Robust Localization Based on Mixed-Norm Minimization Criterion

This paper presents robust positioning methods that use range measurements to estimate location parameters. The existing maximum correntropy criterion-based localization algorithm uses only the <inline-formula> <tex-math notation="LaTeX">l_{2} </tex-math></inline-formu...

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Bibliographic Details
Published in:IEEE access Vol. 10; pp. 57080 - 57093
Main Authors: Park, Chee-Hyun, Chang, Joon-Hyuk
Format: Journal Article
Language:English
Published: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Closed form solutions
Cost function
Criteria
Digital signal processing
distributed information systems
Estimation
Exact solutions
filtering theory
indoor navigation
iterative algorithms
Iterative methods
Kalman filters
Least squares
Line of sight
Localization
Location awareness
Mathematical analysis
Minimization
mobile communication
Noise
Noise level
Noise levels
Optimization
parameter estimation
radio navigation
Robustness
Signal processing algorithms
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:This paper presents robust positioning methods that use range measurements to estimate location parameters. The existing maximum correntropy criterion-based localization algorithm uses only the <inline-formula> <tex-math notation="LaTeX">l_{2} </tex-math></inline-formula> norm minimization. Therefore, the localization performance may not be satisfying because the <inline-formula> <tex-math notation="LaTeX">l_{2} </tex-math></inline-formula> norm minimization is vulnerable to the large error. Therefore, we propose the convex combination of <inline-formula> <tex-math notation="LaTeX">l_{1} </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">l_{2} </tex-math></inline-formula> norm because the <inline-formula> <tex-math notation="LaTeX">l_{1} </tex-math></inline-formula> norm minimization is effective in the large noise condition. The mixed-norm maximum Versoria criterion-based unscented Kalman filter, mixed-norm least lncosh unscented Kalman filter, mixed-norm maximum Versoria criterion iterative reweighted least-squares, mixed-norm least lncosh iterative reweighted least squares and closed-form localization approaches are proposed for mixed line-of-sight/ non-line-of-sight environments. The proposed mixed-norm unscented Kalman filter-based algorithms are more superior to the other methods as the line-of-sight noise level increases by the use of the convex combination of <inline-formula> <tex-math notation="LaTeX">l_{1} </tex-math></inline-formula> norm and <inline-formula> <tex-math notation="LaTeX">l_{2} </tex-math></inline-formula> norm. The iterative reweighted least sqaures-based methods employ a weight matrix. The closed-form weighted least squares algorithm has an advantage that its computational complexity is lower than that of other methods. Simulation and experiments illustrate the localization accuracies of the proposed unscented Kalman filter-based methods are found to be superior to those of the other algorithms under large noise level conditions.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3177838