CN113419222A - Method and system for extracting bridge vibration frequency based on radar signals - Google Patents

Abstract

The invention discloses a method and a system for extracting bridge vibration frequency based on radar signals, and relates to the technical field of radar observation, wherein the method comprises the steps of monitoring bridge vibration through a radar, collecting echo signals, determining a distance unit where a bridge vibration point is located, extracting original echo time sequence signals at the distance unit, carrying out EMD (empirical mode decomposition) to obtain a cluster of IMF (inertial measurement framework) components, carrying out WVD (WVD) conversion one by one to obtain time-frequency spectrograms, and comparing time-frequency spectrogram energy values corresponding to each IMF component to obtain the bridge vibration frequency. The bridge vibration frequency acquisition method disclosed by the invention avoids the problems that the traditional method cannot acquire time information, the time-frequency resolution is limited, the readability of the obtained time-frequency spectrogram is poor, and effective information cannot be distinguished. The invention provides a bridge radar signal vibration frequency extraction method based on echo signal decomposition, which combines WVD and EMD, and can effectively reduce cross terms brought by WVD calculation under the complex condition of monitoring bridge vibration, thereby extracting the vibration frequency of a target from decomposed components.

Description

Method and system for extracting bridge vibration frequency based on radar signal

technical field

The invention relates to the technical field of radar observation, in particular to a method and system for extracting bridge vibration frequencies based on radar signals.

Background technique

Compared with traditional sensor total station, GPS and other monitoring methods, radar monitoring of bridges is widely used due to its advantages of non-contact, unaffected by weather conditions, and high precision. When processing the vibration signal obtained by the radar monitoring the bridge, the bridge vibration will cause the micro-Doppler phenomenon, and the generated Doppler frequency is a function of time. The time-varying Doppler features analyzed in the time-frequency domain can be used for target detection and identification, but the vibration frequency and deformation curve of the bridge at a certain moment cannot be obtained.

There are many methods to extract the vibration frequency of the bridge, for example: first use phase inversion to extract the deformation variable, and then do FFT (Fast Fourier transform, Fourier transform) based on the extracted deformation variable to calculate the vibration frequency. However, the phase-based deformation inversion is sensitive to noise, and it is difficult to directly extract the accurate deformation value to calculate the frequency. It needs to perform filtering and other operations based on experience. Another example: the linear time-frequency distribution method (mainly including short-time Fourier transform, wavelet transform, etc.) is used to directly extract the vibration frequency and deformation curve of the bridge from the echo. Although this method has the advantages of simple calculation and no cross term, etc., However, due to the uncertainty principle (Uncertainty Principle), its time-frequency resolution is limited to a certain extent, and the time-frequency resolution of short-time Fourier transform and other means is difficult to achieve the optimal time-frequency resolution, and the optimal frequency and time resolution cannot be achieved at the same time. The high-resolution vibration frequency and deformation curve of the bridge at a certain moment; another example: bilinear time-frequency distribution (also known as Cohen-like time-frequency distribution) is used to extract, which mainly uses the quadratic integral of the radar signal , calculate the curve of the signal energy density with time. Although this method has good time-frequency focusing, it introduces a cross term in the calculation process. Since the bridge vibration signal obtained by the radar is a nonlinear and non-stationary signal, With multiple frequency components, the bilinear time-frequency analysis will be affected by the cross term, which makes the readability of the time-spectrogram worse and cannot distinguish valid information. It can be seen that the existing methods for extracting the vibration frequency of the bridge have some shortcomings, which lead to the inability to obtain a more accurate vibration frequency of the bridge at a certain moment.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a method and system for extracting the vibration frequency of a bridge based on a radar signal, by combining the Wigner-Ville Distribution (WVD) and the Empirical Mode Decomposition (EMD), the The vibration frequency of the bridge is extracted from the signal, instead of the traditional method used to obtain the vibration frequency from the bridge deformation. In the face of the complex situation of monitoring bridge vibration, it can effectively reduce the cross term brought by the calculation of WVD, so that in EMD From the decomposed components, the bridge vibration frequency with high precision is extracted.

In order to achieve the above object, the present invention provides the following technical solutions:

A method for extracting bridge vibration frequency based on radar signal, comprising:

The echo signal is collected by monitoring the bridge vibration by radar, and based on the echo signal, the distance unit where the bridge vibration point is located is determined, and the original echo time series signal at the distance unit where the bridge vibration point is located is extracted;

Perform EMD decomposition on the original echo time series signal at the distance unit where the bridge vibration point is located to obtain a cluster of IMF components;

The time-spectrogram is obtained by performing WVD transformation on the IMF components one by one, and the bridge vibration frequency is obtained by comparing the energy value of the time-spectrogram corresponding to each of the IMF components.

