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baroshi木虫 (正式写手)
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An asynchronous sensor bias estimation algorithm utilizing targets’ positions only [ 发自手机版 https://muchong.com/3g ] |
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baroshi: 金币+5, ★★★★★最佳答案 2015-07-31 12:40:09
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baroshi: 金币+5, ★★★★★最佳答案 2015-07-31 12:40:09
心静_依然: LS-EPI+1, 感谢应助 2015-07-31 12:56:58
SCI未检索  EI已检索 Accession number:20152600974870 Accession number:20152600974870 Title: An asynchronous sensor bias estimation algorithm utilizing targets' positions only Authors: Yong, Xiaoju1 Email author yongxiaoju1987@126.com; Fang, Yangwang1 Email author ywfang2008@sohu.com; Wu, Youli1 Email author ylwu@126.com; Yang, Pengfei1 Email author pfyang@126.com Author affiliation: 1 School of Aeronautics and Astronautics Engineering, Air Force Engineering University, Xi'an, Shanxi, China Corresponding author: Yong, Xiaoju Source title: Information Fusion Abbreviated source title: Inf. Fusion Volume: 27 Issue date: June 23, 2016 Publication year: 2015 Pages: 54-63 Language: English ISSN: 15662535 Document type: Journal article (JA) Publisher: Elsevier Abstract: Bias estimation is a critical problem in multi-sensor tracking systems, and most existing research has focused on the bias estimation of synchronous sensors; however, in practical applications, sensor measurements are usually asynchronous. The primary contribution of this paper is that a novel algorithm using B-spline interpolation time registration to achieve asynchronous sensor bias estimation is proposed. First, measurements are transformed into synchronous data using the B-spline interpolation time registration method. The time registration results are expressed as weighted results of the measurements. Second, a pseudo measurement equation is created based on the synchronous data. Compared with the pseudo measurements of other algorithms that use weighting coefficients, which are calculated by the target's state, including the target's velocity and time of arrival (TOA), a pseudo measurement that only depends on the target's position can be derived. Thus, the problem of asynchronous sensor bias estimation, particularly with manoeuvring targets, can be solved effectively by the proposed algorithm. Finally, the effectiveness of the proposed algorithm is verified by simulations with the target performing s-shaped manoeuvres. Monte Carlo simulation results indicate that the Cramer-Rao lower bound (CRLB) is achievable; thus, the proposed algorithm is statistically efficient. © 2015 Elsevier B.V. All rights reserved. Number of references: 19 Main heading: Algorithms Controlled terms: Cramer-Rao bounds - Data fusion - Intelligent systems - Interpolation - Monte Carlo methods Uncontrolled terms: Asynchronous sensor - B-spline interpolations - Bias estimation - Cramer-rao lower bound - Manoeuvring target - Multisensor tracking system - Spatial registrations - Weighting coefficient Classification code: 723 Computer Software, Data Handling and Applications - 921 Mathematics - 921.6 Numerical Methods - 922.2 Mathematical Statistics DOI: 10.1016/j.inffus.2015.05.003 |
2楼2015-07-31 07:55:36