Classification of Sonar Targets in Air—A Neural Network Approach

Abstract

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.