Radar near-field target polarization characteristic analysis method based on vector Green function

Abstract

The invention relates to the technical field of radar target characteristic analysis, in particular to a radar near-field target polarization characteristic analysis method based on a vector Green function. Firstly, a target three-dimensional scattering point model is constructed, a coordinate-based scattering coefficient is defined, a full-polarization observation mode is designed, a near-field measurement matrix is calculated based on a vector Green function, and an observation model is constructed. Secondly, designing a fusion far-field constraint orthogonal matching pursuit (OMP) algorithm, realizing high-precision inversion of scattering coefficients, calculating a target far-field single-station RCS (HH / HV / VV mode) through dynamic polarization-based projection and phase compensation, and establishing a near-far field scattering characteristic mapping model; according to the method, near-field three-dimensional imaging and far-field polarization characteristic analysis are fused, the problem of insufficient cross polarization component recovery in a traditional method is solved, and the method has the advantages of high-resolution imaging, multi-physical field collaborative optimization, adaptive noise suppression and the like, and can be applied to the fields of stealth target recognition, radar cross section testing, unmanned aerial vehicle navigation and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of radar target characteristic analysis, and particularly to a method for analyzing the polarization characteristics of radar near-field targets based on the dyadic Green's function. Background Art

[0002] Traditional algorithms have one thing in common, that is, they are all calculated based on the near-field Green's function according to the electromagnetic field in the near field. The scalar Green's function is conditional on the parallelism between the electric field vector and the point source vector. Under the near-field condition, since a single point vector source cannot control the direction of the vector after it is emitted, the direction of the vector field is not fixed, but there are electromagnetic components in three dimensions. The same is true when the electromagnetic wave is scattered by the target and returns to the receiving antenna. Traditional radar target near-field imaging algorithms only compensate for the phase and amplitude information caused by the range history, that is, scalar compensation, which is not accurate; second, under different polarization transmitting and receiving modes, the scattering characteristics of the target are different. Therefore, it is not enough to describe the target characteristics with only one set of scattering functions. That is to say, for different polarization modes, the positions of the scattering points and the scattering functions may be different and need to be solved separately. This leads to the polarization mode corresponding to the measurement result determining the working mode of the measurement radar. For example, the HH polarization measurement mode can only obtain the target RCS of the HH polarization and it is difficult to obtain the target characteristics under other polarization modes. To sum up, the traditional near-field imaging model based on the scalar Green's function cannot fully compensate for the three-dimensional spherical wave effect, and it is also difficult to meet the requirements of high precision and high efficiency in near-field measurement. It is necessary to re-establish the measurement model based on the dyadic Green's function. Compared with the traditional imaging algorithm that uses the scalar Green's function for compensation, if compensation can be carried out during the imaging process based on the dyadic Green's function, the problems faced by the traditional imaging algorithm under the three-dimensional spherical wave effect can be solved more precisely, including the single observation dimension and the lack of electromagnetic information.

[0003] To sum up, the scattering information that can be reflected by these radar target three-dimensional imaging results is ultimately limited. Existing data processing means can often only invert local information of individual dimensions from the original echo. Moreover, in fact, the scattering performance of the target changes with frequency. All the above near-field imaging algorithms obtain the fuzzy scattering coefficients at a certain frequency band and cannot calculate the scattering performance separately for each frequency band. These existing technologies are also extremely difficult to recover complete electromagnetic scattering information, including RCS, polarization, and attitude, etc.

[0004] The technology proposed in this patent combines the latest mathematical and electromagnetic mechanisms and methods to achieve more complete inversion of electromagnetic scattering information. It can obtain the electromagnetic scattering information of the target under all polarization modes at each single frequency band in different postures, and realizes radar imaging and target characteristic analysis with higher precision. Summary of the Invention

[0005] In view of the problems existing in the above-mentioned prior art, the present invention is proposed.

[0006] Therefore, the present invention provides a method for analyzing the polarization characteristics of radar near-field targets based on the dyadic Green's function, which solves the problems of static solidification, high misjudgment rate, and inability to adapt to environmental changes in traditional permission management systems.

[0007] To solve the above technical problems, the present invention provides the following technical solution. A method for analyzing the polarization characteristics of radar near-field targets based on the dyadic Green's function includes: constructing a measurement matrix of electromagnetic propagation characteristics in the near field using the dyadic Green's function, and forming a target measurement model with the actual measured echo data.

[0008] Implementing three-dimensional near-field imaging of radar targets using the OMP algorithm with far-field constraints.

[0009] Analysis of target characteristics in all polarization modes.

[0010] As a preferred embodiment of the method for analyzing the polarization characteristics of radar near-field targets based on the dyadic Green's function according to the present invention, wherein: the measurement matrix includes, according to the measurement mode and target attitude, based on the dyadic Green's function, describes the change process of the polarized electric field from the source-target-receiving antenna in the near field, describes the linear relationship between the polarized electric field at the single-station receiving antenna and the coordinate basis scattering coefficients of the scattering points on the target, and describes the complex near-field electric field propagation and scattering process as a large-scale equation group model.

[0011] As a preferred embodiment of the method for analyzing the polarization characteristics of radar near-field targets based on the dyadic Green's function according to the present invention, wherein: the formation of the target measurement model is expressed as

[0012] E = Θ·Ψ + β

[0013] where E is the received electric field, Θ is defined as the observation matrix, and Ψ is the scattering coefficient vector composed of ψ.

[0014] As a preferred embodiment of the method for analyzing the polarization characteristics of radar near-field targets based on the dyadic Green's function according to the present invention, wherein: the implementation of three-dimensional near-field imaging of radar targets includes preprocessing, iterative update solution, and postprocessing.

[0015] As a preferred embodiment of the method for analyzing the polarization characteristics of radar near-field targets based on the dyadic Green's function according to the present invention, wherein: the preprocessing is expressed as

[0016] Adding far-field constraints to the OMP algorithm. Suppose there are Q measurement points for the target far-field RCS, and the coordinate of the measurement point numbered q is r′ Fq , constructing the target far-field electric field measurement matrix Θ Far , expressed as:

[0017]

[0018] Wherein:

[0019]

[0020] Wherein, h = |h|, v = |v|, h and v are dynamic polarization bases. Assuming that the far-field observation point is close to the z-axis and z is the Cartesian coordinate basis, there is:

[0021]

[0022] Then the far-field electric field of the target at the far-field RCS measurement point is expressed as:

[0023] E Far = Θ Far · Ψ + β

[0024] Perform column normalization on the near-field measurement matrix:

[0025]

[0026] Wherein, θ j is the j-th column of Θ. Perform column energy normalization on the far-field matrix:

[0027]

[0028] Wherein, θ far,j is the j-th column of Θ Far .

[0029] As a preferred solution of a method for analyzing the polarization characteristics of radar near-field targets based on the dyadic Green's function according to the present invention, wherein: the iterative update solution is expressed as,

[0030] Calculate the near-field residual correlation:

[0031]

[0032] Wherein, the superscript H represents the conjugate transpose, t represents the number of iterations, and Res (t) represents the residual vector of the t-th iteration. Calculate the objective function that combines the near-field correlation and the far-field energy:

[0033]

[0034] Select the pixel point that makes the greatest comprehensive contribution to the near-field residual and the far-field radiation:

[0035]

[0036] Add the selected atom to the support set:

[0037] S (t) = S (t-1) ∪ {j *}

[0038] Solve the least - squares problem within the support set to optimize the estimated value of the scattering coefficient under the current support set, expressed as:

[0039]

[0040] Update the residual, expressed as:

[0041]

[0042] When the preset termination condition is reached or the result converges significantly, the iteration can be terminated. Assume the maximum number of iterations is t max .

[0043] As a preferred solution of a method for analyzing the polarization characteristics of radar near - field targets based on the dyadic Green's function according to the present invention, wherein: the iterative update solution is expressed as,

[0044] Restore the normalized coefficient to the physical dimension:

[0045]

[0046] Realize the three - dimensional near - field imaging of radar targets based on the OMP algorithm with far - field constraints.