Preferably, the echo signals collected by monitoring the bridge vibration by radar are:

In formula (4), τ is the time shift in the slow time domain, f is the signal frequency, C is the speed of light, R ₀ is the distance of the bridge, f _v is the vibration frequency of the distance unit where the bridge vibration point is located, and R ₀ +Asin ( 2πf _v τ) is used to describe the bridge vibration;

The echo signal is in matrix form, including range direction and azimuth direction.

Further, the distance unit where the bridge vibration point is located is determined based on the echo signal, and the method for extracting the original echo time series signal at the distance unit where the bridge vibration point is located includes:

performing an inverse fast Fourier transform on the echo signal in the distance direction to obtain a pulse compression signal;

Based on the pulse compression signal, a distance unit with a strong reflection point is determined in the distance direction, the distance unit with the strong reflection point includes the distance unit where the bridge vibration point is located, and the distance unit of the distance unit with the strong reflection point is The echo amplitude reaches several times that of the background noise;

The distance unit where the bridge vibration point is located is read from the distance unit with strong reflection points by means of a distance Doppler map, and the original echo time series signal at the distance unit where the bridge vibration point is located is extracted; wherein , the original echo time series signal at the distance unit where the bridge vibration point is located has multiple frequency components, and the frequency components are all integer multiples of the vibration frequency f _v of the bridge.

Specifically, the original echo time series signal at the distance unit where the bridge vibration point is located is:

为信号波长，f_c为信号中心频率。In formula (5),

is the signal wavelength, and f _c is the signal center frequency.

Preferably, the method for performing EMD decomposition on the original echo time-series signal at the distance unit where the bridge vibration point is located to obtain a cluster of IMF components includes:

S1. Obtain the original echo time series signal at the distance unit where the bridge vibration point is located, and mark it as the time series signal to be processed;

S2. Perform local extremum identification on the time series signal to be processed, and perform cubic spline fitting for all identified maxima and all minima, respectively, to obtain an upper envelope and a lower envelope , and calculate the mean envelope;

S3, subtract the mean envelope from the time series signal to be processed to obtain an IMF component;

S4. Determine whether the IMF component satisfies a preset component condition, and if so, save the IMF component as a correct IMF component; if not, mark the IMF component as a time series signal to be processed, and based on the Step S2 and step S3 re-acquire the IMF component, until the obtained IMF component satisfies the preset component condition;

S5, subtracting the correct IMF component acquired last time from the time-series signal to be processed to obtain a new time-series signal;

S6. Mark the new time-series signal as a time-series signal to be processed, and repeat steps S2 to S5 until the new time-series signal is a monotonic function or constant, and change all correct IMF components from high to high. The frequency to low frequency is arranged in order and output.

Specifically, the preset component conditions are:

In the data set of the IMF component, there is at least one extreme point or zero-crossing point, and the number of the extreme value point and the number of the zero-crossing point differ by at most 1; and

For any point in the dataset of IMF components, the mean of the envelope defined by the local maxima and local minima is zero.

Preferably, the IMF components are WVD transformed one by one to obtain a time-spectrogram, and the method for obtaining the bridge vibration frequency by comparing the energy value of the time-spectrogram corresponding to each of the IMF components includes:

Obtain all correct IMF components and perform WVD transformation one by one;

Calculate the energy value of the time-spectrogram corresponding to each IMF component after WVD transformation one by one, until the energy value of the time-spectrogram corresponding to the currently calculated IMF component is larger than the energy value of the time-spectrogram corresponding to the previous IMF component. When the size is 1 order of magnitude lower, stop calculating the energy value of the time-spectrogram corresponding to the IMF component, extract the previous IMF component as the bridge vibration frequency, and output the time-spectrogram corresponding to the bridge vibration frequency.

A system for extracting bridge vibration frequency based on radar signals, comprising a time series signal extraction module, a decomposition module and a frequency extraction module, wherein,

The timing signal extraction module is used to collect echo signals by monitoring the bridge vibration through radar, and based on the echo signals to determine the distance unit where the bridge vibration point is located, and extract the original echo time at the distance unit where the bridge vibration point is located. sequence signal;

The decomposition module is used to perform EMD decomposition on the original echo time series signal at the distance unit where the bridge vibration point is located to obtain a cluster of IMF components;

The frequency extraction module is configured to perform WVD transformation on the IMF components one by one to obtain a time-spectrogram, and obtain the bridge vibration frequency by comparing the energy value of the time-spectrogram corresponding to each of the IMF components.

