Abstract
Linear Frequency Modulated Continuous Waveform (LFMCW) waveforms are widely used in Low Probability of Intercept (LPI) radars. In this work, Wigner- Hough based transforms are used to detect such waveforms. They are investigated for three aims: Minimizing the effects of Wigner-Ville Distribution (WVD) cross-Terms in the transforms, reducing the complexity of hardware implementation and producing the transform results faster. Modified Wigner-Hough Transform (MWHT) is analyzed for the first aim. Sample and hold type intermediate sampling and column-based thresholding techniques are examined for the second and third aims. A signal database (3000 signals with different SNR levels) is created and used to evaluate the performances of 8 alternative methods on probability of detection and probability of chirp rate estimation. The results are compared for -3dB down to -14dB SNR levels. Proposed method is found to be the best candidate to be implemented on Field Programmable Gate Arrays (FPGA) since it produces better performance with less computational density.
Original language | English |
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Title of host publication | 2016 39th International Conference on Telecommunications and Signal Processing, TSP 2016 |
Editors | Norbert Herencsar |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 441-444 |
Number of pages | 4 |
ISBN (Electronic) | 9781509012886 |
DOIs | |
Publication status | Published - 28 Nov 2016 |
Event | 39th International Conference on Telecommunications and Signal Processing, TSP 2016 - Vienna, Austria Duration: 27 Jun 2016 → 29 Jun 2016 |
Publication series
Name | 2016 39th International Conference on Telecommunications and Signal Processing, TSP 2016 |
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Conference
Conference | 39th International Conference on Telecommunications and Signal Processing, TSP 2016 |
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Country/Territory | Austria |
City | Vienna |
Period | 27/06/16 → 29/06/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.
Keywords
- Column-based thresholding
- FPGA
- LFMCW
- LPI radar
- Modified wigner-hough transform