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Thematische Bibliographien / Sensor Fusion and Tracking / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Sensor Fusion and Tracking“

Autor: Grafiati

Veröffentlicht am 30. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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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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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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Xiong, Chao, Moufa Hu, Huanzhang Lu, and Fei Zhao. "Distributed Multi-Sensor Fusion for Multi-Group/Extended Target Tracking with Different Limited Fields of View." Applied Sciences 14, no. 21 (2024): 9627. http://dx.doi.org/10.3390/app14219627.

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The identification of sensing group targets and extended targets is of paramount importance in the context of vehicle tracking and early warning detection. As the scope of target monitoring and tracking extends, conventional single-sensor-based tracking techniques are proving to be inadequate in meeting the practical demands of the field. Consequently, multi-sensor fusion tracking technology has emerged as a viable alternative. However, the use of multiple sensors is constrained by their limited fields of view (FOVs), which leads to issues such as the loss of target detection and the introduct

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Wöhle, Lukas, and Marion Gebhard. "SteadEye-Head—Improving MARG-Sensor Based Head Orientation Measurements Through Eye Tracking Data." Sensors 20, no. 10 (2020): 2759. http://dx.doi.org/10.3390/s20102759.

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This paper presents the use of eye tracking data in Magnetic AngularRate Gravity (MARG)-sensor based head orientation estimation. The approach presented here can be deployed in any motion measurement that includes MARG and eye tracking sensors (e.g., rehabilitation robotics or medical diagnostics). The challenge in these mostly indoor applications is the presence of magnetic field disturbances at the location of the MARG-sensor. In this work, eye tracking data (visual fixations) are used to enable zero orientation change updates in the MARG-sensor data fusion chain. The approach is based on a

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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 meth

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Guo, Xiaoxiao, Yuansheng Liu, Qixue Zhong, and Mengna Chai. "Research on Moving Target Tracking Algorithm Based on Lidar and Visual Fusion." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 5 (2018): 593–601. http://dx.doi.org/10.20965/jaciii.2018.p0593.

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Multi-sensor fusion and target tracking are two key technologies for the environmental awareness system of autonomous vehicles. In this paper, a moving target tracking method based on the fusion of Lidar and binocular camera is proposed. Firstly, the position information obtained by the two types of sensors is fused at decision level by using adaptive weighting algorithm, and then the Joint Probability Data Association (JPDA) algorithm is correlated with the result of fusion to achieve multi-target tracking. Tested at a curve in the campus and compared with the Extended Kalman Filter (EKF) alg

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Haque, Mohammad Mahfuzul, Akbar Ghobakhlou, and Ajit Narayanan. "Multi-Tracking Sensor Architectures for Reconstructing Autonomous Vehicle Crashes: An Exploratory Study." Sensors 24, no. 13 (2024): 4194. http://dx.doi.org/10.3390/s24134194.

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With the continuous development of new sensor features and tracking algorithms for object tracking, researchers have opportunities to experiment using different combinations. However, there is no standard or agreed method for selecting an appropriate architecture for autonomous vehicle (AV) crash reconstruction using multi-sensor-based sensor fusion. This study proposes a novel simulation method for tracking performance evaluation (SMTPE) to solve this problem. The SMTPE helps select the best tracking architecture for AV crash reconstruction. This study reveals that a radar-camera-based centra

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Duan, Bo. "Sensor and sensor fusion technology in autonomous vehicles." Applied and Computational Engineering 52, no. 1 (2024): 132–37. http://dx.doi.org/10.54254/2755-2721/52/20241470.

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The perception and navigation of autonomous vehicles heavily rely on the utilization of sensor technology and the integration of sensor fusion techniques, which play an essential role in ensuring a secure and proficient understanding of the vehicle's environment.This paper highlights the significance of sensors in autonomous vehicles and how sensor fusion techniques enhance their capabilities. Firstly, the paper introduces the different types of sensors commonly used in autonomous vehicles and explains their principles of operation, strengths, and limitations in capturing essential information

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Zhu, Yipeng, Tao Wang, and Shiqiang Zhu. "Adaptive Multi-Pedestrian Tracking by Multi-Sensor: Track-to-Track Fusion Using Monocular 3D Detection and MMW Radar." Remote Sensing 14, no. 8 (2022): 1837. http://dx.doi.org/10.3390/rs14081837.

