CN112613595A - Ultra-wideband radar echo signal preprocessing method based on variational modal decomposition - Google Patents
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Abstract
The utility model relates to an ultra wide band radar life echo signal processing method based on variational modal decomposition, including the following steps: acquiring an echo signal through an ultra-wideband radar life detector; aiming at the characteristics of the collected echo signals, optimizing important parameters alpha and K of variational modal decomposition by applying a genetic variation particle swarm optimization algorithm; performing VMD decomposition on the original signal by using the parameters alpha and K obtained by optimization; selecting corresponding IMF components which can be used for signal reconstruction from the K IMF components according to the central frequency of the signal sent by the radar transmitter, and performing signal reconstruction; and carrying out simple filtering processing on the signal obtained by reconstruction, highlighting the essential characteristics of the signal, weakening noise interference and obtaining the final output signal. Therefore, according to the characteristics of the collected echo signals, two important parameters corresponding to the strain-division modal decomposition are determined in real time, the self-adaptive decomposition of the strain-division modal is realized, and the method can be suitable for more complex fields.
Description
Technical Field
The disclosure relates to the technical field of ultra-wideband radar echo signal preprocessing, in particular to a method for preprocessing an echo signal of a life detector based on an ultra-wideband radar.
Background
The life detection radar technology has wide application prospects in both military and civil aspects, such as wounded rescue after war on a battlefield, rescue of robbed persons in anti-terrorism action, search of survivors in the ruins after earthquake or collapse and the like. In the disaster rescue field, the ultra-wideband radar technology is introduced into the life detection instrument, the anti-interference capability and the penetration capability of the detection instrument are greatly improved, but a large amount of complex noise is still mixed in an echo signal of the ultra-wideband radar life detection instrument, so that the submergence degree of the echo signal is higher, and useful respiration and heartbeat signals cannot be obtained from the echo signal. Therefore, accurately and rapidly separating the useful signal from the echo signal is a key technology for improving the performance of the life detection instrument.
In order to solve the problem of noise in the echo signal of the ultra-wideband radar, the current mainstream methods include a wavelet transform denoising method and an empirical mode decomposition method. Both methods decompose the signal from different angles, and then perform reconstruction selection of the signal according to different components obtained by decomposition. The wavelet transform is a local transformation of time and frequency, and has good time-frequency localization characteristics. The method carries out multi-scale refinement on the signals step by step through the telescopic translation operation, can realize the high-frequency time subdivision and the low-frequency subdivision of the signals, can automatically adapt to the requirements of time-frequency signal analysis, and can focus on any details of the signals. However, when the wavelet transform method is used, the wavelet basis function and the number of decomposition layers need to be manually determined, so that the purpose of denoising cannot be achieved by self-adaptive decomposition according to signal characteristics, and the method is difficult to adapt to a complex and variable disaster relief rescue site. Another empirical mode decomposition method can adaptively decompose a signal into segmented intrinsic mode functions on different characteristic scales, and all mode components are sequentially arranged from small to large according to a time scale. Although the method can realize the self-adaptive decomposition of the signal, the modal aliasing problem and the endpoint effect occur in the decomposition process, and the final decomposition result is influenced. Therefore, the method for preprocessing the echo signals of the ultra-wideband radar has important significance for saving the lives of people.
Disclosure of Invention
In view of the above existing problems, the present invention provides a method for processing a life echo signal of an ultra-wideband radar based on a genetic variation particle swarm optimization (VMD).
According to a first aspect of the present disclosure, there is provided an ultra-wideband radar life echo signal processing method based on variational modal decomposition, including the following steps: acquiring an echo signal through an ultra-wideband radar life detector; aiming at the characteristics of the collected echo signals, optimizing important parameters alpha and K of variational modal decomposition by applying a genetic variation particle swarm optimization algorithm; performing VMD decomposition on the original signal by using the parameters alpha and K obtained by optimization; selecting corresponding IMF components which can be used for signal reconstruction from the K IMF components according to the central frequency of the signal sent by the radar transmitter, and performing signal reconstruction; and carrying out simple filtering processing on the signal obtained by reconstruction, highlighting the essential characteristics of the signal, weakening noise interference and obtaining the final output signal.
