Multi-Target Joint Detection; Tracking and Classification Based on Marginal GLMB Filter and Belief Function Theory

Multi-Target Joint Detection; Tracking and Classification Based on Marginal GLMB Filter and Belief Function Theory

This paper proposes a new solution to multi-target joint detection, tracking and classification based on labeled random finite set (RFS) and belief function theory. A class dependent multi-model marginal generalized labeled multi-Bernoulli (MGLMB) filter is developed to analytically calculate the mu...

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Bibliographic Details
Main Authors: Jun Liang, Minzhe Li, Zhongliang Jing, Han Pan
Format: Article
Language:English
Published: MDPI AG 2020-07-01
Series:Sensors
Subjects:
joint detection
tracking and classification
continuous transferable belief model
marginal GLMB filter
multi-model
Online Access:https://www.mdpi.com/1424-8220/20/15/4235

Internet

https://www.mdpi.com/1424-8220/20/15/4235

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