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

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Miller, Alexander, Boris Miller, and Gregory Miller. "On AUV Control with the Aid of Position Estimation Algorithms Based on Acoustic Seabed Sensing and DOA Measurements." Sensors 19, no. 24 (2019): 5520. http://dx.doi.org/10.3390/s19245520.

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This article discusses various approaches to the control of autonomous underwater vehicles (AUVs) with the aid of different velocity-position estimation algorithms. Traditionally this field is considered as the area of the extended Kalman filter (EKF) application: It became a universal tool for nonlinear observation models and its use is ubiquitous. Meanwhile, the specific characteristics of underwater navigation, such as an incomplete sets of measurements, constraints on the range metering or even impossibility of range measurements, observations provided by rather specific acoustic beacons,

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Deneux, Thomas, and Olivier Faugeras. "EEG-fMRI Fusion of Paradigm-Free Activity Using Kalman Filtering." Neural Computation 22, no. 4 (2010): 906–48. http://dx.doi.org/10.1162/neco.2009.05-08-793.

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We address here the use of EEG and fMRI, and their combination, in order to estimate the full spatiotemporal patterns of activity on the cortical surface in the absence of any particular assumptions on this activity such as stimulation times. For handling such a high-dimension inverse problem, we propose the use of (1) a global forward model of how these measures are functions of the “neural activity” of a large number of sources distributed on the cortical surface, formalized as a dynamical system, and (2) adaptive filters, as a natural solution to solve this inverse problem iteratively along

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Yuan, Yubin, Yu Shen, Jing Peng, Lin Wang, and Hongguo Zhang. "Defogging Technology Based on Dual-Channel Sensor Information Fusion of Near-Infrared and Visible Light." Journal of Sensors 2020 (November 15, 2020): 1–17. http://dx.doi.org/10.1155/2020/8818650.

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Since the method to remove fog from images is complicated and detail loss and color distortion could occur to the defogged images, a defogging method based on near-infrared and visible image fusion is put forward in this paper. The algorithm in this paper uses the near-infrared image with rich details as a new data source and adopts the image fusion method to obtain a defog image with rich details and high color recovery. First, the colorful visible image is converted into HSI color space to obtain an intensity channel image, color channel image, and saturation channel image. The intensity cha

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Albanwan, H., and R. Qin. "ENHANCEMENT OF DEPTH MAP BY FUSION USING ADAPTIVE AND SEMANTIC-GUIDED SPATIOTEMPORAL FILTERING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 227–32. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-227-2020.

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Abstract. Extracting detailed geometric information about a scene relies on the quality of the depth maps (e.g. Digital Elevation Surfaces, DSM) to enhance the performance of 3D model reconstruction. Elevation information from LiDAR is often expensive and hard to obtain. The most common approach to generate depth maps is through multi-view stereo (MVS) methods (e.g. dense stereo image matching). The quality of single depth maps, however, is often prone to noise, outliers, and missing data points due to the quality of the acquired image pairs. A reference multi-view image pair must be noise-fre

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Mensing, Christian, Stephan Sand, and Armin Dammann. "Hybrid Data Fusion and Tracking for Positioning with GNSS and 3GPP-LTE." International Journal of Navigation and Observation 2010 (August 2, 2010): 1–12. http://dx.doi.org/10.1155/2010/812945.

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Global navigation satellite systems (GNSSs) can provide reliable positioning information under optimum conditions, where at least four satellites can be accessed with sufficient quality. In critical situations, for example, urban canyons or indoor, due to blocking of satellites by buildings and severe multipath effects, the GNSS performance can be decreased substantially. To overcome this limitation, we propose to exploit additionally information from communications systems for positioning purposes, for example, by using time difference of arrival (TDOA) information. To optimize the performanc

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Xia, Haoran, Yuanping Zhang, Ming Yang, and Yufang Zhao. "Visual Tracking via Deep Feature Fusion and Correlation Filters." Sensors 20, no. 12 (2020): 3370. http://dx.doi.org/10.3390/s20123370.

