Device Free Localization Using Prior Correction-Based Logistic Regression

Abstract

In this letter, we focus on a device free localization (DFL) system which is based on logistic regression model using received signal strength (RSS) measurements in each link as training and testing data. To reduce the computational complexity of the logistic regression model prediction and improve the localization accuracy by reduction of the redundant training features, we obtain principal component using the measured RSS data through principal component analysis (PCA). Moreover, to overcome the imbalanced data problem, the number of training data belonging to major class is decreased through the sampling technique based on the distance between the reference points. The modified logistic regression model is derived from Bayesian formula based prior correction method due to the change in the ratio of the major and the minor classes in sampled training data. By substituting the transformed principal components of testing dataset to the probability model with the optimal regression coefficients, the localization probabilities of a target are computed at every reference point.