CN116577748A - Scattering center parameter extraction method based on microwave photon signals - Google Patents
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Abstract
The invention discloses a scattering center parameter extraction method based on microwave photon signals, which comprises the following steps: converting a moving target into a rotating target, obtaining an image with good focusing through motion compensation, and obtaining an attribute scattering center model; performing two-dimensional decoupling on the attribute scattering center model by using a polar coordinate algorithm to obtain a final two-dimensional wave number decoupling model; based on a final two-dimensional wave number decoupling model, transforming the attribute scattering center model to an image domain to construct a representation dictionary of the attribute scattering center model, and solving a maximum posterior estimation to construct a first price function; and estimating the parameter value to be estimated in the representation dictionary based on an improved particle swarm optimization algorithm and an orthogonal matching pursuit algorithm, and determining the optimal parameter value to be estimated. By the parameter estimation method, the problem of extracting geometric electromagnetic features from the frequency angle dependence of the target echo under the conditions of a span and a large rotation angle can be realized, and the estimated two-dimensional image quality of the scattering center is improved.
Description
Technical Field
The invention belongs to the technical field of radar signal processing, and particularly relates to a scattering center parameter extraction method based on microwave photon signals.
Background
Accurate prediction and interpretation of electromagnetic scattering characteristics of radar targets are of great importance in the military field. The microwave photon technology enables the radar to have the capability of receiving and transmitting and processing the large bandwidth signals of the span, greatly improves the measuring range of the frequency response of the target, and provides possibility for extracting the frequency dependence of the target echo, thereby being beneficial to better realizing the target electromagnetic characteristic estimation based on the echo data.
Electromagnetic scattering feature extraction can be achieved by estimating parameters of a parameterized model of the scattering center, namely, the problem of electromagnetic feature extraction of the scattering center is equivalent to the problem of electromagnetic parameter estimation. The efficient and accurate extraction of the target features is the basis for achieving target recognition and classification based on the target electromagnetic characteristics.
The microwave photon radar transmits signals with large bandwidth in a span section, the microwave photon radar provides a high-efficiency mode of transmitting, receiving and processing the ultra-high resolution SAR image, and under the condition of a span section radar system, the target information quantity is multiplied compared with that of the traditional radar, so that the structure identification based on frequency factors is facilitated. And the accuracy and the efficiency of target identification and classification are improved. But the increase of bandwidth makes the dependence of the microwave photon radar system on frequency and azimuth angle, pitch angle more prominent. This results in that the conventional method cannot completely extract fine characteristic information contained in the ultra wideband signal. In addition, the increase in distance resolution and the increase in viewing angle cause significant distance azimuth coupling of echo data. Direct imaging without eliminating range-azimuth coupling can severely degrade image quality.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a scattering center parameter extraction method based on microwave photon signals.
The technical problems to be solved by the invention are realized by the following technical scheme:
a scattering center parameter extraction method based on microwave photon signals, the scattering center extraction method comprising:
s1, converting a moving target into a rotating target, obtaining an image with good focusing through motion compensation, and obtaining an attribute scattering center model of the target under an ultra-wideband microwave photon signal;
s2, performing two-dimensional decoupling on the attribute scattering center model by using a polar coordinate algorithm to obtain a final two-dimensional wave number decoupling model;
s3, based on the final two-dimensional wave number decoupling model, transforming the attribute scattering center model to an image domain to construct a representation dictionary of the attribute scattering center model, and solving a maximum posterior estimation to construct a first cost function;
and S4, estimating the parameter values to be estimated in the characterization dictionary based on an improved particle swarm optimization algorithm and an orthogonal matching pursuit algorithm, and determining the optimal parameter values to be estimated so as to obtain final parameters.
Optionally, the attribute scattering center model expresses an echo signal of the attribute scattering center;
the attribute scattering center model is expressed as:
wherein ,echo signals representing scattering centers; f represents frequency in Hz; f (f) c Representing the center frequency; a is that p Representing the signal amplitude; gamma ray i A dependence factor representing the direction angle; c represents the propagation speed of electromagnetic waves, and the unit is m/s; />Indicating azimuth observation angle, wherein the unit is rad; />Parameter set representing scattering center i, A i Is of amplitude, L i For length (I)>For the initial pointing angle of the scattering center, x i For azimuth position, y i For distance to position alpha i ∈[-1，-0.5，0，0.5，1]Is a frequency dependent factor.
