ECATI - Centros de Investigação

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Algorithms for Estimating the Location of Remote Nodes Using Smartphones
(IEEE, 2019) Pedro, Dario; Tomic, Slavisa; Bernardo, Luís; Beko, Marko; Oliveira, Rodolfo; Dinis, Rui; Pinto, Paulo; Amaral, Pedro; Escola de Comunicação, Arquitetura, Artes e Tecnologias da Informação
Locating the position of a remote node on a wireless network is becoming more relevant, as we move forward in the Internet of things and in autonomous vehicles. This paper proposes a new system to implement the location of remote nodes. A new prototype Android application has been developed to collect real measurements and to study the performance of several smartphone's sensors and location algorithms, including an innovative one, based on the second order cone programming (SOCP) relaxation. The application collects theWiFi access points information and the terminal location. An internal odometry module developed for the prototype is used when Android's service is unavailable. This paper compares the performance of existing location estimators given in closed form, an existing SOCP one, and the new SOCP location estimator proposed, which has reduced complexity. An algorithm to merge measurements from non-identical terminals is also proposed. Cooperative and terminal stand-alone operations are compared, showing a higher performance for SOCP-based ones, that are capable of estimating the path loss exponent and the transmission power. The heterogeneous terminals were also used in the tests. Our results show that the accurate positioning of static remote entities can be achieved using a single smartphone. On the other hand, the accurate real-time positioning of the mobile terminal is provided when three or more scattered terminal nodes cooperate sharing the samples taken synchronously.
Bayesian methodology for target tracking using combined RSS and AoA measurements
(Elsevier, 2017) Tomic, Slavisa; Beko, Marko; Dinis, Rui; Tuba, Milan; Bacanin, Nebojsa; Escola de Comunicação, Arquitetura, Artes e Tecnologias da Informação
This work addresses the target tracking problem based on received signal strength (RSS) and angle of arrival (AoA) measurements. The Bayesian methodology, which integrates the information given by observations with prior knowledge extracted from target motion model in order to enhance the estimation accuracy was employed. First, by converting the considered highly non-linear measurement model into a linear one, i.e., a novel linearization technique of the measurement model is proposed. The derived model is then merged with the prior knowledge, and a novel maximum a posteriori (MAP) estimator whose solution is given in closed-form is proposed. It is also shown that the Kalman filter (KF) can be directly applied on top of the linearized observation model, which results in a proposal of a novel KF algorithm. Furthermore, to the best of authors’ knowledge, this paper premierly presents the application of the extended KF (EKF) and the unscented KF (UKF) to the considered tracking problem, by applying first-order linearization technique to the original non-linear model, and by applying the unscented transformation to carefully selected sample points, respectively. Finally, importance weights are computed for a large number of randomly selected sample points to render a well-known particle filter (PF) solution. Simulation results show that the proposed algorithms perform better than a naive one which uses only information from observations. They also confirm the effectiveness of the proposed linearization technique in comparison with the existing one, reducing the estimation error for about 25%.
A bisection-based approach for exact target localization in NLOS environments
(Edições Universitárias Lusófonas, 2017) Tomic, Slavisa; Beko, Marko; Escola de Comunicação, Arquitetura, Artes e Tecnologias da Informação
This work addresses the range-based target localization problem in adverse non-line-of-sight (NLOS) en- vironments. We start by deriving the maximum likelihood (ML) estimator from the measurement model, since it is asymptotically efficient. However, this estimator is highly non-convex and difficult to solve di- rectly. Hence, we convert the localization problem into a generalized trust region sub-problem (GTRS) framework. Although still non-convex in general, the derived estimator is strictly decreasing over a read- ily obtained interval, and thus, can be solved exactly by a bisection procedure. In huge contrast to exist- ing algorithms, which either require the knowledge about the magnitude of the NLOS bias or to a priori distinguish between line-of-sight (LOS) and NLOS links, the new one does not require such prerequi- sites. Also, the computational complexity of the proposed algorithm is linear in the number of reference nodes, unlike the majority of existing ones. Our simulation results show that the new algorithm possesses a steady NLOS bias mitigation capacity and that it represents an excellent alternative in the sense of the trade offbetween accuracy and complexity. To be more specific, it not only matches the performance of existing methods (majority of which significantly more computationally complex) but outperforms them in general. Moreover, the performance of the proposed algorithm is validated through real-indoor exper- imental data.
