مکان‌یابی منبع تشعشع با استفاده از کمترین مربعات خطی در مشاهدات DRSS جدید

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجو

2 عضو هیئت علمی دانشگاه امام حسین (ع)

چکیده

 مکان‌یابی با استفاده از اندازه­گیری توان سیگنال دریافتی ارزان است و پیچیدگی محاسباتی کمی دارد از این رو سبب افزایش طول عمر حسگرها در شبکه حسگر بی‌سیم می‌گردد. نمونه انتشار متداول برای RSS دارای توزیع لگ-نرمال است و چون تابع چگالی احتمال معلوم است بهترین تخمین‌گر برای مکان‌یابی ML است. تخمین‌گر ML غیرخطی است و در مقالات برای حل آن روش‌های بهینه‌سازی محدب و گوس-نیوتن ارائه ‌شده‌اند. این روش‌ها پیچیدگی زیادی به سامانه تحمیل می‌کنند و سبب کاهش عمر باطری می‌گردند. در این مقاله برای حل تخمین‌گر غیرخطی ML، یک تخمین‌گر خطی در دو مرحله به‌کار گرفته شده است. در مرحله اول یک نمونه DRSS جدید ارائه شده است و در ادامه این مرحله ترم­های غیرخطی تابع هزینه ML با متغیر خطی جایگزین شده­اند، همچنین برخلاف تخمینگرهای بر اساس نمونه DRSS متداول، عملکرد این تخمین‌گر با انتخاب تصادفی گره مرجع شماره 1 افت نمی‌کند. در مرحله دوم به کمک سری تیلور خطا ناشی از تقریب مرحله اول به حداقل کاهش یافته است و بدین ترتیب دقت تخمین مکان افزایش می‌یابد. شبیه‌سازی‌ها نشان می‌دهد جذر متوسط انرژی خطا این تخمین‌گر در مقایسه با تخمین‌گرهای موجود تا 13 درصد کاهش یافته است.

کلیدواژه‌ها


عنوان مقاله [English]

Linear Least-Squares Based Source Localization for New DRSS Model

نویسندگان [English]

  • Hassan Nazari 1
  • M. R. Danaee 2
1 student
2 Assistant Professor of Electrical Engineering, Imam Hossein Comprehensive University
چکیده [English]

Localization by the received signal Strength (RSS) measurement is inexpensive and has low computational complexity, thus extending the lifetime of the sensors in the wireless sensor network. The conventional propagation model for RSS has a log-normal distribution and since the probability density function is known, the best estimator for localization is Maximum Likelihood Estimator (MLE). The ML estimator is nonlinear and nonconvex and Gauss-Newton and convex optimization methods are presented in the papers. These methods impose a lot of complexity on the system and reduce the energy of the battery. In this paper, a two-step linear estimator is employed to solve the nonlinear ML estimator. In the first step, a new DRSS model is presented and nonlinear terms of ML cost function are replaced with linear variables. Also, in contrast to the estimators based on the conventional DRSS model, the performance of this estimator doesn't reduce by the random selection of the number 1 reference node. In the second step, the error of approximation of the first step is minimized, thus increasing the accuracy of the location estimation. Simulations show that in both the first and second steps, the accuracy is improved and the average error root error is reduced by up to 13% compared to the existing estimators.

کلیدواژه‌ها [English]

  • Differential Received Signal Strength (DRSS)
  • Source Localization
  • Linear Least Squares
  • Centralized Localization
  • Wireless Sensor Network (WSN)
  • Maximum Likelihood Estimator (MLE)
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