دانلود مقاله ISI انگلیسی شماره 67457
ترجمه فارسی عنوان مقاله

تعیین موقعیت مستقیم تطبیقی توزیع شده از emitters در شبکه های حسگر

عنوان انگلیسی
Distributed adaptive direct position determination of emitters in sensor networks
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
67457 2016 12 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Signal Processing, Volume 123, June 2016, Pages 100–111

ترجمه کلمات کلیدی
محلی سازی Emitter؛ تعیین موقعیت مستقیم تطبیقی (ADPD)؛ انتشار؛ چارچوب توزیع شده؛ شبکه های حسگر
کلمات کلیدی انگلیسی
Emitter localization; Adaptive direct position determination (ADPD); Diffusion; Distributed framework; Sensor networks
پیش نمایش مقاله
پیش نمایش مقاله  تعیین موقعیت مستقیم تطبیقی توزیع شده از emitters در شبکه های حسگر

چکیده انگلیسی

In the conventional centralized adaptive direct position determination (C-ADPD) approach, the emitter position is estimated at the fusion center (usually one of the sensors) with all the available signal samples transmitted from different sensors. This centralized framework may be not suitable for large-scale sensor networks due to the computational capability and energy storage bottleneck of the single fusion center. Furthermore, transmitting all the received signals to the fusion center usually needs multi-hop transmission, which is a big challenge to the communication bandwidth of the sensor networks. In this paper, we propose a fully distributed adaptive direct position determination (D-ADPD) approach for emitter localization. Without a dedicated fusion center in this distributed framework, signal samples received by sensors are transmitted to their corresponding neighbors with single-hop transmission only; the communication cost could be significantly reduced. Every sensor in the network locally estimates the common emitter position with an adaptive algorithm by fusing its information with diffused parameter estimates from its neighbors; the computational complexity is distributed among each sensor. Simulation results validate the improved convergence and steady-state performance of the proposed approach with enhanced elasticity and robustness in different scenarios.