Movlab – Laboratory of Technologies for Interactions and Interfaces

URI permanente desta comunidade:

http://hdl.handle.net/10437/11004

Navegar

Percorrer Movlab – Laboratory of Technologies for Interactions and Interfaces por assunto "ANGLE OF ARRIVAL"

A mostrar 1 - 7 de 7
  • Item
    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%.
  • Item
    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.
  • Item
    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.
  • Item
    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.
  • Item
    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.
  • Item
    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.
  • Item
    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.