Abstract
Convenient indoor positioning has become an urgent need due to the improvement it offers to quality of life, which inspires researchers to focus on device-free indoor location. In areas covered with Wi-Fi, people in different locations will to varying degrees have an impact on the transmission of channel state information (CSI) of Wi-Fi signals. Because space is divided into several small regions, the idea of classification is used to locate. Therefore, a novel localization algorithm is put forward in this paper based on Deep Neural Networks (DNN) and a multi-model integration strategy. The approach consists of three stages. First, the local outlier factor (LOF), the anomaly detection algorithm, is used to correct the abnormal data. Second, in the training phase, 3 DNN models are trained to classify the region fingerprints by taking advantage of the processed CSI data from 3 antennas. Third, in the testing phase, a model fusion method named group method of data handling (GMDH) is adopted to integrate 3 predicted results of multiple models and give the final position result. The test-bed experiment was conducted in an empty corridor, and final positioning accuracy reached at least 97%.
Highlights
As mobile devices get increasingly popular, the demand for positioning accuracy of indoor positioning technology becomes higher and higher
This paper proposes a multi-model integration method based on group method of data handling (GHDM) algorithm
The channel state information (CSI) data collected by the three antennas are separately classified by support vector machines (SVM)
Summary
As mobile devices get increasingly popular, the demand for positioning accuracy of indoor positioning technology becomes higher and higher. Some existing mobile devices with Wi-Fi NICs can be applied to obtain the channel state information (CSI), and this can obviously improve the accuracy of indoor localization [11]. Most researchers are trying to use a separate algorithm model to perform localization processing on the collected CSI data. This paper proposes a multi-model integration method based on group method of data handling (GHDM) algorithm This method can determine multiple prediction results and find the optimal positioning results. ModelF, a novel indoor fingerprinting method using CSI data, is proposed in this paper. A novel indoor localization method based on CSI fingerprinting named ModelF is proposed in this paper. A multi-model fusion method named GMDH is proposed to integrate localization outputs from 3 antennas for purposes of improving localization accuracy.
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