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A New Method for Geolocating of Radiation Sources Based on Evolutionary Computation of TDOA Equations | ||
| Journal of Electrical and Computer Engineering Innovations (JECEI) | ||
| مقاله 5، دوره 4، شماره 2 - شماره پیاپی 8، مهر 2016، صفحه 137-148 اصل مقاله (1.21 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22061/jecei.2016.574 | ||
| نویسندگان | ||
| S.V. Shojaedini* 1؛ M. Rahimi Nejad2؛ R. Kasbgar Haghighi1 | ||
| 1Department of Electrical Engineering and Information Technology, Iranian Research Organization for Science and Technology, Iran. | ||
| 2Department of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran. | ||
| تاریخ دریافت: 23 آبان 1395، تاریخ بازنگری: 11 دی 1395، تاریخ پذیرش: 20 بهمن 1395 | ||
| چکیده | ||
| In this article a new method is introduced for geolocating of signal emitters which is based on evolutionary computation (EC) concept. In the proposed method two well-known members of EC techniques including Bees Algorithm (BA) and Genetic Algorithm (GA), are utilized to estimate the positions of emitters by optimizing the hyperbola equations which have been resulted from Time Difference of Arrival (TDOA) of their radiated signals. To show the effectiveness of the EC concept in positioning the simulation is carried for linear and nonlinear moving emitters in presence of several amounts of noise. Then obtained results are compared with Maximum Likelihood (ML) estimator as one of the most common approaches among traditional methods. The results showed better performance of the EC family compared to ML in such way that they estimate the position of emitters even up to 33% and 30% more accurate than ML in presence of 5 and 10 percent of noise respectively. Furthermore the comparison among the examined methods belong to EC family shows that BA leads to the accuracy of 3 to 12 percent better than GA in estimating positions of radiation sources. | ||
| کلیدواژه‌ها | ||
| Time Difference of Arrival (TDOA)؛ Geolocation؛ EC Paradigm؛ Bee Algorithm (BA)؛ Genetic Algorithm (GA) | ||
| مراجع | ||
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