In the present disclosure, the genetic variation particle swarm optimization algorithm comprises: assuming that in a D-dimensional space, initializing a particle group X consisting of n particles (X1, X2, …, Xn), wherein the ith particle is represented as a D-dimensional vector X ═ (Xi1, Xi2, …, XiD) T, and a corresponding particle velocity V ═ T (Vi1, Vi2, …, ViD); calculating the fitness function of each initial particle according to a given fitness function which takes mutual information as a support, wherein the fitness function is expressed as follows;
the mutual information I (X; Y) between two discrete random variables X, Y is defined as:
p (X, Y) is the joint probability density function of X and Y, and p (X) and p (Y) are the edge probability density functions of X and Y, respectively, the sum of mutual information between modal components is:
wherein, imf (K) is expressed as the K-th modal component of the original echo signal f after VMD decomposition, and K is the modal number of this VMD decomposition.
And the sum of mutual information between each modal component and the original signal f is:
according to the fitness function value of each particle, searching an individual extreme value T ═ (Ti1, Ti2, …, TiD) T corresponding to the population and a population extreme value Pg ═ (Pg1, Pi2, …, PgD) T of the current population;
according to the individual extreme value and the group extreme value, updating the movement speed of each particle after genetic variation through a formula (5), and updating the current position of each particle through a formula (6):
wherein ω is the inertial weight; k is the current iteration number; vid is the particle velocity; c1 and c2 are acceleration factors; r1 and r2 are random integers of the respective given intervals;
recalculating a fitness function value of the position of each particle in the current-generation particle group, wherein the fitness function of the current-generation particle group is the same as the fitness function in the formula (1); calculating an individual extreme value and a group extreme value in the current generation group, comparing the individual extreme value and the group extreme value with the current individual extreme value and the group extreme value, if a certain particle in the current generation is a better particle, keeping the position information of the particle and the fitness function value of the particle, and setting the particle as an optimal particle; judging whether a termination condition is met, and if the termination condition is met, ending the algorithm; if the termination condition is not met, repeating the steps until the condition is met, wherein the termination condition is iteration times; and extracting optimal parameters alpha and K found by the genetic variation particle swarm optimization algorithm, and finishing the optimization process.
According to a second aspect of the present disclosure, there is provided an ultra-wideband radar life echo signal processing apparatus based on variational modal decomposition, comprising: a processor; a memory for storing processor-executable instructions, wherein the processor is configured to execute the instructions to implement the method as previously described.
According to a third aspect of the present disclosure, there is provided a storage medium having instructions that, when executed by a processor of an ultra-wideband radar life-echo signal processing apparatus that optimizes a variational modal decomposition based on a population of genetic variation particles, enable the ultra-wideband radar life-echo signal processing apparatus that optimizes the variational modal decomposition based on the population of genetic variation particles to perform the method as previously described.
According to the method for preprocessing the echo signals of the ultra-wideband radar life detector based on particle swarm optimization, two important parameters corresponding to strain-division modal decomposition are determined in real time according to the characteristics of the acquired echo signals based on the field environment used by the ultra-wideband radar, so that the self-adaptive decomposition of the strain-division modal is realized, and the method can be suitable for more complex fields.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
FIG. 1 is a flow chart of an ultra-wideband radar echo signal processing method based on particle swarm optimization variational modal decomposition according to the invention;
FIG. 2 is a flow chart of a particle swarm optimization algorithm of the present invention;
FIG. 3 is a signal waveform of an example of the present invention;
FIG. 4 is a flowchart of a metamorphic modal decomposition process of the present invention;
FIG. 5 is a variation process of the optimal individual fitness function value in the particle swarm optimization process of the present invention;
FIG. 6 shows the result of the metamorphic mode decomposition of the signal according to the present invention;
FIG. 7 is a graph of the spectrum of each modal component of the present invention;
FIG. 8 is a graph of reconstructed signal results according to the present invention;
FIG. 9 is a diagram of the final processing results of the present invention.
Detailed Description
In order to make the technical solutions of the present disclosure better understood by those of ordinary skill in the art, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
According to one aspect of the disclosure, an ultra-wideband radar echo signal processing method based on particle swarm optimization variational modal decomposition is provided, two important parameters corresponding to the variational modal decomposition are determined in real time according to the characteristics of acquired echo signals based on the field environment used by an ultra-wideband radar, the self-adaptive decomposition of the variational modal is realized, and the method can adapt to more complex fields.