A computer-readable storage medium having computer-readable program instructions stored thereon, the computer-readable program instructions being used to execute the above-mentioned method for extracting a bridge vibration frequency based on a radar signal.

An electronic device comprising:

at least one processor; and,

a memory communicatively coupled to the at least one processor; wherein,

The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described method of extracting a bridge vibration frequency based on a radar signal.

Compared with the prior art, the method and system for obtaining the bridge vibration frequency based on the decomposition of the radar echo signal provided by the present invention have the following beneficial effects:

The method for extracting the bridge vibration frequency based on the radar signal provided by the invention determines the distance unit where the bridge vibration point is located based on the echo signal obtained by monitoring the bridge vibration by the radar, and extracts the original echo time series signal at the distance unit where the bridge vibration point is located. That is, the original echo time series signal containing the bridge vibration information is obtained, and then EMD decomposition is performed based on the original echo time series signal to obtain a cluster of IMF components, one of these IMF components corresponds to the bridge vibration information, and then compare the After these IMF components are transformed by WVD, the corresponding energy value of the time-spectrogram can be extracted from the bridge vibration frequency, which solves the problem that the existing technology cannot obtain the high-resolution vibration frequency of the bridge at a certain moment.

The bridge vibration frequency acquisition system based on the decomposition of radar echo signals provided by the present invention adopts the above method, has good time-frequency focusing performance, reduces the interference of cross terms, makes the time-spectrogram clearly distinguishable, and can also be based on the time-frequency spectrum Quickly determine the frequency of bridge vibrations.

Description of drawings

The accompanying drawings described herein are used to provide further understanding of the present invention and constitute a part of the present invention. 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 attached image:

1 is a schematic flowchart of a method for extracting a bridge vibration frequency based on a radar signal according to an embodiment of the present invention;

2 is a schematic diagram of a one-dimensional range image in an embodiment of the present invention;

3 is a schematic diagram of a range Doppler including a target range unit in an embodiment of the present invention;

4 is a schematic diagram of an EMD decomposition process flow in an embodiment of the present invention;

Fig. 5 is the schematic diagram of EMD decomposition result in the embodiment of the present invention;

6 is a schematic diagram of a time-frequency spectrum diagram corresponding to a bridge vibration frequency in an embodiment of the present invention;

FIG. 7 is a schematic structural diagram of an electronic device in an embodiment of the present invention.

Detailed ways

In order to make the above objects, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present invention.

Example 1

Referring to FIG. 1, an embodiment of the present invention provides a method for extracting a bridge vibration frequency based on a radar signal, including:

The echo signal is collected by monitoring the bridge vibration by radar, and based on the echo signal, the distance unit where the bridge vibration point is located is determined, and the original echo time series signal at the distance unit where the bridge vibration point is located is extracted;

The original echo time series signal at the distance unit where the bridge vibration point is located is decomposed by EMD, and a cluster of IMF components is obtained;

The time-spectrogram is obtained by WVD transforming the IMF components one by one, and the bridge vibration frequency is obtained by comparing the energy value of the time-spectrogram corresponding to each IMF component.

A method for extracting bridge vibration frequency based on radar echo signals provided by an embodiment of the present invention is adopted to obtain the original echo time series signals of the distance unit where the bridge vibration point is located; EMD decomposition is used to reduce the interference of cross terms when calculating WVD , the time-spectrogram is clearly distinguishable, and the frequency of bridge vibration can be quickly determined according to the time-spectrogram.

It should be understood by those skilled in the art that the radar transmits modulated electromagnetic waves, which are reflected by the target and then return to the radar receiver, thereby obtaining the characteristics of the target. In this embodiment, the radar signal adopts linear frequency modulation, and its form is:

为矩形窗函数，T为信号脉冲宽度，K为调频率，t为时间。在对雷达信号进行混频，相位补偿等常见步骤后，最终需要处理的线性调频信号形式为(省略矩形窗函数)：In formula (1),

is a rectangular window function, T is the signal pulse width, K is the modulation frequency, and t is the time. After common steps such as frequency mixing and phase compensation of the radar signal, the final form of the chirp signal to be processed is (the rectangular window function is omitted):

In formula (2), f is the signal frequency, R ₀ is the target distance, and C is the speed of light.

