Adaptive Collaborative Gaussian Mixture Probability Hypothesis Density Filter for Multi-Target Tracking
In this paper, an adaptive collaborative Gaussian Mixture Probability Hypothesis Density (ACo-GMPHD) filter is proposed for multi-target tracking with automatic track extraction. Based on the evolutionary difference between the persistent targets and the birth targets, the measurements are adaptivel...
Main Authors: | Feng Yang, Yongqi Wang, Hao Chen, Pengyan Zhang, Yan Liang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2016-10-01
|
Series: | Sensors |
Subjects: | |
Online Access: | http://www.mdpi.com/1424-8220/16/10/1666 |
Similar Items
-
A Student’s t Mixture Probability Hypothesis Density Filter for Multi-Target Tracking with Outliers
by: Zhuowei Liu, et al.
Published: (2018-04-01) -
Box-Particle Implementation and Comparison of Cardinalized Probability Hypothesis Density Filter
by: L. Song, et al.
Published: (2016-04-01) -
Multiple Nueral Artifacts Suppression Using Gaussian Mixture Modeling and Probability Hypothesis Density Filtering
Published: (2014) -
Robust Student’s t Mixture Probability Hypothesis Density Filter for Multi-Target Tracking With Heavy-Tailed Noises
by: Zhuowei Liu, et al.
Published: (2018-01-01) -
Multi-Sensor Multi-Target Tracking Using Probability Hypothesis Density Filter
by: Long Liu, et al.
Published: (2019-01-01)