Indoor Object Localization and Tracking Using Deep Learning over Received Signal Strength

This paper introduces a new indoor-localization approach using a deep-learning network for which the received signal-strength indicator (RSSI) is adopted as the radiofrequency fingerprint. In our proposed scheme, the RSSIs which are estimated by a channel-propagation emulator software, are adopted as the input features for deep learning. This approach is measurement-free and thus it is very cost-effective and convenient to users. A multilayer perceptron (MLP) is constructed to predict the location(s) of the mobile object(s). The time evolution of the predicted locations of an object will form the predicted trajectory thereby. Because deep-learning networks require tremendous training data to achieve good prediction accuracy, we propose to partition the indoor geometry of interest (ex., a room) into several zones. Preliminary simulation results demonstrate that the AUC (area under the receiver-operating characteristic curve) can reach up to 0.89 for a room partitioned into eight zones. Our proposed new indoor-localization scheme in this work can be a rare but promising localization technology, which is neither passive nor active as other existing prevalent localization methods.