[0047] As a preferred solution of a method for analyzing the polarization characteristics of radar near - field targets based on the dyadic Green's function according to the present invention, wherein: the post - processing includes,

[0048] According to the distribution of the obtained spatial scattering points and their corresponding six scattering coefficients, the HH, HV, and VV polarization RCSs of the target at the far - field measurement points can be obtained. Suppose there are N effective scattering points, and the distance from the observation point r' Fq to the corresponding far - field observation point is r' Fq and the distance from the far - field observation point to the target center is R Far , then the far - field RCS of the target can be expressed as:

[0049]

[0050] A computer device includes a memory and a processor. The memory stores a computer program. It is characterized in that when the processor executes the computer program, it implements the steps of any one of the methods for analyzing the polarization characteristics of radar near - field targets based on the dyadic Green's function.

[0051] A computer-readable storage medium stores a computer program thereon. The computer program, when executed by a processor, implements the steps of any one of the methods for analyzing the polarization characteristics of radar near-field targets based on the dyadic Green's function.

[0052] Advantages of the present invention: First, full-polarization electromagnetic scattering information inversion. Traditional near-field imaging usually only inverses the scattering coefficients of a single polarization (such as HH or VV), ignoring the cross-polarization components (such as HV, VH); far-field RCS calculation relies on empirical formulas or simplified models and cannot accurately reflect the polarization coupling characteristics of complex targets. Full-polarization modeling: By constructing a 6-component scattering coefficient vector, it completely characterizes the anisotropic electromagnetic response of the target; polarization projection mechanism: Dynamically generates horizontal (H) and vertical (V) polarization bases in far-field RCS calculation to accurately calculate the scattering fields of three modes, namely HH, HV, and VV; cross-polarization retention: Retains the cross-polarization terms to inverse the scattering characteristics of the target for any polarized incident wave.

[0053] Second, near-field to far-field joint constraint optimization. Near-field imaging and far-field RCS inversion are usually processed independently, resulting in the inability to ensure the consistency of far-field radiation characteristics for near-field reconstruction results; far-field RCS inversion relies on the ideal point scattering assumption, ignoring the influence of near-field interactions on complex targets. Joint optimization framework: Introduces far-field energy constraints in the OMP iteration, and balances the near-field residual correlation and far-field radiation energy through dynamic weights; physical consistency enhancement: The inverted scattering coefficients simultaneously satisfy the minimization of near-field electric field reconstruction error and the optimality of far-field RCS radiation characteristics; adaptive weight adjustment: Focuses on near-field correlation (high α value) in the initial stage of iteration and gradually enhances far-field constraints (low α value) in the later stage to avoid local optima.

[0054] Third, high-precision three-dimensional imaging and noise robustness. Traditional imaging algorithms (such as the BP algorithm) are limited by the Rayleigh limit, with low resolution and sensitivity to noise; sparse reconstruction methods (such as the standard OMP) do not consider far-field physical constraints and are easily interfered by noise, resulting in false alarms. Dyadic Green's function modeling: Accurately calculates the propagation effects of near-field spherical waves (including amplitude attenuation and phase delay), avoiding the errors of plane wave approximation; column normalization preprocessing: Eliminates the amplitude deviation caused by distance differences between different pixels, improving the stability of the algorithm.

[0055] Fourth: High scalability. Full-polarization imaging requires multiple measurements of different polarization combinations, resulting in a large amount of data and high computational complexity. The far-field RCS calculation relies on point-by-point numerical integration or high-frequency approximations (such as PO, PTD), making it difficult to handle complex targets. By constructing a measurement matrix using the dyadic Green's function, the near-field electromagnetic phenomenon is scientifically and completely reproduced, which can handle the situation where the variable polarization effect is obvious due to the overly large target, resulting in inaccurate analysis of target characteristics, and is suitable for large-scale observation directions; Applicability of a single frequency point: Inverting the full-polarization scattering coefficient based on single-frequency data provides a basis for broadband signal processing (frequency-domain synthesis expansion).

[0056] Fifth: Close to actual engineering and high observation adaptability. Fixed polarization bases (such as axis-aligned) cannot adapt to different observation directions, resulting in polarization projection errors. The far-field RCS measurement relies on a mechanical turntable or an array antenna, with poor flexibility. Calculate the H / V polarization bases in real time according to the observation direction (based on cross-product orthogonalization) to ensure the accuracy of polarization projection at any angle. Adaptive line-of-sight direction: Automatically switch to the axis-aligned mode when the radar is near the z-axis to avoid numerical instability.

