Localization of a wireless device using the time-of-arrivals (TOAs) from different base stations has been studied extensively in the literature. Numerous localization algorithms with different accuracies, computational complexities, a-priori knowledge requirements, and different levels of robustness against non-line-of-sight (NLOS) bias effects also have been reported. However, to our best knowledge, a detailed unified survey of different localization and NLOS mitigation algorithms is not available in the literature. This paper aims to give a comprehensive review of these different TOA-based localization algorithms and their technical challenges, and to point out possible future research directions. Firstly, fundamental lower bounds and some practical estimators that achieve close to these bounds are summarized for line-of-sight (LOS) scenarios. Then, after giving the fundamental lower bounds for NLOS systems, different NLOS mitigation techniques are classified and summarized. Simulation results are also provided in order to compare the performance of various techniques. Finally, a table that summarizes the key characteristics of the investigated techniques is provided to conclude the paper.

A Survey on TOA Based Wireless Localization and NLOS Mitigation Techniques

Over the coming decades, high-definition situationally-aware networks have the potential to create revolutionary applications in the social, scientific, commercial, and military sectors Ultrawide bandwidth (UWB) technology is a viable candidate for enabling accurate localization capabilities through time-of-arrival (TOA)-based ranging techniques These techniques exploit the fine delay resolution property of UWB signals by estimating the TOA of the first signal path Exploiting the full capabilities of UWB TOA estimation can be challenging, especially when operating in harsh propagation environments, since the direct path may not exist or it may not be the strongest In this paper, we first give an overview of ranging techniques together with the primary sources of TOA error (including propagation effects, clock drift, and interference) We then describe fundamental TOA bounds (such as the Cramer-Rao bound and the tighter Ziv-Zakai bound) in both ideal and multipath environments These bounds serve as useful benchmarks in assessing the performance of TOA estimation techniques We also explore practical low-complexity TOA estimation techniques and analyze their performance in the presence of multipath and interference using IEEE 802154a channel models as well as experimental data measured in indoor residential environments

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Ranging With Ultrawide Bandwidth Signals in Multipath Environments

Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network.

A Survey of Localization in Wireless Sensor Network

Wireless local area networks (WLANs) have become a promising choice for indoor positioning as the only existing and established infrastructure, to localize the mobile and stationary users indoors. However, since WLANs have been initially designed for wireless networking and not positioning, the localization task based on WLAN signals has several challenges. Amongst the WLAN positioning methods, WLAN fingerprinting localization has recently garnered great attention due to its promising performance. Notwithstanding, WLAN fingerprinting faces several challenges and hence, in this paper, our goal is to overview these challenges and corresponding state-of-the-art solutions. This paper consists of three main parts: 1) conventional localization schemes; 2) state-of-the-art approaches; and 3) practical deployment challenges. Since all proposed methods in the WLAN literature have been conducted and tested in different settings, the reported results are not readily comparable. So, we compare some of the representative localization schemes in a single real environment and assess their localization accuracy, positioning error statistics, and complexity. Our results depict illustrative evaluation of the approaches in the literature and guide to future improvement opportunities.

Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges

The widespread deployment of wireless networks presents an opportunity for localization and mapping using only signal-strength measurements. The current state of the art is to use Gaussian process latent variable models (GP-LVM). This method works well, but relies on a signature uniqueness assumption which limits its applicability to only signal-rich environments. Moreover, it does not scale computationally to large sets of data, requiring O (N3) operations per iteration. We present a GraphSLAM-like algorithm for signal strength SLAM. Our algorithm shares many of the benefits of Gaussian processes, yet is viable for a broader range of environments since it makes no signature uniqueness assumptions. It is also more tractable to larger map sizes, requiring O (N2) operations per iteration. We compare our algorithm to a laser-SLAM ground truth, showing it produces excellent results in practice.

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Efficient, generalized indoor WiFi GraphSLAM

Three or more base stations (BS) making time-of-arrival measurements of a signal from a mobile station (MS) can locate the MS. However, when some of the measurements are from non-line-of-sight (NLOS) paths, the location errors can be very large. This paper proposes a residual test (RT) that can simultaneously determine the number of line-of-sight (LOS) BS and identify them. Then, localization can proceed with only those LOS BS. The RT works on the principle that when all measurements are LOS, the normalized residuals have a central Chi-Square distribution, versus a noncentral distribution when there is NLOS. The residuals are the squared differences between the estimates and the true position. Normalization by their variances gives a unity variance to the resultant random variables. In simulation studies, for the chosen geometry and NLOS and measurement noise errors, the RT can determine the correct number of LOS-BS over 90% of the time. For four or more BS, where there are at least three LOS-BS, the estimator has variances that are near the Cramer--Rao lower bound.

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Wing-Yue Tsui

Papers

Time-of-arrival based localization under NLOS conditions