Classification of sonar targets in air: A neural network approach

Patrick K. Kroh*, Ralph Simon, Stefan J. Rupitsch

*Corresponding author for this work

    Research output: Contribution to JournalArticleAcademicpeer-review


    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.

    Original languageEnglish
    Article number1176
    Pages (from-to)1-17
    Number of pages17
    JournalSensors (Switzerland)
    Issue number5
    Publication statusPublished - 7 Mar 2019

    Bibliographical note

    Special Issue: Eurosensors 2018 Selected Papers


    • Feature extraction
    • Neural networks
    • Sonar detection
    • Sonar measurements


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