Collaborative self-Localization and Target Tracking under Sparse Communication

被引：0
|
作者：
Lyu, Yang [1 ]
Pan, Quan [1 ]
Hu, Jinwen [1 ]
Zhao, Chunhui [1 ]
Li, Zhuoyi [1 ]
Zhang, Houxin [1 ]
机构：
[1] Northwestern Polytech Univ, Sch Automat, Key Lab Informat Fus, Minist Educ, Xian 710072, Shaanxi, Peoples R China
来源：
2018 15TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV) | 2018年
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
CONSENSUS; FILTER; NETWORKS;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The problem of collaborative self-localization and target tracking method under challenge environment is studied in this paper. Specifically, the scenario with general nonlinear process and sensing model as well as sparse communication is considered by combining the distributed tracking (DT) and the collaborative localization (CL) techniques. To better characterize the statistics after nonlinear transformations, the unscented transformation (UT) approach is adopted. Simulations are extensively studied to show that the proposed method have better performance on both self-localization and target tracking than the solo CL or DT method.
引用
收藏
页码：2020 / 2025
页数：6
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