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Visual tracking is a fundamental vision task that tries to figure out instances of several object classes from videos and images. It has attracted much attention for providing the basic semantic information for numerous applications. Over the past 10 years, visual tracking has made a great progress, but huge challenges still exist in many real-world applications. The facade of a target can be transformed significantly by pose changing, occlusion, and sudden movement, which possibly leads to a sudden target loss. This paper builds a hybrid tracker combining the deep feature method and correlati

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Dang, Xiaochao, Xiong Si, Zhanjun Hao, and Yaning Huang. "A Novel Passive Indoor Localization Method by Fusion CSI Amplitude and Phase Information." Sensors 19, no. 4 (2019): 875. http://dx.doi.org/10.3390/s19040875.

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With the rapid development of wireless network technology, wireless passive indoor localization has become an increasingly important technique that is widely used in indoor location-based services. Channel state information (CSI) can provide more detailed and specific subcarrier information, which has gained the attention of researchers and has become an emphasis in indoor localization technology. However, existing research has generally adopted amplitude information for eigenvalue calculations. There are few research studies that have used phase information from CSI signals for localization p

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Bezmen, P. A. "Integration of Mobile Robot Control System Data Using the Extended Kalman Filter." Proceedings of the Southwest State University 23, no. 2 (2019): 53–64. http://dx.doi.org/10.21869/2223-1560-2019-23-2-53-64.

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Purpose of research. The article deals with the adaptation of the algorithm of the extended Kalman filter for the integration of data from sensors of physical values of a mobile robotMethods. Integration of data is the process of information (data) fusion for determination or prediction of the state of an object. Integration provides increased robustness of robot control and accuracy of machine perception of information. This process is similar to repeated experiments in order to determine in direct and/or indirect ways the value of a physical quantity with the required accuracy. In the contro

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Kim, H., and I. Lee. "LOCALIZATION OF A CAR BASED ON MULTI-SENSOR FUSION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1 (September 26, 2018): 247–50. http://dx.doi.org/10.5194/isprs-archives-xlii-1-247-2018.

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<p><strong>Abstract.</strong> The vehicle localization is an essential component for stable autonomous car operation. There are many algorithms for the vehicle localization. However, it still needs much improvement in terms of its accuracy and cost. In this paper, sensor fusion based localization algorithm is used for solving this problem. Our sensor system is composed of in-vehicle sensors, GPS and vision sensors. The localization algorithm is based on extended Kalman filter and it has time update step and measurement update step. In the time update step, in-vehicle sensors

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Kashinath, Shafiza Ariffin, Salama A. Mostafa, David Lim, Aida Mustapha, Hanayanti Hafit, and Rozanawati Darman. "A general framework of multiple coordinative data fusion modules for real-time and heterogeneous data sources." Journal of Intelligent Systems 30, no. 1 (2021): 947–65. http://dx.doi.org/10.1515/jisys-2021-0083.

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Abstract Designing a data-responsive system requires accurate input to ensure efficient results. The growth of technology in sensing methods and the needs of various kinds of data greatly impact data fusion (DF)-related study. A coordinative DF framework entails the participation of many subsystems or modules to produce coordinative features. These features are utilized to facilitate and improve solving certain domain problems. Consequently, this paper proposes a general Multiple Coordinative Data Fusion Modules (MCDFM) framework for real-time and heterogeneous data sources. We develop the MCD

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Li, Shugang, Ru Wang, Yuqi Zhang, Hanyu Lu, Nannan Cai, and Zhaoxu Yu. "Potential social media influencers discrimination for concept marketing in online brand community." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 317–29. http://dx.doi.org/10.3233/jifs-201809.

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Identifying potential social media influencers (SMIs) accurately can achieve a long-time and effective concept marketing at a lower cost, and then promote the development of the corporate brand in online communities. However, potential SMIs discrimination often faces the problem of insufficient available information of the long-term evolution of the network, and the existing discriminant methods based on link analysis fail to obtain more accurate results. To fill this gap, a consensus smart discriminant algorithm (CSDA) is proposed to identify the potential SMIs with the aid of attention conce

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Foltz, Steven M., Qingsong Gao, Christopher J. Yoon, et al. "Comprehensive Multi-Omics Analysis of Gene Fusions in a Large Multiple Myeloma Cohort." Blood 132, Supplement 1 (2018): 1898. http://dx.doi.org/10.1182/blood-2018-99-117245.

