This project investigates different approaches to data compression on common types of signals in the context of localization by estimating time difference of arrival (TDOA). This project includes evaluation of the compression schemes using recorded data, collected as part of the thesis work. This evaluation shows that compression is possible while preserving localization accuracy.
The recorded data is backed up with more extensive simulations using a free space propagation model without attenuation. The signals investigated are flat spectrum signals, signals using phase-shift keying and single side band speech signals. Signals with low bandwidth are given precedence over high bandwidth signals, since they require more data in order to get an accurate localization estimate.
The compression methods used are transform based schemes. The transforms utilized are the Karhunen-Loéve transform and the discrete Fourier transform. Different approaches for quantization of the transform components are examined, one of them being zonal sampling.
Localization is performed in the Fourier domain by calculating the steered response power from the cross-spectral density matrix. The simulations are performed in MATLAB using three recording nodes in a symmetrical geometry. The performance of localization accuracy is compared with the Cramér-Rao bound for flat spectrum signals using the standard deviation of the localization error from the compressed signals.
Source: Linköping University
Authors: Arbring, Joel | Hedström, Patrik