Comunicações Não-coerentes em Sistemas MIMO: Desenho do Receptor e Construção de Códigos
(Edições Universitárias Lusófonas, 2009) Beko, Marko; CICANT (FCT) - Centro de Investigação em Comunicação Aplicada, Cultura e Novas Tecnologias
Este trabalho aborda o problema do desenho de códigos espácio-temporais para sistemas de comunicação multiple-input multiple-output (MIMO) sem fios. Considera-se o contexto realista e desafiante da recepção não-coerente (a realização do canal é desconhecida no receptor). O detector conhecido como generalized likelihood ratio test (GLRT)é implementado no receptor e, ao contrário da maioria das abordagens actuais, permite-se uma estrutura de correlação arbitrária para o ruído gaussiano de observação. Apresenta-se uma análise teórica para a probabilidade de erro do detector, em ambos os regimes assimptóticos de relação sinal-ruído (SNR) alta e baixa. Essa análise conduz a um critério de optimalidade para desenho de códigos e permite uma re-interpretação geométrica do problema abordado como um problema de empacotamento óptimo num producto Cartesiano de espaço projectivos. A construção dos códigos implica a resolução de um problema de optimização não-linear, não-diferenciável e de dimensão elevada, o qual foi abordado aqui em duas fases. A primeira fase explora uma relaxação convexa do problema original para obter uma estimativa inicial. A segunda fase, refina essa estimativa através de um algoritmo iterativo de descida do gradiente ao longo de geodésicas, explorando-se assim a geometria Riemanniana imposta pelas restricões de potência sobre os códigos espáciotemporais. Mostra-se que o desempenho dos novos códigos obtidos por este método excede o das soluções previamente conhecidas. De facto, para algumas configurações particulares, estas novas constelações atingem o limiar de Rankin e são por isso garantidamente óptimas.
Distributed localization with complemented RSS and AOA measurements : theory and methods
(Edições Universitárias Lusófonas, 2019) Tomic, Slavisa; Beko, Marko; Matos, Luís M. Camarinha de; Oliveira, Luís Bica; Escola de Comunicação, Arquitetura, Artes e Tecnologias da Informação
Remarkable progress in radio frequency and micro-electro-mechanical systems integrated circuit design over the last two decades has enabled the use of wireless sensor networks with thousands of nodes. It is foreseen that the fifth generation of networks will provide significantly higher bandwidth and faster data rates with potential for interconnecting myriads of heterogeneous devices (sensors, agents, users, machines, and vehicles) into a single network (of nodes), under the notion of Internet of Things. The ability to accurately determine the physical location of each node (stationary or moving) will permit rapid development of new services and enhancement of the entire system. In outdoor environments, this could be achieved by employing global navigation satellite system (GNSS) which offers a worldwide service coverage with good accuracy. However, installing a GNSS receiver on each device in a network with thousands of nodes would be very expensive in addition to energy constraints. Besides, in indoor or obstructed environments (e.g., dense urban areas, forests, and canyons) the functionality of GNSS is limited to non-existing, and alternative methods have to be adopted. Many of the existing alternative solutions are centralized, meaning that there is a sink in the network that gathers all information and executes all required computations. This approach quickly becomes cumbersome as the number of nodes in the network grows, creating bottle-necks near the sink and high computational burden. Therefore, more effective approaches are needed. As such, this work presents a survey (from a signal processing perspective) of existing distributed solutions, amalgamating two radio measurements, received signal strength (RSS) and angle of arrival (AOA), which seem to have a promising partnership. The present article illustrates the theory and offers an overview of existing RSS-AOA distributed solutions, as well as their analysis from both localization accuracy and computational complexity points of view. Finally, the article identifies potential directions for future research.
