Abstract
Wavelet denoising is applied in time delay estimation between signals received at two spatially separated sensors in the presence of noise. Prior to cross correlation, each of the sensor outputs is denoised according to a novel thresholding rule in order to increase the input signal-to-noise ratio. Unlike conventional generalized cross correlators (GCCs), it does not require spectral estimation of the source signal and the corrupting noises which may introduce large delay variance. It is proved that the delay estimate provided by the proposed method is globally convergent to the true value with a high probability. Computer simulations illustrate that the technique outperforms other GCCs for different SNRs when the sampling rate is sufficiently high.
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