TY  - JOUR
AU  - Rosić Vitas, Maja
AU  - Sedak, Miloš
AU  - Simić, Mirjana
AU  - Pejović, Predrag V.
PY  - 2023
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/5360
AB  - This paper addresses the problem of time difference of arrival (TDOA) based passive target localizationand proposes an improved chaos-driven hybrid differential evolution (DE) algorithm and butterfly optimization algorithm (BOA), named ICDEBOA, to solve this complex optimization problem. The proposed algorithm consists of a new mutation strategy with the mechanisms of the BOA algorithm incorporated into the DE algorithm. To boost optimization effectiveness, chaos theory is employed to adjust the control parameter values. The considered localization problem is formulated using the maximum likelihood estimator. To perform the accuracy comparison, the convex constrained weighting least squares algorithm is applied to the considered localization problem as the widely used method in literature. The statistical analysis shows that the proposed modifications to the ICDEBOA algorithm improve its optimization performance, as demonstrated by the improved performance on the CEC2014 benchmark problems. The ICDEBOA algorithm is also shown to be more robust than existing algorithms in noisy environments. Numerical simulation results show that the proposed ICDEBOA algorithm meets the CRLB and achieves better performance than the CWLS, DE, and BOA algorithms.
PB  - MDPI, Basel, Switzerland
T2  - Applied Sciences
T1  - An Improved Chaos Driven Hybrid Differential Evolution and Butterfly Optimization Algorithm for Passive Target Localization Using TDOA Measurements
IS  - 2
VL  - 13
DO  - 10.3390/app13020684
ER  - 

@article{
author = "Rosić Vitas, Maja and Sedak, Miloš and Simić, Mirjana and Pejović, Predrag V.",
year = "2023",
abstract = "This paper addresses the problem of time difference of arrival (TDOA) based passive target localizationand proposes an improved chaos-driven hybrid differential evolution (DE) algorithm and butterfly optimization algorithm (BOA), named ICDEBOA, to solve this complex optimization problem. The proposed algorithm consists of a new mutation strategy with the mechanisms of the BOA algorithm incorporated into the DE algorithm. To boost optimization effectiveness, chaos theory is employed to adjust the control parameter values. The considered localization problem is formulated using the maximum likelihood estimator. To perform the accuracy comparison, the convex constrained weighting least squares algorithm is applied to the considered localization problem as the widely used method in literature. The statistical analysis shows that the proposed modifications to the ICDEBOA algorithm improve its optimization performance, as demonstrated by the improved performance on the CEC2014 benchmark problems. The ICDEBOA algorithm is also shown to be more robust than existing algorithms in noisy environments. Numerical simulation results show that the proposed ICDEBOA algorithm meets the CRLB and achieves better performance than the CWLS, DE, and BOA algorithms.",
publisher = "MDPI, Basel, Switzerland",
journal = "Applied Sciences",
title = "An Improved Chaos Driven Hybrid Differential Evolution and Butterfly Optimization Algorithm for Passive Target Localization Using TDOA Measurements",
number = "2",
volume = "13",
doi = "10.3390/app13020684"
}

Rosić Vitas, M., Sedak, M., Simić, M.,& Pejović, P. V.. (2023). An Improved Chaos Driven Hybrid Differential Evolution and Butterfly Optimization Algorithm for Passive Target Localization Using TDOA Measurements. in Applied Sciences
MDPI, Basel, Switzerland., 13(2).
https://doi.org/10.3390/app13020684

Rosić Vitas M, Sedak M, Simić M, Pejović PV. An Improved Chaos Driven Hybrid Differential Evolution and Butterfly Optimization Algorithm for Passive Target Localization Using TDOA Measurements. in Applied Sciences. 2023;13(2).
doi:10.3390/app13020684 .

