2,964 research outputs found
Emitter Location Finding using Particle Swarm Optimization
Using several spatially separated receivers, nowadays positioning techniques, which are implemented to determine the location of the transmitter, are often required for several important disciplines such as military, security, medical, and commercial applications. In this study, localization is carried out by particle swarm optimization using time difference of arrival. In order to increase the positioning accuracy, time difference of arrival averaging based two new methods are proposed. Results are compared with classical algorithms and Cramer-Rao lower bound which is the theoretical limit of the estimation error
Location-Verification and Network Planning via Machine Learning Approaches
In-region location verification (IRLV) in wireless networks is the problem of
deciding if user equipment (UE) is transmitting from inside or outside a
specific physical region (e.g., a safe room). The decision process exploits the
features of the channel between the UE and a set of network access points
(APs). We propose a solution based on machine learning (ML) implemented by a
neural network (NN) trained with the channel features (in particular, noisy
attenuation values) collected by the APs for various positions both inside and
outside the specific region. The output is a decision on the UE position
(inside or outside the region). By seeing IRLV as an hypothesis testing
problem, we address the optimal positioning of the APs for minimizing either
the area under the curve (AUC) of the receiver operating characteristic (ROC)
or the cross entropy (CE) between the NN output and ground truth (available
during the training). In order to solve the minimization problem we propose a
twostage particle swarm optimization (PSO) algorithm. We show that for a long
training and a NN with enough neurons the proposed solution achieves the
performance of the Neyman-Pearson (N-P) lemma.Comment: Accepted for Workshop on Machine Learning for Communications, June 07
2019, Avignon, Franc
AN ADAPTIVE LOCALIZATION SYSTEM USING PARTICLE SWARM OPTIMIZATION IN A CIRCULAR DISTRIBUTION FORM
Tracking the user location in indoor environment becomes substantial issue in recent research High accuracy and fast convergence are very important issues for a good localization system. One of the techniques that are used in localization systems is particle swarm optimization (PSO). This technique is a stochastic optimization based on the movement and velocity of particles. In this paper, we introduce an algorithm using PSO for indoor localization system. The proposed algorithm uses PSO to generate several particles that have circular distribution around one access point (AP). The PSO generates particles where the distance from each particle to the AP is the same distance from the AP to the target. The particle which achieves correct distances (distances from each AP to target) is selected as the target. Four PSO variants, namely standard PSO (SPSO), linearly decreasing inertia weight PSO (LDIW PSO), self-organizing hierarchical PSO with time acceleration coefficients (HPSO-TVAC), and constriction factor PSO (CFPSO) are used to find the minimum distance error. The simulation results show the proposed method using HPSO-TVAC variant achieves very low distance error of 0.19 mete
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