year 8, Issue 1 (Journal of Acoustical Engineering Society of Iran 2020)                   مجله انجمن علوم صوتی ایران (مهندسی صوتیات سابق) 2020, 8(1): 88-103 | Back to browse issues page

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Masoomi R, Sadeghi H. Accuracy and acceleration improvement of Fisher-based infrasound signal detection using ‎Genetic ‎Algorithm (Research Article). مجله انجمن علوم صوتی ایران (مهندسی صوتیات سابق) 2020; 8 (1) :88-103
URL: http://joasi.ir/article-1-161-en.html
Abstract:   (2896 Views)
Sound waves with frequencies below the human hearing threshold in the range of 0.002 Hz to 20 Hz‎, ‎which are traveling through the atmosphere‎, ‎are referred to as infrasound‎. ‎Wind is the main noise in the above-mentioned frequency range‎. ‎The operation of receiving and detecting of infrasound are often hampered by wind‎. ‎Therefore‎, ‎high quality detectors are required‎. ‎For this purpose‎, ‎sensor arrays and array signal processing techniques are utilized‎. Fisher ratio-based signal detection is a widely used and powerful method in the field of infrasound‎. ‎The main drawback of this approach is its high computational time due to the repeated computation of test statistics for each element of the slowness grid‎. ‎Thus‎, ‎the researchers use a relatively low-resolution slowness grid in order to save time in processing‎. ‎On the other hand‎, ‎low resolution results in an error in the values of estimated parameters of infrasound waves‎. In this study‎, ‎a genetic algorithm based detection method is proposed in order to overcome the fundamental problems of the Fisher method‎. ‎In the proposed method‎, ‎the slowness grid components (px, py) are defined as the chromosome for the genetic algorithm‎. ‎Despite the previous methods‎, ‎the genetic algorithm has created the advantage that searching could be conducted in a continuous slowness grid‎. ‎Therefore‎, ‎the continuity of the network and searching only a limited number of slowness vectors reduce error rates and processing time respectively‎. ‎The apparent velocity and incoming angles became 0.5923 and 0.0710 respectively‎, ‎and the processing time decreased considerably from 25835.07 seconds to 533.55 seconds on average‎.
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Type of Study: Research | Subject: Signal Processing
Received: 2019/10/7 | Accepted: 2020/08/14 | Published: 2020/09/10

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