Robust Drone Detection for Acoustic Monitoring Applications. Ohlenbusch, M., Ahrens, A., Rollwage, C., & Bitzer, J. In 2020 28th European Signal Processing Conference (EUSIPCO), pages 6-10, Aug, 2020.
Robust Drone Detection for Acoustic Monitoring Applications [pdf]Paper  doi  abstract   bibtex   
Commercially available light-weight unmanned aerial vehicles (UAVs) present a challenge for public safety, e.g. espionage, transporting dangerous goods or devices. Therefore, countermeasures are necessary. Usually, detection of UAVs is a first step. Along many other modalities, acoustic detection seems promising. Recent publications show interesting results by using machine and deep learning methods. The acoustic detection of UAVs appears to be particularly difficult in adverse situations, such as in heavy wind noise or in the presence of construction noise. In this contribution, the typical feature set is extended to increase separation of background noise and the UAV signature noise. The decision algorithm utilized is support vector machine (SVM) classification. The classification is based on an extended training dataset labeled to support binary classification. The proposed method is evaluated in comparison to previously published algorithms, on the basis of a dataset recorded from different acoustic environments, including unknown UAV types. The results show an improvement over existing methods, especially in terms of false-positive detection rate. For a first step into real-time embedded systems a recursive feature elimination method is applied to reduce the model dimensionality. The results indicate only a slight decreases in detection performance.
@InProceedings{9287433,
  author = {M. Ohlenbusch and A. Ahrens and C. Rollwage and J. Bitzer},
  booktitle = {2020 28th European Signal Processing Conference (EUSIPCO)},
  title = {Robust Drone Detection for Acoustic Monitoring Applications},
  year = {2020},
  pages = {6-10},
  abstract = {Commercially available light-weight unmanned aerial vehicles (UAVs) present a challenge for public safety, e.g. espionage, transporting dangerous goods or devices. Therefore, countermeasures are necessary. Usually, detection of UAVs is a first step. Along many other modalities, acoustic detection seems promising. Recent publications show interesting results by using machine and deep learning methods. The acoustic detection of UAVs appears to be particularly difficult in adverse situations, such as in heavy wind noise or in the presence of construction noise. In this contribution, the typical feature set is extended to increase separation of background noise and the UAV signature noise. The decision algorithm utilized is support vector machine (SVM) classification. The classification is based on an extended training dataset labeled to support binary classification. The proposed method is evaluated in comparison to previously published algorithms, on the basis of a dataset recorded from different acoustic environments, including unknown UAV types. The results show an improvement over existing methods, especially in terms of false-positive detection rate. For a first step into real-time embedded systems a recursive feature elimination method is applied to reduce the model dimensionality. The results indicate only a slight decreases in detection performance.},
  keywords = {Support vector machines;Training;Training data;Acoustics;Safety;Classification algorithms;Drones;Drone detection;UAV;public safety;binary classification;acoustic event detection;feature selection},
  doi = {10.23919/Eusipco47968.2020.9287433},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2020/pdfs/0000006.pdf},
}
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