Rosić Vitas, Maja, Sedak, Miloš, Simić, Mirjana, Pejović, Predrag V., "An Improved Chaos Driven Hybrid Differential Evolution and Butterfly Optimization Algorithm for Passive Target Localization Using TDOA Measurements" in Applied Sciences, 13, no. 2 (2023),
https://doi.org/10.3390/app13020684 . .

TY  - JOUR
AU  - Rosić Vitas, Maja
AU  - Sedak, Miloš
AU  - Simić, Mirjana
AU  - Pejović, Predrag V.
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/5365
AB  - This paper considers the problem of finding the position of a passive target using noisy time difference of arrival (TDOA) measurements, obtained from multiple transmitters and a single receiver. The maximum likelihood (ML) estimator’s objective function is extremely nonlinear and non-convex, making it impossible to use traditional optimization techniques. In this regard, this paper proposes the chaos-enhanced adaptive hybrid butterfly particle swarm optimization algorithm, named CAHBPSO, as the hybridization of butterfly optimization (BOA) and particle swarm optimization (PSO) algorithms, to estimate passive target position. In the proposed algorithm, an adaptive strategy is employed to update the sensory fragrance of BOA algorithm, and chaos theory is incorporated into the inertia weight of PSO algorithm. Furthermore, an adaptive switch probability is employed to combine global and local search phases of BOA with the PSO algorithm. Additionally, the semidefinite programming is employed to convert the considered problem into a convex one. The statistical comparison on CEC2014 benchmark problems shows that the proposed algorithm provides a better performance compared to well-known algorithms. The CAHBPSO method surpasses the BOA, PSO and semidefinite programming (SDP) algorithms for a broad spectrum of noise, according to simulation findings, and achieves the Cramer–Rao lower bound (CRLB).
PB  - MDPI, Basel, Switzerland
T2  - Sensors
T1  - Chaos-Enhanced Adaptive Hybrid Butterfly Particle Swarm Optimization Algorithm for Passive Target
IS  - 15
VL  - 22
DO  - doi.org/10.3390/s22155739
ER  - 

@article{
author = "Rosić Vitas, Maja and Sedak, Miloš and Simić, Mirjana and Pejović, Predrag V.",
year = "2022",
abstract = "This paper considers the problem of finding the position of a passive target using noisy time difference of arrival (TDOA) measurements, obtained from multiple transmitters and a single receiver. The maximum likelihood (ML) estimator’s objective function is extremely nonlinear and non-convex, making it impossible to use traditional optimization techniques. In this regard, this paper proposes the chaos-enhanced adaptive hybrid butterfly particle swarm optimization algorithm, named CAHBPSO, as the hybridization of butterfly optimization (BOA) and particle swarm optimization (PSO) algorithms, to estimate passive target position. In the proposed algorithm, an adaptive strategy is employed to update the sensory fragrance of BOA algorithm, and chaos theory is incorporated into the inertia weight of PSO algorithm. Furthermore, an adaptive switch probability is employed to combine global and local search phases of BOA with the PSO algorithm. Additionally, the semidefinite programming is employed to convert the considered problem into a convex one. The statistical comparison on CEC2014 benchmark problems shows that the proposed algorithm provides a better performance compared to well-known algorithms. The CAHBPSO method surpasses the BOA, PSO and semidefinite programming (SDP) algorithms for a broad spectrum of noise, according to simulation findings, and achieves the Cramer–Rao lower bound (CRLB).",
publisher = "MDPI, Basel, Switzerland",
journal = "Sensors",
title = "Chaos-Enhanced Adaptive Hybrid Butterfly Particle Swarm Optimization Algorithm for Passive Target",
number = "15",
volume = "22",
doi = "doi.org/10.3390/s22155739"
}

Rosić Vitas, M., Sedak, M., Simić, M.,& Pejović, P. V.. (2022). Chaos-Enhanced Adaptive Hybrid Butterfly Particle Swarm Optimization Algorithm for Passive Target. in Sensors
MDPI, Basel, Switzerland., 22(15).
https://doi.org/doi.org/10.3390/s22155739

Rosić Vitas M, Sedak M, Simić M, Pejović PV. Chaos-Enhanced Adaptive Hybrid Butterfly Particle Swarm Optimization Algorithm for Passive Target. in Sensors. 2022;22(15).
doi:doi.org/10.3390/s22155739 .

