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Optimal Estimation of Sensor Biases for Asynchronous Multi-Sensor Data Fusion

发布时间:2018年09月13日 10:16   浏览次数:
报告人 刘亚锋 副研究员 (中国科学院) 年月 2018-09
14

报告题目:Optimal Estimation of Sensor Biases for Asynchronous Multi-Sensor Data Fusion

报告人:刘亚锋 副研究员

报告人单位:中国科学院数学与系统科学研究院

报告时间:9月14日(周五)下午4:30-5:30

报告地点:数学学院东212

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 talk, we propose a novel nonlinear least squares (LS) 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 LS problems and semidefinite programs (SDPs). 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 (RMSE).

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