مکان‌یابی بر اساس تفاضل توان سیگنال دریافتی با به کارگیری بهینه‌سازی محدب در شبکه حسگر بی‌سیم
محورهای موضوعی : مهندسی برق و کامپیوتر
حسن نظری
1
(دانشگاه جامع امام حسین (ع))
میثم رئیس دانایی
2
(دانشگاه جامع امام حسین (ع))
مرتضی سپه‌وند
3
(دانشگاه جامع امام حسین (ع))
چکیده مقاله :
برای انجام مکان‌یابی بر اساس تفاضل توان سیگنال دریافتی در شبکه حسگر بی‌سیم می‌بایست توان دریافتی آلوده به نویز را توسط تعدادی حسگر مرجع جمع‌آوری نمود. به دلیل مشخص‌بودن تابع چگالی احتمال نویز استفاده از تخمین‌گر حداکثر درست‌نمایی بهترین انتخاب خواهد بود. تابع هزینه این تخمین‌گر غیر خطی و غیر محدب است و تا کنون برای آن جواب تحلیلی ارائه نشده است. یکی از راه‌حل‌ها برای غلبه بر این مشکل استفاده از روش‌های بهینه‌سازی محدب است. در این مقاله برای غلبه بر این مشکل تابع هزینه تخمین‌گر حداکثر درست‌نمایی را به دست می‌آوریم و آن را با روش آزادسازی نیمه‌معین حل می‌کنیم. شبیه‌سازی‌های کامپیوتری نشان می‌دهد در شرایطی که حسگرهای شبکه به صورت غیر منظم در محیط پخش شوند تخمین‌گر جدید نسبت به سایر تخمین‌گرها جذر متوسط انرژی خطای مکان‌یابی کمتری را نشان می‌دهد، یعنی دقت مکان‌یابی بالاتری دارد. در روش جدید دقت مکان‌یابی نسبت به سایر روش‌ها تا 20% افزایش می‌یابد و پیچیدگی محاسباتی آن نیز نسبت به روش‌های بهینه‌سازی محدب 30% کمتر است.
Localization with differential received signal strength measurement in recent years has been very much considered. Due to the fact that the probability density function is known for given observations, the maximum likelihood estimator is used. This estimator can be asymptotically represented the optimal estimation of the location. After the formation of this estimator, it is observed that the corresponding cost function is highly nonlinear and non-convex and has a lot of minima, so there is no possibility of achieving the global minimum with Newton method and the localization error will be high. There is no analytical solution for this cost function. To overcome this problem, two methods are existed. First, the cost function is approximated by a linear estimator. But this estimator has poor accuracy. The second method is to replace the non-convex cost function with a convex one with the aid of convex optimization methods, in which case the global minimum is obtained. In this paper, we proposed new convex estimator to solve cost function of maximum likelihood estimator. The results of the simulations show that the proposed estimator has up to 20 percent performance improvement compared with existing estimators, moreover, the execution time of proposed estimator is 30 percent faster than other convex estimators.
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