|Implementation of a High Accuracy Acoustic Ranging System using Mobile Devices|
|R & D|
|CS CE SE|
|Mobile-phone programming (Microsoft Windows Mobile, IPhone, Android), basic web development skills|
|We are interested in high accuracy ranging using only the most basic set of
commodity hardware capabilities (i.e., a speaker, a microphone, and some
form of interdevice communication) on commercial off-the-shelf (COTS) mobile
High accuracy ranging is typically achieved through measuring
time-of-arrival (TOA) information of acoustic or radio signal.
The distance is thus the product of the signal speed and the time of flight
of the signal traveling between two devices.
The ranging accuracy depends on the signal speed and the precision of TOA
The relatively slow propagation velocity of acoustic waveform facilitates
better compensation of the various (timing) errors, and hence, results in
more accurate TOA measurements .
But the precision of TOA measurement remains a big challenge in any system
Cross-correlation is a popular signal processing technique used to estimate
the TOA, but requires processing a large sample set to obtain the final
Recent work has shown that TOA estimates of the same order of accuracy as
cross-correlation can be obtained by processing only one-third of the entire
sample set .
This method referred to as cross-correlation via sparse representation is a
new framework for ranging based on "L1-minimization".
The key idea is to compress the signal samples on the hardware platform by
efficient random projections and transfer them to a central device, where a
convex optimization process estimates the range by exploiting its sparsity
in the correlation domain.
This project seeks to design and implement an online ranging service using
mobile devices by amalgamating the efficacy of acoustic waveforms and sparse
The first phase of this project includes replicating the functionality of
the BeepBeep system  that popularized acoustic ranging on mobile devices.
This would be followed by modifying the cross-correlation algorithm used by
BeepBeep to sparse cross-correlation in the second phase of developement.
The final results should be made available in a meaningful manner on an
online web portal.
 Nissanka Bodhi Priyantha. "The Cricket Indoor Location System." PhD thesis, Massachusetts Institute Of Technology, 2005.
 P. Misra, W. Hu, M. Yang, S. Jha, "Efficient Cross-Correlation via
Sparse Representation in Sensor Networks", (Appearing) In ACM/IEEE IPSN, 2012. http://www.cse.unsw.edu.au/~pkmisra/Resources/ipsn2012-misra.pdf
 Peng Chunyi, Shen Guobin, Zhang Yongguang, Li Yanlin, and Tan Kun "Beepbeep: a high accuracy acoustic ranging system using cots mobile devices.", In SenSys, pages 1-14. ACM, 2007.
|Please contact Prasant Misra (email@example.com) who will be the associated staff working on this project|
|Past Student Reports|
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