Method and device for processing turbulence response signals in flutter tests

Abstract

The embodiment of the present invention discloses a method and device for signal processing. The signal processing method includes: obtaining a power spectrum density function matrix based on multiple channel turbulence response signals through parameter spectrum analysis; performing singular value decomposition on the power spectrum density function matrix to calculate the maximum singular value curve; and obtaining the modal parameters of the multi-modal system through orthogonal polynomial fitting based on the maximum singular value curve. Through the present invention, the problem of low accuracy of frequency domain modal parameter estimation due to the contradiction between spectral line smoothing and spectral line density in the prior art is solved, the purpose of improving the accuracy of modal parameter estimation is achieved, and the technical effect of realizing autoregressive modeling of turbulence response signals and parameterized spectrum analysis thereof is beneficial to the subsequent analysis of turbulence response signals is achieved.

Description

Technical Field

[0001] The present invention relates to the field of aviation technology applications, and in particular to a signal processing method and device. Background Art

[0002] Flutter design is an important design requirement in the overall design of new aircraft. Sufficient flutter envelope provides a guarantee for the safe flight of the aircraft. Flutter test flight is a key link in verifying flutter design. Flutter test flights at different airspeeds are crucial to verify unstable aeroelastic phenomena. The test signals collected during the test flight are an important basis for calculating the natural frequency domain and damping of dynamic aeroelastic structures at different speeds. In view of the characteristics of random excitation of turbulent response, the power spectral density function of the multi-channel turbulent response signal is analyzed and calculated. Based on the orthogonality of the frequency domain, the singular value decomposition is performed on each frequency point to obtain the maximum singular value of the entire frequency band. Considering the relationship between the maximum singular value curve and the system's self-power spectral density function, the modal parameters of the multi-channel turbulent response signal can be estimated. It is worth noting that for multi-modal frequency domain fitting, the smoothness of the maximum singular value curve and the spectral line density have an important influence on the modal parameter estimation results. In order to achieve spectral line smoothing in the relevant signal processing methods, the power spectral density function of the turbulent response signal is calculated by the periodogram method. However, the power spectral density function calculated by the periodogram method based on windowing logic reduces the spectral line density while ensuring spectral line smoothness, which has a certain impact on the modal parameter estimation results.

[0003] The problem of low accuracy of frequency domain modal parameter estimation due to the contradiction between spectral line smoothing and spectral line density in the existing technology has not been effectively solved yet. Summary of the invention

[0004] The embodiments of the present invention provide a signal processing method and device to at least solve the problem of low accuracy of frequency domain modal parameter estimation due to the contradiction between spectral line smoothing and spectral line density in the prior art.

[0005] According to one aspect of an embodiment of the present invention, a signal processing method is provided, comprising: obtaining a power spectral density function matrix through parameter spectrum analysis based on multiple channel turbulence response signals; performing singular value decomposition on the power spectral density function matrix to calculate the maximum singular value curve; and obtaining modal parameters of a multi-modal system through orthogonal polynomial fitting based on the maximum singular value curve.

[0006] Optionally, obtaining a power spectral density function matrix based on multiple channel turbulence response signals through parameter spectrum analysis includes: calculating the power spectrum of the turbulence response signal through a high-order autoregressive spectrum to obtain a power spectral density function matrix of the multiple channel turbulence response signals.

[0007] Further, optionally, calculating the power spectrum of the turbulence response signal through a high-order autoregressive spectrum to obtain a power spectrum density function matrix of multiple channel turbulence response signals includes: based on autoregressive modeling, calculating the multiple channel turbulence response signals through a high-order autoregressive spectrum to obtain a power spectrum density function matrix of multiple channel turbulence response signals.

[0008] Optionally, performing singular value decomposition on the power spectral density function matrix and calculating the maximum singular value curve includes: performing singular value decomposition on the parameterized power spectral density function matrix of multiple channel turbulence response signals at each frequency point according to the orthogonality of the frequencies of each frequency point to obtain the maximum singular value curve.

[0009] Further, optionally, the method further comprises: performing frequency domain fitting of the maximum singular value curve according to the relationship between the system transfer function, the conjugate transpose of the system transfer function and the maximum singular value curve.

[0010] Optionally, obtaining the modal parameters of the multimodal system by fitting the maximum singular value curve through an orthogonal polynomial includes: fitting the maximum singular value curve through an orthogonal polynomial in the frequency domain to obtain the transfer function of the system; and obtaining the modal parameters of the multimodal system based on the identified system transfer function.

