UWB technology provides an excellent means for wireless positioning due to its high resolution capability in the time domain. Its ability to resolve multipath components makes it possible to obtain accurate location estimates without the need for complex estimation algorithms. In this article, theoretical limits for TOA estimation and TOA-based location estimation for UWB systems have been considered. Due to the complexity of the optimal schemes, suboptimal but practical alternatives have been emphasized. Performance limits for hybrid TOA/SS and TDOA/SS schemes have also been considered. Although the fundamental mechanisms for localization, including AOA-, TOA-, TDOA-, and SS-based methods, apply to all radio air interface, some positioning techniques are favored by UWB-based systems using ultrawide bandwidths.

Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks

tions. Bootstrap has found many applications in engineering field, including artificial neural networks, biomedical engineering, environmental engineering, image processing, and radar and sonar signal processing. Basic concepts of the bootstrap are summarized in each section as a step-by-step algorithm for ease of implementation. Most of the applications are taken from the signal processing literature. The principles of the bootstrap are introduced in Chapter 2. Both the nonparametric and parametric bootstrap procedures are explained. Babu and Singh (1984) have demonstrated that in general, these two procedures behave similarly for pivotal (Studentized) statistics. The fact that the bootstrap is not the solution for all of the problems has been known to statistics community for a long time; however, this fact is rarely touched on in the manuscripts meant for practitioners. It was first observed by Babu (1984) that the bootstrap does not work in the infinite variance case. Bootstrap Techniques for Signal Processing explains the limitations of bootstrap method with an example. I especially liked the presentation style. The basic results are stated without proofs; however, the application of each result is presented as a simple step-by-step process, easy for nonstatisticians to follow. The bootstrap procedures, such as moving block bootstrap for dependent data, along with applications to autoregressive models and for estimation of power spectral density, are also presented in Chapter 2. Signal detection in the presence of noise is generally formulated as a testing of hypothesis problem. Chapter 3 introduces principles of bootstrap hypothesis testing. The topics are introduced with interesting real life examples. Flow charts, typical in engineering literature, are used to aid explanations of the bootstrap hypothesis testing procedures. The bootstrap leads to second-order correction due to pivoting; this improvement in the results due to pivoting is also explained. In the second part of Chapter 3, signal processing is treated as a regression problem. The performance of the bootstrap for matched filters as well as constant false-alarm rate matched filters is also illustrated. Chapters 2 and 3 focus on estimation problems. Chapter 4 introduces bootstrap methods used in model selection. Due to the inherent structure of the subject matter, this chapter may be difficult for nonstatisticians to follow. Chapter 5 is the most impressive chapter in the book, especially from the standpoint of statisticians. It provides real data bootstrap applications to illustrate the theory covered in the earlier chapters. These include applications to optimal sensor placement for knock detection and land-mine detection. The authors also provide a MATLAB toolbox comprising frequently used routines. Overall, this is a very useful handbook for engineers, especially those working in signal processing.

Probability and Random Processes

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

Positioning in wireless networks is mainly used for safety, gaming, and commercial services It is expected to increase in popularity when emergency call services become mandatory as well as with the advent of more advanced location-based services and mobile gaming In this article, we discuss and illustrate the possibilities and fundamental limitations associated with mobile positioning based on available wireless network measurements The possibilities include a sensor fusion approach and model-based filtering, while the fundamental limitations provide hard bounds on the accuracy of position estimates, given the information in the measurements in the most favorable situation The focus of this article is to illustrate the relation between performance requirements, such as those stated by the Federal Communications Commission (FCC), and the available measurements Specific issues on accuracy limitation in each measurement, such as synchronization and multipath problems, are briefly commented upon A geometrical example, as well as a realistic example adopted from a cell planning tool, are used for illustration

Mobile positioning using wireless networks: possibilities and fundamental limitations based on available wireless network measurements

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

/pdf/ranging-with-ultrawide-bandwidth-signals-in-multipath-illizjdvoz.pdf

Ranging With Ultrawide Bandwidth Signals in Multipath Environments

We present an analysis of the time-of-arrival (TOA), time-difference-of-arrival (TDOA), angle-of-arrival (AOA) and signal strength (SS) based positioning methods in a non-line-of-sight (NLOS) environment. Single path (line-of-sight (LOS) or NLOS) propagation is assumed. The best geolocation accuracy is evaluated in terms of the Cramer-Rao lower bound (CRLB) or the generalized CRLB (G-CRLB), depending on whether prior statistics of NLOS induced errors are unavailable or available. We then show that the maximum likelihood estimator (MLE) using only LOS estimates and the maximum a posteriori probability (MAP) estimator using both LOS and NLOS data can asymptotically achieve the CRLB and the G-CRLB, respectively. Hybrid schemes that adopt more than one type of position-pertaining data and the relationship among the four methods in terms of their positioning accuracy are also investigated.

Analysis of wireless geolocation in a non-line-of-sight environment

Wireless geolocation in a multipath environment is of particular interest for wideband communications and fast-developing ultrawideband (UWB) technologies. Conventional methods are solely based on first arriving signals. In this paper, it is investigated whether and under what conditions processing delay estimates of multipath components in addition to first arrivals can enhance the positioning accuracy. It is shown that the enhancement depends on two principal factors: strength of multipath components and variance of nonline-of-sight (NLOS) delays. Analytical results, which are derived as an extension of a single-path propagation case (IEEE Trans. Wireless Commun., Feb. 2006), are presented first. Their practical implications are then discussed by examining several numerical examples. Finally, modified schemes of practical interest in a multipath environment are proposed

On Time-of-arrival Positioning in a Multipath Environment

The time-of-arrival (TOA), time-difference-of-arrival (TDOA) and signal strength (SS) methods have been widely accepted as three principal techniques for positioning a mobile station (MS) in a wireless communication system. To the best of our knowledge, previous studies tend to treat these methods separately, and less analytical results on their relationship have been reported. We first examine the link between the TOA and TDOA methods. We provide an analytical explanation for the claim that, given a set of BS locations and an MS position, the TOA method should achieve a higher positioning precision than its TDOA counterpart. However, the two positioning methods may attain the same level of accuracy under certain conditions. We then investigate the tradeoff between the accuracy limits of the TOA and SS based methods, which leads to our proposal of a hybrid distance estimation scheme that combines both TOA and SS data.

/pdf/on-relation-among-time-delay-and-signal-strength-based-ugsj4vemk1.pdf

On relation among time delay and signal strength based geolocation methods

A wireless positioning apparatus includes a multipath delay estimator configured to estimate multipath delay components from one or more received signals; a selector configured to select appropriate delay estimates from the estimated multipath delay components according to a predetermined criterion; a storage configured to store an NLOS delay function defining prior statistics of information about non-line-of-sight delays; and a position estimator configured to estimate a position of a mobile station based on the selected delay estimates and the NLOS delay function read out from the storage.

Wireless positioning approach using time delay estimates of multipath components

Wireless geolocation in a multipath environment is of particular interest for wideband communications. The conventional approach makes use of first-arriving signals only. In this paper, we investigate whether and under what conditions processing multipath delays should enhance the positioning accuracy. The best achievable positioning accuracy is evaluated in terms of the Cramer-Rao low bound (CRLB) and the generalized-CRLB (G-CRLB), depending on whether prior statistics of non-line-sight (NLOS) induced errors are available. We then show that such prior statics are critical to the accuracy improvement when the multipath delays are processed. Furthermore, the degree of accuracy enhancement depends on two major factors: the strength of multipath components and the variance of NLOS induced errors. The corresponding positioning receivers are also discussed. In previous work (Y. Qi and H. Kobayashi, Proc. 2002 IEEE Int. Conf. on Acoustic Speech and Sig. Proc., pp. 2473-2476, 2002, and Proc. IEEE Vehicle Tech. Conf., pp. 285-288, 2002), we developed an analysis of the time-of-arrival (TOA) positioning method in an NLOS environment, assuming single path propagation. The main results obtained there are extended and applied to the multipath case in this paper.

Yihong Qi

Papers

Analysis of wireless geolocation in a non-line-of-sight environment

On Time-of-arrival Positioning in a Multipath Environment

On relation among time delay and signal strength based geolocation methods

Wireless positioning approach using time delay estimates of multipath components

On time-of-arrival positioning in a multipath environment