Optionally, step S2 includes:
s2.1, willIntroducing an attribute scattering center model to obtain a two-dimensional wave number decoupling model under a rectangular coordinate system;
s2.2, obtaining a final two-dimensional wave number decoupling model based on the two-dimensional wave number decoupling model under a rectangular coordinate system, wherein the final two-dimensional wave number decoupling model is expressed as:
wherein ,
optionally, the representation dictionary includes parameters to be estimated of the attribute scattering center, and includes all parameter values to be estimated of the parameters to be estimated within an estimated value range;
the step S3 comprises the following steps:
s3.1, transforming the two-dimensional wave number decoupling model into an image domain through inverse Fourier transform to obtain a parameter value to be estimated;
s3.2, constructing a representation dictionary by using the parameter set to be estimated;
s3.3, constructing a first cost function by solving a maximum posterior estimation formed based on a statistical model of the target in the image domain.
Optionally, step S3.3 includes:
s3.31, performing discrete decomposition on the scattered field model, and taking a model of the decomposed residual error to obtain a preliminary cost function;
s3.32, adding the amplitude of the image vector to the amplitude of the residual image vector to obtain an amplitude adding result, and obtaining an image with the precisely aligned envelope based on the amplitude adding result to construct a final cost function;
s3.33, carrying out statistical modeling on the amplitude addition result to obtain a maximized posterior probability density function of the value addition result;
s3.34, performing maximum posterior probability estimation on the maximum posterior probability density function to obtain a cost function of distribution diffusion constraint;
and S3.35, introducing peak amplitude constraint into a cost function of distribution diffusion constraint to obtain a first cost function.
Optionally, the first cost function is expressed as:
wherein ,gp ＝q p (Θ p )+n，n is noise, lambda 1 、λ 2 To regularize parameters to control weights between two terms, μ p Is q p M, N are the distance direction and azimuth direction points, respectively, Θ p To accumulate angles.
Optionally, step S4 includes:
s4.1, minimizing the first cost function by utilizing an improved particle swarm optimization algorithm to solve the representation dictionary so as to obtain the position information of the current optimal particles;
s4.2, inputting the position information of the current optimal particle obtained in the step S4.1 into the final two-dimensional wave number decoupling model by using an orthogonal matching pursuit algorithm, and introducing a second cost function to estimate an optimal frequency correlation factor;
s4.3, inputting the position information of the current optimal particles obtained in the step S4.1 into the final two-dimensional wave number decoupling model, and introducing a third price function to estimate the amplitude of the scattering center by using a least square method;
and S4.4, determining the parameter value contained in the position information of the current optimal particle obtained in the step S4.1, the optimal frequency correlation factor and the amplitude of the scattering center as optimal parameter values to be estimated.
Optionally, the second cost function is:
wherein ,Rp (x, y) represents a signal skew equation, (x, y) is coordinates in the north-east coordinate system,in order to accumulate the angle of the angle,is formed by theta p Structured echo signal, alpha i ∈[-1，-0.5，0，0.5，1]，() * Is complex conjugate.
Optionally, the third cost function is:
wherein ,Ai Representing the magnitude of the scattering center i.
Optionally, the improved particle swarm optimization algorithm comprises:
step a, randomly generating particle swarms;
step b, calculating an objective function value of each particle in the particle swarm in an initial state;
step c, determining an extremum of particles in the particle swarm and a global optimal solution in the particle swarm based on the objective function value of each particle;
step d, updating the speed and position parameters of particles in the particle swarm;
step e, after updating, calculating the objective function value of the particles in the particle swarm;
step f, updating a particle extremum in the particle swarm and a global optimal solution in the particle swarm based on the objective function value of each particle;
step g, performing simulated annealing neighborhood search on the extreme values of particles in the particle swarm, updating the function value of each particle, performing optimal selection, and updating the global optimal solution;
step h, repeating the iteration step g until the maximum iteration times or the minimum transformation criterion is met, stopping the iteration and jumping to the step i, otherwise jumping to the step d;
step i, determining the position information of the current optimal particles in the particle swarm, and generating a target particle swarm with position parameters based on the position information;
step g, searching a particle extremum and a global optimal solution in the target particle swarm;
step k, repeating the steps d to h until the maximum iteration times or the minimum transformation criterion is met, stopping iterating and jumping to the step l, otherwise jumping to the step g;
step l, outputting the position information of the current optimal particles;
wherein the location information includes: parameter values to be estimated.