[0011] According to another aspect of an embodiment of the present invention, a signal processing device is provided, comprising: an analysis module, used to obtain a power spectral density function matrix through parameter spectrum analysis based on multiple channel turbulence response signals; a decomposition module, used to perform singular value decomposition on the power spectral density function matrix and calculate the maximum singular value curve; a fitting module, used to obtain the modal parameters of the multi-modal system through orthogonal polynomial fitting based on the maximum singular value curve.

[0012] Optionally, the analysis module includes: an analysis unit, used to calculate the power spectrum of the turbulence response signal through a high-order autoregressive spectrum to obtain a power spectrum density function matrix of the turbulence response signals of multiple channels.

[0013] Further, optionally, the analysis unit includes: an analysis subunit, which is used to calculate the multiple channel turbulence response signals through high-order autoregressive spectra based on autoregressive modeling to obtain a power spectrum density function matrix of the multiple channel turbulence response signals.

[0014] Optionally, the decomposition module includes: a decomposition unit, used to perform singular value decomposition on the parameterized power spectral density function matrix of the multi-channel turbulence response signal at each frequency point according to the orthogonality of the frequencies of each frequency point, to obtain a maximum singular value curve.

[0015] In the embodiment of the present invention, a power spectrum density function matrix is ​​obtained based on the turbulence response signals of multiple channels through parameter spectrum analysis; the power spectrum density function matrix is ​​subjected to singular value decomposition to calculate the maximum singular value curve; and the modal parameters of the multi-modal system are obtained through orthogonal polynomial fitting based on the maximum singular value curve. In other words, the embodiment of the present invention can solve the problem of low accuracy of modal parameter estimation in the frequency domain due to the contradiction between spectral line smoothing and spectral line density in the prior art in the related art, achieve the purpose of improving the accuracy of modal parameter estimation, and realize the technical effect that the autoregressive modeling of the turbulence response signal and its parametric spectrum analysis are beneficial to the subsequent analysis of the turbulence response signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0017] Figure 1 A schematic flow chart of a signal processing method provided by an embodiment of the present invention;

[0018] Figure 2 A schematic flow chart of another signal processing method provided by an embodiment of the present invention;

[0019] Figure 3 A schematic diagram of a signal processing device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first", "second" and the like in the specification, claims and drawings of the present invention are used to distinguish different objects rather than to limit a specific order.

[0022] In the related art, there are mainly the following signal processing methods for frequency domain decomposition method and processing of turbulence response signals of flutter test:

[0023] In order to obtain a smooth maximum singular value curve in the frequency domain decomposition and its improved method, power spectrum analysis is performed based on the periodogram method;

[0024] Multi-order modal parameter estimation based on random subspace in turbulence response signal analysis of flutter test.

[0025] However, the above signal processing solutions have good effects under certain technical conditions, but they also have certain technical defects:

[0026] The power spectrum analysis of the periodogram method improves the smoothness of the spectrum by adding windows to correspond multiple time domain points to one spectrum line. It has a certain effect on the smoothing of the spectrum line of the maximum singular value curve, but it also reduces the density of the spectrum line. The subsequent frequency domain fitting based on the maximum singular value curve leads to a large estimation error due to the spectrum line density problem.

[0027] Through multi-order modeling analysis, the random subspace obtains the modal parameter estimation results of the stable mode based on the steady-state diagram calculation. However, due to the modeling problems of different orders, the time complexity of the signal processing process increases, which has a certain impact on real-time online processing.

[0028] Therefore, a signal processing method provided in an embodiment of the present application takes the turbulence response signal of the flutter test flight as the object, calculates the power spectral density function of the turbulence response signals of multiple channels based on the autoregressive spectrum analysis method, further solves the contradiction between spectral line density and spectral line smoothing, and provides a good foundation for the subsequent frequency domain multimodal fitting of the maximum singular value curve.

[0029] The signal processing method provided in the embodiment of the present application is to improve the frequency domain decomposition method, improve the spectral line density and spectral line smoothness of the power spectral density function through parameterized spectral analysis, and perform frequency domain multimodal fitting based on the relationship between the maximum singular value curve and the product of the system's autopower spectral density function and its conjugate transpose to improve the accuracy of modal parameter estimation. The autoregressive modeling of the turbulence response signal and its parameterized spectral analysis are beneficial to the subsequent turbulence response signal analysis.

