Abstract
Source localization can be achieved by making use of the time-of-arrival (TOA) measurements, but it is not a trivial task because the TOAs have nonlinear relationships with the source coordinates. This paper exploits a neural network technique, namely, Lagrange programming neural networks, for TOA-based localization. We also investigate the local stability of our formulation. Simulation results demonstrate that the performance of the proposed location estimator approaches the optimality benchmark of Cram\({\rm\acute{e}}\)r-Rao lower bound.
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Leung, CS., So, H.C., Chan, F.K.W., Constantinides, A.G. (2012). Analog Neural Network Approach for Source Localization Using Time-of-Arrival Measurements. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34481-7_29
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DOI: https://doi.org/10.1007/978-3-642-34481-7_29
Publisher Name: Springer, Berlin, Heidelberg
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