Compared with the prior art, the invention has the beneficial effects that:
by the parameter estimation method, the problem of extracting geometric electromagnetic features from the frequency angle dependence of the target echo under the conditions of a span and a large rotation angle can be realized, and the estimated two-dimensional image quality of the scattering center is improved.
The present invention will be described in further detail with reference to the accompanying drawings.
Drawings
Fig. 1 is a schematic flow chart of a scattering center parameter extraction method based on a microwave photon signal according to an embodiment of the present invention;
FIG. 2 is a schematic illustration of two-dimensional polar interpolation provided by an embodiment of the present invention;
FIG. 3 is a graph of the change of objective function provided by an embodiment of the present invention;
FIG. 4 is a diagram of a 6G bandwidth satellite geometry model provided by an embodiment of the present invention;
FIG. 5 is a diagram of simulation imaging results provided by an embodiment of the present invention;
fig. 6 is a diagram of parameter estimation situations under different signal-to-noise ratios according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but embodiments of the present invention are not limited thereto.
Example 1
Referring to fig. 1, fig. 1 is a flow chart of a scattering center parameter extraction method based on a microwave photon signal according to an embodiment of the present invention, and the invention provides a scattering center parameter extraction method based on a microwave photon signal, which includes:
s1, converting a moving target into a rotating target, obtaining an image with good focusing through motion compensation, and obtaining an attribute scattering center model of the target under an ultra-wideband microwave photon signal, wherein the attribute scattering center model expresses echo signals of an attribute scattering center.
In particular, in the case of a large bandwidth and high resolution, the backscattering characteristic of the scattering center exhibits a strong dependence characteristic on azimuth angle and frequency. Parametric modeling of the scattering center of the target and accurate and efficient description of the scattering properties of the target in the high frequency region are the preconditions for the algorithm design. The attribute scattering center model can describe the geometric and physical characteristics of a typical scattering component by using different combinations of a set of characteristic parameters, and has concise expression and more accurate description. It uses a power function to model the frequency dependence of the scattering center amplitude; the azimuthal dependence of the scattering center amplitude was modeled using a sinc function. Due to the many advantages of the attributed scattering center, the frequency and angle dependence of the scattering center is described using the attributed scattering center as a theoretical model.
According to the geometric diffraction theory and the physical optical theory, when the object is an electrically large-sized object, the scattered echo thereof can be approximately expressed as a coherent superposition of scattered echoes from a plurality of scattering points. The response and frequency and angle dependence of the scattering center p is determined by the geometry of the scattering point, the electromagnetic mechanism and the polarization properties.
When estimating parameters of a scatterer by using an attribute scattering center model, decoupling of a position parameter and other parameters is performed first. However, under the condition of large rotation angle, the relative rotation of the target can cause the target signal to have obvious azimuth distance coupling, so that the image is defocused, the imaging quality is reduced, and accurate position information cannot be obtained. Therefore, a polar format algorithm is introduced, the coupling of the echo envelope azimuth distance is removed, and the acquisition of the well-focused image helps to estimate the required position parameters from the coarse imaging result.
In this embodiment, the expression of the attribute scattering center model, i.e., the echo signal, is:
wherein ,echo signals representing scattering centers; f represents frequency in Hz; f (f) c Representing the center frequency; a is that p Representing the amplitude; gamma ray i A dependence factor representing the direction angle; c represents the propagation speed of electromagnetic waves, and the unit is m/s; />Indicating azimuth observation angle, wherein the unit is rad; />Parameter set representing scattering center i, A i Is of amplitude, L i For length (I)>For the initial pointing angle of the scattering center, x i For azimuth position, y i For distance to position alpha i ∈[-1，-0.5，0，0.5，1]Is a frequency dependent factor.
In this embodiment, the fringe field model is expressed as:
wherein ,the noise is P, and the total number of scattering points is P;
the geometrical and physical properties of a typical scattering component are determined by the parameter values of the scattering centerDescription.
And S2, performing two-dimensional decoupling on the attribute scattering center model by using a polar coordinate algorithm to obtain a two-dimensional wave number decoupling model.
Specifically, resampling is performed on the attribute scattering center model in the frequency domain, so that the attribute scattering center model is subjected to interpolation transformation from the polar coordinate to the rectangular coordinate system, and a two-dimensional frequency decoupling model in the rectangular coordinate system is obtained.
