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.

Received Signal StrengthBased Indoor Localization Using Hierarchical Classification Commercial interests in indoor localization have been increasing in the past decade. The success of many applications relies at least partially on indoor localization that is expected to provide reliable indoor position information. WiFi received signal strength (RSS)based indoor localization techniques have attracted extensive attentions because WiFi access points (APs) are widely deployed and we can obtain the WiFi RSS measurements without extra hardware cost. In this paper, we propose a hierarchical classificationbased method as a new solution to the indoor localization problem. Within the developed approach, we first adopt an improved KMeans clustering algorithm to divide the area of interest into several zones and they are allowed to overlap with one another to improve the generalization capability of the following indoor positioning process. To find the localization result, the KNearest Neighbor (KNN) algorithm and support vector machine (SVM) with the oneversusone strategy are employed. The proposed method is implemented on a tablet, and its performance is evaluated in realworld environments. Experiment results reveal that the proposed method offers an improvement of 1.4% to 3.2% in terms of position classification accuracy and a reduction of 10% to 22% in terms of average positioning error compared with several benchmark methods.