Rosić Vitas, Maja, Sedak, Miloš, Simić, Mirjana, Pejović, Predrag V., "Chaos-Enhanced Adaptive Hybrid Butterfly Particle Swarm Optimization Algorithm for Passive Target" in Sensors, 22, no. 15 (2022),
https://doi.org/doi.org/10.3390/s22155739 . .

TY  - JOUR
AU  - Rosić Vitas, Maja
AU  - Simić, Mirjana
AU  - Pejović, Predrag V.
PY  - 2021
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3614
AB  - This paper considers a passive target localization problem based on the noisy time of arrival measurements obtained from multiple receivers and a single transmitter. The maximum likelihood (ML) estimator for this localization problem is formulated as a highly nonlinear and non-convex optimization problem, where conventional optimization methods are not suitable for solving such a problem. Consequently, this paper proposes an improved adaptive hybrid firefly differential evolution (AHFADE) algorithm, based on hybridization of firefly algorithm (FA) and differential evolution (DE) algorithm to estimate the unknown position of the target. The proposed AHFADE algorithm dynamically adjusts the control parameters, thus maintaining high population diversity during the evolution process. This paper aims to improve the accuracy of the global optimal solution by incorporating evolutionary operators of the DE in different searching stages of the FA. In this regard, an adaptive parameter is employed to select an appropriate mutation operator for achieving a proper balance between global exploration and local exploitation. In order to efficiently solve the ML estimation problem, this paper proposes the well-known semidefinite programming (SDP) method to convert the non-convex problem into a convex one. The simulation results obtained from the proposed AHFADE algorithm and well-known algorithms, such as SDP, DE and FA, are compared against Cramer-Rao lower bound (CRLB). The statistical analysis has been performed to compare the performance of the proposed AHFADE algorithm with several well-known algorithms on CEC2014 benchmark problems. The obtained simulation results show that the proposed AHFADE algorithm is more robust in high-noise environments compared to existing algorithms.
PB  - Springer, New York
T2  - Soft Computing
T1  - An improved adaptive hybrid firefly differential evolution algorithm for passive target localization
EP  - 5585
IS  - 7
SP  - 5559
VL  - 25
DO  - 10.1007/s00500-020-05554-8
ER  - 

@article{
author = "Rosić Vitas, Maja and Simić, Mirjana and Pejović, Predrag V.",
year = "2021",
abstract = "This paper considers a passive target localization problem based on the noisy time of arrival measurements obtained from multiple receivers and a single transmitter. The maximum likelihood (ML) estimator for this localization problem is formulated as a highly nonlinear and non-convex optimization problem, where conventional optimization methods are not suitable for solving such a problem. Consequently, this paper proposes an improved adaptive hybrid firefly differential evolution (AHFADE) algorithm, based on hybridization of firefly algorithm (FA) and differential evolution (DE) algorithm to estimate the unknown position of the target. The proposed AHFADE algorithm dynamically adjusts the control parameters, thus maintaining high population diversity during the evolution process. This paper aims to improve the accuracy of the global optimal solution by incorporating evolutionary operators of the DE in different searching stages of the FA. In this regard, an adaptive parameter is employed to select an appropriate mutation operator for achieving a proper balance between global exploration and local exploitation. In order to efficiently solve the ML estimation problem, this paper proposes the well-known semidefinite programming (SDP) method to convert the non-convex problem into a convex one. The simulation results obtained from the proposed AHFADE algorithm and well-known algorithms, such as SDP, DE and FA, are compared against Cramer-Rao lower bound (CRLB). The statistical analysis has been performed to compare the performance of the proposed AHFADE algorithm with several well-known algorithms on CEC2014 benchmark problems. The obtained simulation results show that the proposed AHFADE algorithm is more robust in high-noise environments compared to existing algorithms.",
publisher = "Springer, New York",
journal = "Soft Computing",
title = "An improved adaptive hybrid firefly differential evolution algorithm for passive target localization",
pages = "5585-5559",
number = "7",
volume = "25",
doi = "10.1007/s00500-020-05554-8"
}

