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.
Highlights
Air-based ultrasonic sonar sensors are often used for obstacle avoidance and navigation purposes in application areas such as automotive, factory automation as well as mobile ground and airborne robotics [1]
Artificial neural networks (ANNs) are an option, which were used for ultrasonic targets in air [4] and for classification of spherical targets consisting of different materials in water
We used narrowband chirp signals as we were interested in the performance that may be achieved if we used a common narrowband ultrasonic sensor, such as a piezoelectric-based transducer
Summary
Air-based ultrasonic sonar sensors are often used for obstacle avoidance and navigation purposes in application areas such as automotive, factory automation as well as mobile ground and airborne robotics [1]. In addition to identifying the distance to the closest obstacle, it is favorable to be able to classify targets, which may be used as landmarks for navigation and positioning. Echolocating bats are known to use broadband signals as soon as they have to resolve objects in front of vegetation [2] and it was shown that they are able to classify different geometrical objects independent of their size [3]. We focus on classification of geometrically different shaped targets and the extraction of suitable sonar features
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