Combined extended FIR/Kalman filtering for indoor robot localization via triangulation
- Submitting institution
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City, University of London
- Unit of assessment
- 12 - Engineering
- Output identifier
- 374
- Type
- D - Journal article
- DOI
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10.1016/j.measurement.2013.12.045
- Title of journal
- Measurement: Journal of the International Measurement Confederation
- Article number
- -
- First page
- 236
- Volume
- 50
- Issue
- 1
- ISSN
- 0263-2241
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The unbiased finite impulse response (UFIR) is combined with the Kalman filter (KF) and a hybrid algorithm, designed and applied to improve the performance of mobile robot localization in indoor environments, using the triangulation method. The importance lies in that this hybrid UFIR/KF approach combines the robustness of the UFIR filter and optimality of Kalman filtering. To solve the nonlinear localization problem, the extended UFIR (EFIR) and extended KF (EKF) algorithms are also combined. The EFIR/EKF algorithm is tested in indoor environments and is shown to produce increased accuracy and higher robustness than EFIR and EKF algorithms separately
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -