Establishing a Multibeam Sonar Evaluation Test Bed near Sidney, British Columbia

The Canadian Hydrographic Service (CHS), Naval Oceanographic Office (NAVOCEANO) and the Ocean Mapping Group of the University of New Brunswick (OMG) collaborated on establishing a multibeam sonar test bed in the vicinity of the Institute of Ocean Sciences in Sidney, British Columbia Canada. This paper describes the purpose of the sonar evaluation test bed, the trials and tribulations of two foreign governments collaborating on projects of mutual interest, the evaluation areas and their characteristics for sonar testing, and sample results of sonar evaluations using this test bed. Some target detection comparisons of several systems over a range of artificial sonar targets will also be given

The Use of Multi-beam Sonars to Image Bubbly Ship Wakes

During the past five years, researchers at Penn State University (PSU) have used upward-looking multi-beam (MB) sonar to image the bubbly wakes of surface ships. In 2000, a 19-beam, 5° beam width, 120° sector, 250 kHz MB sonar integrated into an autonomous vehicle was used to obtain a first-of-a-kind look at the three-dimensional variability of bubbles in a large ship wake. In 2001 we acquired a Reson 8101 MB sonar, which operates at 240 kHz and features 101-1.5º beams spanning a 150º sector. In July 2002, the Reson sonar was deployed looking upward from a 1.4 m diameter buoy moored at 29.5 m depth in 550 m of water using three anchor lines. A fiber optic cable connected the sonar to a support ship 500 m away. Images of the wake of a small research vessel provided new information about the persistence of bubble clouds in the ocean. An important goal is to use the MB sonar to estimate wake bubble distributions, as has been done with single beam sonar. Here we show that multipath interference and strong, specular reflections from the sea surface adversely affect the use of MB sonars to unambiguously estimate wake bubble distributio

Balancing SoNaR: IPR versus Processing Issues in a 500-Million-Word Written Dutch Reference Corpus

In The Low Countries, a major reference corpus for written Dutch is beingbuilt. We discuss the interplay between data acquisition and data processingduring the creation of the SoNaR Corpus. Based on developments in traditionalcorpus compiling and new web harvesting approaches, SoNaR is designed tocontain 500 million words, balanced over 36 text types including bothtraditional and new media texts. Beside its balanced design, every text sampleincluded in SoNaR will have its IPR issues settled to the largest extentpossible. This data collection task presents many challenges because everydecision taken on the level of text acquisition has ramifications for the levelof processing and the general usability of the corpus. As far as thetraditional text types are concerned, each text brings its own processingrequirements and issues. For new media texts - SMS, chat - the problem is evenmore complex, issues such as anonimity, recognizability and citation right, allpresent problems that have to be tackled. The solutions actually lead to thecreation of two corpora: a gigaword SoNaR, IPR-cleared for research purposes,and the smaller - of commissioned size - more privacy compliant SoNaR,IPR-cleared for commercial purposes as well

Sonar acoustics

The problem of producing a model to determine the beam pattern produced by a sonar set in the form of a circular cylinder with hemispherical end caps is studied. The beam width and the position of the beam centre are also considered and the results of the models are compared with experimental findings. Possible reasons for the discrepancies between these theoretical and experimental results are examined, providing insight into developing more sophisticated mathematical models. The beam patterns were produced using a combination of Matlab and Fortran 77 programs incorporating subroutines from the NAG library. Experimental results and data are included with the kind permission of Thomson Marconi Sonar Systems Ltd

Improving Sonar Image Patch Matching via Deep Learning

Matching sonar images with high accuracy has been a problem for a long time, as sonar images are inherently hard to model due to reflections, noise and viewpoint dependence. Autonomous Underwater Vehicles require good sonar image matching capabilities for tasks such as tracking, simultaneous localization and mapping (SLAM) and some cases of object detection/recognition. We propose the use of Convolutional Neural Networks (CNN) to learn a matching function that can be trained from labeled sonar data, after pre-processing to generate matching and non-matching pairs. In a dataset of 39K training pairs, we obtain 0.91 Area under the ROC Curve (AUC) for a CNN that outputs a binary classification matching decision, and 0.89 AUC for another CNN that outputs a matching score. In comparison, classical keypoint matching methods like SIFT, SURF, ORB and AKAZE obtain AUC 0.61 to 0.68. Alternative learning methods obtain similar results, with a Random Forest Classifier obtaining AUC 0.79, and a Support Vector Machine resulting in AUC 0.66.Comment: Author versio

Size constancy in bat biosonar?

Perception and encoding of object size is an important feature of sensory systems. In the visual system object size is encoded by the visual angle (visual aperture) on the retina, but the aperture depends on the distance of the object. As object distance is not unambiguously encoded in the visual system, higher computational mechanisms are needed. This phenomenon is termed "size constancy". It is assumed to reflect an automatic re-scaling of visual aperture with perceived object distance. Recently, it was found that in echolocating bats, the 'sonar aperture', i.e., the range of angles from which sound is reflected from an object back to the bat, is unambiguously perceived and neurally encoded. Moreover, it is well known that object distance is accurately perceived and explicitly encoded in bat sonar. Here, we addressed size constancy in bat biosonar, recruiting virtual-object techniques. Bats of the species Phyllostomus discolor learned to discriminate two simple virtual objects that only differed in sonar aperture. Upon successful discrimination, test trials were randomly interspersed using virtual objects that differed in both aperture and distance. It was tested whether the bats spontaneously assigned absolute width information to these objects by combining distance and aperture. The results showed that while the isolated perceptual cues encoding object width, aperture, and distance were all perceptually well resolved by the bats, the animals did not assign absolute width information to the test objects. This lack of sonar size constancy may result from the bats relying on different modalities to extract size information at different distances. Alternatively, it is conceivable that familiarity with a behaviorally relevant, conspicuous object is required for sonar size constancy, as it has been argued for visual size constancy. Based on the current data, it appears that size constancy is not necessarily an essential feature of sonar perception in bats