[0030] Specifically, according to one aspect of an embodiment of the present invention, a signal processing method is provided. Figure 1 A flow chart of a signal processing method provided by an embodiment of the present invention. Figure 1 As shown, the signal processing method provided in the embodiment of the present application includes:

[0031] Step S102, obtaining a power spectrum density function matrix through parameter spectrum analysis based on multiple channel turbulence response signals;

[0032] Optionally, in step S102, obtaining a power spectral density function matrix based on multiple channel turbulence response signals through parameter spectrum analysis includes: analyzing the power spectra of multiple channel turbulence response signals through high-order autoregressive spectrum analysis to obtain a power spectral density function matrix of multiple channel turbulence response signals.

[0033] Further, optionally, analyzing the power spectra of multiple channel turbulence response signals through a high-order autoregressive spectrum to obtain a power spectrum density function matrix of the multiple channel turbulence response signals includes: based on autoregressive modeling, calculating the multiple channel turbulence response signals through a high-order autoregressive spectrum to obtain a power spectrum density function matrix of the multiple channel turbulence response signals.

[0034] Specifically, Figure 2 A flow chart of another signal processing method provided by an embodiment of the present invention is as follows: Figure 2 As shown in the figure, for the power spectrum analysis problem of turbulence response, the power spectrum of turbulence response signal can be fitted by high-order autoregressive spectrum analysis, which is called parameterized spectrum analysis. However, due to the high order of parameterized modeling, there is a problem of false mode elimination when estimating modal parameters directly based on the autoregressive model. Therefore, the autopower spectrum density function matrix of turbulence response signal is calculated based on autoregressive modeling only.

[0035] Step S104, performing singular value decomposition on the power spectrum density function matrix to calculate the maximum singular value curve;

[0036] Optionally, in step S104, performing singular value decomposition on the power spectral density function matrix and calculating the maximum singular value curve includes: performing singular value decomposition on the parameterized power spectral density function matrix of multiple channel turbulence response signals at each frequency point according to the orthogonality of the frequencies of each frequency point to obtain the maximum singular value curve.

[0037] Further, optionally, the signal processing method provided in the embodiment of the present application also includes: performing frequency domain fitting of the maximum singular value curve according to the relationship between the system transfer function, the conjugate transpose of the system transfer function and the maximum singular value curve.

[0038] Specifically, Figure 2 As shown, according to the orthogonality of the frequencies at each frequency point, the density function matrix of the parameterized power spectrum of the turbulence response signals of multiple channels is subjected to singular value decomposition at each frequency point, and the maximum singular value curve is analyzed based on the relationship between the maximum singular value curve and the system transfer function and its conjugate transpose.

[0039] Step S106, obtaining the modal parameters of the multi-modal system by fitting an orthogonal polynomial according to the maximum singular value curve.

[0040] Optionally, obtaining the modal parameters of the multimodal system by fitting the maximum singular value curve through an orthogonal polynomial in step S106 includes: fitting the maximum singular value curve through an orthogonal polynomial in the frequency domain to obtain the transfer function of the system; and obtaining the modal parameters of the multimodal system based on the identified system transfer function.

[0041] Further, optionally, the signal processing method provided in the embodiment of the present application also includes: identifying modal parameters of the multi-modal system according to the transfer function.

[0042] Specifically, Figure 2 As shown, the maximum singular value curve is fitted by orthogonal polynomials in the frequency domain to obtain the transfer function of the system, and the modal parameters of the system are identified based on the transfer function.

[0043] In summary, in combination with steps S102 to S106, the signal processing method provided in the embodiment of the present application is aimed at analyzing the turbulence response signal in the flutter test flight. Through parameterized spectral analysis and frequency domain multimodal fitting, the accuracy of frequency domain modal parameter estimation is improved while ensuring spectral smoothness and spectral density.

[0044] In the embodiment of the present invention, a power spectrum density function matrix is ​​obtained by parametric spectrum analysis based on multiple channel turbulence response signals; a maximum singular value curve is obtained by singular value decomposition based on the power spectrum density function matrix; and the modal parameters of the multi-modal system are obtained by orthogonal polynomial fitting based on the maximum singular value curve. In other words, the embodiment of the present invention can solve the problem of low accuracy of frequency domain modal parameter estimation due to the contradiction between spectral line smoothing and spectral line density in the prior art in the related art, achieve the purpose of improving the accuracy of modal parameter estimation, and realize the technical effect that the autoregressive modeling of the turbulence response signal and its parametric spectrum analysis are beneficial to the subsequent analysis of the turbulence response signal.