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Abstract Introduction: Gene fusions are the result of genomic rearrangements that create hybrid protein products or bring the regulatory elements of one gene into close proximity of another. Fusions often dysregulate gene function or expression through oncogene overexpression or tumor suppressor underexpression (Gao, Liang, Foltz, et al. Cell Rep 2018). Some fusions such as EML4--ALK in lung adenocarcinoma are known druggable targets. Fusion detection algorithms utilize discordantly mapped RNA-seq reads. Careful consideration of detection and filtering procedures is vital for large-scale fusio

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Han, Yanling, Xi Shi, Shuhu Yang, Yun Zhang, Zhonghua Hong, and Ruyan Zhou. "Hyperspectral Sea Ice Image Classification Based on the Spectral-Spatial-Joint Feature with the PCA Network." Remote Sensing 13, no. 12 (2021): 2253. http://dx.doi.org/10.3390/rs13122253.

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Sea ice is one of the most prominent causes of marine disasters occurring at high latitudes. The detection of sea ice is particularly important, and the classification of sea ice images is an important part of sea ice detection. Traditional sea ice classification based on optical remote sensing mostly uses spectral information only and does not fully extract rich spectral and spatial information from sea ice images. At the same time, it is difficult to obtain samples and the resulting small sample sizes used in sea ice classification has limited the improvement of classification accuracy to a

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Schön, Steffen, Claus Brenner, Hamza Alkhatib, et al. "Integrity and Collaboration in Dynamic Sensor Networks." Sensors 18, no. 7 (2018): 2400. http://dx.doi.org/10.3390/s18072400.

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Global Navigation Satellite Systems (GNSS) deliver absolute position and velocity, as well as time information (P, V, T). However, in urban areas, the GNSS navigation performance is restricted due to signal obstructions and multipath. This is especially true for applications dealing with highly automatic or even autonomous driving. Subsequently, multi-sensor platforms including laser scanners and cameras, as well as map data are used to enhance the navigation performance, namely in accuracy, integrity, continuity and availability. Although well-established procedures for integrity monitoring e

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Jalal Abadi, Marzieh, Luca Luceri, Mahbub Hassan, Chun Tung Chou, and Monica Nicoli. "A Cooperative Machine Learning Approach for Pedestrian Navigation in Indoor IoT." Sensors 19, no. 21 (2019): 4609. http://dx.doi.org/10.3390/s19214609.

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This paper presents a system based on pedestrian dead reckoning (PDR) for localization of networked mobile users, which relies only on sensors embedded in the devices and device- to-device connectivity. The user trajectory is reconstructed by measuring step by step the user displacements. Though step length can be estimated rather accurately, heading evaluation is extremely problematic in indoor environments. Magnetometer is typically used, however measurements are strongly perturbed. To improve the location accuracy, this paper proposes a novel cooperative system to estimate the direction of

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Liu, Long, Hui-Hui Wang, Sen Qiu, Yun-Cui Zhang, and Zheng-Dong Hao. "Paddle Stroke Analysis for Kayakers Using Wearable Technologies." Sensors 21, no. 3 (2021): 914. http://dx.doi.org/10.3390/s21030914.

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Proper stroke posture and rhythm are crucial for kayakers to achieve perfect performance and avoid the occurrence of sport injuries. The traditional video-based analysis method has numerous limitations (e.g., site and occlusion). In this study, we propose a systematic approach for evaluating the training performance of kayakers based on the multiple sensors fusion technology. Kayakers’ motion information is collected by miniature inertial sensor nodes attached on the body. The extend Kalman filter (EKF) method is used for data fusion and updating human posture. After sensor calibration, the ka

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Shi, Qiaoqiao, Wei Li, Ran Tao, Xu Sun, and Lianru Gao. "Ship Classification Based on Multifeature Ensemble with Convolutional Neural Network." Remote Sensing 11, no. 4 (2019): 419. http://dx.doi.org/10.3390/rs11040419.

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As an important part of maritime traffic, ships play an important role in military and civilian applications. However, ships’ appearances are susceptible to some factors such as lighting, occlusion, and sea state, making ship classification more challenging. This is of great importance when exploring global and detailed information for ship classification in optical remote sensing images. In this paper, a novel method to obtain discriminative feature representation of a ship image is proposed. The proposed classification framework consists of a multifeature ensemble based on convolutional neur

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Lai, Y. C., C. C. Chang, C. M. Tsai, S. Y. Lin, and S. C. Huang. "DEVELOPMENT OF A PEDESTRIAN INDOOR NAVIGATION SYSTEM BASED ON MULTI-SENSOR FUSION AND FUZZY LOGIC ESTIMATION ALGORITHMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4/W5 (May 11, 2015): 81–86. http://dx.doi.org/10.5194/isprsarchives-xl-4-w5-81-2015.

