Deep Convolutional AutoEncoder based Indoor Light Positioning Using RSS Temporal Image Visible light communication is a promising candidate for achieving lowcost, high data rate and massive coverage in future wireless network. To fulfill the multimedia or broadcasting service, the prerequisite is to determine the useru0027s position. However, traditional RSS fingerprint based positioning approach suffers the random signal fluctuation problem which significantly limits the positioning performance. To tackle this problem, this paper presents a deep learning scheme which utilize a regression DNN combined with convolutional autoencoder (CAE) to provide robust indoor positioning. Instead of using the separate fluctuated RSS reading for positioning, the proposed method takes the RSS temporal image (RTI) composed from a set of consecutive RSS readings as input. By leveraging the spatial and temporal dependency of RTI data with stacked denoising CAE, the proposed network is expected to learn more complex and consistent features from the fluctuating RSS readings and perform better location estimation. Simulation results have shown that the proposed method is capable of providing more stable positioning result from fluctuating RSS data than traditional DNN based method.

DeepLocNet: Deep Observation Classification and Ranging Bias Regression for Radio Positioning Systems WiFi technology has been used pervasively in finegrained indoor localization, gesture recognition, and adaptive communication. Achieving better performance in these tasks generally boils down to differentiating LineOfSight (LOS) from NonLineOfSight (NLOS) signal propagation reliably which generally requires expensivespecialized hardware due to the complex nature of indoor environments. Hence, the development of lowcost accurate positioning systems that exploit available infrastructure is not entirely solved. In this paper, we develop a framework for indoor localization and tracking of ubiquitous mobile devices such as smartphones using onboard sensors. We present a novel deep LOSNLOS classifier which uses the Received Signal Strength Indicator (RSSI), and can classify the input signal with an accuracy of 85%. The proposed algorithm can globally localize and track a smartphone (or robot) with a priori unknown location, and with a semiaccurate prior map (error within 0.8m) of the WiFi Access Points (AP). Through simultaneously solving for the trajectory and the map of access points, we recover a trajectory of the device and corrected locations for the access points. Experimental evaluations of the framework show that localization accuracy is increased by using the trained deep network; furthermore, the system becomes robust to any error in the map of APs.