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Abstract
Location sensing provides endless opportunities for a wide range of applications in GPS-obstructed environments; where, typically, there is a need for a higher degree of accuracy.
This dissertation focuses on robust range estimation, an important prerequisite for fine-grained localization.
Motivated by the promise of acoustics in delivering high ranging accuracy, it presents the design, implementation and evaluation of acoustic (both ultrasound and audible) ranging systems.
It distills the limitations of acoustic ranging; and presents efficient signal designs, detection algorithms, and information processing frameworks to overcome the challenges of coverage, range, accuracy/resolution, tolerance to the Doppler effect, audible intensity, and energy consumption.
This dissertation studies the unidirectionality problem of commercially available narrowband ultrasound sensors, and their impact on the coverage of ultrasound localization systems; and demonstrates how a simple omnidirectional ultrasonic receiver unit is effective in improving system performance.
It evaluates the proposed signal designs and detection techniques experimentally on TWEET, a low-power platform, purpose-built for acoustic ranging applications.
Experimental results demonstrate an operational range of 20 m (outdoor) and an average accuracy of approximately 2 cm in the ultrasound domain.
It presents the design of an audible-range acoustic tracking service that encompasses the benefits of a near-inaudible acoustic broadband chirp, and an approximately two fold increase in Doppler tolerance to achieve better performance.
Furthermore, considering the resource constraints of low-cost and low-power sensor platforms, this dissertation introduces a new computing model for ranging: cross-correlation via sparse representation.
Its underlying mechanism, based on the theory of sparse approximation, achieves similar ranging accuracy and precision levels as the standard cross-correlation, but by using approximately one-third of the required information only.
Thus, it significantly reduces energy consumption.