The vibration distance R ₀ (t) of the distance unit where the bridge vibration point is located is expressed in sinusoidal form as:

R ₀ (t)=R ₀ +Asin(2πf _v τ) (3);

In formula (3), R ₀ is the distance between the bridge vibration point and the radar, f _v is the vibration frequency of the distance unit where the bridge vibration point is located, which is the vibration frequency of the bridge, and τ is the time shift in the slow time domain;

Substitute equation (3) into equation (2) to obtain the echo signal collected by radar monitoring bridge vibration, that is, the echo signal received by radar:

In formula (4), τ is the time shift in the slow time domain. Since f and τ are both vectors, the echo signal received by the radar is a matrix, one dimension is the range direction, and one dimension is the azimuth direction. Further, the distance unit where the bridge vibration point is located is determined based on the echo signal, and the original echo time series signal at the distance unit where the bridge vibration point is located is extracted, and the specific method includes:

Inverse fast Fourier transform is performed on the echo signal in the distance direction to obtain a pulse compression signal;

Based on the pulse compression signal, a distance unit with a strong reflection point is determined in the distance direction, the distance unit with a strong reflection point includes the distance unit where the bridge vibration point is located, and the echo amplitude of the distance unit with a strong reflection point is higher than Amplitude of background noise;

The distance unit where the bridge vibration point is located is read from the distance unit with strong reflection points by means of the distance Doppler map, and the original echo time series signal at the distance unit where the bridge vibration point is located is extracted; among them, the bridge vibration point The original echo time series signal at the distance unit has multiple frequency components, and the frequency components are all integer multiples of the vibration frequency f _v of the bridge. Among them, the original echo time series signal at the distance unit where the bridge vibration point is located is:

为信号波长，f_c为信号中心频率。In formula (5),

is the signal wavelength, and f _c is the signal center frequency.

Those skilled in the art should understand that, Inverse Fast Fourier Transform (IFFT) is an inverse transform of FFT, and FFT is a method for rapidly calculating discrete Fourier transform (Discrete Fourier Transformation, DFT) of a sequence. Fourier analysis transforms a signal from the original domain (usually time or space) to the frequency domain, or vice versa. The FFT will quickly compute such transforms by decomposing the DFT matrix into the product of sparse (mostly zero) factors. Therefore, it can reduce the complexity of computing the DFT from O(n ² ) required by the DFT definition calculation to O(nlogn), where n is the data size. Pulse compression technology is a practical application of matched filtering theory and related receiving theory. The method is to transmit a large time width and bandwidth signal at the transmitting end to improve the speed measurement accuracy and speed resolution of the signal, and at the receiving end, compress the wide pulse signal into narrow pulses to improve the range resolution accuracy of the radar to the target. and distance resolution.

After obtaining the pulse compression signal, the one-dimensional range image (HRRP) method is used to determine the distance unit with strong reflection points in the distance direction based on the pulse compression signal. Please refer to Figure 2. Figure 2 is a schematic diagram of the one-dimensional range image, which is actually: The scattering intensity distribution map of each distance unit on the target bridge shows the relationship between the echo amplitude and distance in the distance direction. It can be seen that the echo amplitude of the distance unit with strong reflection points is often very large, which is higher than the background noise. The amplitude can even reach several times of the background noise, for example, more than 2 times. The distance cells with strong reflection points are often at the peak position, so the distance cells with strong reflection points can be screened out according to the different noise intensity.

Since the distance cells with strong reflection points are not necessarily all distance cells where the bridge vibration points are located, the distance Doppler map is further used to read the distance cells where the bridge vibration points are located from the distance cells with strong reflection points. Distance unit, extract the original echo time series signal at the distance unit where the bridge vibration point is located, the distance Doppler map can show the moving direction of the target, when the platform where the transmitter is located is static, if the target is static, Then its feedback is displayed as 0 Doppler, and if the target is not stationary, the feedback is displayed as a non-zero value. In this embodiment, it is shown as follows: the range unit with strong reflection points determined in the one-dimensional range image, if it is a static target, only has zero frequency on the range unit of the range Doppler map; if it is vibration The target is shown as having multiple frequency components on the distance unit of the distance Doppler map, and each frequency component is an integer multiple of f _v , which is generally called a harmonic frequency. According to such characteristics, the bridge vibration point can be determined. The distance unit in which it is located.

Referring to Figure 4, EMD decomposition is performed on the original echo time series signal at the distance unit where the bridge vibration point is located, and the method for obtaining a cluster of IMF components includes:

S1. Obtain the original echo time series signal at the distance unit where the bridge vibration point is located, and mark it as the time series signal to be processed;

S2. Perform local extremum identification on the time series signal to be processed, and perform cubic spline fitting for all identified maximum values and all minimum values to obtain the upper envelope and lower envelope, and calculate mean envelope;

S3, subtract the mean envelope from the time series signal to be processed to obtain an IMF component;

S4, determine whether the IMF component satisfies the preset component conditions, if so, save the IMF component as the correct IMF component; if not, mark the IMF component as a time series signal to be processed, and based on step S2 and step S3 obtains the IMF component again, until the obtained IMF component meets the preset component condition;

S5. Subtract the correct IMF component obtained last time from the time series signal to be processed to obtain a new time series signal;

S6. Mark the new time series signal as the time series signal to be processed, repeat the above steps S2 to S5 until the new time series signal is a monotonic function or constant, and press all correct IMF components from high frequency to low frequency by pressing Arrange and output in order, as shown in Figure 5, the EMD decomposition result.

