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Data Augmentation Schemes for Deep Learning in an Indoor Positioning Application

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Data Augmentation Schemes for Deep Learning in an Indoor Positioning Application electronics Article Data Augmentation Schemes for Deep Learning in an Indoor Positioning Application Rashmi Sharan Sinha 1, Sang-Moon Lee 2, Minjoong Rim 1 and Seung-Hoon Hwang 1,* 1 Division of Electronics and Electrical Engineering, Dongguk University-Seoul, Seoul 04620, Korea; [email protected] (R.S.S.); [email protected] (M.R.) 2 JMP Systems Co., Ltd, Gyeonggi-do 12930, Korea; [email protected] * Correspondence: [email protected]; Tel.: +82-2-2260-3994 Received: 25 April 2019; Accepted: 13 May 2019; Published: 17 May 2019 Abstract: In this paper, we propose two data augmentation schemes for deep learning architecture that can be used to directly estimate user location in an indoor environment using mobile phone tracking and electronic fingerprints based on reference points and access points. Using a pretrained model, the deep learning approach can significantly reduce data collection time, while the runtime is also significantly reduced. Numerical results indicate that an augmented training database containing seven days’ worth of measurements is sufficient to generate acceptable performance using a pretrained model. Experimental results find that the proposed augmentation schemes can achieve a test accuracy of 89.73% and an average location error that is as low as 2.54 m. Therefore, the proposed schemes demonstrate the feasibility of data augmentation using a deep neural network (DNN)-based indoor localization system that lowers the complexity required for use on mobile devices. Keywords: augmentation; deep learning; CNN; indoor positioning; fingerprint 1. Introduction Identifying the location of a mobile user is an important challenge in pervasive computing because their location provides a lot of information about the user with which adaptive computer systems can be created. The challenge of accurately estimating position both indoors and outdoors has thus received significant attention within both industry and academia. In particular, the successful application of Global Positioning System (GPS) has enabled travelers to move around the world more freely. However, GPS is sensitive to occlusion and does not work in indoor environments. A large number of methods has been proposed to overcome this limitation, including estimating indoor location using mobile devices such as smartphones. Measuring the intensity of a received signal makes interior localization using wireless signals such as Wi-Fi possible. Measurement-based wireless positioning systems infer position based on the time of arrival (TOA) or time difference of arrival (TDOA) of the signals. However, general wireless signal receivers are not capable of measuring round-trip times or angles. Additional devices are thus required, which makes this type of system impractical for many applications. An alternative option is the use of fingerprint-based approaches, which do not need any special devices and are thus more feasible. The fingerprint method proposed in the present study consists of two stages (Figure1). In the o ffline stage, the received signal strength indications (RSSIs) from all access points (APs) are collected from known positions, referred to as reference points (RPs), to build a fingerprint database for the environment. Therefore, each RP has a fingerprint characterized by its position and the captured RSSIs from all APs at that location. At the positioning stage, the currently captured RSSIs are matched with those of the RPs and position is determined using the positions of several of the best-fitting RPs. However, a major issue for accurate fingerprint-based Electronics 2019, 8, 554; doi:10.3390/electronics8050554 www.mdpi.com/journal/electronics Electronics 2019, 8, 554 2 of 19 Electronics 2019, 7, x FOR PEER REVIEW 2 of 20 localization is the variation in RSSIs due to the fluctuating nature of wireless signals, caused by multipathnature of fading wireless and signals, attenuation caused by by static multipath or dynamic fading objectsand attenuation such as walls by static or moving or dynamic people. objects such as walls or moving people. FigureFigure 1. 