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KL-Divergence Kernel Regression For Non-Gaussian Fingerprint Based Localization

机译:基于非高斯指纹的KL散度核回归

摘要

Embodiments are directed to mobile localization, and more specifically, but not exclusively, to tracking mobile devices. Embodiments include methods that consider probability kernels with distance-like metrics between distributions. Also described are probabilistic kernels that can be used for a regression of location, which can achieve up to about inn accuracy in an office environment.
机译:实施例针对移动本地化,并且更具体但非排他地针对跟踪移动设备。实施例包括考虑在分布之间具有类似距离的度量的概率核的方法。还描述了可以用于位置回归的概率内核,该内核可以在办公室环境中达到大约客栈的准确性。

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