Elephant Herding Optimization for Energy-Based Localization
(MDPI, 2018) Correia, Sérgio; Beko, Marko; Cruz, Luís Alberto da Silva; Tomic, Slavisa; Escola de Comunicação, Arquitetura, Artes e Tecnologias da Informação
This work addresses the energy-based source localization problem in wireless sensors networks. Instead of circumventing the maximum likelihood (ML) problem by applying convex relaxations and approximations, we approach it directly by the use of metaheuristics. To the best of our knowledge, this is the first time that metaheuristics are applied to this type of problem. More specifically, an elephant herding optimization (EHO) algorithm is applied. Through extensive simulations, the key parameters of the EHO algorithm are optimized such that they match the energy decay model between two sensor nodes. A detailed analysis of the computational complexity is presented, as well as a performance comparison between the proposed algorithm and existing non-metaheuristic ones. Simulation results show that the new approach significantly outperforms existing solutions in noisy environments, encouraging further improvement and testing of metaheuristic methods.
Energy-based acoustic localization by improved elephant herding optimization
(Edições Universitárias Lusófonas, 2020) Correia, Sérgio; Beko, Marko; Tomic, Slavisa; Cruz, Luís Alberto da Silva; Escola de Comunicação, Arquitetura, Artes e Tecnologias da Informação
The present work proposes a new approach to address the energy based acoustic localization problem. The proposed approach represents an enhanced version of evolutionary optimization based on Elephant Herding Optimization (EHO), where two major contributions are introduced. Firstly, instead of random initialization of elephant population, we exploit particularities of the problem at hand to develop an intelligent initialization scheme. More precisely, distance estimates obtained at each reference point are used to determine the regions in which a source is most likely to be located at. Secondly, rather than letting elephants to simply wander around in their search for an update in the source location, we base their motion on a local search scheme which is found on a discrete gradient method. Such a methodology significantly accelerates the convergence of the proposed algorithm, and comes at a very low computational cost, since discretization allows us to avoid the actual gradient computations. Our simulation results show that the enhanced algorithm significantly outperforms the standard EHO method for low noise and matches its performance for high noise, in terms of localization accuracy. Moreover, they show that the proposed enhanced version requires significantly less number of iterations to converge.
Estimating Directional Data From Network Topology for Improving Tracking Performance
(Multidisciplinary Digital Publishing Institute (MDPI), 2019) Tomic, Slavisa; Beko, Marko; Dinis, Rui; Montezuma, Paulo; Escola de Comunicação, Arquitetura, Artes e Tecnologias da Informação
This work proposes a novel approach for tracking a moving target in non-line-of-sight (NLOS) environments based on range estimates extracted from received signal strength (RSS) and time of arrival (TOA) measurements. By exploiting the known architecture of reference points to act as an improper antenna array and the range estimates, angle of arrival (AOA) of the signal emitted by the target is first estimated at each reference point. We then show how to take advantage of these angle estimates to convert the problem into a more convenient, polar space, where a linearization of the measurement models is easily achieved. The derived linear model serves as the main building block on top of which prior knowledge acquired during the movement of the target is incorporated by adapting a Kalman filter (KF). The performance of the proposed approach was assessed through computer simulations, which confirmed its effectiveness in combating the negative effect of NLOS bias and superiority in comparison with its naive counterpart, which does not take prior knowledge into consideration.
Exact Robust Solution to TW-ToA-Based Target Localization Problem With Clock Imperfections
(Institute of Electrical and Electronics Engineers Inc., 2018) Tomic, Slavisa; Beko, Marko; Escola de Comunicação, Arquitetura, Artes e Tecnologias da Informação
This letter addresses the problem of target localization based on two-way time of arrival (TW-ToA) measurements with clock imperfections. In addition to the target location, the turn-around times and clock skews are considered unknown. Since an optimal estimator for this problem cannot be tackled directly, we approximate it by a suboptimal, robust one, formulated as a generalized trust region subproblem. Even though nonconvex in general, exact solution of the derived estimator can be obtained by just a bisection procedure. Simulation results validate the effectiveness of the proposed technique, matching the performance of the state of the art with significantly lower computational complexity.
