CN113742916B - Frequency Hopping RCS Reconstruction Method Based on GTD Parameter Extraction - Google Patents
Frequency Hopping RCS Reconstruction Method Based on GTD Parameter Extraction Download PDFInfo
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Abstract
The application relates to a frequency hopping RCS reconstruction method based on GTD parameter extraction, which comprises the following steps: sparse modeling is carried out on darkroom step frequency RCS measurement data in a frequency hopping mode, so that sparse representation of a backward scattered field GTD scattering model of a radar target is obtained; performing optimization solution on a matrix form of the GTD scattering model by adopting an IRLS algorithm to obtain a sparse optimal solution; after threshold cut-off processing is carried out on the sparse optimal solution, GTD scattering parameter extraction is carried out, and scattering parameters of an equivalent scattering center are obtained; the scattering parameters include scattering location, scattering intensity, and scattering type; substituting the scattering parameters into a GTD scattering model to obtain an RCS reconstruction result under a darkroom RCS measurement frequency hopping mode. The GTD scattering parameter can be extracted under the condition that the RCS data is non-uniform and incomplete, and the RCS reconstruction purpose based on the GTD scattering parameter extraction under the frequency hopping mode is further achieved.
Description
Technical Field
The application relates to the technical field of radars, in particular to a frequency hopping RCS reconstruction method based on GTD parameter extraction.
Background
The radar scattering cross section (radar cross section, RCS) provides a data base for radar scientific research and application practice, and is important to radar target characteristic inversion, radar target imaging, radar target intelligent identification and the like. With the advent of the big data age and the development of modern radar super-resolution technology, people are urgent to have more and more abundant and complete radar target RCS data.
Currently, the main modes of radar target RCS data acquisition include electromagnetic simulation calculation, darkroom scaling measurement and outfield actual measurement. Compared with other two modes, the darkroom shrinkage ratio measurement mode has the advantages of good authenticity, strong confidentiality and the like, but the traditional darkroom step frequency RCS measurement adopts a sweep frequency mode, and all step frequency points of each target gesture need to be swept in sequence, so that the darkroom shrinkage ratio measurement mode has the problems of long measurement period, large data storage space and the like. RCS reconstruction can solve the above problems to some extent, mainly by extracting scattering center parameters. GTD (geometric theory of diffraction, GTD) scattering parameter extraction based on modern spectral estimation has achieved good results. However, in the process of implementing the present invention, the inventor has found that the aforementioned conventional RCS reconstruction technique has a technical problem that non-uniform incomplete RCS data cannot be effectively processed.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method for reconstructing a frequency-hopped RCS based on GTD parameter extraction, a device for reconstructing a frequency-hopped RCS based on GTD parameter extraction, a computer device, and a computer-readable storage medium, which can effectively process non-uniform incomplete RCS data to implement RCS reconstruction.
In order to achieve the above object, the embodiment of the present invention adopts the following technical scheme:
in one aspect, an embodiment of the present invention provides a method for reconstructing a frequency hopping RCS based on GTD parameter extraction, including the steps of:
sparse modeling is carried out on darkroom step frequency RCS measurement data in a frequency hopping mode, so that sparse representation of a backward scattered field GTD scattering model of a radar target is obtained;
Performing optimization solution on a matrix form of the GTD scattering model by adopting an IRLS algorithm to obtain a sparse optimal solution;
After threshold cut-off processing is carried out on the sparse optimal solution, GTD scattering parameter extraction is carried out, and scattering parameters of an equivalent scattering center are obtained; the scattering parameters include scattering location, scattering intensity, and scattering type;
Substituting the scattering parameters into a GTD scattering model to obtain an RCS reconstruction result under a darkroom RCS measurement frequency hopping mode.
On the other hand, still provide a hopping RCS reconstruction device based on GTD parameter extraction, include:
The sparse processing module is used for performing sparse modeling on darkroom step frequency RCS measurement data in a frequency hopping mode to obtain sparse representation of a backward scattered field GTD scattering model of a radar target;
the optimization solving module is used for carrying out optimization solving on the matrix form of the GTD scattering model by adopting an IRLS algorithm to obtain a sparse optimal solution;
The parameter extraction module is used for carrying out threshold truncation processing on the sparse optimal solution and then carrying out GTD scattering parameter extraction to obtain scattering parameters of the equivalent scattering center; the scattering parameters include scattering location, scattering intensity, and scattering type;
And the reconstruction processing module is used for substituting the scattering parameters into the GTD scattering model to obtain an RCS reconstruction result under the darkroom RCS measurement frequency hopping mode.
In yet another aspect, a computer device is provided, including a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the above-mentioned frequency hopping RCS reconstruction method based on GTD parameter extraction of any one of the above-mentioned steps when executing the computer program.