Optionally, resampling the attribute scattering center model in the frequency domain to enable the attribute scattering center model to be interpolated and transformed from the polar coordinate to the rectangular coordinate system, and obtaining the two-dimensional frequency decoupling model in the rectangular coordinate system comprises the following steps:
s2.1, willIntroducing an attribute scattering center model to obtain a two-dimensional wave number decoupling model under a rectangular coordinate system, wherein the two-dimensional wave number decoupling model under the rectangular coordinate system is expressed as follows:
under the condition of large rotation angle, the coupling can generate defocusing phenomenon, and the imaging quality of the image is reduced. The PFA realizes rotation compensation by resampling the wave number domain signal, interpolates and transforms the data in the polar coordinate format to the rectangular coordinate, realizes decoupling of distance and azimuth, and then uses two-dimensional FFT imaging.
S2.2, obtaining a final two-dimensional wave number decoupling model based on the two-dimensional wave number decoupling model under the rectangular coordinate system.
Specifically, analyze E i (f x ,f y ；Θ i ) The final two-dimensional wave number decoupling model is determined by the property, and analysis shows that gamma is due to i The effect on backscattering is negligible, and therefore the final two-dimensional wavenumber decoupling model is:
wherein ,(fx ,f y ) Is in the form of a rectangular coordinate system,is a polar coordinate system>
Referring to fig. 2, fig. 2 is a schematic diagram of 2-dimensional polar interpolation according to an embodiment of the invention. The corresponding attribute scattering center expression is changed through two-dimensional wave number domain resampling of the echo signals. Fig. 2 a is a 2-dimensional polar coordinate interpolation schematic diagram, fig. b is a grid schematic diagram under a rectangular coordinate system, a conventional attribute scattering center model is recorded under a polar coordinate format, and a data collection range is f c ·(1-β/2)～f c (1+β/2), where f c Is the center frequency, β is the relative bandwidth; at an angle ofChange between (I) and (II)>Is the beam width.
S3, based on the two-dimensional wave number decoupling model, the attribute scattering center model is transformed to an image domain to construct a representation dictionary of the attribute scattering center model, a first price function is constructed by solving maximum posterior estimation, parameters to be estimated of the attribute scattering center are contained in the representation dictionary, and all parameter values to be estimated of the parameters to be estimated are contained in an estimated value range.
In a specific embodiment, step S3 may include:
s3.1, transforming the two-dimensional wave number decoupling model into an image domain through inverse Fourier transform to obtain a parameter value to be estimated, wherein the two-dimensional wave number decoupling model of the image domain is as follows:
wherein ,a two-dimensional wavenumber decoupling model representing the image domain.
S3.2, constructing a representation dictionary by utilizing the parameter set to be estimated.
Optionally, the representation dictionary is:
s3.3, constructing a first cost function by solving a maximum posterior estimation formed based on a statistical model of the target in the image domain.
S3.31, performing discrete decomposition on the scattered field model, and taking a modulus of the decomposed residual error to obtain a preliminary cost function.
Specifically, discrete decomposition is performed on the scattered field model:
the modulo residual error can be expressed as:
the preliminary cost function is:
wherein ,representing a preliminary cost function.
S3.32, adding the amplitude of the image vector to the amplitude of the residual image vector to obtain an amplitude adding result, namelyObtaining an image after accurate alignment of the envelope based on the result of the amplitude addition to construct a final cost function, the aligned image being denoted g p ＝q p (Θ p ) +n, constructing a final cost function as:
s3.33 result q of the addition of the amplitude values p (Θ p ) And carrying out statistical modeling to obtain a maximized posterior probability density function of the value addition result.
Specifically, the solution to the final cost function is converted to a solution to q p (Θ p ) Solving and solving for q p (Θ p ) Statistical modeling is carried out to obtain q p (Θ p ) The maximum posterior probability density function of (2) is:
s3.34, pair q p (Θ p ) The maximum posterior probability density function is subjected to maximum posterior probability estimation, and a cost function of distribution diffusion constraint is obtained:
s3.35, introducing peak amplitude constraint into a cost function of distribution diffusion constraint to obtain a cost function of distribution diffusion-peak amplitude joint constraint, namely a first cost function:
wherein ,gp ＝q p (Θ p )+n，n is noise, lambda 1 、λ 2 To regularize parameters to control weights between two terms, μ p Is q p M, N are the distance direction and azimuth direction points, respectively, Θ p To accumulate angles.
