.png){kind=icon}
返回
Probability hypothesis density filter with adaptive parameter estimation for tracking multiple maneuvering targets
.png){kind=icon}
OA
AI
DOI：10.1016/j.cja.2016.09.010.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The probability hypothesis density (PHD) filter has been recognized as a promising technique for tracking an unknown number of targets. The performance of the PHD filter, however, is sensitive to the available knowledge on model parameters such as the measurement noise variance and those associated with the changes in the maneuvering target trajectories. If these parameters are unknown in advance, the tracking performance may degrade greatly. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation (APE) method in the PHD filter so that the model parameters, which may be static and/or time-varying, can be estimated jointly with target states. The resulting APE-PHD algorithm is implemented using the particle filter (PF), which leads to the PF-APE-PHD filter. Simulations show that the newly proposed algorithm can correctly identify the unknown measurement noise variances, and it is capable of tracking multiple maneuvering targets with abrupt changing parameters in a more robust manner, compared to the multi-model approaches. (C) 2016 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd.
Keyword:
Adaptive parameter estimation
Multiple target tracking
Multivariate Gaussian distribution
Particle filter
Probability hypothesis density
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:
5.7
论文数:
4.8K
被引数:
1.4W
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 lymphomaBloodOAAI
BloodIF0
OAAI分享收藏
查看更多内容