In yet another aspect, there is also provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of any one of the above-described method for reconstructing a frequency-hopped RCS based on GTD parameter extraction.
One of the above technical solutions has the following advantages and beneficial effects:
According to the frequency hopping RCS reconstruction method based on GTD parameter extraction, the sparse reconstruction theory is combined with the GTD scattering model to model radar target RCS data, the fact that the RCS data are sparse in a space domain is utilized, sparse representation of the radar target RCS data in a frequency hopping mode is given based on the GTD scattering model, and the GTD scattering parameter extraction problem is converted into the l p norm optimization problem. And further solving the l p norm by using an iterative weighted Least Squares (ITERATIVELY-REWEIGHED-Least-Squares, IRLS) algorithm with the l 2 norm as a criterion, so as to realize the extraction of the GTD scattering parameters under different postures of the target. And finally, substituting the extracted scattering parameters into a GTD scattering model to realize RCS reconstruction. The GTD scattering parameters can be extracted under the condition that RCS data are non-uniform and incomplete, the purpose of RCS reconstruction based on GTD scattering parameter extraction under a frequency hopping mode is further achieved, and the method has high engineering application value on target scattering characteristics under the condition that analysis data are limited, the cycle of reducing darkroom step frequency RCS measurement and amplifying radar target RCS data.
Drawings
Fig. 1 is a flow diagram of a method for reconstructing a frequency hopping RCS based on GTD parameter extraction in one embodiment;
FIG. 2 is a flow diagram of a sparse modeling process for RCS data in one embodiment;
FIG. 3 is a schematic flow diagram of an optimization solution using IRLS algorithm in one embodiment;
fig. 4 is a schematic flow chart of a simulation implementation of a frequency hopping RCS reconstruction method based on GTD parameter extraction in one embodiment;
FIG. 5 is a graph showing the average results of simulated scattering parameters at a signal-to-noise ratio of 25dB in one embodiment; wherein, (a) is the average result of 100 times of Mont Carlo simulation scattering parameters when the sparsity ρ=100.0%, and (b) is the average result of 100 times of Mont Carlo simulation scattering parameters when the sparsity ρ=50.0%;
FIG. 6 is a graph showing the average results of simulated RCS reconstruction at 25dB signal-to-noise ratio in one embodiment; wherein, (a) is the average result of 100 Mont Carlo simulation RCS reconstruction when the sparsity ρ=100.0%, and (b) is the average result of 100 Mont Carlo simulation RCS reconstruction when the sparsity ρ=50.0%;
FIG. 7 is a schematic diagram of a CAD model of a two-segment cone assembly target in one embodiment;
FIG. 8 is a schematic diagram of the result of parameter extraction and reconstruction of a two-segment cone assembly target at radar azimuth θ=1.05° in one embodiment; wherein, (a) is an extraction result of a scattering parameter, and (b) is a reconstruction result of an RCS sequence;
FIG. 9 is a schematic of the RCS sequence of a two-segment cone assembly target in one embodiment; wherein, (a) is an original RCS sequence, (b) is a reconstructed RCS sequence (sparsity ρ=50%);
fig. 10 is a schematic block diagram of a frequency hopping RCS reconstruction device based on GTD parameter extraction in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
In addition, the technical solutions of the embodiments of the present invention may be combined with each other, but it is necessary to be based on the fact that those skilled in the art can implement the technical solutions, and when the technical solutions are contradictory or cannot be implemented, it should be considered that the technical solutions are not combined, and are not within the scope of protection claimed by the present invention.
The traditional RCS reconstruction technology cannot be used for processing non-uniform incomplete RCS data, is limited by the Nyquist sampling theorem, and frequency dependent factors of the RCS reconstruction technology can introduce high-order approximation errors in series expansion. Furthermore, acquiring complete RCS data by measurement also requires a significant space-time overhead. Therefore, the research on how to effectively reduce the space-time overhead of darkroom RCS measurement and amplify radar RCS data has a great application prospect.
Aiming at the technical problem that the traditional RCS reconstruction technology cannot effectively process non-uniform incomplete RCS data, the invention provides a frequency hopping RCS reconstruction method based on GTD parameter extraction, the general design concept is to model radar target RCS data by combining a sparse reconstruction theory with a GTD scattering model, and the sparse representation of the radar target RCS data in a frequency hopping mode is given based on the GTD scattering model by utilizing the fact that the RCS data is sparse in a space domain, and the GTD scattering parameter extraction problem is converted into a l p norm optimization problem. And solving the l p norm optimization problem by further adopting an IRLS algorithm taking the l 2 norms as a criterion so as to realize the extraction of the GTD scattering parameters under different postures of the target. And finally, substituting the extracted scattering parameters into a GTD scattering model to realize RCS reconstruction.
