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Filters
Multi-Target State Estimation Using Interactive Kalman Filter for Multi-Vehicle Tracking
2019
IEEE transactions on intelligent transportation systems (Print)
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illustrate the effectiveness of the 17 proposed filter in vehicle tracking. 18 Index Terms-Distributed Kalman filter, interactive Kalman 19 filter, multi agent systems, multi-target state estimation, ...
20 multi-vehicle tracking, time-varying weights. 21 68 The idea of interaction between targets in MASs is used in 69 this paper to improve multi-vehicle tracking using KFs. 70 B. ...
filters corresponding to adjacent targets to optimize state 751 and estimations of the desired target in a multi-target track-752 ing environment. ...
doi:10.1109/tits.2019.2902664
fatcat:4lcqfyhanfbp3f7tqtmarptz4q
A Multi-Sensor Interacted Vehicle-Tracking Algorithm with Time-Varying Observation Error
2022
Remote Sensing
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Vehicle tracking in the field of intelligent transportation has received extensive attention in recent years. Multi-sensor-based vehicle tracking system is widely used in some critical environments. ...
Therefore, in this paper, we propose a multi-sensor interacted vehicle-tracking algorithm with time-varying observation error (MI-TVOE). ...
[16] proposed a hierarchical fusion estimation method with link failures, which used the Kalman filter algorithm to ensure the consistency of the collected data and used a CI (Covariance Interaction ...
doi:10.3390/rs14092176
fatcat:4aks4627hfgfpbdd2v5e4ifboe
Asynchronous Sensor Fusion using Multi-rate Kalman Filter
다중주기 칼만 필터를 이용한 비동기 센서 융합
2014
The Transactions of The Korean Institute of Electrical Engineers
다중주기 칼만 필터를 이용한 비동기 센서 융합
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We propose a multi-rate sensor fusion of vision and radar using Kalman filter to solve problems of asynchronized and multi-rate sampling periods in object vehicle tracking. ...
A model based prediction of object vehicles is performed with a decentralized multi-rate Kalman filter for each sensor (vision and radar sensors.) ...
We propose a multi-rate sensor fusion using a Kalman filter for object vehicle tracking. ...
doi:10.5370/kiee.2014.63.11.1551
fatcat:qbpwj7gl6ja5nijlbw7afeb7ze
An Interacting Multiple Model Approach for Target Intent Estimation at Urban Intersection for Application to Automated Driving Vehicle
2020
Applied Sciences
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In order to estimate the target intent in such situations, an interacting multiple model (IMM)-based intersection-target-intent estimation algorithm is proposed. ...
A driver model is developed to represent the driver's maneuvering on the intersection using an IMM-based target intent classification algorithm. ...
Since the IMM approach using extended Kalman filters (EKFs) for multi-target state estimation of intelligent vehicle has already been verified by Figure 4 , and in the work of Suh et al. ...
doi:10.3390/app10062138
fatcat:mnic6yorbja6bev2s75bnpl7dm
Online Multi-Target Tracking for Maneuvering Vehicles in Dynamic Road Context
[article]
2019
arXiv
pre-print
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Kalman Filter (KF) is adopted as the backbone for multi-object tracking. ...
This paper proposes an online multi-object tracking (MOT) framework with tracking-by-detection for maneuvering vehicles under motion uncertainty in dynamic road context. ...
Multi-target tracking Single target tracking (SOT) can be well addressed by Bayesian filters such as Kalman filter [1] and particle filter [2] , [3] as well as their variants; while multi-object tracking ...
arXiv:1912.00603v1
fatcat:tus4retakfexjpmsch4c5aldou
Target Tracking for Mobile Sensor Networks Using Distributed Motion Planning and Distributed Filtering
[chapter]
2011
Multi-Robot Systems, Trends and Development
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state estimation is still possible and can be used for accurate localization of the target, as well as for tracking of target's trajectory by the individual mobile sensors (autonomous vehicles), (ii) ...
, or similarly the centralized Unscented Kalman Filter since, (i) if a local filter is subject to a fault then state estimation is still possible and can be used for accurate localization of the target ...
doi:10.5772/12925
fatcat:kgfskcquhbenvfwqntpxohbv6m
Distributed multi-target tracking in a self-configuring camera network
2009
2009 IEEE Conference on Computer Vision and Pattern Recognition
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For tracking the targets as they move through the area covered by the cameras, we propose a special application of the distributed estimation algorithm known as Kalman-Consensus filter through which each ...
Combining these ideas with single-view analysis, we have a completely distributed approach for multi-target tracking and camera network self-configuration. ...
The other targets are tracked using the Kalman-Consensus filtering approach, but are not marked for clarity. ...
doi:10.1109/cvprw.2009.5206773
fatcat:jmywyr7t6fdmnc53ihmah6vddu
Distributed multi-target tracking in a self-configuring camera network
2009
2009 IEEE Conference on Computer Vision and Pattern Recognition
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For tracking the targets as they move through the area covered by the cameras, we propose a special application of the distributed estimation algorithm known as Kalman-Consensus filter through which each ...