Rosić Vitas, M., Simić, M.,& Pejović, P. V.. (2021). An improved adaptive hybrid firefly differential evolution algorithm for passive target localization. in Soft Computing
Springer, New York., 25(7), 5559-5585.
https://doi.org/10.1007/s00500-020-05554-8

Rosić Vitas M, Simić M, Pejović PV. An improved adaptive hybrid firefly differential evolution algorithm for passive target localization. in Soft Computing. 2021;25(7):5559-5585.
doi:10.1007/s00500-020-05554-8 .

Rosić Vitas, Maja, Simić, Mirjana, Pejović, Predrag V., "An improved adaptive hybrid firefly differential evolution algorithm for passive target localization" in Soft Computing, 25, no. 7 (2021):5559-5585,
https://doi.org/10.1007/s00500-020-05554-8 . .

TY  - JOUR
AU  - Rosić Vitas, Maja
AU  - Simić, Mirjana
AU  - Pejović, Predrag V.
PY  - 2020
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3350
AB  - This paper considers a passive target localization problem in Wireless Sensor Networks (WSNs) using the noisy time of arrival (TOA) measurements, obtained from multiple receivers and a single transmitter. The objective function is formulated as a maximum likelihood (ML) estimation problem under the Gaussian noise assumption. Consequently, the objective function of the ML estimator is a highly nonlinear and nonconvex function, where conventional optimization methods are not suitable for this type of problem. Hence, an improved algorithm based on the hybridization of an adaptive differential evolution (ADE) and Nelder-Mead (NM) algorithms, named HADENM, is proposed to find the estimated position of a passive target. In this paper, the control parameters of the ADE algorithm are adaptively updated during the evolution process. In addition, an adaptive adjustment parameter is designed to provide a balance between the global exploration and the local exploitation abilities. Furthermore, the exploitation is strengthened using the NM method by improving the accuracy of the best solution obtained from the ADE algorithm. Statistical analysis has been conducted, to evaluate the benefits of the proposed modifications on the optimization performance of the HADENM algorithm. The comparison results between HADENM algorithm and its versions indicate that the modifications proposed in this paper can improve the overall optimization performance. Furthermore, the simulation shows that the proposed HADENM algorithm can attain the Cramer-Rao lower bound (CRLB) and outperforms the constrained weighted least squares (CWLS) and differential evolution (DE) algorithms. The obtained results demonstrate the high accuracy and robustness of the proposed algorithm for solving the passive target localization problem for a wide range of measurement noise levels.
PB  - Hindawi Ltd, London
T2  - Journal of Sensors
T1  - Passive Target Localization Problem Based on Improved Hybrid Adaptive Differential Evolution and Nelder-Mead Algorithm
VL  - 2020
DO  - 10.1155/2020/3482463
ER  - 