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Accurate and reliable tracking of multi-pedestrian is of great importance for autonomous driving, human-robot interaction and video surveillance. Since different scenarios have different best-performing sensors, sensor fusion perception plans are believed to have complementary modalities and be capable of handling situations which are challenging for single sensor. In this paper, we propose a novel track-to-track fusion strategy for multi-pedestrian tracking by using a millimeter-wave (MMW) radar and a monocular camera. Pedestrians are firstly tracked by each sensor according to the sensor cha

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Patino, Luis, Michael Hubner, Martin Litzenberger, and James Ferryman. "Tracking of objects in a multi-sensor fusion system for border surveillance." Journal of Defence & Security Technologies 5, no. 1 (2022): 29–43. http://dx.doi.org/10.46713/jdst.005.02.

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In this work, we present a Fusion and Tracking system developed within the EU project FOLDOUT aimed to facilitate border guards work by fusing separate sensor information and presenting automatic tracking of objects detected in the surveillance area. The focus of FOLDOUT is on through-foliage detection in the inner and outermost regions of the EU. Fusing several sensor signals increases the effectiveness of detection, particularly in forested and other areas hidden by foliage. We use weighted maps (also called Heatmaps) to combine multi-sensor information; tracking is performed on the resultin

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Takyu, Osamu, Keiichiro Shirai, Mai Ohta, and Takeo Fujii. "ID Insertion and Data Tracking with Frequency Offset for Physical Wireless Parameter Conversion Sensor Networks." Sensors 19, no. 4 (2019): 767. http://dx.doi.org/10.3390/s19040767.

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As the applications of the internet of things are becoming widely diversified, wireless sensor networks require real-time data reception, accommodation of access from several sensors, and low power consumption. In physical wireless parameter conversion sensor networks (PhyC-SN), all the sensors use frequency shift keying as the modulation scheme and then access the channel to the fusion center, simultaneously. As a result, the fusion center can recognize the statistical tendency of all the sensing results at a time from the frequency spectrum of the received signal. However, the information so

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Li, Shenglin, and Hwan-Sik Yoon. "Sensor Fusion-Based Vehicle Detection and Tracking Using a Single Camera and Radar at a Traffic Intersection." Sensors 23, no. 10 (2023): 4888. http://dx.doi.org/10.3390/s23104888.

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Recent advancements in sensor technologies, in conjunction with signal processing and machine learning, have enabled real-time traffic control systems to adapt to varying traffic conditions. This paper introduces a new sensor fusion approach that combines data from a single camera and radar to achieve cost-effective and efficient vehicle detection and tracking. Initially, vehicles are independently detected and classified using the camera and radar. Then, the constant-velocity model within a Kalman filter is employed to predict vehicle locations, while the Hungarian algorithm is used to associ

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Luo, Junhai, Zhiyan Wang, Yanping Chen, Man Wu, and Yang Yang. "An Improved Unscented Particle Filter Approach for Multi-Sensor Fusion Target Tracking." Sensors 20, no. 23 (2020): 6842. http://dx.doi.org/10.3390/s20236842.

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In this paper, a new approach of multi-sensor fusion algorithm based on the improved unscented particle filter (IUPF) and a new multi-sensor distributed fusion model are proposed. Additionally, we employ a novel multi-target tracking algorithm that combines the joint probabilistic data association (JPDA) algorithm and the IUPF algorithm. To improve the real-time performance of the UPF algorithm for the maneuvering target, minimum skew simplex unscented transform combined with a scaled unscented transform is utilized, which significantly reduces the calculation of UPF sample selection. Moreover

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Mingxuan, Gu. "Research on Path Tracking Control Algorithm for Unmanned Driving Based on Multi-Sensor Fusion." Journal of Theory and Practice in Engineering and Technology 1, no. 4 (2024): 11–16. https://doi.org/10.5281/zenodo.14535348.