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This paper presents a pedestrian indoor navigation system based on the multi-sensor fusion and fuzzy logic estimation algorithms. The proposed navigation system is a self-contained dead reckoning navigation that means no other outside signal is demanded. In order to achieve the self-contained capability, a portable and wearable inertial measure unit (IMU) has been developed. Its adopted sensors are the low-cost inertial sensors, accelerometer and gyroscope, based on the micro electro-mechanical system (MEMS). There are two types of the IMU modules, handheld and waist-mounted. The low-cost MEMS

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Muresan, Mircea Paul, Ion Giosan, and Sergiu Nedevschi. "Stabilization and Validation of 3D Object Position Using Multimodal Sensor Fusion and Semantic Segmentation." Sensors 20, no. 4 (2020): 1110. http://dx.doi.org/10.3390/s20041110.

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The stabilization and validation process of the measured position of objects is an important step for high-level perception functions and for the correct processing of sensory data. The goal of this process is to detect and handle inconsistencies between different sensor measurements, which result from the perception system. The aggregation of the detections from different sensors consists in the combination of the sensorial data in one common reference frame for each identified object, leading to the creation of a super-sensor. The result of the data aggregation may end up with errors such as

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Adriano, Bruno, Junshi Xia, Gerald Baier, Naoto Yokoya, and Shunichi Koshimura. "Multi-Source Data Fusion Based on Ensemble Learning for Rapid Building Damage Mapping during the 2018 Sulawesi Earthquake and Tsunami in Palu, Indonesia." Remote Sensing 11, no. 7 (2019): 886. http://dx.doi.org/10.3390/rs11070886.

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This work presents a detailed analysis of building damage recognition, employing multi-source data fusion and ensemble learning algorithms for rapid damage mapping tasks. A damage classification framework is introduced and tested to categorize the building damage following the recent 2018 Sulawesi earthquake and tsunami. Three robust ensemble learning classifiers were investigated for recognizing building damage from Synthetic Aperture Radar (SAR) and optical remote sensing datasets and their derived features. The contribution of each feature dataset was also explored, considering different co

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Ping, Xianyao, Shuo Cheng, Wei Yue, Yongchang Du, Xiangyu Wang, and Liang Li. "Adaptive estimations of tyre–road friction coefficient and body’s sideslip angle based on strong tracking and interactive multiple model theories." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 234, no. 14 (2020): 3224–38. http://dx.doi.org/10.1177/0954407020941410.

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Vehicle dynamic states and parameters, such as the tyre–road friction coefficient and body’s sideslip angle especially, are crucial for vehicle dynamics control with close-loop feedback laws. Autonomous vehicles also have strict demands on real-time knowledge of those information to make reliable decisions. With consideration of the cost saving, some estimation methods employing high-resolution vision and position devices are not for the production vehicles. Meanwhile, the bad adaptability of traditional Kalman filters to variable system structure restricts their practical applications. This p

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Silva, Luiz A. Z. da, Vinicius F. Vidal, Leonardo M. Honório, Mário A. R. Dantas, Milena Faria Pinto, and Miriam Capretz. "A Heterogeneous Edge-Fog Environment Supporting Digital Twins for Remote Inspections." Sensors 20, no. 18 (2020): 5296. http://dx.doi.org/10.3390/s20185296.

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The increase in the development of digital twins brings several advantages to inspection and maintenance, but also new challenges. Digital models capable of representing real equipment for full remote inspection demand the synchronization, integration, and fusion of several sensors and methodologies such as stereo vision, monocular Simultaneous Localization and Mapping (SLAM), laser and RGB-D camera readings, texture analysis, filters, thermal, and multi-spectral images. This multidimensional information makes it possible to have a full understanding of given equipment, enabling remote diagnos

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Hu, Jie, Zhongli Wu, Xiongzhen Qin, Huangzheng Geng, and Zhangbin Gao. "An Extended Kalman Filter and Back Propagation Neural Network Algorithm Positioning Method Based on Anti-lock Brake Sensor and Global Navigation Satellite System Information." Sensors 18, no. 9 (2018): 2753. http://dx.doi.org/10.3390/s18092753.