Referring to fig. 1, in one aspect, the present invention provides a method for reconstructing a frequency hopping RCS based on GTD parameter extraction, including steps S12 to S18 as follows:
and S12, sparse modeling is carried out on darkroom step frequency RCS measurement data in a frequency hopping mode, and sparse representation of a backward scattered field GTD scattering model of the radar target is obtained.
It is understood that in the optical region, radar target electromagnetic scattering can be equivalently a coherent combination of multiple local strong scattering centers.
Referring to fig. 2, in some embodiments, step S12 may include the following processing steps S121 to S124:
S121, characterizing darkroom RCS measurement data based on the GTD scattering model.
It will be appreciated that a general GTD scattering model is:
Wherein y represents a backward scattering echo of a radar target, I represents the number of equivalent scattering centers, f 0 represents the initial frequency of a transmitted signal, c represents the propagation speed of electromagnetic waves, a i represents the scattering intensity of the ith scattering center, α i = { -1, -0.5,0,0.5,1} represents the scattering type of the ith scattering center, and r i represents the phase distance of the ith scattering center on a radar line.
Specifically, the step frequency darkroom RCS measurement system operates by sequentially transmitting coherent step frequency signals. Assuming that the frequency interval of the coherent step frequency signal is Δf, the frequency of the nth step frequency signal is f n＝f0 +nΔf, (n=0, 1, …, N-1). Therefore, the step frequency darkroom RCS measurement is equivalent to performing uniform discretization processing on the formula (1), so that the scattered data corresponding to the darkroom step frequency RCS measurement adopts sparse representation of the following GTD scattering model:
Where y n represents the RCS value of the radar target at frequency f n and Δf represents the frequency spacing of the coherent step frequency signal, then the frequency of the nth step frequency signal is f n＝f0 +nΔf, (n=0, 1, …, N-1). The bandwidth of the test signal is b=nΔf.
S122, performing uniform gridding processing on the maximum non-fuzzy distance of the radar.
It will be appreciated that conventional radars have a radial distance resolution of Δ r =c/2B. In order to obtain a more accurate scattering position distribution, a uniform gridding treatment is performed on the maximum blur-free distance R U =c/2Δf.
S123, deducing the GTD scattering model after uniform discretization according to the candidate grids of the scattering positions after uniform gridding treatment to obtain the GTD scattering model after gridding treatment.
Specifically, assuming that L is an improvement multiple of the distance resolution, the resolution after uniform meshing isCandidate grid of scattering locations is/>Wherein/>When the range resolution after gridding is high enough, all scattering centers can be approximately distributed on the discrete range grids, namely, the darkroom step frequency RCS measurement data is represented by adopting a GTD scattering model after discrete gridding, so that the formula (2) can be further deduced as follows:
therefore, the GTD scattering model after gridding is expressed by formula (3). y n denotes the RCS value of the radar target at frequency f n, r m denotes the scattering position corresponding to the mth range grid, Indicates the scattering intensity corresponding to the mth distance grid, α m indicates the scattering type corresponding to the mth distance grid, and LN indicates the number of discrete distance grids. When/>When this distance element is considered to be free of strong scattering distribution.
S124, sparse representation is carried out on RCS data in a frequency hopping RCS measurement mode by using the meshed GTD scattering model, and the RCS data is converted into a matrix form.
It will be appreciated that the swept mode of step-frequency darkroom RCS measurement requires full-band swept measurements for each pose of the target. Under the assumption that the bandwidth to be measured is b=nΔf, N measurements need to be made for each target pose. In the frequency hopping RCS measurement mode, Q frequency points are only required to be randomly extracted from the N step frequency points to be measured to carry out RCS measurement, and then the purpose of acquiring the full-band RCS is further achieved through an RCS reconstruction method.
Specifically, assuming that V is an index set of the actual measured frequency in the frequency hopping RCS measurement mode, V contains Q elements in total andFor convenience of description, a sparsity ρ is defined:
Since 5 different scattering types can be selected on the same distance grid r m Considering the influence of system additive noise, formula (3) is written in a matrix form:
y＝SΦx+w (3)
Wherein, The method is characterized in that the method is used for representing a column vector formed by the RCS to be measured in a random frequency hopping RCS measurement mode, namely a frequency hopping RCS sequence for short, and Q is used for representing the number of frequency points randomly extracted from N stepping frequency points to be measured.
The column vector, which is composed of all candidate scattering centers in the LN discrete distance grids, contains information such as scattering position, scattering intensity, scattering type and the like.
Represents the downsampling matrix corresponding to the frequency hopping RCS measurement mode, and/>V q denotes an index of the actual measured frequency point q in the frequency hopping RCS measurement mode.