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<em>Unmanned driving technology advances so fast that the problem about path tracking control becomes one core problem in an unmanned operating system. The traditional methodology of path tracking control includes PID and LQR, which are simple but not that effective in complicated environment conditions. In recent years, high-order control approaches like model predictive control and sliding mode control have ensured better performance in path tracking, especially while facing run-time variations and nonlinear systems. However, the significant computational intricacy associated with these meth

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Sun, Sungu, Yuri Lee, and Daekyo Seo. "Target Tracking based on Kernelized Correlation Filter Using MWIR and SWIR Sensors." Journal of the Korea Institute of Military Science and Technology 26, no. 1 (2023): 22–30. http://dx.doi.org/10.9766/kimst.2023.26.1.022.

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When tracking small UAVs and drone targets in cloud clutter environments, MWIR sensors are often unable to track targets continuously. To overcome this problem, the SWIR sensor is mounted on the same gimbal. Target tracking uses sensor information fusion or selectively applies information from each sensor. In this case, parallax correction using the target distance is often used. However, it is difficult to apply the existing method to small UAVs and drone targets because the laser rangefinder's beam divergence angle is small, making it difficult to measure the distance. We propose a tracking

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Ge, Bing, and Yi Yu. "Application of Multi-Sensors Data Fusion Technology in the Theodolite Tracking System." Applied Mechanics and Materials 392 (September 2013): 783–86. http://dx.doi.org/10.4028/www.scientific.net/amm.392.783.

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The task of multi-sensors data fusion technology is to obtain more precise estimate of object state and light path than single sensor through dealing with the information from different sensors. The paper puts forward the ideal of applying the data fusion theory to O-E theodolite system, based on the data fusion theory and Kalman filter and estimate theory. At the condition of losing and covering object, the theodolite tracks object normally. The theory of multi-sensors data fusion is validated improving acquiring and tracking ability of the theodolite effectively in practice.

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Wang, Hai, Junhao Liu, Haoran Dong, and Zheng Shao. "A Survey of the Multi-Sensor Fusion Object Detection Task in Autonomous Driving." Sensors 25, no. 9 (2025): 2794. https://doi.org/10.3390/s25092794.

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Multi-sensor fusion object detection is an advanced method that improves object recognition and tracking accuracy by integrating data from different types of sensors. As it can overcome the limitations of a single sensor in complex environments, the method has been widely applied in fields such as autonomous driving, intelligent monitoring, robot navigation, drone flight and so on. In the field of autonomous driving, multi-sensor fusion object detection has become a hot research topic. To further explore the future development trends of multi-sensor fusion object detection, we introduce the ma

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Zhang, Shixue, Houfeng Wang, Liduo Song, Hongwen Li, and Shuai Liu. "A Fusion Tracking Algorithm for Electro-Optical Theodolite Based on the Three-State Transition Model." Sensors 24, no. 17 (2024): 5847. http://dx.doi.org/10.3390/s24175847.

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This study presents a novel approach to address the autonomous stable tracking issue in electro-optical theodolite operating in closed-loop mode. The proposed methodology includes a multi-sensor adaptive weighted fusion algorithm and a fusion tracking algorithm based on a three-state transition model. A refined recursive formula for error covariance estimation is developed by integrating attenuation factors and least squares extrapolation. This formula is employed to formulate a multi-sensor weighted fusion algorithm that utilizes error covariance estimation. By assigning weighted coefficients

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Zhang, Yongquan, Fan Yang, Wenbo Zhang, Aomen Shang, and Zhibin Li. "A Multi-Active and Multi-Passive Sensor Fusion Algorithm for Multi-Target Tracking in Dense Group Clutter Environments." Remote Sensing 16, no. 22 (2024): 4120. http://dx.doi.org/10.3390/rs16224120.

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Multi-target tracking (MTT) of multi-active and multi-passive sensor (MAMPS) systems in dense group clutter environments is facing significant challenges in measurement fusion. Due to the difference in measurement information characteristics in MAMPS fusion, it is difficult to effectively correlate and fuse different types of sensors’ measurements, leading to difficulty in taking full advantage of various types of sensors to improve target tracking accuracy. To this end, we present a novel MAMPS fusion algorithm, which is based on centralized measurement association fusion (MAF) and distribute

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Yao, Ya Chuan, Yi Yao, Ren Yi Zhang, and Qiang Han. "Design of the Multi-Sensor Target Tracking System Based on Data Fusion." Advanced Materials Research 219-220 (March 2011): 1407–10. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.1407.

