Optimal source localization problem based on TOA measurements
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 optimiza...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Faculty of Technical Sciences in Cacak
2017-01-01
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| Series: | Serbian Journal of Electrical Engineering |
| Subjects: | |
| Online Access: | http://www.doiserbia.nb.rs/img/doi/1451-4869/2017/1451-48691701161R.pdf |
| _version_ | 1819134580660109312 |
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| author | Rosić Maja Simić Mirjana Pejović Predrag Bjelica Milan |
| author_facet | Rosić Maja Simić Mirjana Pejović Predrag Bjelica Milan |
| author_sort | Rosić Maja |
| collection | DOAJ |
| description | 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. [Project of the Serbian Ministry
of Education, Science and Technological Development, Grant no. TR32028] |
| first_indexed | 2024-12-22T10:05:27Z |
| format | Article |
| id | doaj.art-b812e7e6817e47e78a7c018ba81a42f4 |
| institution | Directory Open Access Journal |
| issn | 1451-4869 2217-7183 |
| language | English |
| last_indexed | 2024-12-22T10:05:27Z |
| publishDate | 2017-01-01 |
| publisher | Faculty of Technical Sciences in Cacak |
| record_format | Article |
| series | Serbian Journal of Electrical Engineering |
| spelling | doaj.art-b812e7e6817e47e78a7c018ba81a42f42022-12-21T18:29:58ZengFaculty of Technical Sciences in CacakSerbian Journal of Electrical Engineering1451-48692217-71832017-01-0114116117610.2298/SJEE1701161R1451-48691701161ROptimal source localization problem based on TOA measurementsRosić Maja0Simić Mirjana1Pejović Predrag2Bjelica Milan3School of Electrical Engineering, BelgradeSchool of Electrical Engineering, BelgradeSchool of Electrical Engineering, BelgradeSchool of Electrical Engineering, BelgradeDetermining 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. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. TR32028]http://www.doiserbia.nb.rs/img/doi/1451-4869/2017/1451-48691701161R.pdfgenetic algorithmlocalizationsignal-to-noise ratiotime of arrivalwireless sensor networks |
| spellingShingle | Rosić Maja Simić Mirjana Pejović Predrag Bjelica Milan Optimal source localization problem based on TOA measurements Serbian Journal of Electrical Engineering genetic algorithm localization signal-to-noise ratio time of arrival wireless sensor networks |
| title | Optimal source localization problem based on TOA measurements |
| title_full | Optimal source localization problem based on TOA measurements |
| title_fullStr | Optimal source localization problem based on TOA measurements |
| title_full_unstemmed | Optimal source localization problem based on TOA measurements |
| title_short | Optimal source localization problem based on TOA measurements |
| title_sort | optimal source localization problem based on toa measurements |
| topic | genetic algorithm localization signal-to-noise ratio time of arrival wireless sensor networks |
| url | http://www.doiserbia.nb.rs/img/doi/1451-4869/2017/1451-48691701161R.pdf |
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