A regularized least squares approach for ultra-wideband time-of-arrival estimation with wavelet denoising

Wireless localization and ranging are challenging tasks which demand high signal-to-noise ratio (SNR) for an increase in accuracy. Impulsive ultra-wideband (UWB) technology is a promising signaling alternative that is capable of high-resolution ranging with minimal cost on SNR. Unfortunately, typical time-of-arrival (ToA) estimation algorithms are complicated and perform poorly in the low SNR environment. In this paper, we propose a regularized least squares approach with wavelet denoising to improve the estimator accuracy at low SNR. Our approach estimates the ToA as a by-product of the channel estimation; furthermore, it is simple and enables fast, on-the-fly, accurate ToA estimation applicable to real-time application. We demonstrate the robustness of our algorithm by simulation where it is shown to outperform other high-resolution algorithms in ToA estimation.