Results 321 to 330 of about 7,845,678 (386)
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IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019
In this paper, a new robust Kalman filtering framework for a linear system with non-Gaussian heavy-tailed and/or skewed state and measurement noises is proposed through modeling one-step prediction and likelihood probability density functions as Gaussian
Yulong Huang +5 more
semanticscholar +1 more source
In this paper, a new robust Kalman filtering framework for a linear system with non-Gaussian heavy-tailed and/or skewed state and measurement noises is proposed through modeling one-step prediction and likelihood probability density functions as Gaussian
Yulong Huang +5 more
semanticscholar +1 more source
Multi-Frame Track-Before-Detect Algorithm for Maneuvering Target Tracking
IEEE Transactions on Vehicular Technology, 2020Multi-frame track-before-detect (MF-TBD) is a model-based batch processing method. Assuming a particular model for the evolution of target states (e.g.
Wei Yi +4 more
semanticscholar +1 more source
Conference Proceedings 1991 IEEE International Conference on Systems, Man, and Cybernetics, 2002
The neural data association (DA) process during apparent motion perception in the human visual system is investigated and principles of the associated process are examined. From the theory of human motion perception, a novel DA algorithm was developed and the algorithm was applied to the DA problem arising in radar target tracking.
K. Kim, B. Shafai
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The neural data association (DA) process during apparent motion perception in the human visual system is investigated and principles of the associated process are examined. From the theory of human motion perception, a novel DA algorithm was developed and the algorithm was applied to the DA problem arising in radar target tracking.
K. Kim, B. Shafai
openaire +1 more source
IEEE Transactions on Neural Networks and Learning Systems, 2019
This paper is concerned with the target tracking of underactuated autonomous surface vehicles with unknown dynamics and limited control torques. The velocity of the target is unknown, and only the measurements of line-of-sight range and angle are ...
Lu Liu +4 more
semanticscholar +1 more source
This paper is concerned with the target tracking of underactuated autonomous surface vehicles with unknown dynamics and limited control torques. The velocity of the target is unknown, and only the measurements of line-of-sight range and angle are ...
Lu Liu +4 more
semanticscholar +1 more source
SiamBAN: Target-Aware Tracking With Siamese Box Adaptive Network
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022Variation of scales or aspect ratios has been one of the main challenges for tracking. To overcome this challenge, most existing methods adopt either multi-scale search or anchor-based schemes, which use a predefined search space in a handcrafted way and
Zedu Chen +6 more
semanticscholar +1 more source
Adaptive Consensus-Based Distributed Target Tracking With Dynamic Cluster in Sensor Networks
IEEE Transactions on Cybernetics, 2019This paper is concerned with the target tracking problem over a filtering network with dynamic cluster and data fusion. A novel distributed consensus-based adaptive Kalman estimation is developed to track a linear moving target.
H. Zhang +4 more
semanticscholar +1 more source
2006 Proceeding of the Thrity-Eighth Southeastern Symposium on System Theory, 2006
Tracking moving targets is one of the important tasks in autonomous systems such as security and surveillance systems and unmanned aerial vehicles (UAV). When the camera system is stationery, background subtraction is a commonly used technique for segmenting out moving objects in a scene.
S.S. Polmottawegedara +2 more
openaire +1 more source
Tracking moving targets is one of the important tasks in autonomous systems such as security and surveillance systems and unmanned aerial vehicles (UAV). When the camera system is stationery, background subtraction is a commonly used technique for segmenting out moving objects in a scene.
S.S. Polmottawegedara +2 more
openaire +1 more source
Coarse-to-Fine UAV Target Tracking With Deep Reinforcement Learning
IEEE Transactions on Automation Science and Engineering, 2019The aspect ratio of a target changes frequently during an unmanned aerial vehicle (UAV) tracking task, which makes the aerial tracking very challenging.
Wei Zhang +3 more
semanticscholar +1 more source
SPIE Proceedings, 2012
Steady-state performance of a tracking filter is traditionally evaluated immediately after a track update. However, there is commonly a further delay (e.g., processing and communications latency) before the tracks can actually be used. We analyze the accuracy of extrapolated target tracks for four tracking filters: Kalman filter with the Singer ...
openaire +1 more source
Steady-state performance of a tracking filter is traditionally evaluated immediately after a track update. However, there is commonly a further delay (e.g., processing and communications latency) before the tracks can actually be used. We analyze the accuracy of extrapolated target tracks for four tracking filters: Kalman filter with the Singer ...
openaire +1 more source
Fast Tracking for Video Target Tracking
Applied Mechanics and Materials, 2013Fast video tracking can result in irregular sampling tracking problem. This paper transforms the irregular sampling measurement to the time-varying parameters and develops a model with adaptive parameters on line by the autocorrelation function of Markov random processing.
Xue Bo Jin, Jing Jing Du, Jia Bao
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