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The paper is about designing fusion tracking system based on multi-sensor information processing which combined with data fusion. It solves the multiple sensor nodes collaborative work problems of the target tracking system, which makes wireless sensor networks can process a large number of instantaneous data in time. Its practicability becomes strong after practicing and simulating.

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Wang, Kuiwu, Qin Zhang, Guimei Zheng, and Xiaolong Hu. "Multi-Target Tracking AA Fusion Method for Asynchronous Multi-Sensor Networks." Sensors 23, no. 21 (2023): 8751. http://dx.doi.org/10.3390/s23218751.

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Aiming at the problem of asynchronous multi-target tracking, this paper studies the AA fusion optimization problem of multi-sensor networks. Firstly, each sensor node runs a PHD filter, and the measurement information obtained from different sensor nodes in the fusion interval is flood communicated into composite measurement information. The Gaussian component representing the same target is associated with a subset by distance correlation. Then, the Bayesian Cramér–Rao Lower Bound of the asynchronous multi-target-tracking error, including radar node selection, is derived by combining the comp

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Kim, Taeklim, and Tae-Hyoung Park. "Extended Kalman Filter (EKF) Design for Vehicle Position Tracking Using Reliability Function of Radar and Lidar." Sensors 20, no. 15 (2020): 4126. http://dx.doi.org/10.3390/s20154126.

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Detection and distance measurement using sensors is not always accurate. Sensor fusion makes up for this shortcoming by reducing inaccuracies. This study, therefore, proposes an extended Kalman filter (EKF) that reflects the distance characteristics of lidar and radar sensors. The sensor characteristics of the lidar and radar over distance were analyzed, and a reliability function was designed to extend the Kalman filter to reflect distance characteristics. The accuracy of position estimation was improved by identifying the sensor errors according to distance. Experiments were conducted using

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Wei, Lei, Jun Chen, Yi Ding, Fei Wang, and Jianjiang Zhou. "Adaptive Tracking of High-Maneuvering Targets Based on Multi-Feature Fusion Trajectory Clustering: LPI’s Purpose." Sensors 22, no. 13 (2022): 4713. http://dx.doi.org/10.3390/s22134713.

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Since the passive sensor has the property that it does not radiate signals, the use of passive sensors for target tracking is beneficial to improve the low probability of intercept (LPI) performance of the combat platform. However, for the high-maneuvering targets, its motion mode is unknown in advance, so the passive target tracking algorithm using a fixed motion model or interactive multi-model cannot match the actual motion mode of the maneuvering target. In order to solve the problem of low tracking accuracy caused by the unknown motion model of high-maneuvering targets, this paper firstly

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Veigas, Dr John Prakash. "Smart Helmet with Sensor Fusion and Mobile Connectivity." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 5754–59. https://doi.org/10.22214/ijraset.2025.71545.

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Abstract: The integration of sensor technologies in personal safety devices is transforming how we approach risk management in various high-risk environments. The Smart Helmet with Sensor Fusion and Mobile Connectivity provides an advanced solution for enhancing rider safety by leveraging multiple sensors like accelerometers, gyroscopes, GPS, and communication modules. This helmet detects critical events, such as accidents or falls, using data from the MPU6050 sensor (for motion tracking) and GPS data from the NEO-6M to pinpoint precise locations of incidents. Additionally, it communicates wit

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Shi, Junren, Yingjie Tang, Jun Gao, Changhao Piao, and Zhongquan Wang. "Multitarget-Tracking Method Based on the Fusion of Millimeter-Wave Radar and LiDAR Sensor Information for Autonomous Vehicles." Sensors 23, no. 15 (2023): 6920. http://dx.doi.org/10.3390/s23156920.

