Localization of wireless communication emitters using Time Difference of Arrival (TDOA) methods in noisy channels

The ability to provide position information of wireless emitters comprises a very important communication tool and has extremely valuable applications to military as well as civilian life. GSM is the most popular method of modulation adopted around the world, for mobile telephony. This thesis is focused on the Time Difference Of Arrival (TDOA) estimation, applied to GSM signals, in noisy channels. Improvements in denoising, in conjunction with wavelet processing, are proposed for estimating the TDOA of signals received at two spatially separated sensors. Wavelet denoising based on a modified maximum likelihood method and a higher order moment method is proposed, to improve the performance. A numerical evaluation of the methods, when unequal SNR conditions prevail, is presented. The performance of the proposed denoising methods in a jamming environment is also addressed. Simple excision schemes to improve the performance when jamming is present, are evaluated. Simulation results indicate good performance of the methods and improved estimates relative to the ones obtained using no denoising. Jamming presence degrades the performance but still the extracted estimates are improved.http://archive.org/details/localizationofwi109452507Hellenic Navy autho

Approximate Maximum Likelihood Delay Estimation via Orthogonal Wavelet Transform

A novel approximate maximum likelihood algorithm is proposed for estimating the time difference of arrival between signals received at two spatially separated sensors. Prior to cross correlation, one of the channel outputs is optimally weighted at different frequency bands with the use of an orthogonal wavelet transform. It composes an array of multirate filters and is a time domain implementation of the generalized cross correlation method. However, it does not suffer from the performance degradation due to the errors inherent in spectral estimation obtained from finite length data and is computationally efficient. A simple decision rule is also provided for automatically determining the requisite levels of wavelet decomposition. The effectiveness of the method is demonstrated by comparing with the direct cross correlator, the Eckart processor and the Cram'er-Rao lower bound for different noise conditions and wavelet filter lengths. Corresponding Author: H. C. So Department of Electro..