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Relevant bibliographies by topics / Sensor Fusion and Tracking

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Academic literature on the topic 'Sensor Fusion and Tracking'

Author: Grafiati

Published: 30 June 2021

Last updated: 29 July 2025

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Journal articles on the topic "Sensor Fusion and Tracking"

1

Et. al., M. Hyndhavi,. "DEVELOPMENT OF VEHICLE TRACKING USING SENSOR FUSION." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (2021): 731–39. http://dx.doi.org/10.17762/itii.v9i2.406.

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The development of vehicle tracking using sensor fusion is presented in this paper. Advanced driver assistance systems (ADAS) are becoming more popular in recent years. These systems use sensor information for real-time control. To improve the standard and robustness, especially in the presence of environmental noises like varying lighting, weather conditions, and fusion of sensors has been the center of attention in recent studies. Faced with complex traffic conditions, the single sensor has been unable to meet the security requirements of ADAS and autonomous driving. The common environment p

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Liu, Yan Ju, Chun Xiang Xie, and Jian Hui Song. "Research on Fusion Tracking Technology in Heterogeneous Multi-Sensor." Advanced Materials Research 1056 (October 2014): 158–61. http://dx.doi.org/10.4028/www.scientific.net/amr.1056.158.

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Heterogeneous multi-sensor’s fusion tracking can detect precise distance and angle to the target. For heterogeneous multi-sensor issues, radar, infrared sensor and laser sensor’s data fusion, and target tracking are studied, weighted fusion algorithm based on Lagrange and unscented kalman filter are adopted, which make date fusion and tracking filtering for target. Simulation results show that the radar / infrared / laser sensors can realize data fusion and tracking to the target, and the accuracy is significantly higher than radar and infrared/laser, and then tracking effect is better.

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3

Senel, Numan, Gordon Elger, Klaus Kefferpütz, and Kristina Doycheva. "Multi-Sensor Data Fusion for Real-Time Multi-Object Tracking." Processes 11, no. 2 (2023): 501. http://dx.doi.org/10.3390/pr11020501.

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Sensor data fusion is essential for environmental perception within smart traffic applications. By using multiple sensors cooperatively, the accuracy and probability of the perception are increased, which is crucial for critical traffic scenarios or under bad weather conditions. In this paper, a modular real-time capable multi-sensor fusion framework is presented and tested to fuse data on the object list level from distributed automotive sensors (cameras, radar, and LiDAR). The modular multi-sensor fusion architecture receives an object list (untracked objects) from each sensor. The fusion fr

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Wang, Kuiwu, Qin Zhang, and Xiaolong Hu. "Improved Distributed Multisensor Fusion Method Based on Generalized Covariance Intersection." Journal of Sensors 2022 (October 28, 2022): 1–22. http://dx.doi.org/10.1155/2022/6348938.

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In response to the multitarget tracking problem of distributed sensors with a limited detection range, a distributed sensor measurement complementary Gaussian component correlation GCI fusion tracking method is proposed on the basis of the probabilistic hypothesis density filtering tracking theory. First, the sensor sensing range is extended by complementing the measurements. In this case, the multitarget density product is used to classify whether the measurements belong to the intersection region of the detection range. The local intersection region is complemented only once to reduce the co

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Lee, Seokwon, Zongjian Yuan, Ivan Petrunin, and Hyosang Shin. "Impact Analysis of Time Synchronization Error in Airborne Target Tracking Using a Heterogeneous Sensor Network." Drones 8, no. 5 (2024): 167. http://dx.doi.org/10.3390/drones8050167.

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This paper investigates the influence of time synchronization on sensor fusion and target tracking. As a benchmark, we design a target tracking system based on track-to-track fusion architecture. Heterogeneous sensors detect targets and transmit measurements through a communication network, while local tracking and track fusion are performed in the fusion center to integrate measurements from these sensors into a fused track. The time synchronization error is mathematically modeled, and local time is biased from the reference clock during the holdover phase. The influence of the time synchroni

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Qin, Y., Xue Hui Wang, Ming Jun Feng, Zhen Zhou, and L. J. Wang. "Research of Asynchronous Multi-Type Sensors Data Fusion." Advanced Materials Research 142 (October 2010): 16–20. http://dx.doi.org/10.4028/www.scientific.net/amr.142.16.