[0057] Through the full-polarization joint optimization framework that integrates near-field three-dimensional imaging and far-field RCS inversion, the present invention breaks through the bottlenecks of traditional methods in terms of information integrity, physical consistency, and noise robustness, providing a high-precision and high-efficiency solution for the electromagnetic characteristic analysis and identification of complex targets. Description of the Drawings

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0059] Figure 1 Schematic diagram of the near-field measurement mode of an electrically large target for a method for analyzing the polarization characteristics of a radar near-field target based on the dyadic Green's function provided by an embodiment of the present invention.

[0060] Figure 2 Schematic diagram of the incidence and scattering of electromagnetic waves in the near field for a method for analyzing the polarization characteristics of a radar near-field target based on the dyadic Green's function provided by an embodiment of the present invention.

[0061] Figure 3 Schematic diagram of a measurement example for a method for analyzing the polarization characteristics of a radar near-field target based on the dyadic Green's function provided by an embodiment of the present invention.

[0062] Figure 4The target (a) HH, (b) HV, and (c) VV radar images of a method for analyzing the polarization characteristics of radar near-field targets based on the dyadic Green's function provided by an embodiment of the present invention.

[0063] Figure 5 The schematic diagram of the near-field scattered electric field of the target of a method for analyzing the polarization characteristics of radar near-field targets based on the dyadic Green's function provided by an embodiment of the present invention.

[0064] Figure 6 The target (a) HH, (b) HV, and (c) VV far-field RCS of a method for analyzing the polarization characteristics of radar near-field targets based on the dyadic Green's function provided by an embodiment of the present invention. Detailed implementation manners

[0065] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the detailed implementation manners of the present invention with reference to the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0066] Embodiment 1

[0067] Referring to Figures 1-6 , which is the first embodiment of the present invention. This embodiment provides a method for analyzing the polarization characteristics of radar near-field targets based on the dyadic Green's function.

[0068] For high-value targets, especially military targets, it is very important to diagnose and measure their far-field radar cross-section (RCS) during production or maintenance. However, high transmission power and large land area mean that far-field measurement has the disadvantages of high cost, long time consumption, and vulnerability to environmental interference. Therefore, measuring the far-field RCS of indoor or short-range targets through near-field to far-field transformation has received attention. In the application of target characteristic analysis, the working mode of the measurement equipment, especially the polarization mode, should be considered. When choosing the polarization mode of the measurement radar system, the detection performance, anti-interference ability, and system complexity are mainly considered. Most measurement radars adopt a single polarization mode because this can simplify the design. Generally speaking, the obtained target RCS is usually also under a specific polarization mode, and the most common ones are HH polarization or VV polarization. With the development of stealth technology, more and more measured targets have both electrically large size and low scattering characteristics. Therefore, in order to obtain the single polarization RCS of the target, dual polarization or multi-polarization technology should be considered to more effectively detect stealth targets, especially in target recognition under low signal-to-noise ratio and complex backgrounds. The radar transmits and receives two different polarizations (usually HH and VV, or HH, HV, etc.) to obtain richer target characteristic information and improve the detection ability for complex environments and stealth targets. However, this system has higher complexity and greater cost, so it is mainly used in specific high-demand scenarios. Therefore, considering the practicality of the proposed technology, the present invention uses the method of single polarization near-field measurement to obtain the radar images and far-field RCS of the target under different polarizations.

[0069] S1: Obtain the near-field echo of the target using the measurement mode of the antenna array surface in the near field, and construct a target measurement model based on the dyadic Green's function according to the measurement mode.

[0070] The measurement mode is as Figure 1 shown. The present invention uses the point scattering model to describe the measured target. The spatial size of the target is D. Let the scattering distribution function of the target be Γ(r p , k). The antenna is located on a planar array with a total of M independent array elements. The coordinate of the array element numbered m is defined as r′ m , with the center of the target as the origin of the coordinate system. The target is fixed in one posture. The M antenna array elements transmit and receive one by one, that is, each array element transmits and receives one by one. It can be considered that when the measured target is stationary, there are echoes under M measurement angles. The single-station antenna uses linear polarization for transmission and reception, following the backscatter alignment (BSA) convention. The radar illumination wave and the target scattered wave are defined on the same polarization basis, and for the single-station backscatter measurement mode described in this measurement model, the same coordinate system is used for transmission and reception, and it is agreed that the basis vectors of the Cartesian coordinate system in the measurement model are consistent with the polarization basis vectors at the transmitting and receiving antennas, and the antenna pointing is in the negative z-axis direction.

