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Logistic regression-based device-free localization in changeable environments

机译：在可变环境中基于Logistic回归的无设备本地化

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Device-free localization (DFL) is expected to detect and locate a person by measuring the changes of received signals in wireless sensor networks without the need of any device. Fingerprint-based DFL in changeable environments has attracted wide attenuation in recent years. However, the accuracy of fingerprint-based localization could be improved further in changing environments. In this paper, we adopt the logistic regression classifier to counteract the bad influence to the localization in changeable environments by means of selecting the average channel. The experiment results show that the logistic regression classifier has a lower error rate than the k-nearest neighbors classifier and the linear discriminant analysis classifier. When the change of environments is very obvious, the logistic regression classifier achieves a better result than the random forests classifier in fingerprint-based localization.
机译：无设备定位（DFL）有望通过测量无线传感器网络中接收信号的变化来检测和定位人员，而无需任何设备。近年来，在多变的环境中基于指纹的DFL引起了广泛的关注。但是，在不断变化的环境中，可以进一步提高基于指纹的定位的准确性。在本文中，我们采用逻辑回归分类器通过选择平均通道来抵消在多变环境中对本地化的不利影响。实验结果表明，逻辑回归分类器的误码率低于k近邻分类器和线性判别分析分类器。当环境变化非常明显时，在基于指纹的定位中，逻辑回归分类器比随机森林分类器获得更好的结果。

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