Research of the efficiency of Kravchenko weighting functions and combinations based on them in the problem of narrow band interference rejection

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The influence of weight functions (WFs) of preliminary weighting on the quality of narrowband interference rejection based on direct and inverse discrete Fourier transforms has been studied. Classical and modern WFs are considered: Kravchenko, Kravchenko–Dolph–Chebyshev, Kravchenko–Gauss, Kravchenko–Bernstein–Rogozinsky. Quantitative estimates of efficiency were obtained – the coefficients of interference suppression and signal transmission, as well as their product – the total efficiency coefficient. Graphic examples of the behavior of the total efficiency coefficient depending on the interference frequency are shown. Estimates of the indicated quality indicators are presented when a fixed number of frequency samples are removed from the spectrum. The families of dependences of the probabilities of correctly performing a search for a spread spectrum signal on the “interference/signal” ratio when using modern WFs to weigh the implementations of an additive mixture of signal, interference and noise are presented. The significant advantage of modern WFs formed through combinations with Kravchenko functions in the problem of interference rejection has been demonstrated and confirmed.

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作者简介

E. Kuzmin

Siberian Federal University

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Email: ekuzmin@sfu-kras.ru
俄罗斯联邦, Krasnoyarsk

参考

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2. Fig. 1. Dependences of the total coefficient of rejection efficiency of the UP on the normalized frequency when removing Nуд = 50 frequency samples; weighting of the implementation of the Hanna VF (1) and the Kravchenko VF: (2), (3), (4).

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3. Fig. 2. Dependences of the total coefficient of rejection efficiency of the UP on the normalized frequency when removing Nуд = 50 frequency samples; weighting of the Dolph–Chebyshev waveform implementation and a sine window: ДЧ5(1), sine window (2), ДЧ3.5(3), ДЧ3(4).

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4. Fig. 3. Dependences of the total rejection efficiency coefficient of the UP on the normalized frequency; weighting of the Kaiser VF implementation: β = 6, Nуд = 50 (1) and 10 (2); β = 5, Nуд = 50 (3) and 10 (4).

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5. Fig. 4. Dependences of the total coefficient of rejection efficiency of the UP on the normalized frequency when removing Nуд = 50 frequency samples; weighting of the implementation of the VF (1), Parzen (2), (3) and (4).

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6. Fig. 5. Dependences of the total coefficient of rejection efficiency of the UP on the normalized frequency when removing Nуд = 50 frequency samples; weighting of the implementation of the VF (1), (2), Henning (3), (4), (5).

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7. Fig. 6. Dependences of the FM-SRS transmission coefficient on the normalized frequency with a removal of Nуд = 50 frequency samples; weighting of the VF DC5(1) and (2).

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8. Fig. 7. Dependences of the FM-SRS transmission coefficient on the normalized frequency with a removal of Nуд = 50 frequency samples; weighting of the VF (1) and (2).

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9. Fig. 8. Dependences of the probability of correct execution of the search by the FM-SRS delay, observed against the background of the UP and noise, on the “interference/signal” ratio at qep = 45 dBHz, M = 1: FM-SRS search without partial dispersion (curve 1); single WF at Nуд = 10 (2, 3) and 50 (4, 5); WF K4DCh5 (6, 7), Henning WF (8), (9), (10) at Nуд = 50; fixed interference frequency – 1, 2, 4, 6, random – 3, 5, 7 and 8–10.

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10. Fig. 9. Dependences of the probability of correct execution of the search by FM-SRS delay, observed against the background of UP and noise, on the “interference/signal” ratio at qep = 45 dBHz, M = 5, Nуд = 50: VF K4DCH5 (1, 2); Henning (3, 4); (5, 6); (7, 8); odd curves correspond to a fixed interference frequency, even curves – to a random one.

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