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Optimal estimation of sensor biases for asynchronous multi-sensor data fusion
Pu, Wenqiang1; Liu, Ya-Feng2; Yan, Junkun1; Liu, Hongwei1; Luo, Zhi-Quan3
AbstractAn 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.
KeywordBlock coordinate decent algorithm Nonlinear least squares Sensor registration problem Tightness of semidefinite relaxation
Funding ProjectNational 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 AreaComputer Science ; Operations Research & Management Science ; Mathematics
WOS SubjectComputer Science, Software Engineering ; Operations Research & Management Science ; Mathematics, Applied
WOS IDWOS:000435787200014
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Document Type期刊论文
Corresponding AuthorLuo, Zhi-Quan
Affiliation1.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.
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