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Multitarget tracking based on multisensor fusion perception is one of the key technologies to realize the intelligent driving of automobiles and has become a research hotspot in the field of intelligent driving. However, most current autonomous-vehicle target-tracking methods based on the fusion of millimeter-wave radar and lidar information struggle to guarantee accuracy and reliability in the measured data, and cannot effectively solve the multitarget-tracking problem in complex scenes. In view of this, based on the distributed multisensor multitarget tracking (DMMT) system, this paper propo

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Mao, Yao, Wei Ren, Yong Luo, and Zhijun Li. "Optimal Design Based on Closed-Loop Fusion for Velocity Bandwidth Expansion of Optical Target Tracking System." Sensors 19, no. 1 (2019): 133. http://dx.doi.org/10.3390/s19010133.

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Micro-electro-mechanical system (MEMS) gyro is one of the extensively used inertia sensors in the field of optical target tracking (OTT). However, velocity closed-loop bandwidth of the OTT system is limited due to the resonance and measurement range issues of MEMS gyro. In this paper, the generalized sensor fusion framework, named the closed-loop fusion (CLF), is analyzed, and the optimal design principle of filter is proposed in detail in order to improve measurement of the bandwidth of MEMS gyro by integrating information of MEMS accelerometers. The fusion error optimization problem, which i

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Ma, Liang, Kai Xue, and Ping Wang. "Multitarget Tracking with Spatial Nonmaximum Suppressed Sensor Selection." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/148081.

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Multitarget tracking is one of the most important applications of sensor networks, yet it is an extremely challenging problem since multisensor multitarget tracking itself is nontrivial and the difficulty is further compounded by sensor management. Recently, random finite set based Bayesian framework has opened doors for multitarget tracking with sensor management, which is modelled in the framework of partially observed Markov decision process (POMDP). However, sensor management posed as a POMDP is in essence a combinatorial optimization problem which is NP-hard and computationally unacceptab

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Lee, Deok Jin, Kil To Chong, and Dong Pyo Hong. "Target Tracking in Sensor Networks Using Additive Divided Difference Information Filtering Method." Applied Mechanics and Materials 433-435 (October 2013): 503–9. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.503.

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This paper represents a new multiple sensor information fusion algorithm in distributed sensor networks using an additive divided difference information filter for nonlinear estimation and tracking applications. The newly proposed multi-sensor fusion algorithm is derived by utilizing an efficient new additive divided difference filtering algorithm with embedding statistical error propagation method into an information filtering architecture. The new additive divided difference information filter achieves not only the accurate nonlinear estimation solution, but also the flexibility of multiple

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Lv, Yong, and Hairong Zhu. "An Improved Camshift Tracking Algorithm Based on LiDAR Sensor." Journal of Sensors 2021 (November 12, 2021): 1–10. http://dx.doi.org/10.1155/2021/3353032.

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Aiming at the problems of inaccurate interaction point position, interaction point drift, and interaction feedback delay in the process of LiDAR sensor signal processing interactive system, a target tracking algorithm is proposed by combining LiDAR depth image information with color images. The algorithm first fuses the gesture detection results of the LiDAR and the visual image and uses the color information fusion algorithm of the Camshift algorithm to realize the tracking of the moving target. The experimental results show that the multi-information fusion tracking algorithm based on this p

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Girija, G., J. R. Raol, R. Appavu raj, and Sudesh Kashyap. "Tracking filter and multi-sensor data fusion." Sadhana 25, no. 2 (2000): 159–67. http://dx.doi.org/10.1007/bf02703756.

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

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Distributed multitarget tracking (MTT) is suitable for sensors with limited field of view (FoV). Generalized covariance intersection (GCI) fusion is used to solve the MTT problem based on label probability hypothesis density (PHD) filtering in this paper. Because the traditional GCI fusion only has good fusion performance for the targets in the intersection of each sensor’s FoV, and the targets outside the intersection range would be lost, this paper redivides the Gaussian components according to the FoV and distinguishes the Gaussian components of the targets inside and outside the intersecti

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Yang, Boquan, Jixiong Li, and Ting Zeng. "A Review of Environmental Perception Technology Based on Multi-Sensor Information Fusion in Autonomous Driving." World Electric Vehicle Journal 16, no. 1 (2025): 20. https://doi.org/10.3390/wevj16010020.