[0071] Now, a target measurement model based on the dyadic Green's function is constructed according to the measurement mode. The free-space electric dyadic Green's function is as follows:

[0072]

[0073] Let the transmitting polarization mode of element m be For example, when transmitting a horizontally polarized wave, Take (1, 0, 0). The electric field at the transmitting element and its components in each direction can be obtained:

[0074]

[0075] Among them, U tr is a factor depending on the type of antenna used and the transmitting power of the antenna. The superscript "tr" represents transmission, represents the unit vector in the x direction. Then the incident field of the element electric field at this place incident on the SC can be expressed as:

[0076]

[0077] The formula and tensor form of the dyadic Green's function are:

[0078]

[0079] For the convenience of expression, let:

[0080]

[0081] Among them, g(k, r′ m , r p ) is the scalar Green's function. According to mathematical derivation, the analytical expressions of all components of the dyadic Green's function can be obtained. For the sake of concise expression, it is written as:

[0082]

[0083] Among them,

[0084]

[0085] Among them, C1 and C2 are quantities related only to the distance and the wave number, expressed as:

[0086]

[0087] Each component of the dyadic Green's function is actually a correction to the scalar Green's function using the distance between the source and the scattering center and the wave number. It can completely and accurately reflect the changes in amplitude, phase, and polarization direction generated during the near-field propagation of electromagnetic waves.

[0088] In the imaging model based on the dyadic Green's function, a single numerical value cannot reflect the variable polarization characteristics of the scattering center. Therefore, a tensor is defined to describe the scattering characteristics of the scattering center for the electric field components in different directions:

[0089]

[0090] There are nine variable polarization scattering characteristic coefficients in this tensor. Taking ψ xy (k,r p ) as an example, this scattering coefficient describes the ability of the scattering center on the target to scatter the electric field vector in the x direction into the electric field vector in the y direction. However, according to the reciprocity principle, (16) is symmetric about the main diagonal. Therefore, only six scattering coefficients need to be solved actually.

[0091] After the incident electromagnetic wave is scattered by this SC, its amplitude, polarization direction, and phase have all changed. Then the electric field reaching the array element at r′ m is:

[0092]

[0093] Among them, represents the dyadic Green's function along r p to r′ m . According to the derivation of the analytical formula of the dyadic Green's function, it is equal to .

[0094] Suppose the receiving polarization mode of the antenna is By gathering all the electric fields scattered by the scattering center at the receiving array element, the scattered electric field obtained by the receiving array element, that is, the scattering model expression based on the dyadic Green's function, can be obtained as:

[0095]

[0096] According to the measurement model, considering that the antenna array surface cannot receive the electric field vector in the z direction, the electric field components at the receiving antenna are and The transmitting antenna uses a single-polarization transmitting and single-polarization receiving mode, and it is assumed that the horizontal and vertical polarization transmitting powers are the same. Since the polarization basis at the array element is consistent with the basis of the spatial Cartesian coordinate system, the received field does not need to be expressed as a three-dimensional vector, but the component corresponding to the receiving polarization direction in the three-dimensional field. The fields actually received by the antenna in the four modes of H-H, H-V, V-H, and V-V (H-H represents horizontal polarization transmitting and horizontal polarization receiving) can be obtained. For the convenience of expression, the wavenumber in the variable is omitted, and the variable polarization scattering characteristic coefficients to be solved are written as a vector:

[0097] ψ(r p )=(ψ xx (rp ), ψ xy (r p ), ψ xz (r p ), ψ yy (r p ), ψ yz (r p ), ψ zz (r p )) T , #(12)

[0098] where the superscript "T" represents the transpose of a vector. Ignoring the constant factor U tr , after adding complex Gaussian noise β, the target measurement model based on the dyadic Green's function is defined as a linear system:

[0099] E = Θ·Ψ + β, #(13)

[0100] where E is the received electric field, Θ is defined as the observation matrix, which reflects the process of signal propagation and scattering. Since broadband signal imaging is generally used in actual measurements, E is the result at different antenna positions and different wavenumbers, and Ψ is the scattering coefficient vector composed of ψ. Figure 2 Describes the target measurement model based on the dyadic Green's function.

