AbstractsComputer Science

Data Compression with Application to Geo-location

by William Perkins

Institution: Louisiana State University
Department: Electrical & Computer Engineering
Degree: MSEE
Year: 2007
Keywords: decimation; bit allocation; filterbank; digital signal processing; digital communications; quantization; wireless communications; geo-location; data compression
Record ID: 1811198
Full text PDF: http://etd.lsu.edu/docs/available/etd-05302007-170756/


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.