TY  - JOUR
AU  - Rosić Vitas, Maja
AU  - Simić, Mirjana
AU  - Pejović, Predrag V.
AU  - Bjelica, M.
PY  - 2017
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/2718
AB  - Determining an optimal emitting source location based on the time of arrival (TOA) measurements is one of the important problems in Wireless Sensor Networks (WSNs). The nonlinear least-squares (NLS) estimation technique is employed to obtain the location of an emitting source. This optimization problem has been formulated by the minimization of the sum of squared residuals between estimated and measured data as the objective function. This paper presents a hybridization of Genetic Algorithm (GA) for the determination of the global optimum solution with the local search Newton-Raphson (NR) method. The corresponding Cramer-Rao lower bound (CRLB) on the localization errors is derived, which gives a lower bound on the variance of any unbiased estimator. Simulation results under different signal-to-noise-ratio (SNR) conditions show that the proposed hybrid Genetic Algorithm-Newton-Raphson (GA-NR) improves the accuracy and efficiency of the optimal solution compared to the regular GA.
PB  - Univerzitet u Kragujevcu - Fakultet tehničkih nauka, Čačak
T2  - Serbian Journal of Electrical Engineering
T1  - Optimal source localization problem based on TOA measurements
EP  - 176
IS  - 1
SP  - 161
VL  - 14
DO  - 10.2298/SJEE1701161R
ER  - 

@article{
author = "Rosić Vitas, Maja and Simić, Mirjana and Pejović, Predrag V. and Bjelica, M.",
year = "2017",
abstract = "Determining an optimal emitting source location based on the time of arrival (TOA) measurements is one of the important problems in Wireless Sensor Networks (WSNs). The nonlinear least-squares (NLS) estimation technique is employed to obtain the location of an emitting source. This optimization problem has been formulated by the minimization of the sum of squared residuals between estimated and measured data as the objective function. This paper presents a hybridization of Genetic Algorithm (GA) for the determination of the global optimum solution with the local search Newton-Raphson (NR) method. The corresponding Cramer-Rao lower bound (CRLB) on the localization errors is derived, which gives a lower bound on the variance of any unbiased estimator. Simulation results under different signal-to-noise-ratio (SNR) conditions show that the proposed hybrid Genetic Algorithm-Newton-Raphson (GA-NR) improves the accuracy and efficiency of the optimal solution compared to the regular GA.",
publisher = "Univerzitet u Kragujevcu - Fakultet tehničkih nauka, Čačak",
journal = "Serbian Journal of Electrical Engineering",
title = "Optimal source localization problem based on TOA measurements",
pages = "176-161",
number = "1",
volume = "14",
doi = "10.2298/SJEE1701161R"
}

Rosić Vitas, M., Simić, M., Pejović, P. V.,& Bjelica, M.. (2017). Optimal source localization problem based on TOA measurements. in Serbian Journal of Electrical Engineering
Univerzitet u Kragujevcu - Fakultet tehničkih nauka, Čačak., 14(1), 161-176.
https://doi.org/10.2298/SJEE1701161R

Rosić Vitas M, Simić M, Pejović PV, Bjelica M. Optimal source localization problem based on TOA measurements. in Serbian Journal of Electrical Engineering. 2017;14(1):161-176.
doi:10.2298/SJEE1701161R .

Rosić Vitas, Maja, Simić, Mirjana, Pejović, Predrag V., Bjelica, M., "Optimal source localization problem based on TOA measurements" in Serbian Journal of Electrical Engineering, 14, no. 1 (2017):161-176,
https://doi.org/10.2298/SJEE1701161R . .