Title page for ETD etd-05302007-170756

Type of Document Master's Thesis
Author Perkins, William
Author's Email Address wperki2@lsu.edu
URN etd-05302007-170756
Title Data Compression with Application to Geo-location
Degree Master of Science in Electrical Engineering (M.S.E.E.)
Department Electrical & Computer Engineering
Advisory Committee
Advisor Name Title
Jorge L. Aravena Committee Chair
Guoxiang Gu Committee Member
Morteza Naraghi-Pour Committee Member
  • decimation
  • bit allocation
  • filterbank
  • digital signal processing
  • digital communications
  • quantization
  • wireless communications
  • geo-location
  • data compression
Date of Defense 2007-05-10
Availability unrestricted
A common way to locate an emitter within a wireless sensor network requires the estimation of

time-difference-of-arrival (TDOA) parameters using data collected by a set of spatially separated

sensors. Compressing the data that is shared among the sensors can provide tremendous savings

in terms of the energy and transmission latency. Traditional MSE and perceptual based data

compression schemes fail to accurately capture the effects of compression on the TDOA

estimation task; therefore, it is necessary to investigate compression algorithms suitable for

TDOA parameter estimation.

This thesis explores the effects of data compression on TDOA parameter estimation accuracy.

The first part of this document investigates the decimation of band-limited communication

signals which are oversampled to achieve high precision in the TDOA estimate. In the second

part, we follow the work of [19-22] in implementing a Fisher Information-based subband

encoding scheme, an approach that has been shown to provide better results than the traditional

MSE-based approach. A pseudo-QMF filter bank [8] is implemented, which is computationally

more efficient than wavelet packet filter banks, at the cost of relaxing perfect reconstruction

conditions. Additionally, a suboptimal bit allocation algorithm is developed which further

lessens the sensor resource requirements for compression.

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