Acoustic indoor-localization system for smart phones

Höflinger, Fabian; Hoppe, Joachim; Zhang, Rui; Ens, Alexander; Reindl, L.; Wendeberg, Johannes; Schindelhauer, Christian

doi:10.1109/ssd.2014.6808774

Cited by 20 publications

(14 citation statements)

References 8 publications

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“…Signal strength, form recognition and travel time are the common methods used to derive the location. Hoflinger et al [44] used signal amplitude envelope detection on received chirp-form signals. Rishabh et al [45] used time of arrival (ToA) to calculate the position.…”

Section: Lbs Categorymentioning

confidence: 99%

Indoor location based services challenges, requirements and usability of current solutions

Basiri

Lohan

Moore

et al. 2017

Computer Science Review

216

163

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Indoor Location Based Services (LBS), such as indoor navigation and tracking, still have to deal with both technical and non-technical challenges. For this reason, they have not yet found a prominent position in people's everyday lives. Reliability and availability of indoor positioning technologies, the availability of up-to-date indoor maps, and privacy concerns associated with location data are some of the biggest challenges to their development. If these challenges were solved, or at least minimized, there would be more penetration into the user market. This paper studies the requirements of LBS applications, through a survey conducted by the authors, identifies the current challenges of indoor LBS, and reviews the available solutions that address the most important challenge, that of providing seamless indoor/outdoor positioning. The paper also looks at the potential of emerging solutions and the technologies that may help to handle this challenge.

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Section: Lbs Categorymentioning

confidence: 99%

Indoor location based services challenges, requirements and usability of current solutions

Basiri

Lohan

Moore

et al. 2017

Computer Science Review

216

163

View full text Add to dashboard Cite

show abstract

“…Further, we show localization with the integrated inertial measurement unit and compare the results with a reference motion tracking system. Furthermore, we showed in [3] an optimized receiver hardware to increase sensitivity and accuracy of the localization system.…”

Section: Related Workmentioning

confidence: 99%

Acoustic Self-Calibrating System for Indoor Smart Phone Tracking

Ens

Höflinger

Wendeberg

et al. 2015

International Journal of Navigation and Observation

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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 calibrationfree 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.

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“…The PER is defined through the relative 181 number of erroneous packets compared to the total number of sent packets, where a 182 packet error is any packet that includes at least one bit error. For the PER we consider bit 183 errors according to (45).…”

Section: Bit Error Rate and Packet Error Rate Simulations 177mentioning

confidence: 99%

“…7, which was assembled temporarily inside a 143 building. The transmitter and receiver hardware are a modified version of the indoor 144 localization system [45], as we published before [17,38].…”

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confidence: 99%

Asynchronous Chirp Slope Keying for Underwater Acoustic Communication

Schott

Gabbrielli

Xiong

et al. 2021

Preprint

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We propose an asynchronous acoustic chirp slope keying to map bit sequences on single or multiple bands without preamble or error correction coding on the physical layer. Details of the implementation are disclosed and discussed, the performance verified on laboratory scale in a pool measurement, as well as simulated for a channel containing Rayleigh fading and Additive White Gaussian Noise. For time-bandwidth products of 50 in single band mode, a raw data rate of 100~bit/s is simulated to achieve bit error rates below 0.001 for signal-to-noise ratios above -6~dB. In dual-band mode and a data rate of 200~bit/s, this bit error level was achieved for signal-to-noise ratios above 0~dB for time-bandwidth product of 25. The packet error rates follow this behavior with an offset of 1~dB.

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Acoustic indoor-localization system for smart phones

Cited by 20 publications

References 8 publications

Indoor location based services challenges, requirements and usability of current solutions

Indoor location based services challenges, requirements and usability of current solutions

Acoustic Self-Calibrating System for Indoor Smart Phone Tracking

Asynchronous Chirp Slope Keying for Underwater Acoustic Communication

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