Fig. 1 shows a flow chart of an ultra-wideband radar echo signal processing method based on particle swarm optimization variational modal decomposition according to the present disclosure.
Referring to fig. 1, first, in step 101, an echo signal is acquired.
Specifically, an echo signal is acquired by the ultra-wideband radar life detector, and the waveform diagram of the echo signal is shown in fig. 2.
According to the embodiment of the present disclosure, in step 102, the decomposition mode number K and the penalty factor a are optimized through a genetic variation particle swarm optimization algorithm.
Specifically, aiming at the characteristics of the collected echo signals, the genetic variation particle swarm optimization algorithm is applied to optimize important parameters alpha and K of VMD decomposition. First, important parameters of the particle swarm algorithm are initialized. The number of initialization maximum iterations is 25, the number of particles in the population is 20, the velocity limits range-3, the alpha range [200,3000], the K value range [3,12], and the initial velocity and particle position of the random particles in the specified range. Since the optimized parameters α and K must be in integer form, the initialized particle position and velocity and the corresponding step size should be in integer form.
Fig. 3 shows a flow diagram of a particle swarm optimization algorithm, according to an embodiment of the present disclosure.
Referring to fig. 3, in step 301, a particle group and a velocity are initialized.
Specifically, assume that in a D-dimensional space, a particle group X consisting of n particles is initialized (X)1,X2,…,Xn) Where the ith particle is represented as a D-dimensional vector X ═ X (X)i1,Xi2,…,XiD)TAnd the corresponding particle velocity V ═ V (V)i1,Vi2,…,ViD)T。
In step 302, a fitness value for each particle is calculated.
Specifically, a fitness function value of each initial particle is calculated according to a given fitness function which takes mutual information as support, and the fitness function determining method is as follows;
according to an aspect of the disclosure, mutual information I (X; Y) between two discrete random variables X, Y is defined as:
where p (X, Y) is the joint probability density function of X and Y, and p (X) and p (Y) are the edge probability density functions of X and Y, respectively.
According to aspects of the present disclosure, a sum of mutual information between modal components is calculated:
wherein, imf (K) is expressed as the K-th modal component of the original echo signal f after VMD decomposition, and K is the modal number of this VMD decomposition.
According to an aspect of the disclosure, the sum of mutual information between each modal component and the original signal f is calculated:
according to aspects of the present disclosure, a fitness function is derived:
according to the aspect of the disclosure, when the function is used as a fitness function, the modal components after decomposition can be better separated from each other while the original signal characteristics are kept, and the occurrence of modal aliasing and characteristic information loss is effectively avoided. FIG. 5 shows the optimal individual fitness function value variation process in the particle swarm optimization process of the present invention.
In step 303, individual extrema and population extrema are found.
Specifically, according to the fitness function value of each particle, an individual extreme value T ═ T (T) corresponding to the population is foundi1,Ti2,…,TiD)TAnd the group extremum P of the current populationg＝(Pg1,Pi2,…,PgD)T。
In step 304, a velocity update and a location update are performed.
Specifically, according to the individual extremum and the population extremum obtained in step 303, the motion speed of each particle after genetic variation is updated through formula (5), and then the current position of each particle is updated through formula (6):
wherein ω is the inertial weight; k is the current iteration number; vidIs the particle velocity; c. C1And c2Is the acceleration factor; r is1And r2Are random integers of the respective given interval. Here, requirement c1r1And c2r2The value of (a) is the integer part of the currently calculated value, so that alpha and K still keep the integer form after being updated for a plurality of times.
In step 305, the fitness of each current particle is calculated.
Specifically, the fitness function value of the position of each particle in the current-generation particle group is recalculated, and the fitness function is the same as the fitness function in step 302;
in step 306, the individual extrema and the population extrema are updated.
Specifically, individual extremum and group extremum in the current generation group are calculated. And comparing the current individual extreme value with the current group extreme value, if a particle in the current generation is a better particle, keeping the position information of the particle and the fitness function value of the particle, and setting the particle as the optimal particle. Specifically, a genetic variation particle swarm optimization algorithm is introduced to solve the problem of local extremum of the traditional particle swarm optimization algorithm, and the particle position updating and the fitness value updating are carried out. The variant inheritance process is as follows: the current maximum value is judged to be kept as an algebraic value, and according to the value, the corresponding particle variation probability is shown in table 1:
TABLE 1 probability table of particle variation
In step 307, it is determined whether a termination condition is satisfied, and if the termination condition is satisfied, the algorithm is ended; if the termination condition is not satisfied, repeating steps 304-306 until the condition is satisfied. The termination condition here is the maximum number of iterations.
