جستجوی مقالات مرتبط با کلیدواژه « بهینه سازی غیرخطی مقید » در نشریات گروه « پدافند غیرعامل »

In this paper, a device-based localization method is proposed based on the weighted least square error. The most important challenge in localization is the effect of non-line of sight signals (NLoS) at reference nodes which cause outliers and degrade the estimation accuracy of localization. To meet this challenge and avoid such consequences, a new method is introduced based on the combination of weighted reference nodes method and identification and elimination of the NLoS signals method. Another challenge is the dependency of NLoS signals on the transmission environment. Based on this reason, obtaining a probability density function (PDF) to analyze the behavior of NLoS signals is complex and time-consuming, specifically in device-based localization methods that run on mobile wireless targets with limited battery. Therefore, in this paper, a low-complexity method of identification and weighting of NLoS signals is proposed without requiring priority knowledge regarding NLoS bias PDFs. In this method, the frequency of reference nodes in different estimation groups is used to identify and weight the NLoS signals. Finally, the target location is modeled via a constraint non-linear optimization problem and is solved through the Lagrange method.Simulation results illustrate that the proposed method improves the performance of localization in  comparison to linear and nonlinear unweighted-localization methods. In the proposed method, 35% of   localization errors are lower than 0.25 m showing approximately 30% improvement in the localization   performance.  Moreover, 95% of localization errors are lower than 2 m, and the performance increase by 20% in comparison to the unweighted-localization methods. In the case that the number of reference nodes is small, the proposed method provides higher reliability in the location estimation and specifically, when 35% of reference nodes are the line of sight, the estimation accuracy is increased significantly.