[0101] Because the scattering coefficient not only has spatial-varying characteristics but also changes with frequency, a block at a certain frequency point of the target measurement model is intercepted, and the expansion form is as follows:

[0102]

[0103] where the subscript represents the transmit-receive polarization mode. The expressions for the H-V and V-H modes are the same and do not need to be repeated. The observation matrix Θ is composed of the observation vector Φ, and Φ contains the position information of the scattering center and the antenna:

[0104]

[0105] This model can be understood as follows: On the premise of default backscattering, when the polarization basis is consistent with the three-dimensional image coordinate basis, if the signal emitted by the antenna is in the H polarization mode, according to the dyadic Green's function, when the scattering point on the target deviates from the radar-target center line of sight axis, when the incident wave arrives at this scattering center in a spherical form, the electric field vector of the incident wave is no longer the polarization mode at the time of emission, but has three components in the x, y, and z directions. After the incident wave is scattered by the scattering point, the new electric field vector also corresponds to the components in three directions. The relationship between the two electric field vectors can be solved through the target polarization scattering matrix. When the scattered wave returns to the receiving antenna in a spherical form, the three components of its electric field vector also change. The component in the x direction corresponds to the result obtained by the H receiving polarization form of the receiving antenna, the component in the y direction corresponds to the result obtained by the V receiving polarization form of the receiving antenna, and the component in the z direction cannot be measured.

[0106] S2: OMP three-dimensional imaging integrated with far-field constraints.

[0107] The coefficients of Θ are composed of non-linear combinations of range histories, so Ψ cannot be solved through the process of traditional algorithms. The number of rows of Θ is 3 times the number of array elements, and the number of columns is 6 times the number of pixels in the target space image. Under the requirement of high resolution, the number of pixels in the target space image is much larger than the number of array elements, Θ is an underdetermined matrix, and Ψ has infinitely many solutions. Now, the OMP algorithm with far-field constraints is used to implement three-dimensional near-field imaging of radar targets. The imaging steps are divided into three steps: preprocessing, iterative update of the solution, and postprocessing.

[0108] 1. Preprocessing. In order for the inverted scattering coefficients to be applicable to both near and far fields, a far-field constraint needs to be added to the OMP algorithm. Suppose there are Q measurement points for the target far-field RCS, and the coordinates of the measurement point numbered q are r′ Fq , and construct the target far-field electric field measurement matrix Θ Far :

[0109]

[0110] Where:

[0111]

[0112] Among them, h = |h|, v = |v|, h and v are dynamic polarization bases. Assuming that the far-field observation point is close to the z-axis and z is the Cartesian coordinate basis, there is:

[0113]

[0114] Then the far-field electric field of the target at the far-field RCS measurement point can be expressed as:

[0115] E Far = Θ Far ·Ψ + β#(26)

[0116] Next, perform column normalization on the near-field measurement matrix:

[0117]

[0118] where θ j is the j-th column of Θ. The purpose of this step is to eliminate the amplitude variation caused by the distance difference among different pixels. Then, perform column energy normalization on the far-field matrix:

[0119]

[0120] where θ far,j is the j-th column of Θ Far . This step can quantify the radiation energy distribution of pixels in all far-field directions.

[0121] 2. Iteratively update the solution vector. The iterative steps are subdivided into 4 steps:

[0122] (1) Atom selection.