In step 308, the optimal parameter combination is obtained.
Specifically, the optimal parameters alpha and K found by the genetic variation particle swarm optimization algorithm are extracted, and the optimization process is finished.
In an aspect of the present disclosure, referring back to fig. 1, in step 103, VMD decomposition is performed on the original signal by applying the parameters α ═ 2052 and K ═ 4 optimized in step 102.
According to an embodiment of the present disclosure, the construction assumes that each modality is a finite bandwidth with a center frequency, described as seeking K modalities such that the sum of the estimated bandwidths of each modality is minimized, with the constraint that the sum of the modalities is equal to the variational problem of the input signal f. The model is as follows:
according to the embodiment of the disclosure, a secondary penalty factor α and a lagrangian penalty operator λ (t) are introduced for the above problems to ensure the reconstruction accuracy of the signal in a noise environment, and the constrained variation problem is converted into an unconstrained variation problem, so that an augmented lagrangian expression is obtained as follows:
according to the embodiment of the disclosure, the variation problem is solved by applying an alternative direction multiplier method, and the variation problem is updated through iteration
And
and seeking a 'saddle point' of the augmented Lagrange expression to obtain an optimal solution of the constraint variation model, wherein the specific steps are shown in FIG. 4.
Referring to FIG. 4, first, initialization { u }k 1}、{ωk 1}、{λk 1And n.
Then, the iterative counting variable self-addition is started, and n is equal to n + 1. At the same time to
To solve, the problem can be described as:
wherein ω iskIs equivalent to
Is equivalent to
Then, the equation (9) is converted into the frequency domain by using Parseval/Plancherel Fourier equidistant transformation, and the equation (10) is obtained:
by omega-omegakThe variable ω replacing the first term in (10):
converting equation (11) to the form of integration of non-negative frequency bins:
the secondary optimization problem solution to be solved is obtained as follows:
converting the central frequency value problem into a frequency domain:
the center frequency is solved as follows:
wherein,
corresponding to the current residual amount
Wiener filtering of (1);
to pair
Performing inverse Fourier transform to obtain a real part of
And performing self-addition operation on the resolvable modal values, namely k is k +1, and repeating the steps.
When K is K, according to:
and updating and calculating the lambda value.
Next, it is determined whether or not a given discrimination accuracy is satisfied, and if an iteration stop condition shown by equation (17) is satisfied:
according to the embodiment of the disclosure, the whole VMD decomposition process is finished, the output result is K IMF modal components, otherwise, the above steps are repeated. This results in an exploded view as shown in fig. 6 and a spectrogram of each modal component as shown in fig. 7.
In accordance with the present disclosure, referring back to fig. 1, in step 104, the appropriate components are selected for signal reconstruction according to the frequency characteristics.
Specifically, the corresponding IMF component which can be used for signal reconstruction is selected from the K IMF components according to the central frequency of the signal sent by the radar transmitter, and signal reconstruction is carried out. Fig. 8 shows a reconstructed signal diagram.
In step 105, the reconstructed signal is further processed.
Specifically, the reconstructed signal is subjected to simple filtering processing, so that the essential characteristics of the signal are highlighted, noise interference is weakened, and a final output signal is obtained, as shown in fig. 9.
In addition, in the current embodiment, the acquisition of the echo signal by the life detector of the ultra-wideband radar can be described by taking the first-order gaussian pulse of the ultra-wideband radar as an example.
According to the expression of the gaussian pulse after normalization:
and solving a first derivative of the signal to obtain a first-order Gaussian signal expression:
according to the formula, a plurality of Gaussian pulses are generated, random noise interference is added on the basis of the signals, and a final echo signal f is obtained through simulation. Where the parameter a is commonly referred to as the pulse shape factor, this value determines the width and amplitude of the pulse, and as a decreases, the pulse amplitude increases, the pulse width narrows, and the pulse energy spectral density becomes more concentrated.