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Telematics box (T-Box) chip-level Global Navigation Satellite System (GNSS) receiver modules usually suffer from GNSS information failure or noise in urban environments. In order to resolve this issue, this paper presents a real-time positioning method for Extended Kalman Filter (EKF) and Back Propagation Neural Network (BPNN) algorithms based on Antilock Brake System (ABS) sensor and GNSS information. Experiments were performed using an assembly in the vehicle with a T-Box. The T-Box firstly use automotive kinematical Pre-EKF to fuse the four wheel speed, yaw rate and steering wheel angle dat

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You, Changhui, Hong Zheng, Zhongyuan Guo, Tianyu Wang, and Xiongbin Wu. "Multiscale Content-Independent Feature Fusion Network for Source Camera Identification." Applied Sciences 11, no. 15 (2021): 6752. http://dx.doi.org/10.3390/app11156752.

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In recent years, source camera identification has become a research hotspot in the field of image forensics and has received increasing attention. It has high application value in combating the spread of pornographic photos, copyright authentication of art photos, image tampering forensics, and so on. Although the existing algorithms greatly promote the research progress of source camera identification, they still cannot effectively reduce the interference of image content with image forensics. To suppress the influence of image content on source camera identification, a multiscale content-ind

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Shao, Xiaorui, Chang-Soo Kim, and Palash Sontakke. "Accurate Deep Model for Electricity Consumption Forecasting Using Multi-channel and Multi-Scale Feature Fusion CNN–LSTM." Energies 13, no. 8 (2020): 1881. http://dx.doi.org/10.3390/en13081881.

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Electricity consumption forecasting is a vital task for smart grid building regarding the supply and demand of electric power. Many pieces of research focused on the factors of weather, holidays, and temperatures for electricity forecasting that requires to collect those data by using kinds of sensors, which raises the cost of time and resources. Besides, most of the existing methods only focused on one or two types of forecasts, which cannot satisfy the actual needs of decision-making. This paper proposes a novel hybrid deep model for multiple forecasts by combining Convolutional Neural Netwo

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Chengqi Zhang*, Ling Guan**, and Zheru Chi. "Introduction to the Special Issue on Learning in Intelligent Algorithms and Systems Design." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 6 (1999): 439–40. http://dx.doi.org/10.20965/jaciii.1999.p0439.

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Learning has long been and will continue to be a key issue in intelligent algorithms and systems design. Emulating the behavior and mechanisms of human learning by machines at such high levels as symbolic processing and such low levels as neuronal processing has long been a dominant interest among researchers worldwide. Neural networks, fuzzy logic, and evolutionary algorithms represent the three most active research areas. With advanced theoretical studies and computer technology, many promising algorithms and systems using these techniques have been designed and implemented for a wide range

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Nguyen, Nhan Duc, Duong Trong Bui, Phuc Huu Truong, and Gu-Min Jeong. "Position-Based Feature Selection for Body Sensors regarding Daily Living Activity Recognition." Journal of Sensors 2018 (September 13, 2018): 1–13. http://dx.doi.org/10.1155/2018/9762098.

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This paper proposes a novel approach to recognize activities based on sensor-placement feature selection. The method is designed to address a problem of multisensor fusion information of wearable sensors which are located in different positions of a human body. Precisely, the approach can extract the best feature set that characterizes each activity regarding a body-sensor location to recognize daily living activities. We firstly preprocess the raw data by utilizing a low-pass filter. After extracting various features, feature selection algorithms are applied separately on feature sets of each

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Polakova, Katerina Machova, Vojtech Kulvait, Jana Linhartova, et al. "Algorithms and Processing Pipeline For Error Correction and Detection Of Significant Mutations In The Kinase Domain Of BCR-ABL Analyzed By Next-Generation Sequencing: Implications For Clinical Practice Of Chronic Myeloid Leukemia." Blood 122, no. 21 (2013): 3987. http://dx.doi.org/10.1182/blood.v122.21.3987.3987.

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Abstract High sequencing depths of NGS may cause false-positive variant calls of minor subclones (up to 10%). Errors inserted into the NGS pipeline during sample preparation and sequencing manifest by erroneous detections of mutations including point mutations. Thus, there is a need for algorithms for filtering data which occur by error from relevant biological information. We developed algorithms and a processing pipeline for error correction and detection of significant mutations after NGS of BCR-ABL kinase domain (KD). We validated our algorithms on the retrospective NGS analysis of 135 sam

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Qi, Ji Yuan, and Xiao Jun Hu. "Track Handing Fusion in Data Fusion and Database." Advanced Materials Research 468-471 (February 2012): 959–62. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.959.