Among them, the preset component conditions are: in the data set of the IMF component, there is at least one extreme value point or zero-crossing point, and the number of extreme value points and the number of zero-crossing points differ by at most 1; and the data for the IMF component At any point in the concentration, the mean of the envelope defined by the local maxima and local minima is zero.

As will be appreciated by those skilled in the art, EMD is an adaptive decomposition method whose decomposition is based on the assumption that any data is synthesized from a simple single vibration. This method decomposes the signal according to the time scale characteristics of the data itself, and does not need to set any basis function in advance. This is essentially different from the Fourier decomposition and wavelet decomposition methods based on a priori harmonic basis functions and wavelet basis functions. It is because of this characteristic that the EMD method can theoretically be applied to the decomposition of any type of signal, so it has obvious advantages in processing non-stationary and nonlinear data, and is suitable for analyzing nonlinear and non-stationary signal sequences. Very high signal-to-noise ratio. The key to this method is empirical mode decomposition, which can decompose complex signals into a limited number of intrinsic mode functions (IMFs). . The EMD method can smoothen the non-stationary data, and then perform the Hilbert transform to obtain the spectrogram and obtain the frequency with physical meaning. Compared with methods such as short-time Fourier transform and wavelet decomposition, this method is intuitive, direct, a posteriori and adaptive because the basis functions are obtained by decomposing the data itself. Since the decomposition is based on the local properties of the time scale of the signal sequence, it is adaptive. The use of EMD can effectively reduce the cross term brought by the calculation of WVD, so that the vibration frequency of the target can be extracted from the decomposed components.

In specific implementation, the EMD process can be implemented in the following ways:

Identify local extrema on the data, and then fit all maxima and minima with cubic splines, respectively. The lower envelope a ⁺ (t) and the lower envelope a ^- (t) are obtained, where the envelope is the curve of amplitude versus time. Take the mean of them to get the mean envelope m ₁ (t):

Subtract the original data from S ₁ (τ) to get the first IMF component h′ ₁ (t):

h′ ₁ (t)=S ₁ (t)−m ₁ (t) (7);

However, in practice, h′ ₁ (t) often does not meet the preset component conditions, so the first two steps must be repeated n times to obtain the first correct IMF component, denoted as the first component h ₁ (t );

(3) Subtract the first component h ₁ (t) from the original data S ₁ (τ) to obtain a new signal r ₁ (t):

r ₁ (t)=S ₁ (t)-h ₁ (t) (8);

The process of obtaining the first component is repeated to obtain other components, and when the residual component r _n (t) is a monotonic function or constant, the decomposition is stopped. Finally, a series of eigenmode functions and a residual component are obtained, which are related as follows:

Among them, the IMF components are WVD transformed one by one, and the method of obtaining the bridge vibration frequency by comparing the energy value of the time-spectrogram corresponding to each IMF component includes: obtaining all correct IMF components, and performing WVD transformation one by one; After performing WVD, the obtained The time-spectrogram is a matrix. By finding the largest row in the time-spectrogram matrix corresponding to each IMF component one by one, and adding them to obtain the energy value of the time-spectrogram corresponding to each IMF component after WVD transformation, until the current When the calculated energy value of the time-spectrogram corresponding to the IMF component is one order of magnitude lower than the energy value of the time-spectrogram corresponding to the previous IMF component, stop calculating the energy value of the time-spectrogram corresponding to the IMF component, and The previous IMF component is extracted as the bridge vibration frequency, and the time-frequency spectrum corresponding to the bridge vibration frequency is output at the same time. Figure 6 shows a time-frequency spectrum corresponding to the bridge vibration frequency.

It should be understood by those skilled in the art that WVD belongs to a type of Cohen-like bilinear distribution, and the window function is a special case of Equation (1). Its general form is:

In formula (10), S is the signal, t is the signal time, and τ is the time shift in the slow time domain, which realizes the Fourier transform of the instantaneous autocorrelation function of the signal.

