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 Keywords
- decimation
- bit allocation
- filterbank
- digital signal processing
- digital communications
- quantization
- wireless communications
- geo-location
- data compression
Date of Defense 2007-05-10 Availability unrestricted Abstract A common way to locate an emitter within a wireless sensor network requires the estimation oftime-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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