%0 Book Section %1 Wang2016 %A Wang, Hao %A Feng, Ruibin %A Leung, Chi-Sing %B Neural Information Processing: 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16--21, 2016, Proceedings, Part I %C Cham %D 2016 %E Hirose, Akira %E Ozawa, Seiichi %E Doya, Kenji %E Ikeda, Kazushi %E Lee, Minho %E Liu, Derong %I Springer International Publishing %K ml4pos neuralnetwork %P 367--375 %R 10.1007/978-3-319-46687-3_41 %T A Robust TOA Source Localization Algorithm Based on LPNN %U https://doi.org/10.1007/978-3-319-46687-3_41 %X 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. %@ 978-3-319-46687-3

@inbook{Wang2016, 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.}, added-at = {2017-07-21T15:50:48.000+0200}, address = {Cham}, author = {Wang, Hao and Feng, Ruibin and Leung, Chi-Sing}, biburl = {https://www.bibsonomy.org/bibtex/2250d177864d0813f073cc3914af0bf70/alexgrimm94}, booktitle = {Neural Information Processing: 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16--21, 2016, Proceedings, Part I}, doi = {10.1007/978-3-319-46687-3_41}, editor = {Hirose, Akira and Ozawa, Seiichi and Doya, Kenji and Ikeda, Kazushi and Lee, Minho and Liu, Derong}, interhash = {dc81c4c99871f0130814b15dbcaf0eed}, intrahash = {250d177864d0813f073cc3914af0bf70}, isbn = {978-3-319-46687-3}, keywords = {ml4pos neuralnetwork}, pages = {367--375}, publisher = {Springer International Publishing}, timestamp = {2017-07-21T15:50:48.000+0200}, title = {A Robust TOA Source Localization Algorithm Based on LPNN}, url = {https://doi.org/10.1007/978-3-319-46687-3_41}, year = 2016 }