Hibridna optimizaciona metoda bazirana na genetskom algoritmu za tačno određivanje nepoznate lokacije predajnika

Abstract

U ovom radu, prikazan je TOA (Time of Arrival) model pozicioniranja radi određivanja nepoznate lokacije predajnika. Definisan je kriterijum optimalnosti - funkcija cilja, koja predstavlja sumu kvadrata greške pozicioniranja. Za rešavanje postavljenog optimizacionog modela primenjena je nova metoda GA-NM, koje je bazirana na hibridizaciji Genetskog algoritma i Nelder-Mead metode. Predložena hibridna metoda na efikasan način kombinuje globalnu i lokalnu pretragu datih algoritama kako bi se poboljšale optimizacione performanse i tačnost rešenja. Pored ovoga, u radu je izvedena i Kramer-Rao donja granica CRLB (Cramer-Rao Lower Bound) varijanse procene nepoznate lokacije predajnika za TOA metodu pozicioniranja. Rezultati simulacije pokazuju da predložena hibridna GA-NM metoda postiže značajano poboljšanje performansi u odnosu na postojeće metode.

This paper considers the problem of estimating the position of a target based on the time of arrival (TOA) measurements from a set of receivers whose positions are known. The weighted least square (WLS) technique is applied as an efficient existing approach. The optimization problem is formulated by the minimization of the sum of squared residuals between estimated and measured data as the objective function. The hybrid Genetic Algorithm-Nelder-Mead (GA-NM) method is proposed that combines the global search and local search abilities in an effective way in order to improve the performance and the solution accuracy. The corresponding Cramer-Rao lower bound (CRLB) on the localization errors is derived as a benchmark. Simulation results show that the proposed hybrid GA-NM method achieves a significant performance improvement compared to existing methods.