Representing an additive complex gaussian white noise column vector. /(I)Represented is a redundant dictionary matrix:
Wherein, A sub-matrix is shown containing a total of 5 candidate scattering types. Thus,/>Can be further written as:
In the middle of Is the basis vector of the redundant dictionary matrix Φ. According to formula (3)/>Can be determined by the following formula (8):
for ease of analysis, note:
ψ＝SΦ (9)
Where ψ εQ×5 (LN-1) represents the random hopping dictionary matrix.
And S14, carrying out optimization solution on a matrix form of the GTD scattering model by adopting an IRLS algorithm to obtain a sparse optimal solution.
It can be understood that the column vector x represents sparse components of the radar target RCS sequence, and the GTD scattering parameter extraction can be realized by optimizing and solving x through the frequency hopping RCS sequence. Equation (5) is a linear underdetermined inverse problem, containing an infinite set of solutions. To avoid directly solving the l 0 norm optimization problem, the IRLS algorithm converts the l 0 norm optimization problem to a l p norm optimization problem:
In the middle of On the premise that p is more than or equal to 0 and less than or equal to 1, the formula (10) can be approximated by the norm of l 2, and further converted into the following optimization problem:
Assuming omega k (t) is the weighting coefficient updated by the t-th iteration of x k, take The method comprises the following steps:
both sides of formula (12) are multiplied by ω k (t) simultaneously and summed to obtain:
Equation (13) shows that the IRLS algorithm can quickly approximate the solution of the minimum l p norm optimization problem by optimizing the minimum l 2 norms. Therefore, the sparse optimal solution corresponding to the GTD scattering parameter can be obtained by alternately updating the objective function (11)
Referring to fig. 3, in some embodiments, the step S14 may specifically include the following processing steps S141 to S144:
S141, initializing the iteration initial times, regularization parameters and column vectors in a matrix form.
Specifically, when solving the solution vector x by adopting the IRLS algorithm, the iteration initial times t, the regularization parameter epsilon and the solution vector x need to be initialized at first:
s142, updating an iteration weighting coefficient matrix of the IRLS algorithm.
It will be appreciated that it can be determined theoretically according to formula (12)The corresponding weight coefficient ω k (t), but in order to avoid the occurrence of a disease state due to the matrix operation (ψ HW(t)ψ)-1) in equation (17) ω k (t) →0, a regularization parameter ε [0,1]. Specific, the weight coefficient ω k (t) may be determined by the following equation:
accordingly, the weight coefficient matrix W (t) may be defined by The composition is as follows:
Where epsilon (t-1) represents the regularization parameter at iteration (t-1), Representing the size of the kth element of column vector x (t-1) at iteration (t-1), LN represents the number of discrete distance grids.
S143, updating column vectors in a matrix form.
Specifically, in the regularization parameter updating mode, the t-th updating x (t) of the solution vector x can be realized by substituting the weight coefficient matrix in the formula (16) into the formula (17):
x(t)＝W(t)ψH(ψHW(t)ψ)-1y (17)
Where x (t) represents the column vector of the t-th iteration, ψ represents the random hopping dictionary matrix, (·) H represents the conjugate transpose operation, and(·) -1 represents the matrix inversion operation.
S144, updating regularization parameters, and if the ratio of the two norms of the adjacent solution vector difference values to the two norms of the solution vector is not more than the set solution precision or the iteration number is more than the maximum iteration number, terminating the iteration and taking the finally updated column vector as a sparse optimal solution.
Specifically, the updating mode of the regularization parameter is as follows:
Where ε (t) represents the regularization parameter for the t-th iteration. To accelerate the convergence speed of x (t), the regularization parameter ε (t) is updated according to equation (18).
Sequentially iterating the updating (15), (16), (17) and (18) and performing the operation of t=t+1 until the ratio of the two norms of the adjacent solution vector difference values to the two norms of the solution vector, i.e., |x (t)-x(t-1)||2/||x(t)||2 < Δ (Δ is the solution precision, which may be set to 10 -6, for example, or t+.gtoreq.t (T is the maximum number of iterations, which may be set to 300, for example, or not limited to 300)), terminating the iteration, then the finally updated x (end) may be regarded as a sparse optimal solution
S16, carrying out threshold truncation treatment on the sparse optimal solution, and then carrying out GTD scattering parameter extraction to obtain scattering parameters of an equivalent scattering center; the scattering parameters include scattering location, scattering intensity, and scattering type.
It can be appreciated that to ensure the effectiveness of GTD scattering parameter extraction, a sparse optimal solution is developedThreshold truncation is performed to remove virtual scattering centers due to sidelobes or noise interference.
