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

Advances on localization techniques for wireless sensor networks

The term “ranging” is often used to indicate the operations that make it possible to estimate the distance between two nodes by processing some signals generated and/or received by the nodes. In wireless systems, a very popular ranging method makes use of the radio signal strength (RSS), which is a measure of the received radio signal power. However, RSS-based ranging is considered very inaccurate, particularly in indoor environments, mainly because of the randomness of the received signal power. In this tutorial paper, we provide an in-depth analysis of the main factors that affect the variability of the received signal power and the accuracy of the RSS measurements. Starting from a survey of the most common and widely accepted models for the radio signal propagation and the RSS-based ranging, we then focus our attention on some technological and procedural pitfalls that are often overlooked, but may significantly affect the accuracy of the RSS-based ranging, and we suggest possible techniques to alleviate such problems. The theoretical argumentation is backed up by a set of empirical results in different scenarios. We conclude this paper by providing some best-practice recommendations for proper RSS-based ranging estimation in wireless networks and discussing new approaches and open research challenges.

Best Practice in RSS Measurements and Ranging

This tutorial reviews algorithms which turn measured ranges into position solutions. From their basic mathematical principles, we relate and compare relevant aspects of these algorithms. Special attention is given to the direct (non-iterative) algorithms, which are frequently applied in indoor positioning. Most of them are shown to be essentially the same, as they can be related through applying different weighting schemes. This tutorial is intended as a useful guide to help researchers and system designers evaluate and select appropriate range-based positioning algorithms for their applications at hand.

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Review of range-based positioning algorithms

In cellular communication systems where non-line-of-sight (NLOS) channels are dominant as compared to line-of-sight (LOS) channels, a time difference of arrival (TDOA) localization method can outperform a time of arrival (TOA) localization method, although the TDOA method loses one degree of freedom in the number of usable range estimates when selecting a range estimate as the reference and doubles the variance of range sampling error when subtracting the reference range estimate. In this paper, we first show the computer simulation result on the localization performance of conventional TOA and TDOA methods in mixed LOS/NLOS environments, and then validate it by theoretical analysis. Here, we analyze the root mean square localization error by decomposing it into two factors; for one factor, which is the contribution from range sampling error, we evaluate it by the Cramer-Rao lower-bound, whereas for the other factor, which is the contribution from positively biased NLOS range error, we analyze it by perturbation method. We show that the theoretical result can well explain the localization performance by computer simulation for both the TOA and TDOA methods, and furthermore, we derive a simple condition among the number of cells, the average and variance of NLOS range error distribution which can correctly predict whether the TDOA method outperforms the TOA method or not for the case of all NLOS channels.

A Perturbation Analysis on the Performance of TOA and TDOA Localization in Mixed LOS/NLOS Environments

Linear least squares (LLS) estimation is a low complexity but sub-optimum method for estimating the location of a mobile terminal (MT) from some distance measurements. It requires selecting one of the fixed terminals (FTs) as a reference FT for obtaining a linear set of expressions. However, selection of the reference FT is commonly performed arbitrarily in the literature. In this paper, a method for selection of the reference FT is proposed, which improves the location accuracy compared to a fixed selection of the reference FT. Moreover, a covariance- matrix based LLS estimator is proposed in line of sight (LOS) and non-LOS (NLOS) environments which further improves accuracy since the correlations between the observations are exploited. Simulation results prove the effectiveness of the proposed techniques.

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H. Inamura

Papers

Enhancements to Linear Least Squares Localization Through Reference Selection and ML Estimation