Abstract
Recently, radar based micro-Doppler signature analysis has been successfully applied in various sectors including both defence and civilian applications. A joint radar micro-Doppler and deep learning technology for End-Of-Line (EOL)test of loudspeakers is proposed in this paper. This approach offers the potential benefits of characterizing the mechanical motion of a loudspeaker in a noisy environment as a production line, in order to automatically identify and classify defects. Starting from real radar signal, the proposed Bidirectional Long Short-Term Memory (BiLSTM) classifier has been tested on training, validation and test dataset. The results show that the proposed approach produces a probability of correct classification abovethe98%, outperforming the traditional k-NN classifier.
Original language | English |
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Title of host publication | 2020 IEEE Radar Conference (RadarConf20) |
Place of Publication | Piscataway, NJ. |
Publisher | IEEE |
Number of pages | 6 |
ISBN (Electronic) | 9781728189420 |
ISBN (Print) | 9781728189437 |
DOIs | |
Publication status | Published - 4 Dec 2020 |
Event | IEEE Radar Conference 2020 - Florence, Florence, Italy Duration: 21 Sept 2020 → 25 Sept 2020 |
Conference
Conference | IEEE Radar Conference 2020 |
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Country/Territory | Italy |
City | Florence |
Period | 21/09/20 → 25/09/20 |
Keywords
- civilian radar application
- micro-doppler analysis
- loudspeaker analysis
- deep learning