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Acoustic Indoor Smart Phone Tracking

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Acoustic Indoor Smart Phone Tracking Hindawi Publishing Corporation International Journal of Navigation and Observation Volume 2015, Article ID 694695, 15 pages http://dx.doi.org/10.1155/2015/694695 Research Article Acoustic Self-Calibrating System for Indoor Smart Phone Tracking Alexander Ens,1 Fabian Höflinger,1 Johannes Wendeberg,2 Joachim Hoppe,1 Rui Zhang,1 Amir Bannoura,1 Leonhard M. Reindl,1 and Christian Schindelhauer2 1 Department of Microsystems Engineering, University of Freiburg, 79110 Freiburg, Germany 2Department of Computer Science, University of Freiburg, 79110 Freiburg, Germany Correspondence should be addressed to Alexander Ens; [email protected] Received 1 August 2014; Revised 2 February 2015; Accepted 3 February 2015 Academic Editor: Aleksandar Dogandzic Copyright © 2015 Alexander Ens et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This paper presents an acoustic indoor localization system for commercial smart phones that emit high pitched acoustic signals beyond the audible range. The acoustic signals with an identifier code modulated on the signal are detected by self-built receivers which are placed at the ceiling or on walls in a room. The receivers are connected in a Wi-Fi network, such that they synchronize their clocks and exchange the time differences of arrival (TDoA) of the received chirps. The location of the smart phone is calculated by TDoA multilateration. The precise time measuring of sound enables high precision localization in indoor areas. Our approach enables applications that require high accuracy, such as finding products in a supermarket or guiding blind people through complicated buildings. We have evaluated our system in real-world experiments using different algorithms for calibration- free localization and different types of sound signals. The adaptive GOGO-CFAR threshold enables a detection of 48% of the chirp pulses even at a distance of 30 m. In addition, we have compared the trajectory of a pedestrian carrying a smart phone to reference positions of an optic system. Consequently, the localization error is observed to be less than 30 cm. 1. Introduction their environments, for example, to specific products in a supermarket or to particular exhibition booths on trade fairs, From the sustained rise and ubiquitous availability of mobile a more accurate localization system as GPS is needed. Hence, computers, smart phones, and handheld devices in everyday for indoor applications alternative technologies are required life, a multitude of exciting new location-dependent applica- to provide the signal inside buildings with a low cost infras- tions have emerged. Context sensitive applications support tructure. theuserineverydaylife.Oneofthemostimportantcontexts is user location for navigation. The demand for navigation in 2. Related Work largestructuresasrailwaystations,airports,tradefairhalls,or departmentstoresisobvious,sincetheequipment,themobile Today several indoor localization systems are available, based device of the people, is already available. on different methods and technologies. Some of these systems The GPS-Module in commercial off-the-shelf (COTS) work with COTS smart phones. In addition, many partici- smart phones and hand-held devices makes navigation sys- pants have already COTS smart phones, which reduces the tems reliable to assist in outdoor areas [1]. The demand of costs of the localization system. Figure 1 showsanoverviewof localization systems begins to shift towards closed scenarios. the different technologies and the achievable accuracy of For indoor environments, there is the need for new localiza- indoor localization systems based on COTS smart phones tion approaches, since the reliability of GPS vanishes in which were developed by scientific research groups. densely built-up urban areas and is completely void inside We use the principles of smart phone localization from buildings. In addition, to effectively navigate people in our prior work [2] to apply our new developed algorithm 2 International Journal of Navigation and Observation “MoVIPS” Visual information Werner et al., 2011 visual “RADAR” Otsason et al., 2005 Bahl and Padmanabhan, 2000 Wi-Fi GSM Chintalapudi et al., 2010 Martin et al., 2010 RF-signal “Redpin” Wi-Fi Wi-Fi Bolliger, 2008 Li et al., 2012 Wi-Fi “WALRUS” Wi-Fi Liu, et al., 2012 Wi-Fi/sound Borriello et al., 2005 Rishabh et al., 2012 “BeepBeep” Technologies WLAN/sound 16 kHz–18 kHz 2 kHz–6 kHz 21 sound from Peng et al., 2012 kHz 2 6 14 Filonenko et al., loudspeakers kHz– kHz ( m) 2010 Accustic 20 Hz–22 kHz “Beep” “ASSIST” Mandal et al., 2005 Höflinger et al., 2012 4 kHz sound from 18 kHz–21 kHz smart