WVD has a good resolution, especially for a single component, and the instantaneous frequency change is not more than a quadratic formula. It has good mathematical operation properties and can be used to analyze random programs. At the same time, since the WVD time spectrogram of the vibration signal exhibits linear characteristics, the energy calculation is realized by adding the largest row in the time spectrogram matrix, which is convenient and fast.

Synthesize the one-dimensional range image and range Doppler map to obtain the distance unit where the bridge vibration point is located, and extract the original echo time series signal of the distance unit where the bridge vibration point is located; EMD can effectively reduce the crossover caused by WVD calculation. , so that the vibration frequency of the target can be extracted from the decomposed components; WVD has good resolution and good mathematical operation properties, and can be used to analyze random programs. This embodiment utilizes the above-mentioned technology to effectively reduce the cross term caused by WVD calculation under the complex situation of monitoring bridge vibration, so that the bridge vibration frequency with higher accuracy can be extracted from the components decomposed by EMD.

Embodiment 2

Corresponding to the first embodiment, the embodiment of the present invention also provides a system for extracting the vibration frequency of a bridge based on a radar signal. No further description will be given later.

Embodiments of the present invention provide a system for extracting bridge vibration frequencies based on radar signals, including a timing signal extraction module, a decomposition module, and a frequency extraction module, wherein,

The time series signal extraction module is used to monitor the bridge vibration through radar to collect echo signals, and based on the echo signals, determine the distance unit where the bridge vibration point is located, and extract the original echo time series signal at the distance unit where the bridge vibration point is located;

The decomposition module is used to perform EMD decomposition on the original echo time series signal at the distance unit where the bridge vibration point is located to obtain a cluster of IMF components;

The frequency extraction module is used to perform WVD transformation on the IMF components one by one to obtain the time-spectrogram, and obtain the bridge vibration frequency by comparing the energy value of the time-spectrogram corresponding to each IMF component.

The system of the present invention for extracting the vibration frequency of a bridge based on the radar signal adopts the method for extracting the vibration frequency of the bridge based on the radar signal provided in the first embodiment, and can obtain the original echo time series signal of the distance unit where the bridge vibration point is located based on the echo signal; At the same time, the interference of cross terms is reduced, so that the time-spectrogram is clearly discernible, and the frequency of bridge vibration can be quickly determined based on the time-spectrogram. Compared with the prior art, the beneficial effect of the system for extracting bridge vibration frequency based on radar signal provided by the embodiment of the present invention is the same as the beneficial effect of the method for extracting bridge vibration frequency based on radar signal provided in the above-mentioned first embodiment, and in the system. The other technical features of the device are the same as those disclosed by the method in the previous embodiment, and will not be repeated here.

Embodiment 3

An embodiment of the present invention provides a computer-readable storage medium having computer-readable program instructions stored thereon, where the computer-readable program instructions are used to execute the method for extracting a bridge vibration frequency based on a radar signal in the first embodiment.

The computer-readable storage medium provided by the embodiments of the present invention may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, system or device, or any combination of the above. More specific examples of computer readable storage media may include, but are not limited to, electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable Programmable read only memory (EPROM or flash memory), fiber optics, portable compact disk read only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. In this embodiment, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system, or device. Program code embodied on a computer-readable storage medium may be transmitted using any suitable medium including, but not limited to, electrical wire, optical fiber cable, RF (radio frequency), etc., or any suitable combination of the foregoing.

The above-mentioned computer-readable storage medium may be included in the electronic device; or may exist alone without being assembled into the electronic device.

The above-mentioned computer-readable storage medium carries one or more programs, and when the above-mentioned one or more programs are executed by the electronic device, the electronic device: acquires at least two Internet Protocol addresses; A node evaluation request for an Internet Protocol address, wherein the node evaluation device selects an Internet Protocol address from the at least two Internet Protocol addresses and returns it; receives the Internet Protocol address returned by the node evaluation device; wherein, the obtained Internet Protocol addresses indicate edge nodes in the content distribution network.

Alternatively, the above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed by the electronic device, the electronic device: receives a node evaluation request including at least two Internet Protocol addresses; From the at least two Internet Protocol addresses, the Internet Protocol address is selected; the selected Internet Protocol address is returned; wherein, the received Internet Protocol address indicates an edge node in the content distribution network.

Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including object-oriented programming languages—such as Java, Smalltalk, C++, but also conventional Procedural programming language - such as the "C" language or similar programming language. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (eg, using an Internet service provider through Internet connection).

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code that contains one or more logical functions for implementing the specified functions executable instructions. It should also be noted that, in some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It is also noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented in dedicated hardware-based systems that perform the specified functions or operations , or can be implemented in a combination of dedicated hardware and computer instructions.

