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Proceedings of Meetings on Acoustics
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Item-typ:Veröffentlichung, Experimental investigation of a virtual planar array for MIMO sonar systems(American Instut of Physics, 2022); ; ; ; In this paper a way to achieve a virtual two-dimensional planar array for sonar systems using the multiple input multiple output (MIMO) principle is presented. The time delay information of a planar array can be used for imaging sonars and allows the localization of scatterers in three-dimensional space. In a conventional single input multiple output (SIMO) sonar, this would be achieved using a single transmitter and a receiver consisting of a planar array of N² hydrophones, arranged as a rectangular N × N matrix. We show through simulations and experiments how a virtual planar array can be formed using a linear receiver array and linear transmitter array. For this, an experimental MIMO sonar system was constructed with a 32-channel receiver and a 12-channel transmitter, which allowed the realization of a virtual 32 × 12 array. The array design is analyzed through experiment in a harbor basin and through simulations, validating the principle of the virtual planar array. In addition to the experimental investigations, in which only a limited number of transmitter channels are available, further simulations with a 32 × 32 array are performed for further MIMO arrangements to highlight strengths and weaknesses in different application areas.Wissenschaftlicher ArtikelBand:47Heft:191 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Experimental demonstration of the angular resolution enhancement of a monostatic MIMO sonar(American Institut of Physics, 2021); ; ; ; In this contribution we show by experimental tests the improvement of the angular resolution of an active monostatic Sonar system when using the Multiple-Input-Multiple-Output (MIMO) principle. This principle allows the design of a high-resolution sonar with a lower number of transducers required compared to a conventional Sonar, thus allowing the costs of the Sonar to be significantly reduced. For this purpose, a MIMO Sonar demonstrator was built and experiments were performed in a harbor basin. It is shown that the travel time information can be extended in a manner that allows a factoring of the angular resolution of the system by the number of transmitters. The key to this principle is that the respective transmission pulses of the transmitter modules can be separated from each other during signal processing on the receiver side. This can be achieved through different techniques. In this paper novel transmitter signals are presented, which are coded in a way that they can be transmitted in the same frequency and time window and afterwards be sufficiently separated by correlation filters. To evaluate and model the experiment, also a simulation was developed, which uses a simplified model of the acoustic channel to generate the received signals.Wissenschaftlicher ArtikelBand:44Heft:151 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, A study on modern deep learning detection algorithms for automatic target recognition in sidescan sonar images(American Instut. of Physics, 2021); ; ; ; State-of-the art deep learning models have shown remarkable performance on computer vision tasks like object classification or detection. These networks are typically trained on large-scale datasets of natural RGB images. However, sidescan sonar images are gray-scaled images representing acoustic intensities. The fundamental differences between camera and sonar as well as the images itself makes it necessary to investigate the transfer of results achieved on RGB images to the sonar imagery domain. Therefore, we compare the deep learning detection algorithm YOLOv2 with its updated version YOLOv3, both adopted for object detection in sidescan sonar images. In addition to this, a small convolutional neural network (CNN) is trained from scratch and used for detection. The experiments answer two questions: First, whether, as for general computer vision problems, transfer learning of large deep learning models is preferable over training of custom networks when dealing with limited sonar data. Secondly, whether improvements in the YOLO architecture, developed based on RGB images, lead to significant improvements on sonar data as well. Our results show that YOLOv3 indeed performs better than YOLOv2. Furthermore, YOLOv3 achieves a true positive rate of up to 98.2% and outperforms the small CNN.Wissenschaftlicher ArtikelHeft:4482