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

Abstract

Improving accuracy in wireless localization and ranging is a challenging task which often demands an increase in the signal-to-noise ratio (SNR). Impulsive ultra-wideband (UWB) technology is a promising signaling alternative that is capable of high-resolution ranging with minimal cost on SNR. Unfortunately, typical UWB time-of-arrival (ToA) estimators are complicated and perform poorly in the low SNR environment. In this correspondence, we propose a regularized least squares (RLS) approach with wavelet denoising to improve the estimator accuracy at low SNR. Our approach estimates the ToA as a by-product of the RLS channel estimator based on a thresholding technique, which is simple and can enable fast, on-the-fly, accurate ToA estimation applicable to real-time application. In addition to the meticulous selection of a threshold based on the Neyman-Pearson criterion, we demonstrate the robustness of our algorithm first by computer simulation, then applying it to a realistic situation of range estimation via the UWB impulse radio (UWB-IR). In both cases, our algorithm is shown to supersede other high-resolution algorithms in ToA estimation, energy capture and computational complexity when the sampling rate is available.

Dual Stage Bayesian Network with Dual-Tree Complex Wavelet Transformation for Image Denoising

“…Image noise removal is a necessary pre-processing function in diverse optical engineering applications (Liu & Fang, 2015;Nasri & Nezamabadi-pour, 2009;Jin, 2013;Liu et al, 2009;AM Wagh & Todmal, 2015;Sable & Jondhale, 2010). The multiple noises occurred from the image acquisition, and transmission process destroys the image quality.…”