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A data fusion algorithm was established for estimating the state of target tracking system with multi-type sensor. Through Kalman filter regarding the multi-sensors to computer goal estimated value, it can obtain estimation value of goal at moment. And mean square deviation of fusion estimation value was smaller than single sensor's mean square deviation. The simulation results indicated that synchronisms data fusion method was effective to the multi-target tracking problem. Asynchronous multi-sensor fusion process can obtain good control effect in the practice control process.

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Shi, Yifang, Jee Woong Choi, Lei Xu, Hyung June Kim, Ihsan Ullah, and Uzair Khan. "Distributed Target Tracking in Challenging Environments Using Multiple Asynchronous Bearing-Only Sensors." Sensors 20, no. 9 (2020): 2671. http://dx.doi.org/10.3390/s20092671.

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In the multiple asynchronous bearing-only (BO) sensors tracking system, there usually exist two main challenges: (1) the presence of clutter measurements and the target misdetection due to imperfect sensing; (2) the out-of-sequence (OOS) arrival of locally transmitted information due to diverse sensor sampling interval or internal processing time or uncertain communication delay. This paper simultaneously addresses the two problems by proposing a novel distributed tracking architecture consisting of the local tracking and central fusion. To get rid of the kinematic state unobservability proble

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Chen, Bin, Xiaofei Pei, and Zhenfu Chen. "Research on Target Detection Based on Distributed Track Fusion for Intelligent Vehicles." Sensors 20, no. 1 (2019): 56. http://dx.doi.org/10.3390/s20010056.

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Accurate target detection is the basis of normal driving for intelligent vehicles. However, the sensors currently used for target detection have types of defects at the perception level, which can be compensated by sensor fusion technology. In this paper, the application of sensor fusion technology in intelligent vehicle target detection is studied with a millimeter-wave (MMW) radar and a camera. The target level fusion hierarchy is adopted, and the fusion algorithm is divided into two tracking processing modules and one fusion center module based on the distributed structure. The measurement

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Kassem AL-Attabi. "Integration of Sensor Array Techniques for Efficient Tracking of Approaching Individuals and Objects: A Technological Perspective." Journal of Information Systems Engineering and Management 10, no. 4 (2025): 163–76. https://doi.org/10.52783/jisem.v10i4.8793.

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Conventional object tracking systems are confronted with issues regarding accuracy, noise filtering, and object categorization. This work overcomes these issues by integrating several sensor modalities, such as LiDAR, RADAR, Infrared (IR), and Ultrasonic sensors, within a single tracking framework. The research aims to improve tracking accuracy via weighted sensor fusion, apply an optimized Kalman filter for noise filtering, and utilize DBSCAN clustering for optimized object classification. Performance is measured in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Signal

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Yi, Chunlei, Kunfan Zhang, and Nengling Peng. "A multi-sensor fusion and object tracking algorithm for self-driving vehicles." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233, no. 9 (2019): 2293–300. http://dx.doi.org/10.1177/0954407019867492.

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Vehicles need to detect threats on the road, anticipate emerging dangerous driving situations and take proactive actions for collision avoidance. Therefore, the study on methods of target detection and recognition are of practical value to a self-driving system. However, single sensor has its weakness, such as poor weather adaptability with lidar and camera. In this article, we propose a novel spatial calibration method based on multi-sensor systems, and the approach utilizes rotation and translation of the coordinate system. The validity of the proposed spatial calibration method is tested th

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Dissertations / Theses on the topic "Sensor Fusion and Tracking"

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Mathew, Vineet. "Radar and Vision Sensor Fusion for Vehicle Tracking." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1574441839857988.

