Results of using an artificial neural network to distinguish single echoes from multiple sonar echoes

Chang, Wanni; Bosworth, B.; Carter, G. Clifford

doi:10.1121/1.408169

Cited by 5 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Neural networks have been used in sonar and radar signal processing (Chang, Bosworth & Carter, 1993;Widrow & Winter, 1988); for instance, in the identi®cation of ships from observed parametric radar data (Prieve & Marchette, 1987). The motivation behind the use of neural network classi®ers in sonar or radar systems is the desire to emulate the remarkable perception and pattern recognition capabilities of humans and animals, such as the powerful ability of dolphins and bats to extract detailed information about their environments from acoustic echo returns (Au, 1994;Roitblat, Au, Nachtigall, Shizumura & Moons, 1995;Simmons, Saillant, Wotton, Haresign, Ferragamo & Moss, 1995).…”

Section: Introductionmentioning

confidence: 99%

Neural networks for improved target differentiation and localization with sonar

Ayrulu

Barshan

2001

Neural Networks

View full text Add to dashboard Cite

This study investigates the processing of sonar signals using neural networks for robust differentiation of commonly encountered features in indoor robot environments. Differentiation of such features is of interest for intelligent systems in a variety of applications. Different representations of amplitude and time-of-¯ight measurement patterns acquired from a real sonar system are processed. In most cases, best results are obtained with the low-frequency component of the discrete wavelet transform of these patterns. Modular and non-modular neural network structures trained with the back-propagation and generating±shrinking algorithms are used to incorporate learning in the identi®ca-tion of parameter relations for target primitives. Networks trained with the generating±shrinking algorithm demonstrate better generalization and interpolation capability and faster convergence rate. Neural networks can differentiate more targets employing only a single sensor node, with a higher correct differentiation percentage (99%) than achieved with previously reported methods (61±90%) employing multiple sensor nodes. A sensor node is a pair of transducers with ®xed separation, that can rotate and scan the target to collect data. Had the number of sensing nodes been reduced in the other methods, their performance would have been even worse. The success of the neural network approach shows that the sonar signals do contain suf®cient information to differentiate all target types, but the previously reported methods are unable to resolve this identifying information. This work can ®nd application in areas where recognition of patterns hidden in sonar signals is required. Some examples are system control based on acoustic signal detection and identi®cation, map building, navigation, obstacle avoidance, and target-tracking applications for mobile robots and other intelligent systems. q

show abstract

Section: Introductionmentioning

confidence: 99%

Neural networks for improved target differentiation and localization with sonar

Ayrulu

Barshan

2001

Neural Networks

View full text Add to dashboard Cite

show abstract

“…In Ref. 25, artificial neural networks are applied to classifying underwater active sonar returns with different numbers of peaks. Another system can recognize 3-D cubes and tetrahedrons, independent of their orientation with the help of neural networks.…”

Section: Introductionmentioning

confidence: 99%

Radius of curvature estimation and localization of targets using multiple sonar sensors

Barshan

Sekmen

1999

The Journal of the Acoustical Society of America

View full text Add to dashboard Cite

Acoustic sensors have been widely used in time-of-flight ranging systems since they are inexpensive and convenient to use. One of the most important limitations of these sensors is their low angular resolution. To improve the angular resolution and the accuracy, a novel, flexible, and adaptive three-dimensional ͑3-D͒ multi-sensor sonar system is described for estimating the radius of curvature and location of cylindrical and spherical targets. Point, line, and planar targets are included as limiting cases which are important for the characterization of typical environments. Sensitivity analysis of the curvature estimate with respect to measurement errors and certain system parameters is provided. The analysis and the simulations are verified by experiments in 2-D with specularly reflecting cylindrical and planar targets, using a real sonar system. Typical accuracies in range and azimuth are 0.18 mm and 0.1°, respectively. Accuracy of the curvature estimation depends on the target type and system parameters such as transducer separation and operating range. The adaptive configuration brings an improvement varying between 35% and 45% in the accuracy of the curvature estimate. The presented results are useful for target differentiation and tracking applications.

show abstract