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- W2165470874 abstract "In terrestrial cellular wireless communication surroundings, the geometrical relationship between the measurements and the source position, from which the ordinary hyperbolic location algorithm is derived, is often destroyed by the unknown environmental effect of non-line of sight (NLOS) propagation. In this paper, the NLOS effect on mobile localization is first modeled in feature space, which is produced by a nonlinear mapping on both the original range-difference measurements and an ordinary hyperbolic localization estimate. Then a novel kernel information based hyperbolic localization method is proposed to extract the environment kernel information and correct the location bias of the ordinary location method. Simulation results demonstrate the applicability of the kernel information for hyperbolic localization. impossible task in practical wireless networks for a variety of reasons, such as heavy calculation burden or storage requirement, sufficient number of resolvable multipaths, high- resolution joint (temporal, spatial and even Doppler) parameters estimation, ill-posed problem of NLOS location equations, etc. In this paper, we translate the idea behind representer theorem and kernel-based algorithms developed in the machine learning community (15) to address the problem how the range or rang-difference measurement data, after being put through a nonlinear transformation, can be exploited to construct the location error, especially in unknown NLOS environment. Not knowing any prior knowledge of surroundings, we develop a novel representer theorem, which states that the location error can be modeled by linear combination of the training samples in the feature space produced by a nonlinear mapping on the measurement data and the ordinary location estimate. The coefficients of the linear combination are learned from a position-aware training data set and form the optimal kernel matrixes in sense of minimum training error. Finally, a novel kernel-based location method is proposed to compensate for the location error. Simulation results show that the kernel information extracted from training data set is an effective cure for NLOS effect on mobile localization, tough it does not imply optimality in the sense of test error." @default.
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- W2165470874 date "2006-01-18" @default.
- W2165470874 modified "2023-10-03" @default.
- W2165470874 title "Kernel information for hyperbolic localization" @default.
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- W2165470874 doi "https://doi.org/10.1109/vetecf.2005.1559046" @default.
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