In the embodiment according to the present invention, in the decomposition process, when the modal component decomposition is too small, the VMD decomposition may have a phenomenon of modal aliasing or missing a modal, and when the modal component decomposition is too much, an over-decomposition phenomenon may occur, and a signal may have an unnecessary residual component, and in addition, the balance constraint parameter α in the VMD decomposition process also has an influence on a signal decomposition result: the smaller the value of the balance constraint parameter alpha is, the larger the bandwidth of each intrinsic mode component obtained after decomposition is, and the phenomena of center frequency overlapping and mode aliasing are easy to occur; the larger the value of the balance constraint parameter alpha is, the smaller the bandwidth of each eigenmode component is, and the phenomena of center frequency overlapping and mode aliasing disappear.
According to the preferred embodiment of the disclosure, the particle swarm optimization algorithm is adopted to optimize the modal number K of VMD decomposition and the balance constraint parameter alpha, and finally, respective optimization in a specific range is realized by continuously iteratively updating the position and the speed of the particles, so as to find the optimal parameter combination meeting the conditions. The optimization algorithm can effectively solve the problem that the decomposition modal number K and the balance constraint parameter alpha in the variational modal decomposition are difficult to determine, quantizes the original empirical theory, provides a certain theoretical basis for determining the important parameters of the variational modal decomposition, simultaneously realizes the self-adaptive decomposition of the echo signals of the ultra-wideband radar, and has important significance for the processing of the echo signals of the ultra-wideband radar.
According to the present disclosure, in the decomposition process, when the modal component decomposition is too small, the VMD decomposition may have a phenomenon of modal aliasing or missing a modal, and when the modal component decomposition is too large, an over-decomposition phenomenon may occur, and a signal may have an unnecessary residual component, and in addition, the balance constraint parameter α in the VMD decomposition process also has an influence on a signal decomposition result: the smaller the value of the balance constraint parameter alpha is, the larger the bandwidth of each intrinsic mode component obtained after decomposition is, and the phenomena of center frequency overlapping and mode aliasing are easy to occur; the larger the value of the balance constraint parameter alpha is, the smaller the bandwidth of each eigenmode component is, and the phenomena of center frequency overlapping and mode aliasing disappear.
In an embodiment, there is provided an ultra-wideband radar life-echo signal processing apparatus based on metamorphic modal decomposition, comprising a processor and a memory for storing instructions executable by the processor, wherein the processor is configured to execute the instructions to implement the method of the previous embodiment. Moreover, according to an aspect of the present disclosure, the present application may be embodied as a storage medium having instructions that, when executed by a processor of an ultra-wideband radar life-echo signal processing apparatus optimized variational modal decomposition based on a population of genetic variation particles, enable the ultra-wideband radar life-echo signal processing apparatus optimized variational modal decomposition based on the population of genetic variation particles to perform the method as set forth in the preceding embodiments.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (8)
1. A life echo signal processing method of an ultra-wideband radar based on variation modal decomposition is characterized by comprising the following steps:
acquiring an echo signal through an ultra-wideband radar life detector;
aiming at the characteristics of the collected echo signals, optimizing important parameters alpha and K of variational modal decomposition by applying a genetic variation particle swarm optimization algorithm;
performing VMD decomposition on the original signal by using the parameters alpha and K obtained by optimization;
selecting corresponding IMF components which can be used for signal reconstruction from the K IMF components according to the central frequency of the signal sent by the radar transmitter, and performing signal reconstruction; and
and carrying out simple filtering processing on the signal obtained by reconstruction, highlighting the essential characteristics of the signal, weakening noise interference and obtaining a final output signal.
2. The method of claim 1, wherein the population of genetic variations particle optimization algorithm comprises:
assuming that in a D-dimensional space, initializing a particle group X consisting of n particles (X1, X2, …, Xn), wherein the ith particle is represented as a D-dimensional vector X ═ (Xi1, Xi2, …, XiD) T, and a corresponding particle velocity V ═ T (Vi1, Vi2, …, ViD);
calculating the fitness function of each initial particle according to a given fitness function which takes mutual information as a support, wherein the fitness function is expressed as follows;
wherein, I (X; Y) is mutual information between two discrete random variables X and Y, and is expressed as:
p (X, Y) is the joint probability density function of X and Y, and p (X) and p (Y) are the edge probability density functions of X and Y, respectively, the sum of mutual information between modal components is:
wherein, imf (K) is expressed as the K-th modal component of the original echo signal f after VMD decomposition, and K is the modal number of this VMD decomposition.