[0123] First, calculate the near-field residual correlation:

[0124]

[0125] where the superscript H represents the conjugate transpose, t represents the iteration number, Res (t) represents the residual vector at the t-th iteration, representing the error between the near-field electric field calculated based on the currently estimated scattering coefficients and the true measured near-field electric field. Then, calculate the objective function that combines the near-field correlation (degree of matching with the residual) and the far-field energy:

[0126]

[0127] Then, select the atom that maximizes the objective function, that is, select the pixel point that makes the largest comprehensive contribution to the near-field residual and the far-field radiation:

[0128]

[0129] (2) Update the support set. Add the selected atom to the support set:

[0130] S (t) = S (t-1) ∪{j *} #(24)

[0131] (3) Least squares update. Solve the least squares problem within the support set to optimize the estimated value of the scattering coefficients under the current support set

[0132]

[0133] (4) Update the residual:

[0134]

[0135] When the preset termination condition is reached or the result converges significantly, the iteration can be terminated. Assume that the maximum number of iterations is t max 。

[0136] 3. Post-processing. Because of the preprocessing, after obtaining the solution vector, the normalized coefficients need to be restored to the physical dimensions:

[0137]

[0138] Thus, the three-dimensional near-field imaging of radar targets based on the OMP algorithm with far-field constraints is realized.

[0139] S3: Analysis of target characteristics under all polarization modes.

[0140] The third step of the invention: Analysis of target characteristics under all polarization modes.

[0141] According to the distribution of the spatial scattering points that have been obtained and their corresponding six scattering coefficients, the HH, HV, and VV polarization RCSs of the target at the far-field measurement points can be calculated. Suppose there are N effective scattering points and the observation point is r′ Fq The corresponding distance of the far-field observation point is r′ Fq The distance from the target center is R Far , then the far-field RCS of the target can be expressed as:

[0142]

[0143] The above is the principle and process of the present invention.

[0144] Example demonstration of the present invention (MATLAB):

[0145] Suppose there is an aircraft target composed of 100 scattering centers in the target space, with a size of about 16 meters. Set the scattering coefficients of each scattering point of the target to 0.005, 0.001, 0.000001, 0.005, 0.000001, and 0.000001 respectively. That is to say, when observed from the z-axis perspective, the HH and VV polarization images should be similar, and the intensity is slightly higher than the HV polarization image. Set up an array surface 30 meters away from its center, with the size of the array surface being 4m * 3m and 336 array elements evenly distributed. Each array element is a pair of linearly polarized transmitting and receiving antennas, adopting the single-shot and single-receive mode. Each array element works in turn, emitting H-polarized electromagnetic waves and V-polarized electromagnetic waves (frequency 8 GHz), recording the H- and V-polarized received waves, and adding 30 dB complex Gaussian noise (in line with actual engineering). Figure 3 It is a diagram of the target measurement model.

[0146] Assume that the unit length of the blank image pixel is 2 cm, and a measurement matrix is obtained. Its condition number (the maximum singular value divided by the minimum singular value) is 1.0343, indicating that the singular value distribution of the measurement matrix is relatively uniform, the matrix is relatively stable, and the algorithm has a stable solution. According to the imaging algorithm proposed by the present invention, the HH, HV, and VV polarization radar images of the target under the z-axis view are as Figure 4 shown. It can be seen that the algorithm realizes high-precision polarimetric radar imaging of the target.

[0147] Next, through the obtained scattering coefficient vector, the scattered electric field of the target in the near field and the far-field RCS are inverted. 121 observation points are taken at the pitch angle and azimuth angle of ±5° in the z-axis direction. It is assumed that the distance of all far-field observation points from the target center is 10,000 meters. The results are as Figure 5 and Figure 6 shown. It can be seen that whether it is the near-field scattered electric field or the far-field RCS, the inversion values are close to the reference values, achieving the purpose of high-precision target characteristic analysis.

[0148] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not restrictive. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

[0149] Embodiment 2

[0150] The second embodiment of the present invention provides a method for analyzing the polarization characteristics of a radar near-field target based on the dyadic Green's function.

[0151] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the essence of the technical solution of the present invention, or the part that contributes to the prior art, or the part of this technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0152] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definable sequence list of executable instructions for implementing logical functions, which can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in combination with an instruction execution system, apparatus, or device.

[0153] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, a computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or otherwise processing it as appropriate, and then storing it in a computer memory.