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Sikdar, Ankita. "Depth based Sensor Fusion in Object Detection and Tracking." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1515075130647622.

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Moemeni, Armaghan. "Hybrid marker-less camera pose tracking with integrated sensor fusion." Thesis, De Montfort University, 2014. http://hdl.handle.net/2086/11093.

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This thesis presents a framework for a hybrid model-free marker-less inertial-visual camera pose tracking with an integrated sensor fusion mechanism. The proposed solution addresses the fundamental problem of pose recovery in computer vision and robotics and provides an improved solution for wide-area pose tracking that can be used on mobile platforms and in real-time applications. In order to arrive at a suitable pose tracking algorithm, an in-depth investigation was conducted into current methods and sensors used for pose tracking. Preliminary experiments were then carried out on hybrid GPS-

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Lundquist, Christian. "Sensor Fusion for Automotive Applications." Doctoral thesis, Linköpings universitet, Reglerteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-71594.

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Mapping stationary objects and tracking moving targets are essential for many autonomous functions in vehicles. In order to compute the map and track estimates, sensor measurements from radar, laser and camera are used together with the standard proprioceptive sensors present in a car. By fusing information from different types of sensors, the accuracy and robustness of the estimates can be increased. Different types of maps are discussed and compared in the thesis. In particular, road maps make use of the fact that roads are highly structured, which allows relatively simple and powerful model

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Romine, Jay Brent. "Fusion of radar and imaging sensor data for target tracking." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/13324.

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Morton, Peter Michael. "Multi-target tracking using appearance models for identity maintenance." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/11644.

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This thesis considers perception systems for urban environments. It focuses on the task of tracking dynamic objects and in particular on methods that can maintain the identities of targets through periods of ambiguity. Examples of such ambiguous situations occur when targets interact with each other, or when they are occluded by other objects or the environment. With the development of self driving cars, the push for autonomous delivery of packages, and an increasing use of technology for security, surveillance and public-safety applications, robust perception in crowded urban spaces is more

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Moody, Leigh. "Sensors, measurement fusion and missile trajectory optimisation." Thesis, Cranfield University; College of Defence Technology; Department of Aerospace, Power and Sensors, 2003. http://hdl.handle.net/1826/778.

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When considering advances in “smart” weapons it is clear that air-launched systems have adopted an integrated approach to meet rigorous requirements, whereas air-defence systems have not. The demands on sensors, state observation, missile guidance, and simulation for air-defence is the subject of this research. Historical reviews for each topic, justification of favoured techniques and algorithms are provided, using a nomenclature developed to unify these disciplines. Sensors selected for their enduring impact on future systems are described and simulation models provided. Complex internal sys

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Andersson, Naesseth Christian. "Vision and Radar Sensor Fusion for Advanced Driver Assistance Systems." Thesis, Linköpings universitet, Reglerteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-94222.

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The World Health Organization predicts that by the year 2030, road traffic injuries will be one of the top five leading causes of death. Many of these deaths and injuries can be prevented by driving cars properly equipped with state-of-the-art safety and driver assistance systems. Some examples are auto-brake and auto-collision avoidance which are becoming more and more popular on the market today. A recent study by a Swedish insurance company has shown that on roadswith speeds up to 50 km/h an auto-brake system can reduce personal injuries by up to 64 percent. In fact in an estimated 40 perce

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Andersson, Anton. "Offline Sensor Fusion for Multitarget Tracking using Radar and Camera Detection." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208344.

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Autonomous driving systems are rapidly improving and may have the ability to change society in the coming decade. One important part of these systems is the interpretation of sensor information into trajectories of objects. In this master’s thesis, we study an energy minimisation method with radar and camera measurements as inputs. An energy is associated with the trajectories; this takes the measurements, the objects’ dynamics and more factors into consideration. The trajectories are chosen to minimise this energy, using a gradient descent method. The lower the energy, the better the trajecto

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Attalla, Daniela, and Alexandra Tang. "Drones in Arctic Environments: Snow Change Tracking Aid using Sensor Fusion." Thesis, KTH, Mekatronik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235928.

