Abstract
A common measurement model for locating a mobile source is time-of-arrival (TOA). However, when non-line-of-sight (NLOS) bias error exists, the error can seriously degrade the estimation accuracy. This paper formulates the problem of estimating a mobile source position under the NLOS situation as a nonlinear constrained optimization problem. Afterwards, we apply the concept of Lagrange programming neural networks (LPNNs) to solve the problem. In order to improve the stability at the equilibrium point, we add an augmented term into the LPNN objective function. Simulation results show that the proposed method provides much robust estimation performance.
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Acknowledgement
The work was supported by GRF from Hong Kong (Project No.: CityU 115612).
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Han, ZF., Leung, CS., So, H.C., Sum, J., Constantinides, A.G. (2015). Non-Line-of-Sight Mitigation via Lagrange Programming Neural Networks in TOA-Based Localization. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_22
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DOI: https://doi.org/10.1007/978-3-319-26555-1_22
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