And S4, estimating the parameter value to be estimated in the representation dictionary based on an improved particle swarm optimization algorithm and an orthogonal matching pursuit algorithm, and determining the optimal parameter value to be estimated.
In a specific embodiment, step S4 may include:
s4.1, minimizing a first cost function by utilizing an improved particle swarm optimization algorithm so as to solve the representation dictionary and obtain the position information of the current optimal particles.
Specifically, the parameters of the scattering center can be obtained by a minimization and optimization problem, and the optimal point is determined by introducing a correlation function, wherein the cost function is a first cost function. Adopting an improved Particle Swarm Optimization (PSO) to solve the minimization problem of the cost function; outputting the position information of the current optimal individual (particle)Comprising 4 parameters.
In a specific embodiment, referring to fig. 3, fig. 3 is a graph of an objective function change provided by an embodiment of the present invention, and the improved particle swarm optimization algorithm includes:
step a, randomly generating particle swarms;
step b, calculating an objective function value of each particle in the particle swarm in an initial state;
step c, determining an extremum of particles in the particle swarm and a global optimal solution in the particle swarm based on the objective function value of each particle;
step d, updating the speed and position parameters of particles in the particle swarm;
step e, after updating, calculating the objective function value of the particles in the particle swarm;
step f, updating a particle extremum in the particle swarm and a global optimal solution in the particle swarm based on the objective function value of each particle;
step g, performing simulated annealing neighborhood search on the extreme values of particles in the particle swarm, updating the function value of each particle, performing optimal selection, and updating the global optimal solution;
step h, repeating the iteration step g until the maximum iteration times or the minimum transformation criterion is met, stopping the iteration and jumping to the step i, otherwise jumping to the step d;
step i, determining the position information of the current optimal particles in the particle swarm, and generating a target particle swarm with position parameters based on the position information;
step g, searching a particle extremum and a global optimal solution in the target particle swarm;
step k, repeating the steps d to h until the maximum iteration times or the minimum transformation criterion is met, stopping iterating and jumping to the step l, otherwise jumping to the step g;
step l, outputting the position information of the current optimal particles; wherein the location information includes: parameter values to be estimated.
Specifically, the embodiment defines the parameter to be solved corresponding to the optimization problem as the individual position parameter in the PSO processing algorithm:
firstly, each particle group independently searches an optimal solution in a search space, marks the optimal solution as a current individual extremum Prest, shares the individual extremum with the whole particle group, and continuously updates the individual extremum to find the optimal individual extremum as a current global optimal solution Gbest of the whole particle group.
The update formula in step d is:
V i (t+1)＝wV i (t)+c 1 rand 1 (Pbest-X i (t))+c 2 rand 2 (Gbest-X i (t))
X i (t+1)＝X i (t)+V i (t)
wherein ,Vi(t) and Xi (t) the motion and position of the t-th individual in the i-th iteration; w is an inertia factor, the value of the inertia factor is non-negative, and the global optimizing performance of the algorithm can be adjusted by adjusting the size of w; c 1 、c 2 For acceleration constants, weighting factors used to balance individual experience and group experience are typically set to c 1 ＝c 2 =2, take c 1 ＝c 2 ∈[0，4]The method comprises the steps of carrying out a first treatment on the surface of the Prest is the current individual extremum, namely each particle group independently searches the optimal solution in the search space; gbest is the current global optimal solution; rand of 1 and rand2 Representing interval [0,1 ]]Random numbers on the same.
When the population is initialized, the initial population is assigned by adopting inverse trigonometric function logistic mapping, so that the initial population can be uniformly distributed in a decision space, and a good start is provided for algorithm searching.
S4.2, inputting the position information of the current optimal particle obtained in the step S4.1 into a final two-dimensional wave number decoupling model by using an orthogonal matching pursuit algorithm, and introducing a second cost function to estimate an optimal frequency correlation factor.
Specifically, parameter setsThe optimal frequency correlation factor, namely the 5 th parameter, is estimated by substituting the optimal frequency correlation factor into a final two-dimensional decoupling model formula and then introducing a second cost function; the second cost function is:
wherein ,Rp (x, y) represents the signal skew equation,is formed by theta p Structured echo signal, alpha i ∈[-1，-0.5，0，0.5，1](x, y) is the coordinates in the North Tiandong coordinate system, < >>For the accumulation angle () * Is complex conjugate.
