- Forfatter
- Gusland, Daniel
- Rolfsjord, Sigmund Johannes Ljosvoll
- Torvik, Børge
- Publisert
- 2020
- Emneord
- Radar
- Maskinlæring
- Ubemannede luftfarkoster (UAV)
- Dyp læring
- Permalenke
- http://hdl.handle.net/20.500.12242/2840
- DOI
- 10.1109/RADAR42522.2020.9114828
- Samling
- Articles
- Description
- Gusland, Daniel; Rolfsjord, Sigmund Johannes Ljosvoll; Torvik, Børge. Deep temporal detection - A machine learning approach to multiple-dwell target detection. IEEE International Conference on Radar 2020
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- 1857856.pdf
- Size: 1M
- Sammendrag
- Detecting small targets, such as an Unmanned Aerial Vehicle (UAV) in high clutter and non-homogeneous environments is challenging for a radar system. Traditional Constant False Alarm Rate (CFAR) detectors have suboptimal performance in many scenarios. In this paper, we attempt a new approach to radar detection, based on machine learning, to increase the P D while retaining a low F FA . We propose two approaches, using a Convolutional Neural Network (CNN) on the range-Doppler images and stacking multiple range-Doppler images as layers, called the Temporal CNN detector. The models are trained and tested solely on measured radar data by using the estimated position and velocity from a collaborative target UAV. It is shown that training a model based solely on measured data is achievable and performance metrics calculated from the testing data shows that both models outperform the Cell-Averaging Constant False Alarm Rate (CA-CFAR) by having higher P D with the same P FA . The current test results indicate that the temporal CNN is able to increase the detection distance close to 30%, while retaining the same P FA as the CA-CFAR.