Combining these ideas with single-view analysis, we have a completely distributed approach for multi-target tracking and camera network self-configuration. ...
The other targets are tracked using the Kalman-Consensus filtering approach, but are not marked for clarity. ...
doi:10.1109/cvpr.2009.5206773
dblp:conf/cvpr/SotoSC09
fatcat:77hxnz6x4vdbvhk55m5jtov5j4
A Novel Multi-Sensor Environmental Perception Method Using Low-Rank Representation and a Particle Filter for Vehicle Reversing Safety
2016
Sensors
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Secondly, the information fusion algorithm using an adaptive Kalman filter is used to process the data obtained with the multi-sensor environmental perception module, which greatly improves the robustness ...
In this paper, we present a novel multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety. ...
Zutao Zhang, and Yanjun Li designed the multi-sensor environmental perception module and the algorithm for information fusion and target recognition and tracking. ...
doi:10.3390/s16060848
pmid:27294931
pmcid:PMC4934274
fatcat:w7cww4o3rzejpa27oc4nskcgey
Estimation of Vehicle State Based on IMM-AUKF
2022
Symmetry
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Therefore, we propose an adaptive unscented Kalman filter algorithm based on Interacting Multiple Model (IMM) theory to estimate the state of target vehicle in the high-speed driving environment. ...
To be specific, we use the Constant Turn Rate and Acceleration (CTRA) theory to establish the target vehicle kinematics model, simultaneously, in order to overcome the problem of estimator failure when ...
In this paper, we propose an adaptive parameter Interacting Multiple Model unscented Kalman filter algorithm (IMM-AUKF) to achieve accurate real-time target vehicle state estimation performance. ...
doi:10.3390/sym14020222
fatcat:dfy4ytocqrdspb5zmwuk2jqynm
IMMPDA vehicle tracking system using asynchronous sensor fusion of radar and vision
2008
2008 IEEE Intelligent Vehicles Symposium
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This paper focuses on recognition and tracking of maneuvering vehicles in dense traffic situations. ...
We present an asynchronous multi obstacle multi sensor tracking method that fuses information from radar and monocular vision. ...
MULTI SENSOR TRACKING SYSTEM
A. Measurement transformation We use a linear Kalman filter in Cartesian coordinates. ...
doi:10.1109/ivs.2008.4621161
fatcat:ag2fwac6h5b3ze7upa4h66vuua
Performance evaluation of multi-sensor data fusion technique for test range application
2004
Sadhana (Bangalore)
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We have adopted the state-vector fusion technique for fusing multiple sensors track data to provide complete and precise trajectory information about the flight vehicle under test, for the purpose of flight ...
The present paper brings out the performance of the algorithm for different process noise and measurement noise using simulated as well as real track data. ...
The state and stateerror-covariance estimates of Kalman filter (KF) for each of the sensors are then used to obtain the fused state according to the following equations (Saha 1996) : φ θ , , , R R θ, ...
doi:10.1007/bf02703734
fatcat:sjpogfl3wbawvi73yjxqdwkvoe
Computer vision algorithms for intersection monitoring
2003
IEEE transactions on intelligent transportation systems (Print)
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A multi-level tracking approach using Kalman filter is presented for tracking vehicles and pedestrians at intersections. ...
The approach combines low-level image-based blob tracking with high-level Kalman filtering for position and shape estimation. ...
Combinations of position and shape estimation filters that interact with each other indirectly are used for tracking. ...
doi:10.1109/tits.2003.821212
fatcat:ojrmhw6z55b3ho7knagguxq7de
Papertitles
2019
2019 International Conference on Control, Automation and Information Sciences (ICCAIS)
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Multi-Robot Cooperative SLAM Based on Distributed Extended Kalman Filter
Multi-sensor Multi-target Tracking Using Labelled Random Finite Sets with Homography Data
Multi-Target Tracking based on the ...
State Prediction
M 3 A B C D E F G H I J L M N O P R S T V
Maneuvering Target Joint Detection and Tracking Using Multi-Frame Integration
Marine Target Detection Based on Improved Faster R-CNN for ...
doi:10.1109/iccais46528.2019.9074559
fatcat:srkln5llk5czjgcksdtuhlgwdy
A distributed motion planning and distributed filtering approach for target tracking in mobile sensor networks
2011
IFAC Proceedings Volumes
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The paper studies the problem of tracking of a target by a multi-robot system assuming that the target's state vector is not directly measurable and has to be estimated by distributed filtering based on ...
This estimate provides the desirable state vector to be tracked by each one of the mobile robots, (ii) motion planning and control that enables convergence of the vehicles to the goal position and also ...
SIMULATION TESTS
Estimation of target's position usign EIF The number of mobile robots used for target tracking in the simulation experiments was N = 10. ...
doi:10.3182/20110828-6-it-1002.00043
fatcat:s7vb4lkuand6rjdsmhfi6yqdo4
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