@article{
author = "Rosić Vitas, Maja and Simić, Mirjana and Pejović, Predrag V.",
year = "2020",
abstract = "This paper considers a passive target localization problem in Wireless Sensor Networks (WSNs) using the noisy time of arrival (TOA) measurements, obtained from multiple receivers and a single transmitter. The objective function is formulated as a maximum likelihood (ML) estimation problem under the Gaussian noise assumption. Consequently, the objective function of the ML estimator is a highly nonlinear and nonconvex function, where conventional optimization methods are not suitable for this type of problem. Hence, an improved algorithm based on the hybridization of an adaptive differential evolution (ADE) and Nelder-Mead (NM) algorithms, named HADENM, is proposed to find the estimated position of a passive target. In this paper, the control parameters of the ADE algorithm are adaptively updated during the evolution process. In addition, an adaptive adjustment parameter is designed to provide a balance between the global exploration and the local exploitation abilities. Furthermore, the exploitation is strengthened using the NM method by improving the accuracy of the best solution obtained from the ADE algorithm. Statistical analysis has been conducted, to evaluate the benefits of the proposed modifications on the optimization performance of the HADENM algorithm. The comparison results between HADENM algorithm and its versions indicate that the modifications proposed in this paper can improve the overall optimization performance. Furthermore, the simulation shows that the proposed HADENM algorithm can attain the Cramer-Rao lower bound (CRLB) and outperforms the constrained weighted least squares (CWLS) and differential evolution (DE) algorithms. The obtained results demonstrate the high accuracy and robustness of the proposed algorithm for solving the passive target localization problem for a wide range of measurement noise levels.",
publisher = "Hindawi Ltd, London",
journal = "Journal of Sensors",
title = "Passive Target Localization Problem Based on Improved Hybrid Adaptive Differential Evolution and Nelder-Mead Algorithm",
volume = "2020",
doi = "10.1155/2020/3482463"
}

Rosić Vitas, M., Simić, M.,& Pejović, P. V.. (2020). Passive Target Localization Problem Based on Improved Hybrid Adaptive Differential Evolution and Nelder-Mead Algorithm. in Journal of Sensors
Hindawi Ltd, London., 2020.
https://doi.org/10.1155/2020/3482463

Rosić Vitas M, Simić M, Pejović PV. Passive Target Localization Problem Based on Improved Hybrid Adaptive Differential Evolution and Nelder-Mead Algorithm. in Journal of Sensors. 2020;2020.
doi:10.1155/2020/3482463 .

Rosić Vitas, Maja, Simić, Mirjana, Pejović, Predrag V., "Passive Target Localization Problem Based on Improved Hybrid Adaptive Differential Evolution and Nelder-Mead Algorithm" in Journal of Sensors, 2020 (2020),
https://doi.org/10.1155/2020/3482463 . .

TY  - 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 . .

TY  - JOUR
AU  - Rosić Vitas, Maja
AU  - Simić, Mirjana
AU  - Pejović, Predrag V.
PY  - 2018
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/6037
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  - Belgrade : Military Technical Institute
T2  - Scientific Technical Review
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 = "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 = "Belgrade : Military Technical Institute",
journal = "Scientific Technical Review",
title = "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). A Hybrid Genetic Optimization Method for Accurate Target Localization. in Scientific Technical Review
Belgrade : Military Technical Institute., 68(3), 50-55.
https://doi.org/10.5937/str1803050r

Rosić Vitas M, Simić M, Pejović PV. A Hybrid Genetic Optimization Method for Accurate Target Localization. in Scientific Technical Review. 2018;68(3):50-55.
doi:10.5937/str1803050r .

Rosić Vitas, Maja, Simić, Mirjana, Pejović, Predrag V., "A Hybrid Genetic Optimization Method for Accurate Target Localization" in Scientific Technical Review, 68, no. 3 (2018):50-55,
https://doi.org/10.5937/str1803050r . .

TY  - CONF
AU  - Rosić Vitas, Maja
AU  - Simić, Mirjana
AU  - Pejović, Predrag V.
PY  - 2018
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/6028
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  - Belgrade : Military Technical Institute
C3  - Proceedings of the 8th International Scientific Conference on Defensive Technologies, OTEH 2018
T1  - Hybrid Genetic Optimization Method for Accurate Target Localization
EP  - 372
SP  - 367
UR  - https://hdl.handle.net/21.15107/rcub_machinery_6028
ER  - 

