It is widely <span>admitted that the estimation of ultra wide band (UWB) time-of-arrival (TOA) for multiple targets in indoor multipath channels is a very challenging task. The existing algorithms deal with a limited number of targets and require a complex exchange of several messages. In this paper, a novel TOA estimation algorithm for multiple targets is developed. The proposed algorithm estimates the first path (FP) TOA of a number of targets without exchanging messages or using collision avoidance techniques. As a first step, the singular value decomposition (SVD) is employed to extract the first path (FP) of each target and then a matched filter, followed by an iterative threshold crossing algorithm, is used to determine the number of targets and the corresponding FP TOAs. The simulation results with four targets, using the CM1 IEEE 802.15.4a channel model, showed that the proposed novel algorithm can effectively detect the FP of each target and estimate its corresponding TOA.</span>
<span lang="EN-US">Ultra-wideband (UWB) ranging via time-of-arrival (TOA) estimation method has gained a lot of research interests because it can take full advantage of UWB capabilities. Energy detection (ED) based TOA estimation technique is widely used in the area due to its low cost, low complexity and ease of implementation. However, many factors affect the ranging performance of the ED-based methods, especially, non-line-of-sight (NLOS) condition and the integration interval. In this context, a new TOA estimation method is developed in this paper. Firstly, the received signal is denoised using a five-level wavelet decomposition, next, a double sliding window algorithm is applied to detect the change in the variance information of the received signal, the first path (FP) TOA is then calculated according to the first variance sharp increase. The simulation results using the CM1 and CM2 IEEE 802.15.4a channel models, prove that our proposed approach works effectively compared with the conventional ED-based methods.</span>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.