Exploiting orientation information to improve range-based localization accuracy
(Edições Universitárias Lusófonas, 2020) Tomic, Slavisa; Beko, Marko; Tuba, Milan; Escola de Comunicação, Arquitetura, Artes e Tecnologias da Informação
This work addresses target localization problem in precarious surroundings where possibly no links are line of sight. It exploits the known architecture of available reference points to act as an irregular antenna array in order to estimate the azimuth angle between a reference point and a target, based on distance estimates withdrawn from integrated received signal strength (RSS) and time of arrival (TOA) observations. These ctitious azimuth angle observations are then used to linearize the measurement models, which triggers effortless derivation of a new estimator in a closed-form. It is shown here that, by using xed network geometry in which target orientation with respect to a line formed by a pair of anchors can be correctly estimated, the localization performance can be signi cantly enhanced. The new approach is validated through computer simulations, which corroborate our intuition of pro ting from inherent information within a network.
A geometric approach for distributed multi-hop target localization in cooperative networks
(Edições Universitárias Lusófonas, 2020) Tomic, Slavisa; Beko, Marko; Escola de Comunicação, Arquitetura, Artes e Tecnologias da Informação
This work addresses target localization problem in cooperative distributed sensor networks, in which all sensors are capable of measuring Received Signal Strength (RSS), but only some are appropriately equipped to measure Angle Of Arrival (AOA) of the received signal. A novel approach based on simple geometry and multi-hopping is proposed, which allows for natural conversion of the problem into a Generalized Trust Region Sub-Problem (GTRS). The proposed algorithm comprises three main steps, each of them with linear computational cost in the number of neighbors, making it suitable for real-time applications. Our simulation results validate the performance of the new algorithm, surpassing some significantly more complex ones, and almost achieving a lower bound set by an existing algorithm which uses some (unrealistic) assumptions in its favor.
A linear estimator for network localization using integrated RSS and AOA measurements
(Institute of Electrical and Electronics Engineers Inc., 2019-03) Tomic, Slavisa; Beko, Marko; Tuba, Milan; Escola de Comunicação, Arquitetura, Artes e Tecnologias da Informação; Faculdade de Engenharia; COPELABS (FCT) - Centro de Investigação em Computação Centrada nas Pessoas e Cognição (CTS)
This letter addresses the problem of simultaneous localization of multiple targets in three-dimensional cooperative wireless sensor networks. To this end, integrated received signal strength and angle of arrival measurements are employed. By exploiting the convenient nature of spherical representation of the considered problem, the measurement models are linearized and a sub-optimal estimator is formulated. Unlike the maximum likelihood estimator, which is highly non-convex and difficult to tackle directly, the derived estimator is quadratic and has a closed-form solution. Its computational complexity is linear in the number of connections and its accuracy surpasses the accuracy of existing ones in all considered scenarios.
On Target Localization Using Combined RSS and AoA Measurements
(Edições Universitárias Lusófonas, 2018) Tomic, Slavisa; Beko, Marko; Dinis, Rui; Bernardo, Luís; Escola de Comunicação, Arquitetura, Artes e Tecnologias da Informação
This work revises existing solutions for a problem of target localization in wireless sensor networks (WSNs), utilizing integrated measurements, namely received signal strength (RSS) and angle of arrival (AoA). The problem of RSS/AoA-based target localization became very popular in the research community recently, owing to its great applicability potential and relatively low implementation cost. Therefore, here, a comprehensive study of the state-of-the-art (SoA) solutions and their detailed analysis is presented. The beginning of this work starts by considering the SoA approaches based on convex relaxation techniques (more computationally complex in general), and it goes through other (less computationally complex) approaches, as well, such as the ones based on the generalized trust region sub-problems framework and linear least squares. Furthermore, a detailed analysis of the computational complexity of each solution is reviewed. Furthermore, an extensive set of simulation results is presented. Finally, the main conclusions are summarized, and a set of future aspects and trends that might be interesting for future research in this area is identified.
