Results 21 to 30 of about 1,121,998 (275)
Pedestrian Group Tracking Using The Gm-Phd Filter
, 2013 CUAS
Edman, Viktor +3 more
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Parallel particle-PHD filter [PDF]
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014 The complexity of multi-target tracking grows faster than linearly with the increase of the numbers of objects, thus making the design of real-time trackers a challenging task for scenarios with al arge number of targets. The Probability Hypothesis Density (PHD )fi lter is known to help reducing this complexity.
Marco Del Coco, Andrea Cavallaro
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MULTI-TARGET DETECTION FROM FULL-WAVEFORM AIRBORNE LASER SCANNER USING PHD FILTER [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 We propose a new technique to detect multiple targets from full-waveform airborne laser scanner. We introduce probability hypothesis density (PHD) filter, a type of Bayesian filtering, by which we can estimate the number of targets and their positions ...
T. Fuse, D. Hiramatsu, W. Nakanishi
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Marginalized PHD Filters for multi-target filtering [PDF]
2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA), 2012 Multi-target filtering aims at tracking an unknown number of targets from a set of observations. The Probability Hypothesis Density (PHD) Filter is a promising solution but cannot be implemented exactly. Suboptimal implementation techniques include Gaussian Mixture (GM) solutions, which hold only in linear and Gaussian models, and Sequential Monte ...
Yohan Petetin, François Desbouvries
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A Sector-Matching Probability Hypothesis Density Filter for Radar Multiple Target Tracking
Applied Sciences, 2023 The development of high-tech, dim, small targets, such as drones and cruise missiles, brings great challenges to radar multi-target tracking (MTT), making it necessary to extend the beam dwell time to obtain a high signal-to-noise ratio (SNR).
Jialin Yang +6 more
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Robust adaptive multi‐target tracking with unknown measurement and process noise covariance matrices
IET Radar, Sonar & Navigation, 2022 A robust adaptive probability hypothesis density (PHD) filter is proposed to address the degradation of PHD performance due to an unknown process noise and measurement noise covariance matrix.
Peng Gu, Zhongliang Jing, Liangbin Wu
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Unscented Auxiliary Particle Filter Implementation of the Cardinalized Probability Hypothesis Density Filters [PDF]
AUT Journal of Electrical Engineering, 2017 The probability hypothesis density (PHD) filter suffers from lack of precise estimation of the expected number of targets. The Cardinalized PHD (CPHD) recursion, as a generalization of the PHD recursion, remedies this flaw and simultaneously propagates ...
M. R. Danaee, F. Behnia
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SLAM with single cluster PHD filters [PDF]
2012 IEEE International Conference on Robotics and Automation, 2012 Recent work by Mullane, Vo, and Adams has re-examined the probabilistic foundations of feature-based Simultaneous Localization and Mapping (SLAM), casting the problem in terms of filtering with random finite sets. Algorithms were developed based on Probability Hypothesis Density (PHD) filtering techniques that provided superior performance to leading ...
Chee Sing Lee 0001 +2 more
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Measurement + Control, 2021 Under the Gaussian noise assumption, the probability hypothesis density (PHD) filter represents a promising tool for tracking a group of moving targets with a time-varying number.
Weijun Xu
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Multi-target Tracking Method Based on GM-PHD Filtering with Weight Constraint [PDF]
Jisuanji gongcheng, 2017 Concerning that the Gaussian Mixture Probability Hypothesis Density(GM-PHD) filter does not check one-to-one assumption and it is difficult to track crossing targets,an improved multi-target tracking method with weight constraint is proposed based on GM ...
ZHAO Yifeng
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