S4.3, inputting the position information of the current optimal particle obtained in the step S4.1 into a final two-dimensional wave number decoupling model, and introducing a third price function to estimate the amplitude of a scattering center by using a least square method, wherein the amplitude of the scattering center is a 6 th parameter, and the third price function is as follows:
wherein ,Ai Representing the amplitude of the scattering center i,a parameter set representing the scattering center i.
Specifically, a particle swarm optimization algorithm (PSO) is employed to solve the minimization problem of the first cost function. It is a population global optimization algorithm that initializes the global system to a set of random particle solutions, and then the particles search for optimal particles in the solution space as potential solutions. The method has the characteristics of high search dimension, few parameters to be adjusted and simple model.
And S4.4, determining the parameter value, the optimal frequency correlation factor and the amplitude of the scattering center contained in the position information of the current optimal particle obtained in the step S4.1 as optimal parameter values to be estimated so as to obtain final parameters.
After obtaining the parameters to be solved, the two-dimensional image of the attribute scattering center can be determined by the optimal parameter values to be estimated.
Next, the effect of the present invention was verified by a simulation experiment.
Experiment one
Please refer to fig. 4. Fig. 4 is a diagram of a 6G bandwidth satellite geometry model provided by an embodiment of the present invention. Satellite echo data was simulated using the bouncing ray method (SBR). The satellite main body is of a cube structure with the side length of 0.75 m. The connection structure is a cubic structure with the side length of 0.125m, and the solar panel is a cuboid structure with the length of 0.75m, the width of 0.3m and the height of 0.025 m. The scanning frequency is 7-13 GHz, the bandwidth is 6GHz, and the sampling interval is 20MHz; the scanning angle is [ -17 deg., 17 deg. ], and the sampling interval is 0.11 deg.. The two-dimensional resolution was 2.5cm. Times.2.5 cm.
Analysis of experimental results:
please refer to fig. 5. Fig. 5 (a) shows the results of direct imaging in the simulation model, and the PFA-generated image is shown in fig. 5 (b). By using the proposed method, it is apparent that the reconstructed ASCs are complete and accurate, as shown by the image segmentation result in fig. 5 (c) and the reconstructed image after parameter estimation in fig. 5 (d). It can be seen that for distributed ASCs, the target is divided into several parts. This is because the reference method estimates the length L in each ranging unit. If the distributed scattering center is not located in a single ranging unit, the reference method cannot estimate the correct length.
The effect of the invention under different signal to noise ratios can be verified by the following simulation data.
The radar parameters are the same as those of the experiment of the second embodiment, and the signal to noise ratio variation range is [0,30]dB (dB). The estimation performance of the algorithm is evaluated by using the RCS estimation accuracy and the correlation coefficient as criteria. The estimation accuracy is to calculate the relative error of the estimated parameter inversion radar RCS and the reference radar RCS. By means ofCalculating an RCS error, wherein parameterized imaging is performed by calculating an RCS esti ＝∑|x esti (i)| 2 Obtained, raw imaging results are obtained by calculating RCS refer ＝∑|x refer (i)| 2 Obtained. The correlation coefficient r is used to scale the correlation of the reconstructed signal with the original signalDegree of the degree.
Experiment two
Please refer to fig. 6. Fig. 6 is an evaluation index of the proposed method and the reference method at different signal-to-noise levels provided by an embodiment of the present invention. FIGS. 6 (a), (b) and (d) are RMSE between actual and estimated values obtained by two methods; fig. 6 (c) is the actual α and the estimated α fig. 6 (e) is the relative error between the original image and the reconstructed image obtained by two methods; 6 (f) calculating a correlation coefficient between the original image and the reconstructed image by two methods. Monte Carlo tests were performed on the proposed method and the reference method. The two parts were compared in detail: the first part is Root Mean Square Error (RMSE); the second part is the estimation accuracy calculated from the relative error and correlation coefficient between the original image and the reconstructed image. The RMSE of each parameter obtained by the proposed method is much smaller than the RMSE obtained by the reference method. The relative error and correlation coefficient also demonstrate that the method has good robustness.