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Environmental perception is a key technology for autonomous driving, enabling vehicles to analyze and interpret their surroundings in real time to ensure safe navigation and decision-making. Multi-sensor information fusion, which integrates data from different sensors, has become an important approach to overcome the limitations of individual sensors. Each sensor has unique advantages. However, its own limitations, such as sensitivity to lighting, weather, and range, require fusion methods to provide a more comprehensive and accurate understanding of the environment. This paper describes multi

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Abbas, Ash Mohammad. "Target Tracking in Wireless Sensor Networks." Journal of Computer Science and Technology 21, no. 1 (2021): e8. http://dx.doi.org/10.24215/16666038.21.e8.

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A Wireless Sensor Network (WSN) consists of a group of tiny devices called sensors that communicate throughwireless links. Sensors are used to collect data about some parameters and send the collected data for furtherprocessing to a designated station. The designated station is often called command and control center (CCC),fusion center (FC), or sink. Sensors forward the collected data to their leaders or cluster heads, which in turn sendit to the centralized station. There are many applications of a WSN such as environmental monitoring, raisingalarms for fires in forests and multi-storied bui

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Sayed, Amir Hoseini, and Ashraf MohammadReza. "COMPUTATIONAL COMPLEXITY COMPARISON OF MULTI-SENSOR SINGLE TARGET DATA FUSION METHODS BY MATLAB." International Journal of Chaos, Control, Modelling and Simulation (IJCCMS) 2, no. 2 (2022): 01–08. https://doi.org/10.5281/zenodo.6785988.

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Target tracking using observations from multiple sensors can achieve better estimation performance thana single sensor. The most famous estimation tool in target tracking is Kalman filter. There are severalmathematical approaches to combine the observations of multiple sensors by use of Kalman filter. Animportant issue in applying a proper approach is computationalcomplexity. In this paper, four data fusionalgorithms based on Kalman filter are considered including three centralized and one decentralized methods. Using MATLAB, computational loads of these methods are compared while number

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Tian, Qinglin, Kevin I.-Kai Wang, and Zoran Salcic. "An INS and UWB Fusion-Based Gyroscope Drift Correction Approach for Indoor Pedestrian Tracking." Sensors 20, no. 16 (2020): 4476. http://dx.doi.org/10.3390/s20164476.

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Information fusion combining inertial navigation and radio frequency (RF) technologies, is commonly applied in indoor positioning systems (IPSs) to obtain more accurate tracking results. The performance of the inertial navigation system (INS) subsystem is affected by sensor drift over time and the RF-based subsystem aims to correct the position estimate using a fusion filter. However, the inherent sensor drift is usually not corrected during fusion, which leads to increasingly erroneous estimates over a short period of time. Among the inertial sensor drifts, gyroscope drift has the most signif

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Dasari, Mallesham, Ramanujan K. Sheshadri, Karthikeyan Sundaresan, and Samir R. Das. "RoVaR." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, no. 1 (2022): 1–25. http://dx.doi.org/10.1145/3580854.

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The plethora of sensors in our commodity devices provides a rich substrate for sensor-fused tracking. Yet, today's solutions are unable to deliver robust and high tracking accuracies across multiple agents in practical, everyday environments - a feature central to the future of immersive and collaborative applications. This can be attributed to the limited scope of diversity leveraged by these fusion solutions, preventing them from catering to the multiple dimensions of accuracy, robustness (diverse environmental conditions) and scalability (multiple agents) simultaneously. In this work, we ta

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Haas, Lukas, Arsalan Haider, Ludwig Kastner, et al. "Velocity Estimation from LiDAR Sensors Motion Distortion Effect." Sensors 23, no. 23 (2023): 9426. http://dx.doi.org/10.3390/s23239426.

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Many modern automated vehicle sensor systems use light detection and ranging (LiDAR) sensors. The prevailing technology is scanning LiDAR, where a collimated laser beam illuminates objects sequentially point-by-point to capture 3D range data. In current systems, the point clouds from the LiDAR sensors are mainly used for object detection. To estimate the velocity of an object of interest (OoI) in the point cloud, the tracking of the object or sensor data fusion is needed. Scanning LiDAR sensors show the motion distortion effect, which occurs when objects have a relative velocity to the sensor.