1.The The fingerprint fingerprint method method (For (For offline offl inedeep deep neur neuralal network network (DNN) (DNN) training; training; a four-layer a four-layer DNN- DNN-basedbased localizer localizer is trained is trained to extract to extract reliable reliable features features from from massive massive noisy noisy received received signal signal strength strength indicationindication (RSSI) (RSSI) samples samples from from a pre-built a pre-built fingerprint fingerprint data set. data For onlineset. For positioning, online positioning, the preprocessed the RSSIpreprocessed readings are RSSI fed readings into the are localizer fed into to the estimate localizer the to final estimate position). the final position). ItIt is is also also necessary necessary toto collect more more RPs RPs to toallow allow more more accurate accurate positioning, positioning, especially especially when whenthe thetarget target environment environment has has a alarge large area, area, which which lead leadss to toan an extremely extremely large large fingerprint fingerprint database. database. Consequently,Consequently, the the challenge challenge in in wireless wireless positioning positioning is is how how to to extract extract reliable reliable features features and and optimize optimize the mappingthe mapping function function using ausing large a collectionlarge collection of RPs of with RPs widely with widely fluctuating fluctuating RSSI signals. RSSI signals. The methods The representmethods a formrepresent of shallow a form learning of shallow architecture learning that hasarchitecture limited modeling that has and limited representational modeling powerand whenrepresentational dealing with power large and when noisy dealing volumes with of large data. and To extractnoisy volumes complex of structures data. To andextract build complex accurate structures and build accurate internal representations from rich data sources, human information internal representations from rich data sources, human information processing mechanisms (e.g., vision processing mechanisms (e.g., vision and speech) suggest the need for deep learning architecture and speech) suggest the need for deep learning architecture consisting of multiple layers of nonlinear consisting of multiple layers of nonlinear processing [1]. Deep learning simulates the hierarchical processing [1]. Deep learning simulates the hierarchical structure of the human brain, processing data structure of the human brain, processing data from a lower to a higher level, gradually producing from a lower to a higher level, gradually producing more semantic concepts. Deep neural networks more semantic concepts. Deep neural networks (DNNs) have been employed to address these types (DNNs) have been employed to address these types of issue with notable success, outperforming of issue with notable success, outperforming state-of-the-art techniques in certain areas such as vision state-of-the-art[2], audio [3], and techniques robotics in[4,5]. certain However, areas the such use as of vision DNNs [in2], Wi-Fi audio localization [3], and robotics has largely [4,5 ].remained However, theuninvestigated. use of DNNs With in Wi-Fi this in localization mind, we haspresent largely a novel remained Wi-Fi positioning uninvestigated. method With based this on in deep mind, welearning. present aIn novel this Wi-Fipaper, positioning we propose method data augmen based ontation deep schemes learning. to Inestimate this paper, user we position propose and data augmentationdemonstrate schemeshow our tomethods estimate can user be employed position and in convolutional demonstrate neural how our network methods (CNN) can besettings. employed in convolutionalA deep learning neural structure network is (CNN) built to settings.extract features from widely fluctuating massive Wi-Fi data, oneA which deep learning automatically structure performs is built probabilistic to extract features position from estimation. widely fluctuating In addition, massive a CNN-based Wi-Fi data, onelocalizer which automaticallyis introduced performs to formulate probabilistic the relation positionship estimation. between adjacent In addition, position a CNN-based states and localizer to is introducedintuitively reduce to formulate variation the in relationship the estimates. between In this adjacent work, a position Wi-Fi-based states indoor and to RSSI intuitively fingerprint reduce variationpositioning in the system estimates. is proposed In this work,and implement a Wi-Fi-baseded using indoor data RSSI augmentation, fingerprint which positioning is a common system is proposedtechnique and that implemented has been proven using to data benefit augmentation, the training whichof machine is a commonlearning models technique in general that has and been provendeep toarchitecture benefit the in training particular. of machine It either speeds learning up models convergence in general or acts and as deepa regularizer, architecture thus in avoiding particular. It eitheroverfitting speeds and upincreasing convergence generalization or acts aspower a regularizer, [6,7]. Data augmentation thus avoiding typically overfitting involves and applying increasing generalizationa set of transformations power [6,7]. to Data either augmentation the data spac typicallye or the involvesfeature space, applying or both. a set The of transformations most common to eitheraugmentations the data space are orperformed the feature in space,
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