Specifically, the threshold truncation processing method for the sparse optimal solution is as follows:
Wherein, Representing solution vector/>, after threshold truncationIs the k-th element of (a), eta represents the cutoff threshold,/>Representing the sparse optimal solution,/>And the kth element of the sparse optimal solution is represented. Thus,/>Any non-zero element in the spectrum is characterized by a scattering center.
S18, substituting the scattering parameters into a GTD scattering model to obtain an RCS reconstruction result under a darkroom RCS measurement frequency hopping mode.
It will be appreciated that the kth element in x is assumed to beCharacterizing the ith scattering center, the distance grid m where the scattering center is located is:
Where floor (-) represents a rounding down function and mod (-) represents a remainder function.
Correspondingly, its scattering positionIt can be determined that:
Its scattering intensity The method comprises the following steps:
According to the formulas (6), (7) and (8), the scattering type thereof It can be judged that:
Position of equivalent scattering center Intensity/>And type/>Each of which can be determined according to the formulas (21), (22) and (23), respectively. Extracting scattering parameter/>Substituting into (2) can realize RCS reconstruction under the darkroom RCS measurement frequency hopping mode:
According to the frequency hopping RCS reconstruction method based on GTD parameter extraction, the sparse reconstruction theory is combined with the GTD scattering model to model radar target RCS data, the fact that the RCS data are sparse in a space domain is utilized, sparse representation of the radar target RCS data in a frequency hopping mode is given based on the GTD scattering model, and the GTD scattering parameter extraction problem is converted into the l p norm optimization problem.
And further solving the l p norm by adopting an iterative weighted least square algorithm with the l 2 norm as a criterion, so as to realize the extraction of the GTD scattering parameters under different postures of the target. And finally, substituting the extracted scattering parameters into a GTD scattering model to realize RCS reconstruction. The GTD scattering parameters can be extracted under the condition that RCS data are non-uniform and incomplete, the purpose of RCS reconstruction based on GTD scattering parameter extraction under a frequency hopping mode is further achieved, and the method has high engineering application value on target scattering characteristics under the condition that analysis data are limited, the cycle of reducing darkroom step frequency RCS measurement and amplifying radar target RCS data.
In one embodiment, as shown in fig. 4, in order to more intuitively and fully describe the above-mentioned frequency hopping RCS reconstruction method based on the GTD parameter extraction, an example of performing a simulation experiment by applying the above-mentioned frequency hopping RCS reconstruction method based on the GTD parameter extraction is given below.
It should be noted that, the implementation examples given in the present specification are only illustrative, and not the only limitation of the specific embodiments of the present invention, and those skilled in the art may implement experiments and application analysis under different application scenarios by using the above-mentioned frequency hopping RCS reconstruction method based on GTD parameter extraction under the schematic illustration of the implementation examples provided by the present invention.
First, experimental tests were performed with simulation data.
Table 1 lists the scattering parameters of a simulated target that contains a total of 4 scattering centers. Assuming that the radar operates in the X-band, the initial frequency f 0 =8 GHz, the radar bandwidth is b=2 GHz, the step-by-step frequency interval is Δf=20 MHz, and the high-resolution improvement factor is set to l=4.
TABLE 1
Table 2 shows the mean value of simulation results of 100 Mont Carlo simulation scattering parameters at 25dB signal-to-noise ratio. Therefore, the application can realize the extraction of the scattering parameters under the conditions that the sparsity is rho=100.0% and rho=50.0%, and the extraction results of the scattering parameters under the two conditions are similar and approximate to the true value.
TABLE 2
Fig. 5 is the average result of 100 Mont Carlo simulated scattering parameters for a signal-to-noise ratio at 25 dB.
Fig. 5 (a) and (b) are average results of 100 Mont Carlo simulation scattering parameters at a signal-to-noise ratio of 25dB under the conditions of sparseness ρ=100.0% and ρ=50.0%, respectively. Therefore, the scattering positions, scattering intensities and scattering types of the S1, the S2, the S3, the S4 and the S5 under two sparse conditions are correctly estimated, and only the scattering intensities have certain deviation, so that the application is verified to be capable of extracting the GTD scattering parameters under the condition of non-uniformity and incomplete RCS data.
Fig. 6 (a) and (b) are average results of 100 Mont Carlo simulation RCS reconstructions with sparseness ρ=100.0% and ρ=50.0%, respectively, at a signal-to-noise ratio of 25 dB. Therefore, the reconstructed RCS sequence under two sparse conditions is highly matched with the real RCS sequence, and only certain deviation exists at the peak value and the peak valley, so that the RCS sequence can be reconstructed under the condition that the RCS data is non-uniform and incomplete.
Further, the electromagnetic calculation data are used for experimental verification.
