Optimal estimation of sensor biases for asynchronous multi-sensor data fusion

• 成果类型: Article; Proceedings Paper
• 署名作者: Pu, Wenqiang; Liu, Ya-Feng; Yan, Junkun; Liu, Hongwei; Luo, Zhi-Quan
• 署名单位: Xidian University; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; The Chinese University of Hong Kong, Shenzhen; Shenzhen Research Institute of Big Data
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-018-1304-2
• 发表日期: 2018
• 页码: 357-386
• 关键词: Target tracking; REGISTRATION; algorithm; errors

摘要

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.