Logistic regression-based device-free localization in changeable environments

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.