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Track message is obtained with a lot of adopted sensors. For one sensor, the reliability may not the same in different condition. A reliability judgment matrix for each target is constructed based on the relative reliability of information offered by sensors. Then, the reliabilities of all the sensors for each target and their general reliability can be gotten. A numerical example is presented. The well reliability sensor’s information will be chose to merge with Kalman filter algorithm in tracking handling based on data fusion technique, and gain the best estimate track. According to the dema

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Sun, Shasha, Chuanpeng Li, Ning Lv, Xiaoman Zhang, Zhaoyan Yu, and Haibo Wang. "Attention based convolutional network for automatic sleep stage classification." Biomedical Engineering / Biomedizinische Technik 66, no. 4 (2021): 335–43. http://dx.doi.org/10.1515/bmt-2020-0051.

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Abstract Sleep staging is an important basis for diagnosing sleep-related problems. In this paper, an attention based convolutional network for automatic sleep staging is proposed. The network takes time-frequency image as input and predict sleep stage for each 30-s epoch as output. For each CNN feature maps, our model generate attention maps along two separate dimensions, time and filter, and then multiplied to form the final attention map. Residual-like fusion structure is used to append the attention map to the input feature map for adaptive feature refinement. In addition, to get the globa

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Tan, Xiu Hu. "An Information Fusion Algorithm Based on Kalman Filtering." Applied Mechanics and Materials 444-445 (October 2013): 1072–76. http://dx.doi.org/10.4028/www.scientific.net/amm.444-445.1072.

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For the multisensor systems with unknown noise variances, by the statistics method, the mathematical model and the noise statistics are essential, and this limitation was settled by adaptive algorithm. The adaptive Kalman filter was proposed to solve the filtering problem of the system with unknown mathematical model or noise statistics in information fusion. Based on the probability method and the scalar weighting optimal information fusion criterion in the minimum variance sense, the algorithm can not only optimize the multi-channel data, but also obtain the minimum mean square error (MMSE)

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Oh, Eunmi. "A method for track fusing using data association in naval combat system." Ciencia y tecnología de buques 9, no. 18 (2016): 83. http://dx.doi.org/10.25043/19098642.131.

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In today battlefield multi-sensors installed on naval ship are acquiring too much information. Information is used through naval combat system to improve reaction capability to threat more quickly and precise. For acting to threat, we have to make a decision whether same ones what each target from multi sensor and execute track fusion according to result of judgment. So in this paper, we propose the track fusion method using track’s varied information. We predicted and estimated the target state based on dynamic information using data association filter so made valid measurement area what is a

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Atasever, U. H., P. Civicioglu, E. Besdok, and C. Ozkan. "A New Unsupervised Change Detection Approach Based On DWT Image Fusion And Backtracking Search Optimization Algorithm For Optical Remote Sensing Data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7 (September 19, 2014): 15–18. http://dx.doi.org/10.5194/isprsarchives-xl-7-15-2014.

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Change detection is one of the most important subjects of remote sensing discipline. In this paper, a new unsupervised change detection approach is proposed for multi-temporal remotely sensed optic imagery. This approach does not require any prior information about changed and unchanged pixels. The approach is based on Discrete Wavelet Transform (DWT) based image fusion and Backtracking Search Optimization Algorithm (BSA). In the first step of the approach, absolute-valued difference image and absolute-valued log-ratio image is calculated from co-registered and radiometrically corrected multi-

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Liang, Zhuoqian, Ding Pan, and Yuan Deng. "Research on the Knowledge Association Reasoning of Financial Reports Based on a Graph Network." Sustainability 12, no. 7 (2020): 2795. http://dx.doi.org/10.3390/su12072795.

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With increasingly strict supervision, the complexity of enterprises’ annual reports has increased significantly, and the size of the text corpus has grown at an enormous rate. Information fusion for financial reporting has become a research hotspot. The difficulty of this problem is in filtering the massive amount of heterogeneous data and integrating related information distributed in different locations according to decision topics. This paper proposes a Graph NetWork (GNW) model that establishes the overall connection between decentralized information, as well as a graph network generation

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Zeng, Qingxi, Dehui Liu, and Chade Lv. "UWB/Binocular VO Fusion Algorithm Based on Adaptive Kalman Filter." Sensors 19, no. 18 (2019): 4044. http://dx.doi.org/10.3390/s19184044.