[0154] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0155] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A method for analyzing the polarization characteristics of radar near-field targets based on the dyadic Green's function, characterized in that: include, The dyadic Green's function is used to construct a measurement matrix that strictly describes the electromagnetic propagation characteristics in the near field, and together with the actual measured echo data, a target measurement model is formed. This model is actually a mathematical model of an underdetermined set of equations. Taking advantage of the high sparsity of radar images, the OMP algorithm with far-field constraints is used to solve the mathematical model, obtaining a high-precision solution for the scattering coefficient and implementing three-dimensional near-field imaging of radar targets. The far-field constraints in the algorithm ensure that the scattering coefficient can accurately invert the far-field characteristics of the target. Based on the scattering coefficient obtained by the OMP algorithm, the far-field RCS of the target in all polarization modes is calculated through dynamic polarization basis projection and phase compensation, completing the target characteristic analysis in all polarization modes.

2. The method for analyzing the polarization characteristics of radar near-field targets based on the dyadic Green's function according to claim 1, characterized in that: The measurement matrix includes, based on the measurement mode and target posture, a description of the change process of the polarized electric field through the source-target-receiving antenna in the near field based on the dyadic Green's function, a description of the linear relationship between the polarized electric field at the single-station receiving antenna and the coordinate basis scattering coefficient of the scattering point on the target, and a description of the complex near-field electric field propagation and scattering process as a large-scale equation model.

3. The method for analyzing the polarization characteristics of radar near-field targets based on the dyadic Green's function according to claim 2, wherein: The target measurement model is expressed as: E=Θ·Ψ+β Where E is the received electric field, Θ is defined as the observation matrix, and Ψ is the scattering coefficient vector composed of ψ.

4. The method for analyzing the polarization characteristics of radar near-field targets based on the dyadic Green's function according to claim 3, characterized in that: The implementation of radar target three-dimensional near-field imaging includes preprocessing, iterative updating solution and post-processing.

5. The method for analyzing the polarization characteristics of a radar near-field target based on a dyadic Green's function according to claim 4, wherein: The preprocessing is expressed as, Add a far-field constraint to the OMP algorithm. Suppose there are Q target far-field RCS measurement points, and the coordinates of the measurement point numbered q are r′ Fq , and construct the target far-field electric field measurement matrix Θ Far , which is expressed as: in: Where h = |h|, v = |v|, h and v are dynamic polarization bases. Assuming that the far-field observation point is close to the z-axis and z is the Cartesian coordinate basis, we have: Then the far-field electric field of the target at the far-field RCS measurement point is expressed as: E Far =Θ Far ·P+b Perform column normalization on the near-field measurement matrix: Among them, θ j is the jth column of Θ, and the far-field matrix is column-normalized: where θ far,j is the j-th column of Θ Far .

6. The method for analyzing the polarization characteristics of a radar near-field target based on a dyadic Green's function according to claim 5, wherein: The iterative update solution is expressed as, Calculate the near-field residual correlation: Among them, the superscript H represents the conjugate transpose, t represents the number of iterations, and Res (t) Represents the residual vector of the t-th iteration, and calculates the objective function of the comprehensive near-field correlation and far-field energy: Select the pixel that has the largest combined contribution to the near-field residual and far-field radiation: Add the selected atoms to the support set: S (t) = S (t-1) ∪ {j *} Solve the least squares problem within the support set to optimize the estimated value of the scattering coefficient under the current support set, which can be expressed as: Update the residual, expressed as: When the preset termination conditions are reached or the results converge significantly, the iteration can be exited. Assuming that the maximum number of iterations is t max .

7. The method for analyzing the polarization characteristics of radar near-field targets based on the dyadic Green's function according to claim 6, wherein: The iterative update solution is expressed as, Restore the normalized coefficients to physical dimensions: Implementation of three-dimensional near-field imaging of radar targets based on OMP algorithm with far-field constraints.

8. The method for analyzing the polarization characteristics of radar near-field targets based on the dyadic Green's function according to claim 7, wherein: The post-processing includes: According to the obtained distribution of spatial scattering points and their corresponding 6 scattering coefficients, the HH, HV and VV polarization RCS of the target at the far-field measurement point can be calculated. Assume that there are N effective scattering points and the observation point r′ Fq The corresponding far-field observation point distance is r′ Fq The distance to the target center is R Far , then the far-field RCS of the target can be expressed as:

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium, on which a computer program is stored, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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