And the sum of mutual information between each modal component and the original signal f is:
according to the fitness function value of each particle, searching an individual extreme value T ═ (Ti1, Ti2, …, TiD) T corresponding to the population and a population extreme value Pg ═ (Pg1, Pi2, …, PgD) T of the current population;
according to the individual extreme value and the group extreme value, updating the movement speed of each particle after genetic variation through a formula (5), and updating the current position of each particle through a formula (6):
wherein ω is the inertial weight; k is the current iteration number; vid is the particle velocity; c1 and c2 are acceleration factors; r1 and r2 are random integers of the respective given intervals;
recalculating a fitness function value of the position of each particle in the current-generation particle group, wherein the fitness function of the current-generation particle group is the same as the fitness function in the formula (1);
calculating an individual extreme value and a group extreme value in the current generation group, comparing the individual extreme value and the group extreme value with the current individual extreme value and the group extreme value, if a certain particle in the current generation is a better particle, keeping the position information of the particle and the fitness function value of the particle, and setting the particle as an optimal particle;
judging whether a termination condition is met, and if the termination condition is met, ending the algorithm; if the termination condition is not met, repeating the steps until the condition is met, wherein the termination condition is iteration times;
and extracting optimal parameters alpha and K found by the genetic variation particle swarm optimization algorithm, and finishing the optimization process.
3. The method of claim 2, wherein the initialization maximum iteration generation is 25 generations, the number of particles in the particle population is 20, the speed is limited to the range of [ -3,3], the α range is [200,3000], and the K value range is [3,12 ].
4. The method of claim 2, wherein applying the optimized parameters α and K to VMD decompose the original signal comprises:
the construction assumes that each mode is a finite bandwidth with a center frequency, described as seeking K modes such that the sum of the estimated bandwidths of each mode is minimal, with the constraint that the sum of the modes equals the variational problem of the input signal f, and the model is:
aiming at the problems, a secondary penalty factor alpha and a Lagrange penalty operator lambda (t) are introduced to ensure the reconstruction accuracy of the signal in a noise environment, the constraint variation problem is converted into an unconstrained variation problem, and an augmented Lagrange expression is obtained as follows:
solving the variation problem by applying an alternating direction multiplier method, and searching for a saddle point expressed by the augmented Lagrange by iteratively updating ukn +1, omega kn +1 and lambda kn +1 to obtain an optimal solution of the constraint variation model.
5. The method of claim 4, wherein applying an alternating direction multiplier to solve the variational problem comprises:
initializing { uk1}, { ω k1}, { λ k1} and n;
starting to perform iterative counting variable self-addition, let n be n +1, and solving ukn +1, the problem can be described as:
where ω k is equivalent to ω kn +1,
is equivalent to
Converting equation (9) to the frequency domain using Parseval/Plancherel Fourier equidistant transformation to obtain equation (10):
replacing the variable ω of the first term in (10) with ω - ω k:
converting equation (11) to the form of integration of non-negative frequency bins:
the secondary optimization problem solution to be solved is obtained as follows:
converting the central frequency value problem into a frequency domain:
the center frequency is solved as follows:
wherein,
corresponding to the current residual amount
Wiener filtering of (1);
to pair
Performing inverse Fourier transform to obtain a real part of
Performing self-addition operation on the resolvable modal values, namely k is k +1, and repeating the steps;
when K is K, according to:
updating and calculating a lambda value;
judging whether a given discrimination accuracy is satisfied, and if an iteration stop condition shown in equation (17) is satisfied:
and (5) ending the whole VMD decomposition process, outputting results of K IMF modal components, and otherwise, repeating the steps.
6. The method of claim 1, wherein the echo signal received by the ultra-wideband radar signal receiver is represented as:
according to the expression of the gaussian pulse after normalization:
and solving a first derivative of the signal to obtain a first-order Gaussian signal expression:
7. the utility model provides an ultra wide band radar life echo signal processing apparatus based on variational modal decomposition which characterized in that includes:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the method of any one of claims 1 to 6.
