Results 21 to 30 of about 480,134 (154)

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 ...
Mark Coates, Santosh Nannuru
exaly   +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   +3 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

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   +1 more source

Multitarget Tracking Using One Time Step Lagged Delta-Generalized Labeled Multi-Bernoulli Smoothing

IEEE Access, 2020
Aiming at improving the tracking performance of the delta-generalized labeled multi-Bernoulli (δ-GLMB) filter, we present a one time step lagged δ-GLMB smoother in this work, which also inherently outputs targets trajectories and differs ...
Guolong Liang, Quanrui Li, Bin Qi, Longhao Qiu   +3 more
doaj   +1 more source

Multiple‐model generalised labelled multi‐Bernoulli filter with distributed sensors for tracking manoeuvring targets using belief propagation

IET Radar, Sonar &Navigation, Volume 17, Issue 5, Page 845-858, May 2023., 2023
The proposed implementation of the multi‐sensor multi‐target tracking filter is composed of two parts including centralised fusion for distributed sensors and centralised tracking using belief propagation algorithm. The local posteriors are centrally fused to obtain the global filtering density using information matrix fusion method.
Chenghu Cao, Yongbo Zhao
wiley   +1 more source

Continuous-Discrete Multiple Target Filtering: PMBM, PHD and CPHD Filter Implementations [PDF]

IEEE Transactions on Signal Processing, 2020
This article develops models and algorithms for continuous-discrete multiple target filtering, in which the multi-target system is modelled in continuous time and measurements are available at discrete time steps. In order to do so, this paper first proposes a statistical model for multi-target appearance, dynamics and disappearance in continuous time,
Ángel F. García-Fernández, Simon Maskell   +1 more
openaire   +3 more sources