Abstract
Extraction of communication signals from noisy spectrograms is a challenging problem which has not been explored extensively from an intelligent signal processing and computer vision based perspective. In this paper we propose a novel technique of extracting the communications signal from a noisy spectrogram using a combination of fuzzy neighborhood thresholding based self organizing neural network and morphological operations. We show that about 98% detection is achieved at 5% false alarm of a particular scenario outperforming traditional energy detection.
Original language | English |
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Title of host publication | 2015 IEEE Conference on Antenna Measurements & Applications (CAMA) |
Publisher | IEEE |
Number of pages | 4 |
ISBN (Electronic) | 9781467391498 |
DOIs | |
Publication status | Published - 10 Mar 2015 |
Event | IEEE Conference on Antenna Measurements 2015 - Chiang Mai, Thailand Duration: 30 Nov 2015 → 2 Dec 2015 |
Conference
Conference | IEEE Conference on Antenna Measurements 2015 |
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Country/Territory | Thailand |
City | Chiang Mai |
Period | 30/11/15 → 2/12/15 |
Keywords
- Computer Vision
- Spectrogram
- Bi-directional Self-organizing Neural Networks
- Fuzzy Hostility Index
- Morphological Filtering
- Blob Detection