FIG. 7 is a CAD model of an electromagnetic computing target of a two-segment cone assembly. The radar initial line of sight is parallel to the Z-axis and is directed in the nose cone direction of the electromagnetically calculated target. The radar simulation parameters were set as follows: initial frequency f 0 =8 GHz, radar bandwidth of B=2 GHz, step frequency interval of Δf=20 MHz, azimuth interval of Δθ=0.05° and azimuth accumulation angle of 0-6 DEG
Fig. 8 (a) shows the extraction result of the GTD scattering parameter under the condition of the degree of hydrophobicity ρ=50.0% at the radar azimuth θ=1.05°. It can be seen that the scattered echo mainly consists of 5 equivalent scattering centers, and the scattering position and scattering intensity of the scattered echo are matched with a radar one-dimensional profile (RP). In addition, the scattering components whose types of scattering are specular reflection and edge diffraction dominate, consistent with theoretical analysis. Fig. 8 (b) shows the result of the reconstruction of the RCS sequence under the condition of the degree of hydrophobicity ρ=50.0% at the radar azimuth θ=1.05°. It can be seen that the reconstructed RCS sequence is highly identical to the original RCS sequence, and only certain deviations exist at the peak and the peak valley, further verifying the effectiveness of the present application.
Fig. 9 (a) and (b) are the original RCS sequence of the two-segment cone assembly target and the RCS sequence after reconstruction under the condition of sparsity ρ=50%, respectively. It can be seen that the reconstructed RCS sequence is highly identical to the original RCS sequence, with a peak-signal-to-noise ratio (PSNR) of up to 39.0999dB. Both subjective visual effects and objective PSNR indices verify the effectiveness of the present application.
In conclusion, the application can extract the GTD scattering parameters under the condition of non-uniform incomplete RCS data, thereby realizing RCS reconstruction in a frequency hopping mode. The method has higher engineering application value on the target scattering property under the condition of limited analysis data, the cycle of reducing darkroom step frequency RCS measurement and the amplification of radar target RCS data.
It should be understood that, although the steps in the flowcharts of fig. 1 to 3 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Furthermore, at least a portion of the steps of fig. 1-3 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the sub-steps or stages are performed necessarily occur sequentially, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Referring to fig. 10, in an embodiment, a frequency hopping RCS reconstruction device 100 based on GTD parameter extraction is further provided, and includes a sparse processing module 11, an optimization solving module 13, a parameter extraction module 15, and a reconstruction processing module 17. The sparse processing module 11 is used for performing sparse modeling on darkroom step frequency RCS measurement data in a frequency hopping mode to obtain sparse representation of a backward scattered field GTD scattering model of a radar target. The optimization solution module 13 is used for performing optimization solution on the matrix form of the GTD scattering model by adopting an IRLS algorithm to obtain a sparse optimal solution. The parameter extraction module 15 is configured to perform threshold truncation processing on the sparse optimal solution, and then perform GTD scattering parameter extraction to obtain scattering parameters of the equivalent scattering center; the scattering parameters include scattering location, scattering intensity, and scattering type. The reconstruction processing module 17 is configured to substitute the scattering parameter into the GTD scattering model, and obtain an RCS reconstruction result in the darkroom RCS measurement frequency hopping mode.
The above-mentioned frequency hopping RCS reconstruction device 100 based on GTD parameter extraction models radar target RCS data by combining the sparse reconstruction theory with the GTD scattering model through cooperation of each module, and uses the fact that the RCS data is sparse in the airspace to give sparse representation of the radar target RCS data in the frequency hopping mode based on the GTD scattering model, and converts the GTD scattering parameter extraction problem into the i p norm optimization problem. And further solving the l p norm by adopting an iterative weighted least squares IRLS algorithm taking the l 2 norm as a criterion, and realizing the extraction of GTD scattering parameters under different postures of the target. And finally, substituting the extracted scattering parameters into a GTD scattering model to realize RCS reconstruction. The GTD scattering parameters can be extracted under the condition that RCS data are non-uniform and incomplete, the purpose of RCS reconstruction based on GTD scattering parameter extraction under a frequency hopping mode is further achieved, and the method has high engineering application value on target scattering characteristics under the condition that analysis data are limited, the cycle of reducing darkroom step frequency RCS measurement and amplifying radar target RCS data.
In an embodiment, each module of the sparse processing module 11 may be further configured to implement other corresponding sub-steps in each embodiment of the above-described frequency hopping RCS reconstruction method based on GTD parameter extraction.
In one embodiment, each module of the optimization solving module 13 may be further configured to implement other corresponding sub-steps in each embodiment of the above-described frequency hopping RCS reconstruction method based on GTD parameter extraction.
