آشكارسازي سيگنال بر اساس پردازش موازي مبتني بر جي‌پي‌يو در شبكه‌هاي حس‌گري صوتي داراي زيرساخت

Signal Detection Based on GPU-Assisted Parallel Processing for Infrastructure-based Acoustical Sensor Networks

الگوريتم فيشر، يكي از معروف‌ ترين و پركاربردترين روش‌هاي آشكارسازي آرايه‌اي سيگنال‌هاي صوتي بسامدِ پايين در شبكه‌هاي حس‌گري داراي زيرساخت است؛ اما يكي از مشكلات عمده در به‌كارگيري اين الگوريتم، زمان طولاني انجام پردازش در آن است كه در عمل، پياده‌سازي بلادرنگ آشكارساز را با مشكل مواجه مي‌سازد. در اين مقاله، چگونگي پياده‌سازي الگوريتم فيشر را با استفاده از واحد پردازش گرافيك (جي‌پي‌يو) به‌منظور تحقق محاسبات سريع و انجام پردازش‌هاي نزديك به زمانِ واقعي، ارائه مي‌كنيم. به‌خصوص به‌منظور بهبود هرچه بيشتر سرعت محاسبات، الگوريتم آشكارسازي با استفاده از روش پردازش موازي (مبتني بر جي‌پي‌يو) پياده‌سازي شده است. نتايج شبيه‌سازي‌ها، ارتقاي قابل ملاحظه سرعت آشكارساز فيشر را نشان مي‌دهند كه باعث بهبود كارآيي شبكه حس‌گري صوتي خواهد شد.

Nowadays, several infrastructure-based low-frequency acoustical sensor networks are employed in different applications to monitor the activity of diverse natural and man-made phenomena, such as avalanches, earthquakes, volcanic eruptions, severe storms, super-sonic aircraft flights, etc. Two signal detection methods are usually implemented in these networks for the purpose of event occurrence identification, which are the progressive multi-channel correlator (PMCC) and the so-called Fisher detector. But, the Fisher method is more important and applicable in low signal-to-noise (SNR) ratio conditions, which is of a special interest in acoustical monitoring networks. Unfortunately, an important disadvantage of this algorithm is its relative high detection-time; which limits its application for real-time detection scenarios. This disadvantage is fundamentally due to a beam forming process in Fisher algorithm, which requires doing complete search in a slowness-network, constructed from possible incoming wave front directions and speeds. To address this issue, we propose a method for implementation of this beam forming on a graphics processing unit (GPU), in order to realize a fast-computing and/or near real-time signal processing technique. In addition, we also propose a parallel-processing algorithm for further enhancement of the performance of this GPU-based Fisher detector. Simulation results confirm the performance improvement of Fisher detector, in terms of required processing time for acoustical signal detection applications.