KMS Of Academy of mathematics and systems sciences, CAS
| Optimal estimation of sensor biases for asynchronous multi-sensor data fusion | |
Pu, Wenqiang1; Liu, Ya-Feng2 ; Yan, Junkun1; Liu, Hongwei1; Luo, Zhi-Quan3
| |
| 2018-07-01 | |
| Source Publication | MATHEMATICAL PROGRAMMING
 |
| ISSN | 0025-5610 |
| Volume | 170Issue:1Pages:357-386 |
| Abstract | An important step in a multi-sensor surveillance system is to estimate sensor biases from their noisy asynchronous measurements. This estimation problem is computationally challenging due to the highly nonlinear transformation between the global and local coordinate systems as well as the measurement asynchrony from different sensors. In this paper, we propose a novel nonlinear least squares formulation for the problem by assuming the existence of a reference target moving with an (unknown) constant velocity. We also propose an efficient block coordinate decent (BCD) optimization algorithm, with a judicious initialization, to solve the problem. The proposed BCD algorithm alternately updates the range and azimuth bias estimates by solving linear least squares problems and semidefinite programs. In the absence of measurement noise, the proposed algorithm is guaranteed to find the global solution of the problem and the true biases. Simulation results show that the proposed algorithm significantly outperforms the existing approaches in terms of the root mean square error. |
| Keyword | Block coordinate decent algorithm Nonlinear least squares Sensor registration problem Tightness of semidefinite relaxation |
| DOI | 10.1007/s10107-018-1304-2 |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China (NSFC) Key Project Grant[61731018] ; NSFC[11331012] ; NSFC[11631013] ; NSFC[61601340] ; China National Funds for Distinguished Young Scientists Grant[61525105] |
| WOS Research Area | Computer Science ; Operations Research & Management Science ; Mathematics |
| WOS Subject | Computer Science, Software Engineering ; Operations Research & Management Science ; Mathematics, Applied |
| WOS ID | WOS:000435787200014 |
| Publisher | SPRINGER HEIDELBERG |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/30614 |
| Collection | 计算数学与科学工程计算研究所 |
| Corresponding Author | Luo, Zhi-Quan |
| Affiliation | 1.Xidian Univ, Natl Lab Radar Signal Proc, Collaborat Innovat Ctr Informat Sensing & Underst, Xian 710071, Shaanxi, Peoples R China 2.Chinese Acad Sci, State Key Lab Sci & Engn Comp, Inst Computat Math & Sci Engn Comp, Acad Math & Syst Sci, Beijing 100190, Peoples R China 3.Chinese Univ Hong Kong, Shenzhen Res Inst Big Data, Shenzhen 518172, Peoples R China |
| Recommended Citation GB/T 7714 | Pu, Wenqiang,Liu, Ya-Feng,Yan, Junkun,et al. Optimal estimation of sensor biases for asynchronous multi-sensor data fusion[J]. MATHEMATICAL PROGRAMMING,2018,170(1):357-386. |
| APA | Pu, Wenqiang,Liu, Ya-Feng,Yan, Junkun,Liu, Hongwei,&Luo, Zhi-Quan.(2018).Optimal estimation of sensor biases for asynchronous multi-sensor data fusion.MATHEMATICAL PROGRAMMING,170(1),357-386. |
| MLA | Pu, Wenqiang,et al."Optimal estimation of sensor biases for asynchronous multi-sensor data fusion".MATHEMATICAL PROGRAMMING 170.1(2018):357-386. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment