Classification of Sonar Targets in Air: A Neural Network Approach

Journal article


Publication Details

Author(s): Kroh P, Simon R, Rupitsch S
Journal: Sensors
Publication year: 2019
Volume: 19
Journal issue: 5
ISSN: 1424-8220


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.


FAU Authors / FAU Editors

Kroh, Patrick
Lehrstuhl für Sensorik
Rupitsch, Stefan PD Dr.
Lehrstuhl für Sensorik


External institutions with authors

Vrije Universiteit Amsterdam (VU) / University Amsterdam


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).

BibTeX: 

Last updated on 2019-12-04 at 22:08