Hibridna optimizaciona metoda bazirana na genetskom algoritmu za tačno određivanje nepoznate lokacije predajnika
A hybrid genetic optimization method for accurate target localization
Апстракт
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.
Кључне речи:
predajnik / pozicioniranje / optimizacija / hibridna metoda / genetski algoritam / receiver / optimization / localization / hybrid method / genetic algorithmИзвор:
Scientific Technical Review, 2018, 68, 3, 50-55Издавач:
- Vojnotehnički institut, Beograd
Колекције
Институција/група
Mašinski fakultetTY - JOUR AU - Rosić Vitas, Maja AU - Simić, Mirjana AU - Pejović, Predrag V. PY - 2018 UR - https://machinery.mas.bg.ac.rs/handle/123456789/2756 AB - 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. AB - 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. PB - Vojnotehnički institut, Beograd T2 - Scientific Technical Review T1 - Hibridna optimizaciona metoda bazirana na genetskom algoritmu za tačno određivanje nepoznate lokacije predajnika T1 - A hybrid genetic optimization method for accurate target localization EP - 55 IS - 3 SP - 50 VL - 68 DO - 10.5937/str1803050R ER -
@article{ author = "Rosić Vitas, Maja and Simić, Mirjana and Pejović, Predrag V.", year = "2018", 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.", publisher = "Vojnotehnički institut, Beograd", journal = "Scientific Technical Review", title = "Hibridna optimizaciona metoda bazirana na genetskom algoritmu za tačno određivanje nepoznate lokacije predajnika, A hybrid genetic optimization method for accurate target localization", pages = "55-50", number = "3", volume = "68", doi = "10.5937/str1803050R" }
Rosić Vitas, M., Simić, M.,& Pejović, P. V.. (2018). Hibridna optimizaciona metoda bazirana na genetskom algoritmu za tačno određivanje nepoznate lokacije predajnika. in Scientific Technical Review Vojnotehnički institut, Beograd., 68(3), 50-55. https://doi.org/10.5937/str1803050R
Rosić Vitas M, Simić M, Pejović PV. Hibridna optimizaciona metoda bazirana na genetskom algoritmu za tačno određivanje nepoznate lokacije predajnika. in Scientific Technical Review. 2018;68(3):50-55. doi:10.5937/str1803050R .
Rosić Vitas, Maja, Simić, Mirjana, Pejović, Predrag V., "Hibridna optimizaciona metoda bazirana na genetskom algoritmu za tačno određivanje nepoznate lokacije predajnika" in Scientific Technical Review, 68, no. 3 (2018):50-55, https://doi.org/10.5937/str1803050R . .