Andreu Urruela

A non-line-of-sight mitigation technique based on ML-detection

Geolocation in non line of sight (NLOS) environments is an important issue in wireless communication networks. In several recent publications related with the nature and magnitude of the NLOS phenomenon it is concluded that this is the major source of error in position estimators based on time measurements. This article presents a new approach to ameliorate the effect of the NLOS exploiting the redundant time measurements in scenarios with more than the minimum number of base stations (BS). This redundant data allows to formulate the problem as a test of hypothesis performing a hard decision to discard the BS considered to be in a NLOS scenario. Numerical simulations show that the proposed algorithm can discard the NLOS errors in certain scenarios. The algorithm is also compared with some other existing methods to show the advantage of the new approach.

Average performance analysis of circular and hyperbolic geolocation

A comparative performance analysis of four geolocation methods in terms of their theoretical root mean square positioning errors is provided. Comparison is established in two different ways: strict and average. In the strict type, methods are examined for a particular geometric configuration of base stations (BSs) with respect to mobile position, which determines a given noise profile affecting the respective time-of-arrival (TOA) or time-difference-of-arrival (TDOA) estimates. In the average type, methods are evaluated in terms of the expected covariance matrix of the position error over an ensemble of random geometries, so that comparison is geometry independent. Exact semianalytical equations and associated lower bounds (depending solely on the noise profile) are obtained for the average covariance matrix of the position error in terms of the so-called information matrix specific to each geolocation method. Statistical channel models inferred from field trials are used to define realistic prior probabilities for the random geometries. A final evaluation provides extensive results relating the expected position error to channel model parameters and the number of base stations.

Novel closed-form ML position estimator for hyperbolic location

Geolocation of mobile terminals has become in the last decades an important issue in mobile networks. In the literature, there have been presented several closed-form position estimators based on time-difference-of-arrival (TDOA) measurements. Only Fangs estimator can be considered optimum in the maximum likelihood (ML) sense. Unfortunately, it can only be applied to the particular case of two TDOA measurements for the two dimensional (2D) location case. This paper presents an extension of this closed-form estimator to be applied to an arbitrary number of TDOA measurements by means of a transformation in the maximum likelihood function. This allows the ML function minimization to be split in several partial ML minimizations which only consider a subset of the available measurements, where the original Fangs estimator can be applied. Numerical simulations show that the proposed algorithm, that can be considered asymptotically the ML-estimator, attains the theoretical limits for all range of reasonable SNR values and has a low implementation complexity.

Divide-and-Conquer Based Closed-form Position Estimation for AOA and TDOA Measurements

Mobile location using time of arrival (TOA), time difference of arrival (TDOA) or angle of arrival (AOA) measurements has received considerable attention over the last years. Several closed-form algorithms have been presented for the TOA and TDOA case based on approximations of the maximum-likelihood (ML) estimator. In the case of AOA measurements, only ad-hoc estimators have been presented in order to avoid the classical linearization solution that needs an initial guess. This paper presents an approximation of the ML position estimator based on AOA measurements applying the divide-and-conquer approach dividing the ML estimation in smaller problems each one with a closed-form solution. Numerical simulations show that the proposed algorithm outperforms the previous contributions and presents a generic way to combine AOA and TDOA measurements

Efficient mobile location from time measurements with unknown variances in dynamic scenarios

This work is focused on the study of the maximum likelihood (ML) mobile position estimator when the quality of the available measurements is not a-priori known. Based on a statistical analysis, a polynomial time-evolution model is used to simplify the ML function, finding a closed-form approximation of the ML estimator. Numerical simulations show that the proposed algorithm, with a low implementation complexity, attains the Cramer Rao lower bound (CRB) for all reasonable observed window lengths and for any arbitrary distribution of the measurement variances. Although the mathematical development of this closed-form position estimator is quite dense, the obtained algorithm has a very low complexity implementation.

A novel estimator and performance bound for time propagation and Doppler based radio-location

This paper presents the theoretical accuracy limits of the geolocation algorithms based on TOA measurements exploiting the fact that the mobile is moving in a known or unknown direction. The developed expressions show us the possible improvements in terms of accuracy and/or availability due to the diversity created with the movement. A simple algorithm based on the use of TOA drift estimation is also presented in order to compare its performance with the developed theoretical limits. The proposed estimator attains the theoretical limits under certain conditions.

Low complexity tracking for ad-hoc automotive sensor networks

The main focus of this paper is to investigate how the co-operative nature of an ad-hoc sensor network can be exploited in order to reduce the complexity of accurate node locationing algorithms in sensor networks. We propose a novel approach to target tracking, called node-aided tracking. The node-aided tracking algorithm is assisted in the tracking process by the tracked nodes in the network, allowing for a reduction in filter complexity. This approach is shown to be sensitive to network delays. The effects of such delays are investigated, and simple countermeasures are presented that stabilize the algorithm. Further, we present a novel way of evaluating the performance of tracking filters used in automotive safety applications. This new measure, called the time margin difference, takes not only the mean-squared-error into account but also latency in providing location estimates and other important filter characteristics for a fair comparison between different tracking algorithms designed for automotive safety applications. At the end of the paper we present a brief comparison, based on simulation, between the node-aided tracking filter and a traditional high-order Kalman filter with respect to this new performance measure.

Autonomous positioning techniques based on Cramér-Rao lower bound analysis

We consider the problem of autonomously locating a number of asynchronous sensor nodes in a wireless network. A strong focus lies on reducing the processing resources needed to solve the relative positioning problem, an issue of great interest in resource-constrained wireless sensor networks. In the first part of the paper, based on a well-known derivation of the Cramér-Rao lower bound for the asynchronous sensor positioning problem, we are able to construct optimal preprocessing methods for sensor clock-offset cancellation. A cancellation of unknown clock-offsets from the asynchronous positioning problem reduces processing requirements, and, under certain reasonable assumptions, allows for statistically efficient distributed positioning algorithms. Cramér-Rao lower bound theory may also be used for estimating the performance of a positioning algorithm. In the second part of this paper, we exploit this property in developing a distributed algorithm, where the global positioning problem is solved suboptimally, using a divide-and-conquer approach of low complexity. The performance of this suboptimal algorithm is evaluated through computer simulation, and compared to previously published algorithms.

NLOS mitigation based on a Trellis search for wireless location

Wireless location using time of arrival (TOA) and time-difference-of-arrival (TDOA) measurements has received considerable attention over the last years to be the best selection for cell phone location. The major problem related with these types of measurements is the non-line-of-sight problem (NLOS) that happens when the direct path between the base stations (BSs) and the mobile is blocked. This paper presents a new technique to mitigate the NLOS effect in dynamic scenarios based on a Trellis search of the NLOS state. Numerical simulation shows that the proposed technique outperforms the previous contributions because it is able to detect and reject the NLOS measurements.

A novel estimator and theoretical limits for in-car radio-location

This paper is focused on the design of a novel position estimator for in-car applications when some navigation information, such as speed and direction of the movement, can be supplied by additional integrated systems. Concretely, this paper presents a new method based on time-of-arrival (TOA) measurements, exploiting the known, or partially known, drift speed of the mobile. The proposed algorithm transforms the TOA measurements to solve the classic non-linear problem presented in all TOA-based radio-location estimators, obtaining a linear model where the WLS approach can be applied. The associated lower bounds for the known or partially known speed cases are also computed in the paper to conclude that the proposed algorithm attains them asymptotically with large enough observation windows.