8. A storage medium having instructions that, when executed by a processor of an ultra-wideband radar life-echo signal processing apparatus optimized variational modal decomposition based on a population of genetic variation particles, enable the ultra-wideband radar life-echo signal processing apparatus optimized variational modal decomposition based on the population of genetic variation particles to perform the method of any one of claims 1 to 6.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113589795A (en) * | 2021-08-02 | 2021-11-02 | 湖州师范学院 | Multi-oscillation detection method based on intelligent optimization nonlinear chirp modal decomposition algorithm |
| CN114754857A (en) * | 2022-06-14 | 2022-07-15 | 之江实验室 | Two-section type optical fiber sensing underwater acoustic signal compensation method and device |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101216939A (en) * | 2008-01-04 | 2008-07-09 | 江南大学 | A multi-resolution medical image registration method based on quantum behaviors particle swarm algorithm |
| CN107506330A (en) * | 2017-08-14 | 2017-12-22 | 电子科技大学 | A kind of variation mode decomposition algorithm parameter optimization method based on particle cluster algorithm |
| CN107679499A (en) * | 2017-10-12 | 2018-02-09 | 河北工业大学 | Single channel radio mixed signal separation method based on variation mode decomposition |
| CN108664562A (en) * | 2018-04-10 | 2018-10-16 | 华东师范大学 | The text feature selection method of particle group optimizing |
| CN110932297A (en) * | 2019-12-25 | 2020-03-27 | 安徽建筑大学 | Distributed energy storage device scheduling method and device based on centerless particle swarm optimization |
| US20200210864A1 (en) * | 2018-01-15 | 2020-07-02 | Dalian Minzu University | Method for detecting community structure of complicated network |
| CN111487063A (en) * | 2020-03-20 | 2020-08-04 | 天津大学 | Parameter adaptive internal combustion engine noise source separation method |
-
2020
- 2020-12-25 CN CN202011560160.3A patent/CN112613595A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101216939A (en) * | 2008-01-04 | 2008-07-09 | 江南大学 | A multi-resolution medical image registration method based on quantum behaviors particle swarm algorithm |
| CN107506330A (en) * | 2017-08-14 | 2017-12-22 | 电子科技大学 | A kind of variation mode decomposition algorithm parameter optimization method based on particle cluster algorithm |
| CN107679499A (en) * | 2017-10-12 | 2018-02-09 | 河北工业大学 | Single channel radio mixed signal separation method based on variation mode decomposition |
| US20200210864A1 (en) * | 2018-01-15 | 2020-07-02 | Dalian Minzu University | Method for detecting community structure of complicated network |
| CN108664562A (en) * | 2018-04-10 | 2018-10-16 | 华东师范大学 | The text feature selection method of particle group optimizing |
| CN110932297A (en) * | 2019-12-25 | 2020-03-27 | 安徽建筑大学 | Distributed energy storage device scheduling method and device based on centerless particle swarm optimization |
| CN111487063A (en) * | 2020-03-20 | 2020-08-04 | 天津大学 | Parameter adaptive internal combustion engine noise source separation method |
Non-Patent Citations (4)
| Title |
|---|
| W. C. TAN等: "Segmentation and detection of human spermatozoa using modified Pulse Coupled Neural Network optimized by Particle Swarm Optimization with Mutual Information", 《2015 IEEE 10TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA)》, 17 June 2015 (2015-06-17), pages 192 - 197, XP032815868, DOI: 10.1109/ICIEA.2015.7334109 * |
| 李昌喜: "基于变分模态分解的高速列车转向架故障诊断", 《中国优秀硕士学位论文全文数据库：工程科技Ⅱ辑》, no. 10, 15 October 2018 (2018-10-15), pages 1 - 71 * |
| 李艳娟等: "基于PSO和互信息的小波医学图像配准及融合", 《黑龙江工程学院学报》, vol. 29, no. 5, 15 October 2015 (2015-10-15), pages 46 - 51 * |
| 杨大为等: "基于参数优化变分模态分解和多尺度熵偏均值的行星变速箱故障特征提取", 《兵工学报》, vol. 39, no. 9, 15 September 2018 (2018-09-15), pages 1683 - 1691 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113589795A (en) * | 2021-08-02 | 2021-11-02 | 湖州师范学院 | Multi-oscillation detection method based on intelligent optimization nonlinear chirp modal decomposition algorithm |
| CN114754857A (en) * | 2022-06-14 | 2022-07-15 | 之江实验室 | Two-section type optical fiber sensing underwater acoustic signal compensation method and device |
| CN114754857B (en) * | 2022-06-14 | 2022-08-23 | 之江实验室 | Two-section type optical fiber sensing underwater acoustic signal compensation method and device |
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