Sensor Node Localization Using Mobile Acoustic Beacons

In this monograph, we present a mobile acoustic
beacon based sensor node localization method. Our
technique is passive in that the sensor nodes
themselves do not need to generate an acoustic signal
for ranging. This saves cost, power and provides
stealthy operation. Furthermore, the beacon can
generate much more acoustic energy than a severely
resource constrained sensor node, thereby
significantly increasing the range. The acoustic
ranging method uses a linear frequency modulated
signal that can be accurately detected by matched
filtering. This provides longer range and higher
accuracy than the current state-of-the-art. The
localization algorithm was especially designed to
work in such acoustically reverberant environment, as
urban terrain. The algorithm presented handles non
Gaussian ranging errors caused by echoes. Node
locations are computed centrally by solving a global
non-linear optimization problem in an iterative and
incremental fashion.

Autorentext

Manish Kushwaha received BTech from Indian Institute of Technology Delhi India in 2002, and MS from the Vanderbilt University, USA in 2005. Currently, he is pursuing PhD at the Vanderbilt University. His research interests include multisensor fusion, target tracking, Bayesian estimation and Markov Chain Monte Carlo methods.

Klappentext

In this monograph, we present a mobile acoustic beacon based sensor node localization method. Our technique is passive in that the sensor nodes themselves do not need to generate an acoustic signal for ranging. This saves cost, power and provides stealthy operation. Furthermore, the beacon can generate much more acoustic energy than a severely resource constrained sensor node, thereby significantly increasing the range. The acoustic ranging method uses a linear frequency modulated signal that can be accurately detected by matched filtering. This provides longer range and higher accuracy than the current state-of-the-art. The localization algorithm was especially designed to work in such acoustically reverberant environment, as urban terrain. The algorithm presented handles non Gaussian ranging errors caused by echoes. Node locations are computed centrally by solving a global non-linear optimization problem in an iterative and incremental fashion.