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https://doi.org/10.3390/s16050729
| Title: | Systematic error modeling and bias estimation | Authors: | Zhang, F Knoll, A |
Keywords: | Errors Systematic errors Bias Bias estimation Error model Least square methods Transformation process Weighted nonlinear least squares Least squares approximations |
Issue Date: | 2016 | Publisher: | MDPI AG | Citation: | Zhang, F, Knoll, A (2016). Systematic error modeling and bias estimation. Sensors (Switzerland) 16 (5) : 729. ScholarBank@NUS Repository. https://doi.org/10.3390/s16050729 | Rights: | Attribution 4.0 International | Abstract: | This paper analyzes the statistic properties of the systematic error in terms of range and bearing during the transformation process. Furthermore, we rely on a weighted nonlinear least square method to calculate the biases based on the proposed models. The results show the high performance of the proposed approach for error modeling and bias estimation. © 2016 by the authors; licensee MDPI, Basel, Switzerland. | Source Title: | Sensors (Switzerland) | URI: | https://scholarbank.nus.edu.sg/handle/10635/179575 | ISSN: | 1424-8220 | DOI: | 10.3390/s16050729 | Rights: | Attribution 4.0 International |
| Appears in Collections: | Elements Staff Publications |
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