.png){kind=icon}
返回
Maneuvering extended object tracking based on constrained expectation maximization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2022.108729.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper addresses the problem of maneuvering extended object tracking, in which the object extension (OE) and the turn rate are identified simultaneously. Due to its non-linearity with respect to the turn rate, the state transition is converted into a linear form of a newly defined hyper-parametric vector by parameter substitution. For the hyper-parametric equality constraints (HECs) introduced in the transformation, a constrained expectation conditional maximization algorithm is designed. The HECs are projected onto the conditional expectation function for regularization, so as to realize the iterative identification of multi-parameter (i.e., OE and turn rate). The transformation and CECM optimization bring the advantage of avoiding nonlinear model approximation, which is important for the convergence and accuracy of state estimation. Finally, simulation results demonstrate the superiority of the proposed method in terms of both estimation accuracy and identification effectiveness. (C) 2022 Elsevier B.V. All rights reserved.
Keyword:
Object extension
Constrained expectation maximization
Joint identification and estimation
Maneuvering extended object tracking
期刊
IF:
3.6
论文数:
10.0K
被引数:
1.7W
机构
引用论文
A Constant Speed Changing Rate and Constant Turn Rate Model for Maneuvering Target Tracking.png)
SENSORS
OAAI
SENSORSIF3.5
OAAI分享收藏分享收藏
Structure modeling and estimation of multiple resolvable group targets via graph theory and multi-Bernoulli filterAUTOMATICA
AUTOMATICAIF5.9
PREAI
分享收藏A survey on joint tracking using expectation-maximization based techniquesINFORMATION FUSIONOAAI
INFORMATION FUSIONIF15.5
OAAI分享收藏Recursive Noise Adaptive Extended Object Tracking by Variational Bayesian ApproximationIEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容