.png){kind=icon}
返回
An improved multi-target tracking algorithm based on CBMeMBer filter and variational Bayesian approximation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2013.03.027.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Random finite set (RFS) filters have been demonstrating a promising algorithm for tracking an unknown number of targets in real time. However, these methods can only be used in the multi-target tracking systems with known measurement noise variances; otherwise, their tracking performances will decline greatly. To solve this problem, an improved multi-target tracking algorithm is proposed based on the cardinality-balanced multi-target multi-Bernoulli (CBMeMBer) filter and the variational Bayesian (VB) approximation technique to recursively estimate the joint posterior distributions of the multi-target states and the time-varying measurement noise variances. First, the variational calculus method is employed to derive the multi-target estimate recursions, and then the Gaussian and inverse Gamma mixture distributions are introduced to approximate the joint posterior density, and achieve a Gaussian closed-form solution. Simulation results show that the proposed algorithm can effectively estimate the unknown measurement noise variances and has a good performance of multi-target tracking with a strong robustness. (c) 2013 Elsevier B.V. All rights reserved.
Keyword:
Target tracking
Multi-target multi-Bernoulli filter
Variational Bayesian
Inverse Gamma distribution
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
10.0K
被引数:
1.7W
机构
引用论文
Effects of the model PAH phenanthrene on immune function and oxidative stress in the haemolymph of the temperate scallop Pecten maximus.png)
Chemosphere
ChemosphereIF0
PREAI
分享收藏
R-CHOP preceded by blood-brain barrier permeabilization with engineered tumor necrosis factor-α in primary CNS lymphomaBlood
OAAI
BloodIF0
OAAI分享收藏分享收藏分享收藏
查看更多内容