In the present invention, the dependence of the scattering center on the azimuth angle is represented by γ i ,L i ,To determine, wherein gamma i Is the dependent factor of the direction angle, L i For length (I)>Is the initial pointing angle of the scattering center. Scattering centers are divided into local and distributed in the image domain. The local scattering center comprises a dihedral angle, a cap and a sphere; distributed scattering centers include dihedral, cylindrical, planar, and edge scattering. The frequency dependent factor is determined by the original curvature of the scattering mechanism. Through length L i And a frequency dependent factor alpha i Can distinguish between different types of diffuser structures. Please refer to table 1. Table 1 is alpha i and Li Corresponding to a combination of typical scattering structures.
TABLE 1 alpha i and Li Corresponding to different combinations of typical scattering structures
The invention discloses an electromagnetic parameter estimation method based on microwave photon signals and improved particle swarms, which comprises the steps of obtaining an attribute scattering center model of a target scattering center; performing two-dimensional decoupling on the attribute scattering center model by using a polar coordinate algorithm to obtain a two-dimensional wave number decoupling model; based on the frequency domain two-dimensional decoupling model, the attribute scattering center model is transformed to an image domain, an attribute scattering center model representation dictionary is constructed, and based on an improved particle swarm optimization algorithm and an orthogonal matching pursuit algorithm, parameters to be estimated are estimated, and parameter values to be estimated are determined. By the parameter estimation method, the problem of extracting geometric electromagnetic features from the frequency angle dependence of the target echo under the conditions of a span and a large rotation angle can be realized, and the estimated two-dimensional image quality of the scattering center is improved.
It should be noted that the terms "first," "second," and "second" are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implying a number of technical features being indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more features. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Further, one skilled in the art can engage and combine the different embodiments or examples described in this specification.
Although the invention is described herein in connection with various embodiments, other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings and the disclosure. In the description, the word "comprising" does not exclude other elements or steps, and the "a" or "an" does not exclude a plurality. Some measures are described in mutually different embodiments, but this does not mean that these measures cannot be combined to produce a good effect.
The foregoing is a further detailed description of the invention in connection with the preferred embodiments, and it is not intended that the invention be limited to the specific embodiments described. It will be apparent to those skilled in the art that several simple deductions or substitutions may be made without departing from the spirit of the invention, and these should be considered to be within the scope of the invention.
Claims (10)
1. The scattering center parameter extraction method based on the microwave photon signals is characterized by comprising the following steps of:
s1, converting a moving target into a rotating target, obtaining an image with good focusing through motion compensation, and obtaining an attribute scattering center model of the target under an ultra-wideband microwave photon signal;
s2, performing two-dimensional decoupling on the attribute scattering center model by using a polar coordinate algorithm to obtain a final two-dimensional wave number decoupling model;
s3, based on the final two-dimensional wave number decoupling model, transforming the attribute scattering center model to an image domain to construct a representation dictionary of the attribute scattering center model, and solving a maximum posterior estimation to construct a first cost function;
and S4, estimating the parameter values to be estimated in the characterization dictionary based on an improved particle swarm optimization algorithm and an orthogonal matching pursuit algorithm, and determining the optimal parameter values to be estimated so as to obtain final parameters.
2. The method for extracting scattering center parameters based on microwave photon signals according to claim 1, wherein the attribute scattering center model expresses echo signals of the attribute scattering center;
the attribute scattering center model is expressed as:
wherein ,echo signals representing scattering centers; f represents frequency in Hz; f (f) c Representing the center frequency; a is that p Representing the signal amplitude; gamma ray i A dependence factor representing the direction angle; c represents the propagation speed of electromagnetic waves, and the unit is m/s; />Indicating azimuth observation angle, wherein the unit is rad; />Parameter set representing scattering center i, A i Is of amplitude, L i For length (I)>For the initial pointing angle of the scattering center, x i For azimuth position, y i For distance to position alpha i ∈[-1，-0.5，0，0.5，1]Is a frequency dependent factor.
3. The method for extracting parameters of a scattering center based on a microwave photon signal according to claim 2, wherein step S2 comprises:
s2.1, willIntroducing an attribute scattering center model to obtain a two-dimensional wave number decoupling model under a rectangular coordinate system;
s2.2, obtaining a final two-dimensional wave number decoupling model based on the two-dimensional wave number decoupling model under a rectangular coordinate system, wherein the final two-dimensional wave number decoupling model is expressed as:
wherein ,
4. the method for extracting parameters of scattering centers based on microwave photon signals according to claim 1, wherein the characterization dictionary contains parameters to be estimated of the attribute scattering centers and contains all parameter values to be estimated of the parameters to be estimated within an estimated value range;
the step S3 comprises the following steps:
s3.1, transforming the two-dimensional wave number decoupling model into an image domain through inverse Fourier transform to obtain a parameter value to be estimated;
s3.2, constructing a representation dictionary by using the parameter set to be estimated;
s3.3, constructing a first cost function by solving a maximum posterior estimation formed based on a statistical model of the target in the image domain.
