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.

Ultrasonic sonar sensors are commonly used for contactless distance measurements in application areas such as automotive and mobile robotics. They can also be used to identify and classify sound reflecting objects. In the presented work, we classify simple sonar targets of different geometric shape and size. For this purpose, we built a test stand for echo measurements that facilitates defined arbitrary translation and rotation of the targets. Artificial neural networks (ANNs) with multiple hidden layers were used as classifiers and different features were evaluated. The focus was on two features that were derived from the echoes’ cross-correlation functions with their excitation chirp signals. We could distinguish different target geometries with our features and also evaluated the ANNs’ capabilities for size discrimination of targets with the same geometric shape.

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