The modules involved in the embodiments of the present disclosure may be implemented in software or hardware. Among them, the name of the module does not constitute a limitation of the unit itself in some cases. For example, the time series signal extraction module can also be described as "used to monitor the vibration of the bridge through radar to collect echo signals, and based on the echo signals The signal determines the distance unit where the bridge vibration point is located, and extracts the module of the original echo time series signal at the distance unit where the bridge vibration point is located.

The computer-readable storage medium provided by the present invention stores computer-readable program instructions for executing the above-mentioned method for extracting bridge vibration frequency based on radar signals. frequency extraction. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the embodiment of the present invention are the same as those of the method for extracting the bridge vibration frequency based on the radar signal provided in the first embodiment, and are not repeated here.

Embodiment 4

An embodiment of the present invention provides an electronic device, the electronic device includes: at least one processor; and a memory connected in communication with the at least one processor; wherein, the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor. The processor executes, so that at least one processor can execute the method for extracting the bridge vibration frequency based on the radar signal in the first embodiment.

Referring next to FIG. 7 , it shows a schematic structural diagram of an electronic device suitable for implementing an embodiment of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablets), PMPs (portable multimedia players), vehicle-mounted terminals (eg, mobile terminals such as in-vehicle navigation terminals), etc., and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in FIG. 7 is only an example, and should not impose any limitation on the function and scope of use of the embodiments of the present disclosure.

As shown in FIG. 7, an electronic device may include processing means (eg, a central processing unit, a graphics processor, etc.), which may be loaded into a random access memory (RAM) according to a program stored in a read only memory (ROM) or from a storage device to execute various appropriate actions and processes. In the RAM, various programs and data necessary for the operation of the electronic device are also stored. The processing device, the ROM, and the RAM are connected to each other through a bus. Input/output (I/O) interfaces are also connected to the bus.

Typically, the following systems can be connected to the I/O interface: input devices including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; including, for example, liquid crystal displays (LCDs), speakers, vibrators output devices, etc.; storage devices including, for example, magnetic tapes, hard disks, etc.; and communication devices. Communication means may allow electronic devices to communicate wirelessly or by wire with other devices to exchange data. While the figures show electronic devices having various systems, it should be understood that not all of the systems shown are required to be implemented or available. More or fewer systems may alternatively be implemented or provided.

In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the method illustrated in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network via a communication device, or from a storage device, or from a ROM. When the computer program is executed by the processing apparatus, the above-mentioned functions defined in the methods of the embodiments of the present disclosure are executed.

The electronic device provided by the present invention adopts the method for extracting the vibration frequency of the bridge based on the radar signal in the above-mentioned first embodiment, which realizes the rapid, accurate and convenient acquisition of the vibration frequency of the bridge in engineering application, and improves the engineering efficiency and accuracy. Compared with the prior art, the beneficial effects of the electronic device provided by the embodiment of the present invention are the same as the beneficial effects of the method for extracting the vibration frequency of the bridge based on the radar signal provided by the first embodiment, and other technical features in the electronic device are the same as the above. The disclosed features of the method in one embodiment are the same, and are not repeated here.

It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the foregoing description of the embodiments, the particular features, structures, materials or characteristics may be combined in any suitable manner in any one or more of the embodiments or examples.

The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention. should be included within the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims (10)

1. a method for extracting bridge vibration frequency based on radar signal, is characterized in that, comprises: The echo signal is collected by monitoring the bridge vibration by radar, and based on the echo signal, the distance unit where the bridge vibration point is located is determined, and the original echo time series signal at the distance unit where the bridge vibration point is located is extracted; Perform EMD decomposition on the original echo time series signal at the distance unit where the bridge vibration point is located to obtain a cluster of IMF components; The time-spectrogram is obtained by performing WVD transformation on the IMF components one by one, and the bridge vibration frequency is obtained by comparing the energy value of the time-spectrogram corresponding to each of the IMF components, wherein the energy value of the time-spectrogram is: The value obtained by adding the largest row. 2. the method for extracting bridge vibration frequency based on radar signal according to claim 1, is characterized in that, the echo signal collected by radar monitoring bridge vibration is:

