Abstract
One of the traditional models for finding the location of a mobile source is the time-of-arrival (TOA). It usually assumes that the measurement noise follow a Gaussian distribution. However, in practical, outliers are difficult to be avoided. This paper proposes an \(l_1\)-norm based objective function for alleviating the influence of outliers. Afterwards, we utilize the Lagrange programming neural network (LPNN) framework for the position estimation. As the framework requires that its objective function and constraints should be twice differentiable, we introduce an approximation for the \(l_1\)-norm term in our LPNN formulation. From the simulation result, our proposed algorithm has very good robustness.
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Wang, H., Feng, R., Leung, CS. (2016). A Robust TOA Source Localization Algorithm Based on LPNN. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9947. Springer, Cham. https://doi.org/10.1007/978-3-319-46687-3_41
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DOI: https://doi.org/10.1007/978-3-319-46687-3_41
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