5. The method for extracting parameters of a scattering center based on a microwave photon signal as claimed in claim 4, wherein the step S3.3 comprises:
s3.31, performing discrete decomposition on the scattered field model, and taking a model of the decomposed residual error to obtain a preliminary cost function;
s3.32, adding the amplitude of the image vector to the amplitude of the residual image vector to obtain an amplitude adding result, and obtaining an image with the precisely aligned envelope based on the amplitude adding result to construct a final cost function;
s3.33, carrying out statistical modeling on the amplitude addition result to obtain a maximized posterior probability density function of the value addition result;
s3.34, performing maximum posterior probability estimation on the maximum posterior probability density function to obtain a cost function of distribution diffusion constraint;
and S3.35, introducing peak amplitude constraint into a cost function of distribution diffusion constraint to obtain a first cost function.
6. The method for extracting parameters of a scattering center based on a microwave photon signal according to claim 5, wherein the first cost function is expressed as:
wherein ,gp ＝q p (Θ p )+n，n is noise, lambda 1 、λ 2 To regularize parameters to control weights between two terms, μ p Is q p M, N are the distance direction and azimuth direction points, respectively, Θ p To accumulate angles.
7. The method for extracting parameters of a scattering center based on a microwave photon signal as claimed in claim 1, wherein the step S4 comprises:
s4.1, minimizing the first cost function by utilizing an improved particle swarm optimization algorithm to solve the representation dictionary so as to obtain the position information of the current optimal particles;
s4.2, inputting the position information of the current optimal particle obtained in the step S4.1 into the final two-dimensional wave number decoupling model by using an orthogonal matching pursuit algorithm, and introducing a second cost function to estimate an optimal frequency correlation factor;
s4.3, inputting the position information of the current optimal particles obtained in the step S4.1 into the final two-dimensional wave number decoupling model, and introducing a third price function to estimate the amplitude of the scattering center by using a least square method;
and S4.4, determining the parameter value contained in the position information of the current optimal particle obtained in the step S4.1, the optimal frequency correlation factor and the amplitude of the scattering center as optimal parameter values to be estimated.
8. The method for extracting parameters of a scattering center based on a microwave photon signal as claimed in claim 7 wherein the second cost function is:
wherein ,Rp (x, y) represents the signal skew equation,is formed by theta p Structured echo signal, alpha i ∈[-1，-0.5，0，0.5，1]，() * Is complex conjugate.
9. The method for extracting parameters of a scattering center based on a microwave photon signal according to claim 8, wherein the third cost function is:
wherein ,Ai Representing the magnitude of the scattering center i.
10. The method of claim 7, wherein the improved particle swarm optimization algorithm comprises:
step a, randomly generating particle swarms;
step b, calculating an objective function value of each particle in the particle swarm in an initial state;
step c, determining an extremum of particles in the particle swarm and a global optimal solution in the particle swarm based on the objective function value of each particle;
step d, updating the speed and position parameters of particles in the particle swarm;
step e, after updating, calculating the objective function value of the particles in the particle swarm;
step f, updating a particle extremum in the particle swarm and a global optimal solution in the particle swarm based on the objective function value of each particle;
step g, performing simulated annealing neighborhood search on the extreme values of particles in the particle swarm, updating the function value of each particle, performing optimal selection, and updating the global optimal solution;
step h, repeating the iteration step g until the maximum iteration times or the minimum transformation criterion is met, stopping the iteration and jumping to the step i, otherwise jumping to the step d;
step i, determining the position information of the current optimal particles in the particle swarm, and generating a target particle swarm with position parameters based on the position information;
step g, searching a particle extremum and a global optimal solution in the target particle swarm;
step k, repeating the steps d to h until the maximum iteration times or the minimum transformation criterion is met, stopping iterating and jumping to the step l, otherwise jumping to the step g;
step l, outputting the position information of the current optimal particles;
wherein the location information includes: parameter values to be estimated.
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