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Classification of Radar Jammer FM Signals Using a Neural Network Approach.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Classification of Radar Jammer FM Signals Using a Neural Network Approach./
作者:	Mendoza, Ariadna.
面頁冊數:	1 online resource (49 pages)
附註:	Source: Masters Abstracts International, Volume: 56-05.
Contained By:	Masters Abstracts International56-05(E).
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355035285

Classification of Radar Jammer FM Signals Using a Neural Network Approach.
Mendoza, Ariadna.

Classification of Radar Jammer FM Signals Using a Neural Network Approach. - 1 online resource (49 pages)

Source: Masters Abstracts International, Volume: 56-05.

Thesis (M.S.)--The University of Texas at El Paso, 2017.

Includes bibliographical references

A Neural Network (NN) used to classify radar signals is proposed for the purpose of military survivability and lethality analysis. The goal of the NN is to correctly differentiate Frequency-Modulated (FM) signals from Additive White Gaussian Noise (AWGN) using limited signal pre-processing. The FM signals used to test the NN approach are the linear or chirp FM and the power-law FM. Preliminary simulations using the moments of the signals in the time and frequency domain yielded better results in the frequency domain, suggesting that time domain training would not be as effective frequency domain training. To test this hypothesis, we developed a training procedure for the NN using either spectra or autocorrelation sequences as inputs as they require a similar amount of signal preprocessing. Classification performance was done in terms of the probability of false alarm (PFA), probability of detection (PD), and probability of error (PE) as a function signal-to-noise-ratio (SNR). In one case, the NN is trained with a set of spectra with either a noisy FM signal with random carrier frequency and bandwidth or strong bandlimited white noise. Simulations show that at an SNR of 5dB, the NN consistently performs signal classification with a PFA close to 0% and a PD higher than 85%. At a SNR of 10dB, the NN reaches a PE of 0%. In another case, the NN is trained with a set of autocorrelations of either a noisy signal or bandlimited noise. At an SNR of 5dB, the NN consistently performs signal classification with a PFA close to 0% and a PD higher than 99%. At a SNR of 10dB, the NN reaches a PE of 0%. In a third case, the NN is trained with a set of signals, which are either linear FM, power-law FM, or bandlimited white noise. Here, at an SNR of 5dB, the NN consistently performs signal classification with a PE close to 0% for both the spectra and the autocorrelation. The conclusion is the NN at a high SNR level performs exceedingly well for either case. However, at very low SNR, the NN radar signal classifier performs better when its input is the autocorrelation of the signal.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355035285Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Classification of Radar Jammer FM Signals Using a Neural Network Approach.
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520 $a A Neural Network (NN) used to classify radar signals is proposed for the purpose of military survivability and lethality analysis. The goal of the NN is to correctly differentiate Frequency-Modulated (FM) signals from Additive White Gaussian Noise (AWGN) using limited signal pre-processing. The FM signals used to test the NN approach are the linear or chirp FM and the power-law FM. Preliminary simulations using the moments of the signals in the time and frequency domain yielded better results in the frequency domain, suggesting that time domain training would not be as effective frequency domain training. To test this hypothesis, we developed a training procedure for the NN using either spectra or autocorrelation sequences as inputs as they require a similar amount of signal preprocessing. Classification performance was done in terms of the probability of false alarm (PFA), probability of detection (PD), and probability of error (PE) as a function signal-to-noise-ratio (SNR). In one case, the NN is trained with a set of spectra with either a noisy FM signal with random carrier frequency and bandwidth or strong bandlimited white noise. Simulations show that at an SNR of 5dB, the NN consistently performs signal classification with a PFA close to 0% and a PD higher than 85%. At a SNR of 10dB, the NN reaches a PE of 0%. In another case, the NN is trained with a set of autocorrelations of either a noisy signal or bandlimited noise. At an SNR of 5dB, the NN consistently performs signal classification with a PFA close to 0% and a PD higher than 99%. At a SNR of 10dB, the NN reaches a PE of 0%. In a third case, the NN is trained with a set of signals, which are either linear FM, power-law FM, or bandlimited white noise. Here, at an SNR of 5dB, the NN consistently performs signal classification with a PE close to 0% for both the spectra and the autocorrelation. The conclusion is the NN at a high SNR level performs exceedingly well for either case. However, at very low SNR, the NN radar signal classifier performs better when its input is the autocorrelation of the signal.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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650 4 $a Artificial intelligence. $3 559380
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710 2 $a The University of Texas at El Paso. $b Electrical Eng.. $3 1186964
773 0 $t Masters Abstracts International $g 56-05(E).
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