Please note: We are currently experiencing some performance issues across the site, and some pages may be slow to load. We are working on restoring normal service soon. Importing new articles from Word documents is also currently unavailable. We apologize for any inconvenience.

Raoof Masoomi

and 2 more

Special attention is now being paid to the processing of microphone array signals‎. ‎Sensors located in the array receive the source signal at different time intervals‎. ‎In order to find the direction of source‎, ‎the time difference measurement algorithms‎, ‎which are based on the calculation of cross correlation function‎, ‎calculate the correlation between the outputs of the array sensors‎. ‎The maximum peak of the correlation function indicates the time difference of the sensors to each other‎. ‎Then‎, ‎the source signal orientation will be estimated by using the direction of arrival estimation algorithms based on the calculation of the time differences‎. ‎When the signal propagates in a direct path‎, ‎the cross-correlation function is efficient to measure the time differences‎. ‎However‎, ‎in the case the signal propagates in multipath medium‎, ‎the generalized cross-correlation functions will be used due to the presence of multiple peaks in the cross-correlation function‎. ‎The Phase Transform method‎, ‎which is a family of generalized cross-correlation methods‎, ‎yields better results than other methods‎. ‎Nevertheless‎, ‎it performs poorly at low SNRs‎. ‎In this study‎, ‎we add a modification factor to the weighting function of the Phase Transform method‎. ‎In the following‎, ‎the results of time differences obtained from the proposed method are then applied to different DOA methods‎. ‎In the simulations‎, ‎it was observed that the particle swarm optimization method offers the least error among the DOA methods‎, ‎so that the error values of backazimuth and elevation decreased by about 3 degrees and 2 degrees‎, ‎respectively for the factor of 0.65 at low SNRs‎.

Raoof Masoomi

and 2 more

‎Atmospheric sound waves with frequencies below the human hearing threshold in the range of 0.002 Hz to 20 Hz are generally referred to as infrasound‎. ‎Wind is the main noise in the above-mentioned frequency range‎. ‎The operation of receiving and detecting 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 grid results in an error in the values of estimated parameters of infrasound wave‎. ‎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 grid and searching only a limited number of slowness vectors reduce error rates and processing time respectively‎. ‎The error of apparent velocity and incoming orientation became 0.5923 and 0.0710 respectively‎, ‎and the processing time decreased considerably from 25835.07 seconds to 533.55 seconds on average.