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A Signal Recognition Method Using Short-Time Ramanujan Fourier Transform Spectrogram

A technology of Fourier transform and signal recognition, which is applied in the field of signal processing, can solve the problem of low signal recognition rate and achieve good anti-noise effect

Inactive Publication Date: 2019-11-08
TIANJIN UNIVERSITY OF TECHNOLOGY
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] The purpose of the present invention is to overcome the shortcoming that the signal identification rate is low under the condition of low signal-to-noise ratio existing in the signal identification method based on short-time Fourier transform

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  • A Signal Recognition Method Using Short-Time Ramanujan Fourier Transform Spectrogram
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  • A Signal Recognition Method Using Short-Time Ramanujan Fourier Transform Spectrogram

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Embodiment Construction

[0041] The following will refer to Figure 1-11 The specific embodiments of the present invention will be described.

[0042] A signal recognition method, such as figure 1 As shown, the method includes:

[0043] Step 1. To the template signal s i,L,x Calculate the normalized short-time Ramanujan Fourier transform spectrum i is the serial number corresponding to each modulation signal type, L=1,2,...F, L is the number of experiments, x is the noise signal-to-noise ratio (SNR) decibel value, and time k=1, 2,..., N, integer period q=1, 2,...,N, M, F, N are natural numbers;

[0044] Calculate the normalized short-time Ramanujan Fourier transform spectrum Centroid shift spectrum Is the normalized short-time Ramanujan Fourier transform spectrum Center of mass.

[0045] Further, in step 1, the currently commonly used modulation signal types include: linear frequency modulation signal (LFM), single frequency pulse signal (CW), two-phase coded signal (BPSK), binary frequency coded signal...

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Abstract

The invention discloses a signal recognition method by use of a short-term Ramanujan Fourier transformation spectrogram. The method comprises the following steps: computing a centroid shifting spectrogram of a normalized short-term Ramanujan Fourier transformation spectrogram of a template signal; computing an imaginary part mean value and a real part mean value of third-order three-fold pseudo-Zernike moments for the centroid shifting spectrogram, respectively setting a threshold value thLFM_1 and the threshold value thLFM_2 according to the imaginary party mean value and the real part mean value; computing the second-order zero-fold pseudo-Zernike moments and the fifth-order one-fold pseudo-Zernike moments for the centroid shifting spectrogram of other template signal so as to construct a template cluster; computing the centroid shifting spectrogram of the normalized short-term Ramanujan Fourier transformation spectrogram of a to-be-recognized signal, computing the imaginary part mean value and the real part mean value of third-order three-fold pseudo-Zernike moments for the centroid shifting spectrogram of a to-be-recognized signal, and respectively separating a to-be-recognized LFM signal according to the a threshold value thLFM_1 and the threshold value thLFM_2. By use of the signal recognition method disclosed by the invention, the problem that the signal recognition method based on the short-term Fourier transformation is low in signal recognition rate under a low signal to noise ratio condition is solved.

Description

Technical field [0001] The present invention relates to signal processing technology, in particular to a signal identification method using short-time Ramanujan Fourier transform spectrogram. [0002] technical background [0003] With the rapid development of wireless communication technology and the increasingly complex modern communication environment, signal type recognition technology is of great significance in both civil and military applications. However, it is particularly important and urgent to realize reliable signal type recognition under the condition of lack of prior knowledge and low signal-to-noise ratio. [0004] At present, the commonly used methods of signal recognition include two methods based on likelihood function and feature extraction. Time-frequency analysis is one of the feature extraction methods. Since it can establish the instantaneous correspondence between time and frequency, it can be used It has become a research hotspot to analyze the changing law...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L27/00
CPCH04L27/0008H04L27/0012
Inventor 马秀荣刘丹单云龙
Owner TIANJIN UNIVERSITY OF TECHNOLOGY