Extended B distribution pulse signal time-frequency analysis method based on parameter self-adaption

A time-frequency analysis and pulse signal technology, applied in the field of signal processing, can solve the problems that the time-frequency distribution is not optimal for the suppression of cross terms, the amount of computation is small, and the time-frequency analysis cannot be adapted to the time and frequency variation range.

Active Publication Date: 2021-03-16
NANJING SHIHAI ACOUSTIC TECH CO LTD
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Problems solved by technology

[0003] The most classic linear time-frequency analysis method is the short-time Fourier transform. This method is simple to implement and has a small amount of calculation. For multi-component underwater acoustic or radar pulse signals, the time-frequency analysis results do not have the problem of cross-term interference. Distortion shows the time-varying characteristics of multi-component underwater acoustic or radar pulse signals, but because the duration and frequency of underwater acoustic or radar pulse signals vary by thousands of times, the time-frequency resolution of the short-time Fourier transform is limited Due to the shape and width of the window function, its time-frequency resolution cannot be improved at the same time, making it unable to adapt to the time-frequency analysis of underwater acoustic pulse signals with a wide range of time and frequency variations.
[0004] At present, scholars at home and abroad have proposed many quadratic time-frequency analysis methods, such as the Wigner-Willi distribution analysis method, which can obtain the best time-frequency analysis effect of instantaneous frequency for time-varying chirp signals, but For multi-component chirp signals or non-linear FM pulse signals, when this method analyzes the time-frequency characteristics of multi-component signals, there are serious cross-term problems between the instantaneous frequency curves; and based on the B distribution kernel function and its improved The time-frequency analysis method of the modified B distribution kernel function can reduce the influence of cross terms between the instantaneous frequency curves of multi-component pulse signals, but the parameters of this method cannot adaptively adjust the time-frequency parameters according to the multi-component pulse signals to be processed. This leads to a sub-optimal suppression of cross-terms in the resulting time-frequency distribution

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  • Extended B distribution pulse signal time-frequency analysis method based on parameter self-adaption
  • Extended B distribution pulse signal time-frequency analysis method based on parameter self-adaption
  • Extended B distribution pulse signal time-frequency analysis method based on parameter self-adaption

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Effect test

Embodiment 1

[0134] The parameters of the simulated multi-component underwater acoustic pulse signal are set as follows: signal amplitude A=1, initial phase The number of sampling points N=2048, the initial signal frequency f l =400Hz, sampling frequency f s =4000Hz, the modulation rates are: 0Hz / s and 781.25Hz / s, the pulse width length is 0.256s, and the received signal length is 0.512s.

[0135] First calculate the ambiguity function A of the multi-component underwater acoustic pulse signal data s(n) to be processed z (α,β), the calculation result is as figure 2 shown.

[0136] Then calculate the blur function A z The Radon transform of (α,β) R(D a ,θ b ), the calculation result is as image 3 shown.

[0137] Then estimate the radial angle and radial length of the self-term, and get

[0138] Then the delay parameter ξ=0.0533 and the Doppler parameter ε=0.2742 are obtained. Using the parameters ξ, ε to generate the extended B distribution kernel function g(α, β), such as F...

Embodiment 2

[0141] The parameters of the simulated multi-component underwater acoustic pulse signal are set as follows: signal amplitude A=1, initial phase The number of sampling points N=2048, the initial signal frequency f l =400Hz, sampling frequency f s =4000Hz, the modulation rates are: 0Hz / s and -781.25Hz / s, the pulse width length is 0.256s, and the received signal length is 0.512s.

[0142] First calculate the ambiguity function A of the multi-component underwater acoustic pulse signal data s(n) to be processed z (α,β), the calculation result is as Image 6 shown.

[0143] Then calculate the blur function A z The Radon transform of (α,β) R(D a ,θ b ), the calculation result is shown in 7.

[0144] Then estimate the radial angle and radial length of the self-term, and get

[0145] Then the time delay parameter ξ=0.0399 and the Doppler parameter ε=0.2748 are obtained. Using the parameters ξ, ε to generate the extended B distribution kernel function g(α, β), such as Figu...

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Abstract

The invention discloses an extended B distribution pulse signal time-frequency analysis method based on parameter self-adaption, which comprises the following steps of: firstly, obtaining multi-component underwater acoustic or radar pulse signal data to be processed to generate a fuzzy function, then detecting a radial angle and a radial length of a self-term signal in a fuzzy domain, generating an extended modified B distribution kernel function, and filtering the fuzzy function to obtain an extended B distribution kernel function; and finally, converting the filtering signal to obtain a time-frequency analysis result. The method is based on the extended and modified B distribution kernel function, is simple to implement, can adaptively set the parameters of the kernel function accordingto the signal, achieves the better matching between the kernel function and the underwater acoustic or radar pulse signal to be processed, and is suitable for real-time engineering application occasions.

Description

technical field [0001] The invention relates to a time-frequency analysis method for extended B-distribution pulse signals based on parameter self-adaptation, in particular to a time-frequency analysis method for extended B-distribution underwater acoustic or radar pulse signals based on parameter self-adaptation, which belongs to the technical field of signal processing. Background technique [0002] Time-frequency analysis is of great significance in array signal processing applications such as radar, sonar, acoustics, voice and wireless communications, especially in underwater acoustic and electronic reconnaissance processing. The existing time-frequency analysis methods mainly include linear time-frequency analysis method and quadratic time-frequency analysis method. [0003] The most classic linear time-frequency analysis method is the short-time Fourier transform. This method is simple to implement and has a small amount of calculation. For multi-component underwater a...

Claims

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Application Information

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IPC IPC(8): G01S7/36G01S7/537G06F17/14G06F17/16
CPCG01S7/36G01S7/537G06F17/141G06F17/16
Inventor 方世良姚帅方衍安文威
Owner NANJING SHIHAI ACOUSTIC TECH CO LTD
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