D-Fi: Domain adversarial neural network based CSI fingerprint indoor localization

Abstract

Deep learning based channel state information (CSI) fingerprint indoor localization schemes need to collect massive labeled data samples for training, and the parameters of the deep neural network are used as the fingerprints. However, the indoor environment may change, and the previously constructed fingerprint may not be valid for the changed environment. In order to adapt to the changed environment, it requires to recollect massive amount of labeled data samples and perform the training again, which is labor-intensive and time-consuming. In order to overcome this drawback, in this paper, we propose one novel domain adversarial neural network (DANN) based CSI Fingerprint Indoor Localization (D-Fi) scheme, which only needs the unlabeled data samples from the changed environment to update the fingerprint to adapt to the changed environment. Specifically, the previous environment and changed environment are treated as the source domain and the target domain, respectively. The DANN consists of the classification path and the domain-adversarial path, which share the same feature extractor. In the offline phase, the labeled CSI samples are collected as source domain samples to train the neural network of the classification path, while in the online phase, for the changed environment, only the unlabeled CSI samples are collected as target domain samples to train the neural network of the domain-adversarial path to update parameters of the feature extractor. In this case, the feature extractor extracts the common features from both the source domain samples corresponding to the previous environment and the target domain samples corresponding to the changed environment. Experiment results show that for the changed localization environment, the proposed D-Fi scheme significantly outperforms the existing convolutional neural network (CNN) based scheme.