A Robust NLOS Bias Mitigation Technique for RSS-TOA-Based Target Localization
(Edições Universitárias Lusófonas, 2019) Tomic, Slavisa; Beko, Marko; Escola de Comunicação, Arquitetura, Artes e Tecnologias da Informação
This letter proposes a novel robust mitigation technique to address the problem of target localization in adverse nonline- of-sight (NLOS) environments. The proposed scheme is based on combined received signal strength and time of arrival measurements. Influence of NLOS biases is mitigated by treating them as nuisance parameters through a robust approach. Due to a high degree of difficulty of the considered problem, it is converted into a generalized trust region sub-problem by applying certain approximations, and solved efficiently by merely a bisection procedure. Numerical results corroborate the effectiveness of the proposed approach, rendering it the most accurate one in all considered scenarios.
Special issue :localization in wireless sensor networks
(MDPI Journal of Sensor and Actuator Networks, 2020) Tomic, Slavisa; Beko, Marko; Escola de Comunicação, Arquitetura, Artes e Tecnologias da Informação
Target Localization in NLOS Environments Using RSS and TOA Measurements
(Edições Universitárias Lusófonas, 2018) Tomic, Slavisa; Beko, Marko; Tuba, Milan; Correia, Victor M. Franco; Escola de Comunicação, Arquitetura, Artes e Tecnologias da Informação
This letter addresses the problem of target localization in adverse non-line-of-sight environments. By utilizing integrated received signal strength and time of arrival measurements, a novel alternating algorithm is proposed. The new algorithm is derived by converting the original nonconvex problem into a generalized trust region sub-problem framework, which can be solved exactly by just a bisection procedure. Therefore, the proposed algorithm is very light in terms of computational cost, and its excellent estimation accuracy is validated through computer simulations.
Target Localization via Integrated and Segregated Ranging Based on RSS and TOA Measurements
(MDPI, 2019) Tomic, Slavisa; Beko, Marko; Escola de Comunicação, Arquitetura, Artes e Tecnologias da Informação
This work addresses the problem of target localization in adverse non-line-of-sight (NLOS) environments by using received signal strength (RSS) and time of arrival (TOA) measurements. It is inspired by a recently published work in which authors discuss about a critical distance below and above which employing combined RSS-TOA measurements is inferior to employing RSS-only and TOA-only measurements, respectively. Here, we revise state-of-the-art estimators for the considered target localization problem and study their performance against their counterparts that employ each individual measurement exclusively. It is shown that the hybrid approach is not the best one by default. Thus, we propose a simple heuristic approach to choose the best measurement for each link, and we show that it can enhance the performance of an estimator. The new approach implicitly relies on the concept of the critical distance, but does not assume certain link parameters as given. Our simulations corroborate with findings available in the literature for line-of-sight (LOS) to a certain extent, but they indicate that more work is required for NLOS environments. Moreover, they show that the heuristic approach works well, matching or even improving the performance of the best fixed choice in all considered scenarios.
Target Tracking with Sensor Navigation Using Coupled RSS and AoA Measurements
(MDPI, 2017) Tomic, Slavisa; Beko, Marko; Dinis, Rui; Gomes, João Pedro; Escola de Comunicação, Arquitetura, Artes e Tecnologias da Informação
This work addresses the problem of tracking a signal-emitting mobile target in wireless sensor networks (WSNs) with navigated mobile sensors. The sensors are properly equipped to acquire received signal strength (RSS) and angle of arrival (AoA) measurements from the received signal, while the target transmit power is assumed not known. We start by showing how to linearize the highly non-linear measurement model. Then, by employing a Bayesian approach, we combine the linearized observation model with prior knowledge extracted from the state transition model. Based on the maximum a posteriori (MAP) principle and the Kalman filtering (KF) framework, we propose new MAP and KF algorithms, respectively. We also propose a simple and efficient mobile sensor navigation procedure, which allows us to further enhance the estimation accuracy of our algorithms with a reduced number of sensors. Model flaws, which result in imperfect knowledge about the path loss exponent (PLE) and the true mobile sensors’ locations, are taken into consideration. We have carried out an extensive simulation study, and our results confirm the superiority of the proposed algorithms, as well as the effectiveness of the proposed navigation routine.

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