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Koksal, N., M. Jalalmaab, and B. Fidan. "Adaptive Linear Quadratic Attitude Tracking Control of a Quadrotor UAV Based on IMU Sensor Data Fusion." Sensors 19, no. 1 (2018): 46. http://dx.doi.org/10.3390/s19010046.

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In this paper, an infinite-horizon adaptive linear quadratic tracking (ALQT) control scheme is designed for optimal attitude tracking of a quadrotor unmanned aerial vehicle (UAV). The proposed control scheme is experimentally validated in the presence of real-world uncertainties in quadrotor system parameters and sensor measurement. The designed control scheme guarantees asymptotic stability of the close-loop system with the help of complete controllability of the attitude dynamics in applying optimal control signals. To achieve robustness against parametric uncertainties, the optimal tracking

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LIU, Lihua. "A dataset of ship target tracking and trajectory fusion in maritime surveillance." China Scientific Data 9, no. 1 (2024): 1–5. http://dx.doi.org/10.11922/11-6035.csd.2023.0149.zh.

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Real-time trajectory association and trajectory fusion in maritime surveillance pose great challenges and remain hot issues in security, regional situation monitoring, and long-range precision strikes for both military and civilian applications. High-quality datasets play a pivotal role in advancing research in target tracking and fusion technologies within this domain. This paper addresses the data requirements for technological research in target tracking and fusion, as well as the limitations of currently available datasets, including data scarcity, inadequate scene design specificity, unif

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Gu, Junyi, Artjom Lind, Tek Raj Chhetri, Mauro Bellone, and Raivo Sell. "End-to-End Multimodal Sensor Dataset Collection Framework for Autonomous Vehicles." Sensors 23, no. 15 (2023): 6783. http://dx.doi.org/10.3390/s23156783.

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Autonomous driving vehicles rely on sensors for the robust perception of their surroundings. Such vehicles are equipped with multiple perceptive sensors with a high level of redundancy to ensure safety and reliability in any driving condition. However, multi-sensor, such as camera, LiDAR, and radar systems raise requirements related to sensor calibration and synchronization, which are the fundamental blocks of any autonomous system. On the other hand, sensor fusion and integration have become important aspects of autonomous driving research and directly determine the efficiency and accuracy of

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Olufade, Michael Adewale, Emmanuel Aanu Bankole, Olayinka Olubola Victor-Igun, and Adekunle Junior. "Multimodal Sensor Fusion for Autonomous Systems: Integrating Data from Various Sensors to Improve Decision-making in Autonomous Vehicles and Robotics." Journal of Basic and Applied Research International 31, no. 4 (2025): 37–54. https://doi.org/10.56557/jobari/2025/v31i49405.

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Multimodal sensor fusion refers to the combination of data from various sensors to produce a more comprehensive and accurate understanding of the environment, enabling autonomous systems to make informed decisions. With the increasing adoption of autonomous vehicles and robotics, the need for robust and reliable sensor fusion techniques has become paramount. These systems must accurately interpret their environment, detect obstacles, and make rapid decisions to ensure safety and efficiency. Despite the numerous advantages, multimodal sensor fusion faces several challenges, including data synch

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Wang, Liu, Jian Zhao, Lijuan Shi, Yuan Liu, and Jing Zhang. "A GM-JMNS-CPHD Filter for Different-Fields-of-View Stochastic Outlier Selection for Nonlinear Motion Tracking." Sensors 24, no. 10 (2024): 3176. http://dx.doi.org/10.3390/s24103176.

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Most multi-target movements are nonlinear in the process of movement. The common multi-target tracking filtering methods directly act on the multi-target tracking system of nonlinear targets, and the fusion effect is worse under the influence of different perspectives. Aiming to determine the influence of different perspectives on the fusion accuracy of multi-sensor tracking in the process of target tracking, this paper studies the multi-target tracking fusion strategy of a nonlinear system with different perspectives. A GM-JMNS-CPHD fusion technique is introduced for random outlier selection

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