Percorrer por autor "Tuba, Milan"
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Item Bayesian methodology for target tracking using combined RSS and AoA measurements(Elsevier, 2017) Tomic, Slavisa; Beko, Marko; Dinis, Rui; Tuba, Milan; Bacanin, NebojsaThis 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 Exploiting orientation information to improve range-based localization accuracy(IEEE Access, 2020) Tomic, Slavisa; Beko, Marko; Tuba, MilanThis 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array in order to estimate the azimuth angle between a reference point and a target, based on distance estimates withdrawn from integrated received signal strength (RSS) and time of arrival (TOA) observations.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.Item A Linear Estimator for Network Localization Using Integrated RSS and AOA Measurements(IEEE, 2019) Tomic, Slavisa; Beko, Marko; Tuba, MilanThis 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 Target Localization in NLOS Environments Using RSS and TOA Measurements(IEEE, 2018) Tomic, Slavisa; Beko, Marko; Tuba, Milan; Correia, Victor M. FrancoThis 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.