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Multi-component linear frequency modulation signal time-frequency analysis method based on regional kernel function
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A chirp signal, time-frequency analysis technology, applied in the field of signal processing, can solve the problem that the time-frequency distribution is not optimal
Active Publication Date: 2021-09-07
SOUTHEAST UNIV
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For example, the time-frequency analysis method of the extended compact kernel function can reduce the influence of cross-terms, but the parameters of this method are set according to empirical values, and cannot be adaptively changed according to the signal to be processed, which will lead to the obtained time-frequency distribution is not optimal
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Embodiment 1
[0162] The simulation signal parameters are set as follows: signal amplitude A=1, number of sampling points N=2048, initial signal frequency is f l1 = 400Hz and f l2 =200Hz, sampling frequency f s =4000Hz, the modulation rates are: 1.2kHz / s and 1.6kHz / s respectively, the pulse width length is 0.25s, and the signal start times are 0.0625s and 0.125s respectively. Additive white Gaussian noise with a signal-to-noise ratio of 5dB is added to the signal.
[0163] First, calculate the fuzzy function A of the signal data s(a) to be processed z (m,n).
[0164] Then, calculate the blur function A z (m,n) discrete Radon transform R(θ p , d q ).
[0165] Next, estimate the radial angle, radial length, and normal width of the self-term to obtain φ 1 = 74°, r 1 '=0.3226,l 1 '=0.0144. φ 2 = 68°, r 2 '=0.3433, l 2 '=0.0044.
[0166] Then, set the area function B k (m,n), will expand the compact kernel function g(m,n) with the area function B k (m,n) multiplied to get the re...
Embodiment 2
[0169] The simulation signal parameters are set as follows: signal amplitude A=1, number of sampling points N=2048, initial signal frequency is f l1 = 400Hz and f l2 =550Hz, sampling frequency f s =4000Hz, the modulation rates are: 1.2kHz / s and -0.8kHz / s, the pulse widths are both 0.25s, and the signal start times are 0.0625s and 0.125s respectively. Additive white Gaussian noise with a signal-to-noise ratio of 5dB is added to the signal.
[0170] First, calculate the fuzzy function A of the signal data s(a) to be processed z (m,n).
[0171] Then, calculate the blur function A z (m,n) discrete Radon transform R(θ p , d q ).
[0172] Next, estimate the radial angle, radial length, and normal width of the self-term to obtain φ 1 = 102°, r 1 '=0.2711, l 1 '=0.0057. φ 2 = 74°, r 2 '=0.3075, l 2 '=0.0057.
[0173] Then, set the area function B k (m,n), will expand the compact kernel function g(m,n) with the area function B k (m,n) multiplied to get the region adapt...
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Abstract
The invention discloses a multi-component linear frequency modulationsignal time-frequency analysis method based on a regional kernel function. The method comprises the steps of 1 acquiring a to-be-processed multi-component linear frequency modulation pulse signal; 2 calculating a fuzzy function; 3 detecting a signal self-term in a fuzzy domain and estimating a radial angle of the signal self-term in the fuzzy domain; 4 setting a region function according to the distribution of the signal self-term in the fuzzy domain; 5 multiplying the extended compact kernel function by the regional function to obtain a regional adaptive kernel function; and 6 multiplying the generated regional adaptive kernel function by a signal fuzzy function to obtain a signal after fuzzy domain filtering, and converting the signal after fuzzy domain filtering to a time-frequency domain to obtain a time-frequency analysis result of the signal. The method can effectively suppress cross terms and noise interference, does not weaken the self-term as much as possible, and is suitable for high-quality time-frequency analysis of multi-component linear frequency modulation signals.
Description
technical field [0001] The invention belongs to the field of signal processing, in particular to a method for time-frequency analysis of multi-component linear frequency modulation signals based on a regional kernel function. Background technique [0002] Since the traditional single frequency domain or time domain analysis cannot observe the time-frequency relationship of non-stationary underwater acoustic or radar pulse signals at the same time, time-frequency analysis is very important in array signal processing applications such as radar, sonar, acoustics, voice and wireless communications. Significance, especially plays an extremely important role in underwater acoustic and electronic reconnaissance processing. At present, there are mainly linear time-frequency analysis methods and quadratic time-frequency analysis methods for analyzing the time-frequency domain characteristics of signals. [0003] The most classic linear time-frequency analysis method is the short-tim...
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