@conference{
author = "Rosić Vitas, Maja and Simić, Mirjana and Pejović, Predrag V.",
year = "2018",
abstract = "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 = "Belgrade : Military Technical Institute",
journal = "Proceedings of the 8th International Scientific Conference on Defensive Technologies, OTEH 2018",
title = "Hybrid Genetic Optimization Method for Accurate Target Localization",
pages = "372-367",
url = "https://hdl.handle.net/21.15107/rcub_machinery_6028"
}

Rosić Vitas, M., Simić, M.,& Pejović, P. V.. (2018). Hybrid Genetic Optimization Method for Accurate Target Localization. in Proceedings of the 8th International Scientific Conference on Defensive Technologies, OTEH 2018
Belgrade : Military Technical Institute., 367-372.
https://hdl.handle.net/21.15107/rcub_machinery_6028

Rosić Vitas M, Simić M, Pejović PV. Hybrid Genetic Optimization Method for Accurate Target Localization. in Proceedings of the 8th International Scientific Conference on Defensive Technologies, OTEH 2018. 2018;:367-372.
https://hdl.handle.net/21.15107/rcub_machinery_6028 .

Rosić Vitas, Maja, Simić, Mirjana, Pejović, Predrag V., "Hybrid Genetic Optimization Method for Accurate Target Localization" in Proceedings of the 8th International Scientific Conference on Defensive Technologies, OTEH 2018 (2018):367-372,
https://hdl.handle.net/21.15107/rcub_machinery_6028 .

TY  - CONF
AU  - Rosić Vitas, Maja
AU  - Simić, Mirjana
AU  - Pejović, Predrag V.
PY  - 2017
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/6031
PB  - IcETRAN
C3  - Proceedings of the 4th International Conference on Electrical, Electronic and Computing Engineering
T1  - Hybrid genetic optimization algorithm for target localization using TDOA measurements
EP  - TEI2.6.6
SP  - TEI2.6.1
UR  - https://hdl.handle.net/21.15107/rcub_machinery_6031
ER  - 

@conference{
author = "Rosić Vitas, Maja and Simić, Mirjana and Pejović, Predrag V.",
year = "2017",
publisher = "IcETRAN",
journal = "Proceedings of the 4th International Conference on Electrical, Electronic and Computing Engineering",
title = "Hybrid genetic optimization algorithm for target localization using TDOA measurements",
pages = "TEI2.6.6-TEI2.6.1",
url = "https://hdl.handle.net/21.15107/rcub_machinery_6031"
}

Rosić Vitas, M., Simić, M.,& Pejović, P. V.. (2017). Hybrid genetic optimization algorithm for target localization using TDOA measurements. in Proceedings of the 4th International Conference on Electrical, Electronic and Computing Engineering
IcETRAN., TEI2.6.1-TEI2.6.6.
https://hdl.handle.net/21.15107/rcub_machinery_6031

Rosić Vitas M, Simić M, Pejović PV. Hybrid genetic optimization algorithm for target localization using TDOA measurements. in Proceedings of the 4th International Conference on Electrical, Electronic and Computing Engineering. 2017;:TEI2.6.1-TEI2.6.6.
https://hdl.handle.net/21.15107/rcub_machinery_6031 .

Rosić Vitas, Maja, Simić, Mirjana, Pejović, Predrag V., "Hybrid genetic optimization algorithm for target localization using TDOA measurements" in Proceedings of the 4th International Conference on Electrical, Electronic and Computing Engineering (2017):TEI2.6.1-TEI2.6.6,
https://hdl.handle.net/21.15107/rcub_machinery_6031 .