phones (7 m) sound from smart phones (18 m) 6.0 4.03.5 3.0 2.5 1.0 0.75 0.5 0.25 0 Resolution (m) Figure 1: Overview of localization system based on smart phones. (Cone Alignment) and particle filter. Further, we show local- of all, Bluetooth adjusts the signal-strength when the ization with the integrated inertial measurement unit and signal becomes too strong or too weak. Moreover, compare the results with a reference motion tracking system. Bluetooth takes a lot of time to discover new devices. Furthermore, we showed in [3] an optimized receiver hard- As a result, these restrictions make Bluetooth posi- ware to increase sensitivity and accuracy of the localization tioning impractical and not feasible for high precision system. localization. To sum up, the RF systems are susceptible to errors in dynamic environments. For example, the RSSI value 2.1. Basic Indoor Localization depends on the environment and the smart phone. TheRSSIvalueisdistortedbyobjectsinthedirect (i) Many present localization systems use radio fre- path, in the vicinity and by environmental influences, quency (RF) signals for localization. The RF systems like air humidity, and so forth. Additionally, the RSSI use the propagation of radio waves for position calcu- value also depends on the orientation of the antenna. lation. Therefore, the existing infrastructure can often The antenna directivity is influenced by specific smart be used. In the following, a brief description of indoor phone types and the actual orientation to the anchor localization systems based on three different RF nodes. RF localization systems can localize people technologies is presented. Otsason et al. used the GSM with low accuracy (1.5 m–3 m). Through combina- communication with wide signal-strength finger- tion with other technologies, this accuracy can be prints to locate the user in indoor environments [4]. improved. The multimethod approach [9]usesacom- For the localization, no infrastructure is required, but bination of built-in sensors of mobile devices and the the accuracy strongly depends on the environment. capabilities of the end-users, which estimates posi- Another possibility is using the Wi-Fi communica- tions with a scanner application. Redpin considered tion [5–7]. Current smart phones have a Wi-Fi mod- the signal-strength of GSM, Bluetooth, and Wi-Fi ule implemented to communicate with a network. access points on a mobile phone to calculate the RADAR [8] operates with the existing multiple Wi-Fi position [10]. accesspoints.Further,theyusethereceivedsignal- strength indicator (RSSI) to calculate the distances (ii) An alternative technology is pedestrian dead reckon- between the Wi-Fi access points and the mobile ing (PDR) with inertial sensors. By using the inte- phone. The accuracy depends on the number of Wi-Fi grated MEMS sensors (accelerometers, gyroscopes), access points and the environment. The third technol- thecurrentpositioncanbecalculatedrecursively ogyisBluetooth,whichhastheshortestrangeamong basedonthemeasuredaccelerationandangularrate the three technologies. However, the technology has of the movement. Inertial sensors based localization some flaws for accurate positioning application. First work without addition infrastructure. However, the International Journal of Navigation and Observation 3 errors of the sensors are accumulated during the inte- signals is straight forward. A brief description of current gration of the measurement values, which increases indoor localization systems based on sound is presented in the localization error with the investigation time [11]. this section. Therefore, position calculation based only on inertial Most of the research groups uses the time of flight (ToF) sensorsisusuallyfusedwithanabsolutelocation or round trip time (RTT) measurement for smart phone posi- method.ThusKimetal.presentedasmartphone tioning. However, there are several intrinsic uncertainty fac- localization system based on Wi-Fi access points and tors of a ToF measurement which lead to the ranging inaccu- inertial sensors. Zhang et al. presented a smart phone racy. For COTS smart phones, there exists a variable latency, localization system based only on inertial sensors a changeable misalignment between the timestamps of the [12]. Different methods were introduced to provide command from the transmitted signal and the transmitted adaptive step lengths detection by analyzing vertical signal from the loudspeaker. Another problem is the synchro- acceleration data. The experimental results showed nization of the smart phones and receivers. These delays can that the obtained trajectory was able to follow the true easily add up to several milliseconds, which imply a ranging path with an error margin of a meter in a
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