10.1017/s0373463308004827.

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The Kalman filter (KF) is a form of optimal estimator characterized by recursive evaluation, which has been widely applied to the navigation sensor fusion. Utilizing the KF requires that all the plant dynamics and noise processes are completely known, and the noise process is zero mean white noise. If the theoretical behaviour of the filter and its actual behaviour do not agree, divergence problems tend to occur. The adaptive algorithm has been one of the approaches to prevent divergence problems in the Kalman filter when precise knowledge on the system models is not available. Two popular types of adaptive Kalman filter are the innovation-based adaptive estimation (IAE) approach and the adaptive fading Kalman filter (AFKF) approach. In this paper, an approach involving the concept of the two methods is presented. The proposed method is a synergy of the IAE and AFKF approaches. The ratio of the actual innovation covariance based on the sampled sequence to the theoretical innovation covariance will be employed for dynamically tuning two filter parameters – fading factors and measurement noise scaling factors. The method has the merits of good computational efficiency and numerical stability. The matrices in the KF loop are able to remain positive definitive. Navigation sensor fusion using the proposed scheme will be demonstrated. Performance of the proposed scheme on the loosely coupled GPS/INS navigation applications will be discussed.
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47

Zhao, Nan, Dawei Lu, Kechen Hou, Meifei Chen, Xiangyu Wei, Xiaowei Zhang, and Bin Hu. "Fatigue Detection with Spatial-Temporal Fusion Method on Covariance Manifolds of Electroencephalography." Entropy 23, no. 10 (September 30, 2021): 1298. http://dx.doi.org/10.3390/e23101298.

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With the increasing pressure of current life, fatigue caused by high-pressure work has deeply affected people and even threatened their lives. In particular, fatigue driving has become a leading cause of traffic accidents and deaths. This paper investigates electroencephalography (EEG)-based fatigue detection for driving by mining the latent information through the spatial-temporal changes in the relations between EEG channels. First, EEG data are partitioned into several segments to calculate the covariance matrices of each segment, and then we feed these matrices into a recurrent neural network to obtain high-level temporal information. Second, the covariance matrices of whole signals are leveraged to extract two kinds of spatial features, which will be fused with temporal characteristics to obtain comprehensive spatial-temporal information. Experiments on an open benchmark showed that our method achieved an excellent classification accuracy of 93.834% and performed better than several novel methods. These experimental results indicate that our method enables better reliability and feasibility in the detection of fatigued driving.
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48

Zhang, Liang, Pei Yi Shen, Juan Song, Luo Bin Dong, Yan Zheng Zhang, Xiao Xi Zhang, and Jie Qiong Zhang. "A Distributed Multi-Robot Map Fusion Algorithm." Applied Mechanics and Materials 536-537 (April 2014): 917–24. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.917.

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This paper proposes a new approach to the multi-robot map fusion algorithm that enables a team of robots to build a joint map without initial knowledge of their relative pose. First, the relative distance and bearing measurements between two robots are fused together by the covariance intersection method after they detect each other. Second, the transformation equations among multi robots coordinates are derived based on their relative distance and bearing measurements. Third, all the multi robots local maps are merged into one global map by unscented transform based on the transformation equations. Fourth, the possible duplicate features are filtered out by the robots maximal detection area and the features coordinate range, then the Mahalanobis distance is computed to decide the duplicate features correspondence through unscented transform, and the Kalman Filter is used while fusing the duplicate features information. As a means of validation for the proposed method, experimental results obtained from the two robots are presented.
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49

Bayasli, Omar, and Hassen Salhi. "The cubic root unscented kalman filter to estimate the position and orientation of mobile robot trajectory." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (October 1, 2020): 5243. http://dx.doi.org/10.11591/ijece.v10i5.pp5243-5250.

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In this paper we introduce a Cubic Root Unscented Kalman Filter (CRUKF) compared to the Unscented Kalman Filter (UKF) for calculating the covariance cubic matrix and covariance matrix within a sensor fusion algorithm to estimate the measurements of an omnidirectional mobile robot trajectory. We study the fusion of the data obtained by the position and orientation with a good precision to localize the robot in an external medium; we apply the techniques of Kalman Filter (KF) to the estimation of the trajectory. We suppose a movement of mobile robot on a plan in two dimensions. The sensor approach is based on the Cubic Root Unscented Kalman Filter (CRUKF) and too on the standard Unscented Kalman Filter (UKF) which are modified to handle measurements from the position and orientation. A real-time implementation is done on a three-wheeled omnidirectional mobile robot, using a dynamic model with trajectories. The algorithm is analyzed and validated with simulations.
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Osman, Mostafa, Ahmed Hussein, Abdulla Al-Kaff, Fernando García, and Dongpu Cao. "A Novel Online Approach for Drift Covariance Estimation of Odometries Used in Intelligent Vehicle Localization." Sensors 19, no. 23 (November 26, 2019): 5178. http://dx.doi.org/10.3390/s19235178.

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Localization is the fundamental problem of intelligent vehicles. For a vehicle to autonomously operate, it first needs to locate itself in the environment. A lot of different odometries (visual, inertial, wheel encoders) have been introduced through the past few years for autonomous vehicle localization. However, such odometries suffers from drift due to their reliance on integration of sensor measurements. In this paper, the drift error in an odometry is modeled and a Drift Covariance Estimation (DCE) algorithm is introduced. The DCE algorithm estimates the covariance of an odometry using the readings of another on-board sensor which does not suffer from drift. To validate the proposed algorithm, several real-world experiments in different conditions as well as sequences from Oxford RobotCar Dataset and EU long-term driving dataset are used. The effect of the covariance estimation on three different fusion-based localization algorithms (EKF, UKF and EH-infinity) is studied in comparison with the use of constant covariance, which were calculated based on the true variance of the sensors being used. The obtained results show the efficacy of the estimation algorithm compared to constant covariances in terms of improving the accuracy of localization.
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