In formula (4), τ is the time shift in the slow time domain, f is the signal frequency, C is the speed of light, R ₀ is the distance of the bridge, f _v is the vibration frequency of the distance unit where the bridge vibration point is located, and R ₀ +Asin ( 2πf _v τ) is used to describe the bridge vibration; The echo signal is in matrix form, including range direction and azimuth direction. 3. the method for extracting bridge vibration frequency based on radar signal according to claim 2, is characterized in that, based on described echo signal, determine the distance unit where bridge vibration point is located, extract the distance unit at the distance unit where described bridge vibration point is located. Methods for raw echo time series signals include: performing an inverse fast Fourier transform on the echo signal in the distance direction to obtain a pulse compression signal; Based on the pulse compression signal, a distance unit with a strong reflection point is determined in the distance direction, the distance unit with the strong reflection point includes the distance unit where the bridge vibration point is located, and the distance unit of the distance unit with the strong reflection point is The echo amplitude is higher than the background noise amplitude; The distance unit where the bridge vibration point is located is read from the distance unit with strong reflection points by means of a distance Doppler map, and the original echo time series signal at the distance unit where the bridge vibration point is located is extracted; wherein , the original echo time series signal at the distance unit where the bridge vibration point is located has multiple frequency components, and the frequency components are all integer multiples of the vibration frequency f _v of the bridge. 4. the method for extracting bridge vibration frequency based on radar signal according to claim 3, is characterized in that, the original echo time series signal at the distance unit where described bridge vibration point is located is:

In formula (5),

is the signal wavelength, and f _c is the signal center frequency. 5. the method for extracting bridge vibration frequency based on radar signal according to claim 4, is characterized in that, carries out EMD decomposition to the original echo time series signal at the distance unit where described bridge vibration point is located, obtains a cluster of IMF components methods include: S1. Obtain the original echo time series signal at the distance unit where the bridge vibration point is located, and mark it as the time series signal to be processed; S2. Perform local extremum identification on the time series signal to be processed, and perform cubic spline fitting for all identified maxima and all minima, respectively, to obtain an upper envelope and a lower envelope , and calculate the mean envelope; S3, subtract the mean envelope from the time series signal to be processed to obtain an IMF component; S4. Determine whether the IMF component satisfies a preset component condition, and if so, save the IMF component as a correct IMF component; if not, mark the IMF component as a time series signal to be processed, and based on the Step S2 and step S3 re-acquire the IMF component, until the obtained IMF component satisfies the preset component condition; S5, subtracting the correct IMF component acquired last time from the time-series signal to be processed to obtain a new time-series signal; S6. Mark the new time-series signal as a time-series signal to be processed, and repeat steps S2 to S5 until the new time-series signal is a monotonic function or constant, and change all correct IMF components from high to high. The frequency to low frequency is arranged in order and output. 6. the method for extracting bridge vibration frequency based on radar signal according to claim 5, is characterized in that, described preset component condition is: In the data set of the IMF component, there is at least one extreme point or zero-crossing point, and the number of the extreme value point and the number of the zero-crossing point differ by at most 1; and For any point in the dataset of IMF components, the mean of the envelope defined by the local maxima and local minima is zero. 7. the method for extracting bridge vibration frequency based on radar signal according to claim 5 or 6, is characterized in that, described IMF component is carried out WVD transformation one by one to obtain time-spectrogram, by comparing the time corresponding to each described IMF component. The methods of obtaining the bridge vibration frequency from the energy value of the spectrogram include: Obtain all correct IMF components and perform WVD transformation one by one; Calculate the energy value of the time-spectrogram corresponding to each IMF component after WVD transformation one by one, until the energy value of the time-spectrogram corresponding to the currently calculated IMF component is larger than the energy value of the time-spectrogram corresponding to the previous IMF component. When the size is 1 order of magnitude lower, stop calculating the energy value of the time-spectrogram corresponding to the IMF component, extract the previous IMF component as the bridge vibration frequency, and output the time-spectrogram corresponding to the bridge vibration frequency. 8. a system based on radar signal extraction bridge vibration frequency, is characterized in that, comprises time series signal extraction module, decomposition module and frequency extraction module, wherein, The timing signal extraction module is used to collect echo signals by monitoring the bridge vibration through radar, and based on the echo signals to determine the distance unit where the bridge vibration point is located, and extract the original echo time at the distance unit where the bridge vibration point is located. sequence signal; The decomposition module is used to perform EMD decomposition on the original echo time series signal at the distance unit where the bridge vibration point is located to obtain a cluster of IMF components; The frequency extraction module is configured to perform WVD transformation on the IMF components one by one to obtain a time-spectrogram, and obtain the bridge vibration frequency by comparing the energy value of the time-spectrogram corresponding to each of the IMF components. 9. A computer-readable storage medium, characterized in that it has computer-readable program instructions stored thereon, the computer-readable program instructions being used to execute the radar-based radar of any one of claims 1 to 8 Signal extraction method of bridge vibration frequency. 10. An electronic device, characterized in that the electronic device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the execution of any one of claims 1 to 8 The method of extracting bridge vibration frequency based on radar signal.

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