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The Arctic is subject to rapid climate changes that canbe difficult to track. This thesis aims to provide a user case in which researchers in the Arctic benefit from the incorporation of drones in their snow ablation research. This thesis presents a way to measure ablation stakes with the help of a sensor fusion system mounted on a drone. Ablation stakes are stakes placed on a grid over glaciers in the Arctic, below the snow and ice surface, during the winter and then measured during the summer to keep track of the amount of snow that has melted throughout the mass balance year. Each measureme

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Books on the topic "Sensor Fusion and Tracking"

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Koch, Wolfgang. Tracking and Sensor Data Fusion. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-39271-9.

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Kim, Kyungsu. A comparison of nonlinear filters and multi-sensor fusion for tracking boost-phase ballistic missiles. Naval Postgraduate School, 2009.

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International Conference on Information Fusion (7th 2005 Philadelphia, Pa.). 2005 7th International Conference on Information Fusion (FUSION): Philadelphia, PA, 25-28 July, 2005. IEEE, 2005.

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Kadar, Ivan. Signal processing, sensor fusion, and target recognition XIX: 5-7 April 2010, Orlando, Florida, United States. SPIE, 2010.

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5

Ivan, Kadar, and Society of Photo-optical Instrumentation Engineers., eds. Signal processing, sensor fusion, and target recognition VI: 21-24 April 1997, Orlando, Florida. SPIE, 1997.

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Vibeke, Libby, Kadar Ivan, and Society of Photo-optical Instrumentation Engineers., eds. Signal processing, sensor fusion, and target recognition: 20-22 April 1992, Orlando, Florida. SPIE, 1992.

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Ivan, Kadar, and Society of Photo-optical Instrumentation Engineers., eds. Signal processing, sensor fusion, and target recognition VII: 13-15 April 1998, Orlando, Florida. SPIE, 1998.

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Ivan, Kadar, and Society of Photo-optical Instrumentation Engineers., eds. Signal processing, sensor fusion, and target recognition VIII: 5-7 April, 1999, Orlando, Florida. SPIE, 1999.

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Ivan, Kadar, Libby Vibeke, and Society of Photo-optical Instrumentation Engineers., eds. Signal processing, sensor fusion, and target recognition IV: 17-19 April 1995, Orlando, Florida. SPIE, 1995.

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Kadar, Ivan. Signal processing, sensor fusion, and target recognition XX: 25-27 April 2011, Orlando, Florida, United States. Edited by SPIE (Society). SPIE, 2011.

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Book chapters on the topic "Sensor Fusion and Tracking"

1

Koch, Wolfgang. "Integration of Advanced Sensor Properties." In Tracking and Sensor Data Fusion. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39271-9_7.

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2

Nimier, V. "Soft Sensor Management for Multisensor Tracking Algorithm." In Multisensor Fusion. Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2_15.

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Koch, Wolfgang. "Notion and Structure of Sensor Data Fusion." In Tracking and Sensor Data Fusion. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39271-9_1.

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Koch, Wolfgang. "Feed-Back to Acquisition: Sensor Management." In Tracking and Sensor Data Fusion. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39271-9_10.

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Koch, Wolfgang. "Characterizing Objects and Sensors." In Tracking and Sensor Data Fusion. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39271-9_2.

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Koch, Wolfgang. "Bayesian Knowledge Propagation." In Tracking and Sensor Data Fusion. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39271-9_3.

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Koch, Wolfgang. "Sequential Track Extraction." In Tracking and Sensor Data Fusion. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39271-9_4.

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Koch, Wolfgang. "On Recursive Batch Processing." In Tracking and Sensor Data Fusion. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39271-9_5.

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Koch, Wolfgang. "Aspects of Track-to-Track Fusion." In Tracking and Sensor Data Fusion. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39271-9_6.

