Classification of Sonar Targets in Air: A Neural Network Approach
Kroh P, Simon R, Rupitsch S (2019)
Publication Type: Journal article
Publication year: 2019
Journal
Book Volume: 19
Journal Issue: 5
DOI: 10.3390/s19051176
Abstract
Ultrasonic sonar sensors are commonly used for contactless distance measurements in application areas such as automotive and mobile robotics. They can also be exploited to identify and classify sound-reflecting objects (targets), which may then be used as landmarks for navigation. In the presented work, sonar targets of different geometric shapes and sizes are classified with custom-engineered features. Artificial neural networks (ANNs) with multiple hidden layers are applied as classifiers and different features are tested as well as compared. We concentrate on features that are related to target strength estimates derived from pulse-compressed echoes. In doing so, one is able to distinguish different target geometries with a high rate of success and to perform tests with ANNs regarding their capabilities for size discrimination of targets with the same geometric shape. A comparison of achievable classifier performance with wideband and narrowband chirp excitation signals was conducted as well. The research indicates that our engineered features and excitation signals are suitable for the target classification task.
Authors with CRIS profile
Patrick Kroh
Lehrstuhl für Autonome Systeme und Mechatronik
Stefan Rupitsch
Lehrstuhl für Autonome Systeme und Mechatronik
Involved external institutions
Netherlands (NL)How to cite
APA:
Kroh, P., Simon, R., & Rupitsch, S. (2019). Classification of Sonar Targets in Air: A Neural Network Approach. Sensors, 19(5). https://dx.doi.org/10.3390/s19051176
MLA:
Kroh, Patrick, Ralph Simon, and Stefan Rupitsch. "Classification of Sonar Targets in Air: A Neural Network Approach." Sensors 19.5 (2019).
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