Results 21 to 30 of about 355 (164)

Adaptive grid‐driven probability hypothesis density filter for multi‐target tracking

IET Signal Processing, 2021
The probability hypothesis density (PHD) filter and its cardinalised version PHD (CPHD) have been demonstratedasa class of promising algorithms for multi‐target tracking (MTT) with unknown,time‐varying number of targets.
Jinlong Yang, Jiuliu Tao, Yuan Zhang
doaj   +2 more sources

Computationally-Tractable Approximate PHD and CPHD Filters for Superpositional Sensors [PDF]

IEEE Journal of Selected Topics in Signal Processing, 2013
In this paper we derive computationally-tractable approximations of the Probability Hypothesis Density (PHD) and Cardinalized Probability Hypothesis Density (CPHD) filters for superpositional sensors with Gaussian noise. We present implementations of the filters based on auxiliary particle filter approximations.
Santosh Nannuru, Mark Coates, Ronald P. S. Mahler   +2 more
openaire   +3 more sources

A Robust TCPHD Filter for Multi-Sensor Multitarget Tracking Based on a Gaussian–Student’s t-Mixture Model

Remote Sensing
To realize multitarget trajectory tracking under non-Gaussian heavy-tailed noise, we propose a Gaussian–Student t-mixture distribution-based trajectory cardinality probability hypothesis density filter (GSTM-TCPHD).
Shaoming Wei, Yingbin Lin, Jun Wang, Yajun Zeng, Fangrui Qu, Xuan Zhou, Zhuotong Lu   +6 more
doaj   +2 more sources

Trajectory PHD and CPHD Filters for the Pulse Doppler Radar

Remote Sensing
Different from the standard probability hypothesis density (PHD) and cardinality probability hypothesis density (CPHD) filters, the trajectory PHD (TPHD) and trajectory CPHD (TCPHD) filters employ the sets of trajectories rather than the sets of the ...
Mei Zhang, Yongbo Zhao, Ben Niu
doaj   +2 more sources

Hybrid multi-Bernoulli CPHD filter for superpositional sensors [PDF]

Proceedings of SPIE, 2014
We propose, for the super-positional sensor scenario, a hybrid between the multi-Bernoulli filter and the cardinal­ized probability hypothesis density (CPHD) filter. We use a multi-Bernoulli random finite set (RFS) to model existing targets and we use an independent and identically distributed cluster (IIDC) RFS to model newborn ...
Santosh Nannuru, Mark Coates
exaly   +2 more sources

A general cardinalized probability hypothesis density filter

EURASIP Journal on Advances in Signal Processing, 2022
Based on random finite set, the probability hypothesis density (PHD) filter and the cardinalized PHD (CPHD) filter have been proposed for multitarget tracking as they are computational tractable.
Xinglin Shen, Zhiyong Song, Hongqi Fan, Qiang Fu   +3 more
doaj   +1 more source

GCI Fusion-Based Anti-Deception Jamming Algorithm for Distributed Radar [PDF]

Hangkong bingqi, 2022
Aiming at the anti-jamming problem of distributed radar against multiple false targets, an anti-jamming algorithm based on data level fusion is proposed. Firstly, the cardinalized probability hypothesis density (CPHD) filter based on random finite set is
Zhu Yongfeng, Da Kai, Yang Ye
doaj   +1 more source

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
doaj   +1 more source

Distributed GM-CPHD Filter Based on Generalized Inverse Covariance Intersection

IEEE Access, 2021
In this paper, we propose a distributed Gaussian mixture cardinalized probability hypothesis density (GM-CPHD) filter based on generalized inverse covariance intersection that fuses multiple node information effectively for multi-target tracking ...
Woo Jung Park, Chan Gook Park
doaj   +1 more source

High Expression of IGSF10 Confers an Inhibitory Effect on the Progression of Lung Adenocarcinoma. [PDF]

J Cell Mol Med
ABSTRACT Lung cancer is one of the most frequently diagnosed cancers and the leading cause of cancer‐related deaths worldwide. Unlike conventional treatments, the targeted therapies or emerging immunotherapies have shown significant advantages in the management of advanced lung cancer.
Cheng L, Ma B, Zhao Y, Qin C, Jiang L, Ling B.   +5 more
europepmc   +2 more sources