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Wavelet analysis of instantaneous correlations with application to frequency hopped signals

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Author
Khalil, Nabil Hamdy Shaker
Date
1997-09
Advisor
Hippenstiel, Ralph D.
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Abstract
Frequency hopped signals are widely used in various communication applications for their inherent security features. The demand, by civilian and governmental agencies, to intercept communication signals is increasing. The interception task can be summarized by detecting the signal's presence in additive noise, classifying the modulation type, estimating the control parameters, decoding the data, and decrypting the information content. This work addresses the merging of wavelet and correlation concepts to detect, classify and estimate the parameters of frequency hopped signals. We address the interception problem in two ways. The first approach is based on a visual inspection of the wavelet surfaces generated from the instantaneous correlation function of the communication signal and leads to hop start/stop times estimates. In the second approach, we apply an energy-based processing scheme to estimate the hop start and stop times, the hop scale pattern, and the hop frequency. Results show that frequency hopped signals can be identified at an SNR of 3 dB or 6 dB using visual inspection or an automated scheme, respectively
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https://hdl.handle.net/10945/9156
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    Detection and estimation of frequency hopping signals using wavelet transforms 

    Overdyk, Howard F. (Monterey, California. Naval Postgraduate School, 1997-09);
    This thesis investigates the use of wavelet transforms in the detection and estimation of spread spectrum frequency hopping signals. The technique developed in this work makes only two basic assumptions of a minimum hopping ...
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    Detection of frequency hopped signals timing information using the temporal correlation function 

    Cheng, Yuan-Pin. (Monterey California. Naval Postgraduate School, 2008-09);
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    Bootstrap based signal denoising 

    Kan, Hasan Ertam (Monterey, California. Naval Postgraduate School, 2002-09);
    "This work accomplishes signal denoising using the Bootstrap method when the additive noise is Gaussian. The noisy signal is separated into frequency bands using the Fourier or Wavelet transform. Each frequency band is ...
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