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Among the existing wireless indoor positioning systems, UWB (ultra-wideband) is one of the most promising solutions. However, the single UWB positioning system is affected by factors such as non-line of sight and multipath, and the navigation accuracy will decrease. In order to make up for the shortcomings of a single UWB positioning system, this paper proposes a scheme based on binocular VO (visual odometer) and UWB sensor fusion. In this paper, the original distance measurement data of UWB and the position information of binocular VO are merged by adaptive Kalman filter, and the structural d

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Efatmaneshnik, Mahmoud, Allison Kealy, Asghar Tabatabei Balaei, and Andrew G. Dempster. "Information Fusion for Localization Within Vehicular Networks." Journal of Navigation 64, no. 3 (2011): 401–16. http://dx.doi.org/10.1017/s0373463311000075.

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Cooperative positioning (CP) is a localization technique originally developed for use across wireless sensor networks. With the emergence of Dedicated Short Range Communications (DSRC) infrastructure for use in Intelligent Transportation Systems (ITS), CP techniques can now be adapted for use in location determination across vehicular networks. In vehicular networks, the technique of CP fuses GPS positions with additional sensed information such as inter-vehicle distances between the moving vehicles to determine their location within a neighbourhood. This paper presents the results obtained fr

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Xue, Ying Hua, and Jing Li. "Distributed Information Fusion Structure Based on Data Fusion Tree." Advanced Materials Research 225-226 (April 2011): 488–91. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.488.

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A distributed information fusion structure based on data fusion tree is built to realize precise localization and efficient navigation for the mobile robot. The multi-class, multi-level information from robot and environment is fused using different algorithms in different levels, and make the robot have a deeper understanding to the whole environment. Experiments demonstrate that the new model proposed in the paper can improve the positioning precision of robot greatly, and the search efficiency and success rate are also better than traditional mode.

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Wang, Xian Wei, and Fu Cheng Cao. "Research on Data Fusion Technology of Body Posture Detection Based on Kalman Filter." Applied Mechanics and Materials 668-669 (October 2014): 1003–6. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1003.

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This paper discusses the body posture detection problem using low cost Micro-Electro-Mechanical System (MEMS) inertial sensors, for which a complementary sensor fusion solution is proposed. Considering the impact from the noise and bias drifts, through Kalman filter to complete the multi-sensor information fusion, achieved an accurate attitude determination. The experimental results show that, after using Kalman filtering algorithm to fuse acceleration sensor and signal gyroscope, it can effectively eliminate the accumulative error and significantly better dynamic characteristics of attitude a

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Mao, Yi, Yi Yang, and Yuxin Hu. "Research into a Multi-Variate Surveillance Data Fusion Processing Algorithm." Sensors 19, no. 22 (2019): 4975. http://dx.doi.org/10.3390/s19224975.

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Targeted information sources include radar and ADS (Automatic Dependent Surveillance) for civil ATM (Air Traffic Management) systems, and the new navigation system based on satellites has the capability of global coverage. In order to solve the surveillance problem in mid-and-high altitude airspace and approaching airspace, this paper proposes a filter-based covariance matrix weighting method, measurement variance weighting method, and measurement-first weighted fusion method weighting integration algorithm to improve the efficiency of data integration calculation under fixed accuracy. Besides

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Romanovas, Michailas, Lasse Klingbeil, Martin Traechtler, and Yiannos Manoli. "APPLICATION OF FRACTIONAL SENSOR FUSION ALGORITHMS FOR INERTIAL MEMS SENSING." Mathematical Modelling and Analysis 14, no. 2 (2009): 199–209. http://dx.doi.org/10.3846/1392-6292.2009.14.199-209.

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The work presents an extension of the conventional Kalman filtering concept for systems of fractional order (FOS). Modifications are introduced using the Grünwald‐Letnikov (GL) definition of the fractional derivative (FD) and corresponding truncation of the history length. Two versions of the fractional Kalman filter (FKF) are shown, where the FD is calculated directly or by augmenting the state vector with the estimate of the FD. The filters are compared to conventional integer order (IO) Position (P‐KF) and Position‐Velocity (PV‐KF) Kalman filters as well as to an adaptive Interacting Multip

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