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Koch, Wolfgang. "Integration of Advanced Object Properties." In Tracking and Sensor Data Fusion. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39271-9_8.

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Conference papers on the topic "Sensor Fusion and Tracking"

1

Wei, Xinwei, Linao Zhang, Yiru Lin, Jianwei Wei, Chenyu Zhang, and Wei Yi. "Transformer-based Multi-Sensor Hybrid Fusion for Multi-Target Tracking." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706284.

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Tian, Yunlian, Xi Cao, Wujun Li, and Wei Yi. "Incorporating Heading Restrictions for Multilane-Road Target Tracking Using Radar Sensor." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706343.

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Houssineau, Jeremie, Chenbao Xue, Han Cai, Murat Uney, and Emmanuel Delande. "Decentralised multi-sensor target tracking with limited field of view via possibility theory." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706352.

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Li, Qing, Runze Gan, and Simon Godsill. "Decentralised Gradient-based Variational Inference for Multi-sensor Fusion and Tracking in Clutter." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706445.

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Bondarchuk, Jennifer, Anthony Trezza, and Donald J. Bucci. "Efficient Implementation of Multi-sensor Adaptive Birth Samplers for Labeled Random Finite Set Tracking." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706456.

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Yang, Feng, Jingru Niu, Lihong Shi, and Litao Zheng. "Identification and Tracking of Multi-group Targets in Circular Formation under Multi-sensor Networks." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706507.

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Gu, Jiayi, Jiaqi Shi, Kun Shi, Chaoqun Yang, and Xianghui Cao. "IMM-Based PMBM Filter for Maneuvering Extended Target Tracking." In 2024 Sensor Data Fusion: Trends, Solutions, Applications (SDF). IEEE, 2024. https://doi.org/10.1109/sdf63218.2024.10773915.

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Florian, Grabner. "Multi-Sensor Simulation from Target Tracking to a Recognized Air Picture." In 2024 Sensor Data Fusion: Trends, Solutions, Applications (SDF). IEEE, 2024. https://doi.org/10.1109/sdf63218.2024.10773927.

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Cormack, David, and James R. Hopgood. "Sensor Registration and Tracking from Heterogeneous Sensors with Belief Propagation." In 2019 22th International Conference on Information Fusion (FUSION). IEEE, 2019. http://dx.doi.org/10.23919/fusion43075.2019.9011389.

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Helgesen, Oystein Kaarstad, Edmund Forland Brekke, Hakon Hagen Helgesen, and Oystein Engelhardtsen. "Sensor Combinations in Heterogeneous Multi-sensor Fusion for Maritime Target Tracking." In 2019 22th International Conference on Information Fusion (FUSION). IEEE, 2019. http://dx.doi.org/10.23919/fusion43075.2019.9011297.

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Reports on the topic "Sensor Fusion and Tracking"

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

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The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, s

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Norcross, Richard J. HiCASS target tracking sensor study. National Institute of Standards and Technology, 2005. http://dx.doi.org/10.6028/nist.ir.7220.

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Garg, Devendra P., and Manish Kumar. Sensor Modeling and Multi-Sensor Data Fusion. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada440553.

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Akita, Richard, Robert Pap, and Joel Davis. Biologically Inspired Sensor Fusion. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada389747.

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Baim, Paul. Dynamic Database for Sensor Fusion. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada363915.

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Meyer, David, and Jeffrey Remmel. Distributed Algorithms for Sensor Fusion. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada415039.

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Hero, III, Raich Alfred O., and Raviv. Performance-driven Multimodality Sensor Fusion. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada565491.

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ROCKWELL INTERNATIONAL ANAHEIM CA. Multi-Sensor Feature Level Fusion. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada237106.

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Connors, John J., Kevin Hill, David Hanekamp, et al. Sensor fusion for intelligent process control. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/919114.

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Carlson, J. J., A. M. Bouchard, G. C. Osbourn, et al. Sensor-fusion-based biometric identity verification. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/573302.

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