[0045] According to another aspect of an embodiment of the present invention, a signal processing device is provided. Figure 3 A schematic diagram of a signal processing device provided by an embodiment of the present invention. Figure 3 As shown, it includes: an analysis module 32, which is used to obtain a power spectrum density function matrix through parameter spectrum analysis based on multiple channel turbulence response signals; a decomposition module 34, which is used to perform singular value decomposition on the power spectrum density function matrix and calculate the maximum singular value curve; a fitting module 36, which is used to obtain the modal parameters of the multi-modal system through orthogonal polynomial fitting based on the maximum singular value curve.

[0046] Optionally, the analysis module 32 includes: an analysis unit, configured to calculate the power spectrum of the turbulence response signal by using a high-order autoregressive spectrum to obtain a power spectrum density function matrix of the turbulence response signals of multiple channels.

[0047] Further, optionally, the analysis unit includes: an analysis subunit, which is used to calculate the multiple channel turbulence response signals through high-order autoregressive spectra based on autoregressive modeling to obtain a power spectrum density function matrix of the multiple channel turbulence response signals.

[0048] Optionally, the decomposition module 34 includes: a decomposition unit, configured to perform singular value decomposition on a parameterized power spectrum density function matrix of the multi-channel turbulence response signal at each frequency point according to the orthogonality of the frequencies of each frequency point, to obtain a maximum singular value curve.

[0049] Further, optionally, the method further comprises: performing frequency domain fitting of the maximum singular value curve according to the relationship between the system transfer function, the conjugate transpose of the system transfer function and the maximum singular value curve.

[0050] Optionally, obtaining the modal parameters of the multimodal system by fitting the maximum singular value curve through an orthogonal polynomial includes: fitting the maximum singular value curve through an orthogonal polynomial in the frequency domain to obtain the transfer function of the system; and obtaining the modal parameters of the multimodal system based on the identified system transfer function.

[0051] Further, optionally, modal parameters of the multi-modal system are identified based on the transfer function.

[0052] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. A method for processing turbulence response signals in a flutter test, characterized in that: include: According to the multi-channel turbulence response signal, the power spectrum density function matrix is ​​obtained through parameter spectrum analysis; including: Calculating the power spectrum of the turbulence response signal by high-order autoregressive spectrum to obtain the power spectrum density function matrix of the multi-channel turbulence response signal; Performing singular value decomposition on the power spectrum density function matrix to calculate the maximum singular value curve; including: According to the orthogonality of the frequencies at each frequency point, the parameterized power spectrum density function matrix of the multi-channel turbulence response signal is subjected to singular value decomposition at each frequency point to obtain the maximum singular value curve; Performing frequency domain fitting of the maximum singular value curve according to a relationship between a system transfer function, a conjugate transpose of the system transfer function and the maximum singular value curve; According to the maximum singular value curve, the modal parameters of the multi-modal system are obtained by fitting the orthogonal polynomial; including: fitting the maximum singular value curve by the orthogonal polynomial in the frequency domain to obtain the transfer function of the system; The modal parameters of the multi-modal system are obtained according to the identified system transfer function.

2. A device for implementing the signal processing method according to claim 1, characterized in that: include: An analysis module is used to obtain a power spectrum density function matrix through parameter spectrum analysis based on multi-channel turbulence response signals; A decomposition module, used for performing singular value decomposition on the power spectrum density function matrix and calculating a maximum singular value curve; The fitting module is used to obtain the modal parameters of the multi-modal system by fitting the maximum singular value curve through an orthogonal polynomial.

3. The device according to claim 2, characterized in that The analysis module comprises: The analysis unit is used to calculate the power spectrum of the turbulence response signal through a high-order autoregressive spectrum to obtain the power spectrum density function matrix of the multi-channel turbulence response signal.

4. The device according to claim 3, characterized in that The analysis unit comprises: The analysis subunit is used to calculate the multi-channel turbulence response signal through the high-order autoregression spectrum based on autoregression modeling to obtain the power spectrum density function matrix of the multi-channel turbulence response signal.

5. The device according to claim 4, characterized in that The decomposition module comprises: The decomposition unit is used to perform singular value decomposition on the parameterized power spectrum density function matrix of the multi-channel turbulence response signal at each frequency point according to the orthogonality of the frequencies of each frequency point, so as to obtain the maximum singular value curve.

Images