For specific limitations of the frequency hopping RCS reconstruction device 100 based on the GTD parameter extraction, reference may be made to the corresponding limitations of the frequency hopping RCS reconstruction method based on the GTD parameter extraction hereinabove, and the details are not repeated here. The above-described respective modules in the frequency hopping RCS reconstruction device 100 based on GTD parameter extraction may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be stored in a memory of the above device, or may be stored in software, so that the processor may call and execute operations corresponding to the above modules, where the above device may be, but is not limited to, various radar data computing and analyzing devices existing in the art.
In yet another aspect, a computer device is provided, including a memory storing a computer program and a processor, where the processor, when executing the computer program, may implement the steps of: sparse modeling is carried out on darkroom step frequency RCS measurement data in a frequency hopping mode, so that sparse representation of a backward scattered field GTD scattering model of a radar target is obtained; performing optimization solution on a matrix form of the GTD scattering model by adopting an IRLS algorithm to obtain a sparse optimal solution; after threshold cut-off processing is carried out on the sparse optimal solution, GTD scattering parameter extraction is carried out, and scattering parameters of an equivalent scattering center are obtained; the scattering parameters include scattering location, scattering intensity, and scattering type; substituting the scattering parameters into a GTD scattering model to obtain an RCS reconstruction result under a darkroom RCS measurement frequency hopping mode.
In one embodiment, the processor may further implement the steps or sub-steps added in the embodiments of the above-described frequency hopping RCS reconstruction method based on GTD parameter extraction when executing the computer program.
In yet another aspect, there is also provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of: sparse modeling is carried out on darkroom step frequency RCS measurement data in a frequency hopping mode, so that sparse representation of a backward scattered field GTD scattering model of a radar target is obtained; performing optimization solution on a matrix form of the GTD scattering model by adopting an IRLS algorithm to obtain a sparse optimal solution; after threshold cut-off processing is carried out on the sparse optimal solution, GTD scattering parameter extraction is carried out, and scattering parameters of an equivalent scattering center are obtained; the scattering parameters include scattering location, scattering intensity, and scattering type; substituting the scattering parameters into a GTD scattering model to obtain an RCS reconstruction result under a darkroom RCS measurement frequency hopping mode.
In one embodiment, the computer program when executed by the processor may further implement the steps or sub-steps added in the embodiments of the above-described method for reconstructing a frequency hopping RCS based on the extraction of GTD parameters.
Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of a computer program, which may be stored on a non-transitory computer readable storage medium, that when executed may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus dynamic random access memory (Rambus DRAM, RDRAM for short), and interface dynamic random access memory (DRDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it is possible for those skilled in the art to make several variations and modifications without departing from the spirit of the present application, which fall within the protection scope of the present application. The scope of the application is therefore intended to be covered by the appended claims.
Claims (9)
1. The frequency hopping RCS reconstruction method based on GTD parameter extraction is characterized by comprising the following steps:
sparse modeling is carried out on darkroom step frequency RCS measurement data in a frequency hopping mode, so that sparse representation of a backward scattered field GTD scattering model of a radar target is obtained;
performing optimization solution on the matrix form of the GTD scattering model by adopting an IRLS algorithm to obtain a sparse optimal solution;
Carrying out threshold cut-off treatment on the sparse optimal solution, and then carrying out GTD scattering parameter extraction to obtain scattering parameters of an equivalent scattering center; the scattering parameters include scattering location, scattering intensity, and scattering type;
substituting the scattering parameters into the GTD scattering model to obtain an RCS reconstruction result under a darkroom RCS measurement frequency hopping mode;
sparse modeling is performed on darkroom step frequency RCS measurement data in a frequency hopping mode, and a step of sparse representation of a backward scattered field GTD scattering model of a radar target is obtained, wherein the step comprises the following steps:
Characterizing darkroom step frequency RCS measurement data based on the GTD scattering model;
carrying out uniform gridding treatment on the maximum non-fuzzy distance of the radar;
deducing the GTD scattering model after uniform discretization according to candidate grids of the scattering positions after uniform gridding treatment to obtain the GTD scattering model after gridding;
And sparse representation is carried out on RCS data in a frequency hopping RCS measurement mode by using the meshed GTD scattering model, and the RCS data is converted into the matrix form.
2. The method for reconstructing the frequency hopping RCS based on GTD parameter extraction according to claim 1, wherein the step of performing optimization solution on the matrix form of the GTD scattering model by using IRLS algorithm to obtain a sparse optimal solution comprises:
Initializing the iteration initial times, regularization parameters and column vectors of the matrix form;
Updating an iteration weighting coefficient matrix of the IRLS algorithm;
updating column vectors in the matrix form;
Updating the regularization parameters, and if the ratio of the two norms of the adjacent solution vector difference values to the two norms of the solution vector does not exceed the set solution precision or the iteration number exceeds the maximum iteration number, terminating the iteration and taking the finally updated column vector as the sparse optimal solution.