TY  - CONF
AU  - Rosić Vitas, Maja
AU  - Simić, Mirjana
AU  - Lukić, Petar
PY  - 2017
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/2675
AB  - This paper presents a target localization problem based on the time difference of arrival (TDOA) measurements by employing an improved genetic algorithm (GA) for estimation. The weighted least square (WLS) technique is applied as an efficient existing approach. The TDOA target localization problem is formulated as an optimization problem, with a highly nonlinear and multimodal objective function. The hybrid Genetic Algorithm - Newton-Raphson (GA-NR) has been proposed as high accuracy and global convergence algorithm in this sense. Finally, the simulation results of the proposed optimization method show a significant performance improvement over existing WLS approach.
PB  - Institute of Electrical and Electronics Engineers Inc.
C3  - 24th Telecommunications Forum, TELFOR 2016
T1  - TDOA approach for target localization based on improved genetic algorithm
DO  - 10.1109/TELFOR.2016.7818752
ER  - 

@conference{
author = "Rosić Vitas, Maja and Simić, Mirjana and Lukić, Petar",
year = "2017",
abstract = "This paper presents a target localization problem based on the time difference of arrival (TDOA) measurements by employing an improved genetic algorithm (GA) for estimation. The weighted least square (WLS) technique is applied as an efficient existing approach. The TDOA target localization problem is formulated as an optimization problem, with a highly nonlinear and multimodal objective function. The hybrid Genetic Algorithm - Newton-Raphson (GA-NR) has been proposed as high accuracy and global convergence algorithm in this sense. Finally, the simulation results of the proposed optimization method show a significant performance improvement over existing WLS approach.",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
journal = "24th Telecommunications Forum, TELFOR 2016",
title = "TDOA approach for target localization based on improved genetic algorithm",
doi = "10.1109/TELFOR.2016.7818752"
}

Rosić Vitas, M., Simić, M.,& Lukić, P.. (2017). TDOA approach for target localization based on improved genetic algorithm. in 24th Telecommunications Forum, TELFOR 2016
Institute of Electrical and Electronics Engineers Inc...
https://doi.org/10.1109/TELFOR.2016.7818752

Rosić Vitas M, Simić M, Lukić P. TDOA approach for target localization based on improved genetic algorithm. in 24th Telecommunications Forum, TELFOR 2016. 2017;.
doi:10.1109/TELFOR.2016.7818752 .

Rosić Vitas, Maja, Simić, Mirjana, Lukić, Petar, "TDOA approach for target localization based on improved genetic algorithm" in 24th Telecommunications Forum, TELFOR 2016 (2017),
https://doi.org/10.1109/TELFOR.2016.7818752 . .

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 . .

TY  - CONF
AU  - Rosić Vitas, Maja
AU  - Simić, Mirjana
AU  - Pejović, Predrag V.
PY  - 2016
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/6034
AB  - Source localization based on the time of arrival (TOA) measurements is very important in military and civil applications such as Wireless Sensor Networks (WSNs), sonar, radar, security systems, health monitoring, etc. Some of the localization measurements are corrupted by errors which always exist no matter which localization techniques are used. Therefore, TOA source localization model, in the presence of additive noise, can be formulated as an optimization model with the sum of squared residuals as the objective function. This paper presents the application of three nonlinear optimization methods: the Steepest Descent, the Newton-Raphson and the Gauss-Newton methods and their performance is compared with each other in terms of localization accuracy. Numerical simulation results illustrate the performance comparison of these different proposed nonlinear optimization methods with different initial values and signal-to-noise ratio (SNR). 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. Finally, the simulation results analysis of the performance of the proposed gradient-based optimization methods are evaluated and compared with the CRLB and the closed-form LLS method.
PB  - Belgrade : Military Technical Institute
C3  - Proceedings of the 7th International Scientific Conference on Defensive Technologies, OTEH 2016
T1  - Performance Evaluation of Nonlinear Optimization Methods for TOA Localization Techniques
EP  - 471
SP  - 466
UR  - https://hdl.handle.net/21.15107/rcub_machinery_6034
ER  - 

