ArticleSensors (Basel, Switzerland)2019
Classification of Sonar Targets in Air: A Neural Network Approach.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Object Recognition and Positioning with Neural Networks: Single Ultrasonic Sensor Scanning Approach.Sensors (Basel, Switzerland) · 2025Article
- Acoustic traits of bat-pollinated flowers compared to flowers of other pollination syndromes and their echo-based classification using convolutional neural networks.PLoS computational biology · 2021Article
- Bioinspired sonar reflectors as guiding beacons for autonomous navigation.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Classification of Sonar Targets in Air: A Neural Network Approach.Sensors (Basel, Switzerland) · 2019Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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