3. The method for reconstructing frequency hopping RCS based on GTD parameter extraction according to claim 1 or 2, wherein the darkroom step frequency RCS measurement data uses sparse representation of the GTD model as follows:
Wherein y n represents the RCS value of the radar target at the frequency f n, I represents the number of equivalent scattering centers, f 0 represents the initial frequency of the transmitted signal, c represents the propagation speed of electromagnetic waves, a i represents the scattering intensity of the ith scattering center, α i = { -1, -0.5,0,0.5,1} represents the scattering type of the ith scattering center, r i represents the distance of the ith scattering center on the radar line, Δf represents the frequency interval of the coherent step frequency signal, and then the frequency of the nth step frequency signal is f n＝f0 +nΔf, (n=0, 1, …, N-1).
4. The method for reconstructing the frequency hopping RCS based on the GTD parameter extraction as set forth in claim 3, wherein said matrix form is:
y＝SΦx+w
Wherein, The method comprises the steps of representing a frequency hopping RCS sequence formed by the RCS to be measured in a random frequency hopping RCS measurement mode, wherein Q represents the number of frequency points randomly extracted from N stepping frequency points to be measured;
representing a column vector consisting of all candidate scattering centers within the LN discrete distance grid;
Represents the downsampling matrix corresponding to the frequency hopping RCS measurement mode, and/> V q represents the index of the actual measured frequency point q in the frequency hopping RCS measurement mode;
representing an additive complex gaussian white noise column vector;
Representing a redundant dictionary matrix:
Wherein, Representing the sub-matrix.
5. The method for reconstructing the frequency hopping RCS based on GTD parameter extraction as set forth in claim 3, wherein the updating manner of the iterative weighting coefficient matrix of the IRLS algorithm is as follows:
Wherein W (t) represents the iteration weighting coefficient matrix, the weighting coefficient of the t-th iteration Epsilon (t-1) represents the regularization parameter at the (t-1) th iteration, x k (t-1) represents the size of the kth column vector at the (t-1) th iteration, and LN represents the number of discrete distance grids;
The column vector updating mode of the matrix form is as follows:
x(t)＝W(t)ψH(ψHW(t)ψ)-1y
Wherein x (t) represents a column vector of the t-th iteration, ψ represents a random frequency hopping dictionary matrix, ψ=sΦ, S represents a downsampling matrix corresponding to a frequency hopping RCS measurement mode, Φ represents a redundant dictionary matrix, (. Cndot.) H represents a conjugate transpose operation, and (-) -1 represents a matrix inversion operation;
the regularization parameter updating mode is as follows:
where ε (t) represents the regularization parameter for the t-th iteration.
6. The method for reconstructing the frequency hopping RCS based on GTD parameter extraction as set forth in claim 5, wherein the threshold truncation process is performed on the sparse optimal solution by:
Wherein, Representing solution vector/>, after threshold truncationIs the k-th element of (a), eta represents the cutoff threshold,/>Representing the sparse optimal solution,/>And the kth element of the sparse optimal solution is represented.
7. A frequency hopping RCS reconstruction device based on GTD parameter extraction, comprising:
The sparse processing module is used for performing sparse modeling on darkroom step frequency RCS measurement data in a frequency hopping mode to obtain sparse representation of a backward scattered field GTD scattering model of a radar target; characterizing darkroom step frequency RCS measurement data based on the GTD scattering model, carrying out uniform gridding treatment on the radar maximum non-ambiguity distance, deducing the GTD scattering model after uniform discretization treatment according to candidate grids of scattering positions after uniform gridding treatment to obtain the GTD scattering model after gridding, carrying out sparse characterization on RCS data in a frequency hopping RCS measurement mode by using the GTD scattering model after gridding, and converting the RCS data into a matrix form;
The optimization solving module is used for carrying out optimization solving on the matrix form of the GTD scattering model by adopting an IRLS algorithm to obtain a sparse optimal solution;
the parameter extraction module is used for carrying out threshold cut-off processing on the sparse optimal solution and then carrying out GTD scattering parameter extraction to obtain scattering parameters of an equivalent scattering center; the scattering parameters include scattering location, scattering intensity, and scattering type;
And the reconstruction processing module is used for substituting the scattering parameters into the GTD scattering model to obtain an RCS reconstruction result under the darkroom RCS measurement frequency hopping mode.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the GTD parameter extraction based frequency hopping RCS reconstruction method according to any one of claims 1 to 6.
9. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor implements the steps of the GTD parameter extraction based frequency hopping RCS reconstruction method according to any one of claims 1 to 6.
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