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

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.