@conference{
author = "Rosić Vitas, Maja and Simić, Mirjana and Pejović, Predrag V.",
year = "2016",
abstract = "Source localization based on the time of arrival (TOA) measurements is very important in military and civil applications such as Wireless Sensor Networks (WSNs), sonar, radar, security systems, health monitoring, etc. Some of the localization measurements are corrupted by errors which always exist no matter which localization techniques are used. Therefore, TOA source localization model, in the presence of additive noise, can be formulated as an optimization model with the sum of squared residuals as the objective function. This paper presents the application of three nonlinear optimization methods: the Steepest Descent, the Newton-Raphson and the Gauss-Newton methods and their performance is compared with each other in terms of localization accuracy. Numerical simulation results illustrate the performance comparison of these different proposed nonlinear optimization methods with different initial values and signal-to-noise ratio (SNR). 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. Finally, the simulation results analysis of the performance of the proposed gradient-based optimization methods are evaluated and compared with the CRLB and the closed-form LLS method.",
publisher = "Belgrade : Military Technical Institute",
journal = "Proceedings of the 7th International Scientific Conference on Defensive Technologies, OTEH 2016",
title = "Performance Evaluation of Nonlinear Optimization Methods for TOA Localization Techniques",
pages = "471-466",
url = "https://hdl.handle.net/21.15107/rcub_machinery_6034"
}

Rosić Vitas, M., Simić, M.,& Pejović, P. V.. (2016). Performance Evaluation of Nonlinear Optimization Methods for TOA Localization Techniques. in Proceedings of the 7th International Scientific Conference on Defensive Technologies, OTEH 2016
Belgrade : Military Technical Institute., 466-471.
https://hdl.handle.net/21.15107/rcub_machinery_6034

Rosić Vitas M, Simić M, Pejović PV. Performance Evaluation of Nonlinear Optimization Methods for TOA Localization Techniques. in Proceedings of the 7th International Scientific Conference on Defensive Technologies, OTEH 2016. 2016;:466-471.
https://hdl.handle.net/21.15107/rcub_machinery_6034 .

Rosić Vitas, Maja, Simić, Mirjana, Pejović, Predrag V., "Performance Evaluation of Nonlinear Optimization Methods for TOA Localization Techniques" in Proceedings of the 7th International Scientific Conference on Defensive Technologies, OTEH 2016 (2016):466-471,
https://hdl.handle.net/21.15107/rcub_machinery_6034 .

TY  - CONF
AU  - Rosić Vitas, Maja
AU  - Simić, Mirjana
AU  - Pejović, Predrag V.
PY  - 2016
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/2324
PB  - Institute of Electrical and Electronics Engineers Inc.
C3  - 2015 23rd Telecommunications Forum, TELFOR 2015
T1  - Optimal source localization in a real radio channel
EP  - 215
SP  - 212
DO  - 10.1109/TELFOR.2015.7377450
ER  - 

@conference{
author = "Rosić Vitas, Maja and Simić, Mirjana and Pejović, Predrag V.",
year = "2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
journal = "2015 23rd Telecommunications Forum, TELFOR 2015",
title = "Optimal source localization in a real radio channel",
pages = "215-212",
doi = "10.1109/TELFOR.2015.7377450"
}

Rosić Vitas, M., Simić, M.,& Pejović, P. V.. (2016). Optimal source localization in a real radio channel. in 2015 23rd Telecommunications Forum, TELFOR 2015
Institute of Electrical and Electronics Engineers Inc.., 212-215.
https://doi.org/10.1109/TELFOR.2015.7377450

Rosić Vitas M, Simić M, Pejović PV. Optimal source localization in a real radio channel. in 2015 23rd Telecommunications Forum, TELFOR 2015. 2016;:212-215.
doi:10.1109/TELFOR.2015.7377450 .

Rosić Vitas, Maja, Simić, Mirjana, Pejović, Predrag V., "Optimal source localization in a real radio channel" in 2015 23rd Telecommunications Forum, TELFOR 2015 (2016):212-215,
https://doi.org/10.1109/TELFOR.2015.7377450 . .