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 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 t

SUBMITTER: Kroh PK 

PROVIDER: S-EPMC6427766 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature