Electromagnetic inverse scattering imaging method based on plane vortex electromagnetic waves

Abstract

The invention discloses an electromagnetic inverse scattering imaging method based on plane vortex electromagnetic waves, which comprises the following steps: 1, building a test platform, and ensuring that the positions of all equipment and devices meet measurement requirements; 2, the plane vortex electromagnetic wave transmitting module generates incident waves and transmits the incident waves to the interior of an imaging area; 3, sequentially transmitting electromagnetic waves to the imaging area by transmitting antennas uniformly distributed around the imaging area, receiving echo signals by an antenna receiving module after medium scattering and reflection, extracting amplitude and phase information for splicing and combining to obtain a scattering field matrix, and constructing a data set by taking a real dielectric constant distribution image as a label; and 4, training a deep learning imaging module by using the data set to obtain a model with the capability of reconstructing the dielectric constant distribution in the to-be-imaged area. The dielectric constant distribution information in the to-be-imaged area can be reconstructed with higher image resolution, higher environmental interference resistance and higher calculation speed, so that the imaging precision can be improved.

Description

Technical Field

[0001] The invention belongs to the field of electromagnetic backscattering imaging, and specifically is an electromagnetic backscattering imaging method based on plane vortex electromagnetic waves. Background Art

[0002] Plane vortex electromagnetic waves are a special form of vortex electromagnetic waves. Plane vortex electromagnetic waves with different mode numbers can propagate laterally in the same direction at the same time, thus solving the problem of different divergence angles of vortex electromagnetic waves with different modes. This lays the foundation for the application of plane vortex electromagnetic waves in electromagnetic inverse scattering imaging problems.

[0003] Existing electromagnetic inverse scattering imaging methods include Born iteration method, contrast source inversion method, subspace optimization algorithm and deep learning related algorithms.

[0004] The Born iteration method first constructs an initial model by making a preliminary estimate of the dielectric constant distribution in the imaging area. Then, the initial model is continuously optimized using an iterative method to obtain more accurate results. However, the Born iteration method performs poorly under strong scattering conditions and is highly dependent on the initial model. In addition, the iterative process requires a large amount of calculations, resulting in a long calculation time, which makes it difficult to meet real-time requirements.

[0005] The contrast source inversion method is an optimization method based on contrast sources. It gradually optimizes the imaging results by minimizing the error between the observed data and the simulated data. The contrast source inversion method has a rigorous theoretical basis and performs well under weak scattering conditions. However, the contrast source inversion method is sensitive to the choice of initial values ​​and is prone to fall into local optimal solutions under strong scattering conditions. In addition, the high computational complexity leads to limited applicability in real-time imaging scenarios.

[0006] The subspace optimization algorithm reduces computational complexity by converting the imaging problem into an optimization problem in a low-dimensional subspace. The subspace optimization algorithm method has high mathematical rigor in theory and can provide better imaging effects under weak scattering conditions. However, the performance of the subspace optimization algorithm under strong scattering conditions is not as expected, and the selection of subspaces is required to be high, and there are many transmission links, which increases the complexity of the algorithm and the computational error.

[0007] Compared with traditional algorithms, the deep learning method using U-Net has faster computing speed and better imaging effect. However, due to the single dimension of information in traditional plane waves, the data set constructed by its echo contains limited information about scatterers in the imaging area, resulting in limited imaging accuracy.

[0008] Another deep learning-based method is to use generative adversarial networks for electromagnetic inverse scattering imaging. Generative adversarial networks can generate more realistic dielectric constant distributions through adversarial training between generators and discriminators, thereby improving imaging effects. However, there are certain limitations to the application of generative adversarial networks in electromagnetic inverse scattering imaging problems. For example, the training process of the generative adversarial network model is complex, and the quality and quantity of the data set are high. In addition, the stability of the generative adversarial network model is poor, and mode collapse is prone to occur, resulting in a lack of diversity and accuracy in the generated results. Summary of the invention

[0009] The present invention aims to address the deficiencies of the above-mentioned prior art and proposes an electromagnetic inverse scattering imaging method based on plane vortex electromagnetic waves, so as to obtain more scattering information inside the object to be imaged through plane vortex electromagnetic waves with different mode numbers, thereby being able to reconstruct the dielectric constant distribution in the imaging area with higher imaging accuracy, higher system reliability and anti-environmental interference ability, and faster data processing speed.

[0010] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme:

[0011] The electromagnetic inverse scattering imaging method based on plane vortex electromagnetic waves of the present invention is characterized in that it is applied to Planar vortex electromagnetic wave transmitting module, The invention relates to a method for measuring a planar vortex electromagnetic wave in a measurement system of an antenna receiving module, an electromagnetic backscattering imaging network and an object to be imaged; each planar vortex electromagnetic wave transmitting module comprises: a plurality of array antenna units, a phase controller, a power divider, a signal generator, and a reflector; wherein the reflector is installed on the front of the array antenna, and the signal generator is connected to the power divider through a phase controller; each antenna receiving module comprises: a radiation unit, a phase and amplitude controller, a low noise amplifier, a vector signal processor, and a signal synchronizer; the electromagnetic backscattering imaging method comprises the following steps:

[0012] Step 1: Set the phase of the phase controller in each planar vortex electromagnetic wave transmitting module to ,in, is the orbital angular momentum, n is the number of array antenna units in each planar vortex electromagnetic wave transmitting module, and the initialization ;

[0013] Step 2: The signal generator in the j-th planar vortex electromagnetic wave transmitting module generates an electromagnetic wave signal of a preset frequency and sends it to the j-th phase controller, which applies a The phase shift is obtained by the j-th power divider to obtain the shifted electromagnetic wave signal and send it to the j-th power divider, so that the j-th power divider distributes the shifted electromagnetic wave signal to each array antenna unit to form The j-th single-mode plane vortex electromagnetic wave is emitted toward the center direction of the object to be imaged, and at the same time, the j-th reflector is used to enhance the radiation power of the j-th plane vortex electromagnetic wave in the emission direction, thereby forming a uniformly surrounded area with the object to be imaged as the center. Planar vortex electromagnetic wave incident source; j∈[1,M];

[0014] Step 3, the jth incident source emits toward the center of the object to be imaged, and after being scattered and reflected by the object to be imaged, generates the jth echo signal;

[0015] Step 4: Center the object to be imaged. The radiation units in the antenna receiving modules are evenly arranged in a circle, and the jth echo signal is received at the same time, and sent to each phase and amplitude controller. Each phase and amplitude controller adjusts the phase and amplitude of the received echo signal, and obtains A conditioned echo signal;

[0016] Each low noise amplifier performs noise reduction amplification on the respective adjusted echo signal to obtain After the amplified echo signal is transmitted to the corresponding vector signal processor to extract The amplitude and phase information of the echo signal is extracted After the echo signal emitted by the incident source, we get Amplitude and phase information;

[0017] Step 5: The amplitude and phase information are normalized, and the normalized amplitude is assigned to the red channel of the RGB image, the normalized phase information is assigned to the green channel of the RGB image, and the blue channel of the RGB image is filled with 0 to obtain a scattering field image; the real dielectric constant distribution map of the object to be imaged corresponding to the scattering field image is used as the real label, so as to obtain a labeled image sample, and then different objects to be imaged are used to construct a scattering field image. A single-modal image dataset , and Any i-th image in is denoted as , The corresponding true label is recorded as ; i∈[1,n], n is the number of images in the single-modal image dataset;

[0018] Step 6: , and adjust the phase of the phase controller in each planar vortex electromagnetic wave transmitting module, and then follow the process of steps 2 to 5 to construct A single-modal image dataset , and Any i-th image in is denoted as , The corresponding true label is recorded as ;

[0019] Step 7: , and adjust the phase of the phase controller in the plane vortex electromagnetic wave transmitting module, and then follow the process of steps 2 to 5 to construct A single-modal image dataset , and Any i-th image in is denoted as , The corresponding true label is recorded as ;

[0020] Step 8: Construct an electromagnetic inverse scattering imaging network, including three preliminary imaging modules with the same structure but different parameters ( , and ), decision layer fusion module, imaging optimization module ;

[0021] Each preliminary imaging module includes: a preliminary encoder and a preliminary decoder; the imaging optimization module Contains an optimized encoder and an optimized decoder;

[0022] Step 8.1: Input first preliminary imaging module The reconstructed image set is obtained by processing , used to construct the loss function , so that the first preliminary imaging module Perform training to obtain the first preliminary imaging module after training ;Will enter After that, the first preliminary reconstructed image set is obtained , and Any i-th image in is denoted as ;

[0023] Step 8.2: Follow the process in step 8.1 and use Training the second preliminary imaging module , get the second preliminary imaging module after training ,Will enter Get the second preliminary reconstructed image set , and Any i-th image in is denoted as ;

[0024] Step 8.3: Follow the process in step 8.1 and use Training the third preliminary imaging module , get the third preliminary imaging module after training ,Will enter Get the third preliminary reconstructed image set , and Any i-th image in is denoted as ;

[0025] Step 8.4: , and The image in the decision layer fusion module is processed to obtain a multimodal fusion image set ,Will Any i-th fused image in is denoted as , and its corresponding label is :

[0026] The decision layer fusion module extracts , and After the reconstruction information of the red channel, the corresponding image is taken as the i-th multimodal fusion image The data of the three channels are obtained to obtain the i-th fused image ,and The size is ;

[0027] Step 8.5: Input imaging optimization module The optimized reconstructed image set is obtained by processing , and used to construct the loss function , thereby optimizing the input imaging module Perform training to obtain the trained imaging optimization module ;

[0028] Step 9: , , , decision layer fusion module and The trained electromagnetic inverse scattering imaging model is formed and used to reconstruct any input scattering field image to be identified to obtain the prediction result of the dielectric constant distribution.

[0029] The electromagnetic backscattering imaging method based on planar vortex electromagnetic waves described in the present invention is also characterized in that step 8.1 comprises:

[0030] Step 8.1.1. enter In, first go through After the initial encoder processing, the size is Feature map ,in, for The number of pooling layers in the preliminary encoder, for The number of convolution kernels in the last convolution layer in the preliminary encoder;

[0031] Step 8.1.2: enter The final convolutional layer outputs a size of The reconstructed image ,in, is the discrete size of the object to be imaged, for The number of upsampling layers in the preliminary decoder of , and ;

[0032] Step 8.1.3: Use formula (1) to construct the first preliminary imaging module The loss function :

[0033] (1)

[0034] Step 8.1.4: First preliminary imaging module Train and minimize using the Adam optimizer , until Until convergence, we get ;

[0035] Further, the step 8.5 includes:

[0036] Step 8.5.1. enter After being processed by the optimized encoder, the size is Feature map ,in, To optimize the number of pooling layers in the encoder, To optimize the number of convolution kernels in the last convolution layer in the encoder;

[0037] Step 8.5.2: The feature map After being processed by the optimized decoder, the final convolutional layer and a sigmoid activation function output size is The reconstructed image ;

[0038] Step 8.5.3: Use formula (2) to construct an imaging optimization module The loss function :

[0039] (2)

[0040] Step 8.5.4: Optimize the imaging module Train and minimize using the Adam optimizer , until Until convergence, we get ;

[0041] An electronic device of the present invention includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the electromagnetic inverse scattering imaging method, and the processor is configured to execute the program stored in the memory.

[0042] The present invention provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and the computer program executes the steps of the electromagnetic inverse scattering imaging method when the computer program is executed by a processor.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. The present invention introduces plane vortex electromagnetic waves into the electromagnetic inverse scattering imaging problem. Compared with traditional plane waves, plane vortex electromagnetic waves introduce a new degree of freedom, orbital angular momentum, which can capture more diverse information about scatterers and construct a data set containing more scattering information of the objects to be imaged, thereby improving the imaging accuracy of the electromagnetic inverse scattering imaging model;

[0045] 2. The present invention uses decision-level fusion technology to obtain higher imaging accuracy. By fusing the imaging results of plane vortex electromagnetic waves of multiple modes in the target area, the preliminary imaging results of each single-mode data set can be comprehensively considered, thereby effectively reducing the impact of external factors such as environmental noise and vibration on the imaging accuracy of electromagnetic inverse scattering problems based on traditional plane waves;

[0046] 3. The preliminary imaging module and the imaging optimization module in the present invention are based on the autoencoder network, and learn the fusion extraction of high-level and low-level spatial features in a supervised training manner, ensuring the comprehensive capture of spatial information; combining the high-level and low-level spatial features, so as to achieve higher accuracy in the image reconstruction task;

[0047] 4. The present invention adopts a two-stage autoencoder structure. First, the first-stage autoencoder performs preliminary image reconstruction on the single-modality data set, quickly captures the main features and structural information of the image, and performs preliminary reconstruction; then the second-stage autoencoder performs fine reconstruction on the preliminary reconstructed image, further extracting and optimizing the detail features. This two-stage reconstruction method enables the network to gradually improve the quality of the reconstructed image while maintaining computational efficiency, ensuring that the final static detection results are more accurate and clear. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the arrangement of the measuring device of the present invention;

[0049] Figure 2a is a schematic diagram of the network structure of the preliminary imaging module of the present invention;

[0050] Figure 2b It is a schematic diagram of the network structure of the imaging optimization module of the present invention;

[0051] Figure 2c It is a schematic diagram of the electromagnetic inverse scattering imaging model of the present invention;

[0052] Figure 3a is the real dielectric constant label image of the present invention;

[0053] Figure 3b is the input scattered field image of the present invention;

[0054] Figure 3c is a reconstructed image of each preliminary imaging module and optimized imaging module of the present invention;

[0055] Figure 3d They are frequency histograms of the structural similarity index between the imaging results using the traditional plane wave-based imaging method and the imaging results of the present invention and the true dielectric constant label image. DETAILED DESCRIPTION

[0056] In this embodiment, an electromagnetic inverse scattering imaging method based on a planar vortex electromagnetic wave first uses three preliminary imaging modules to generate preliminary reconstruction results of three single-modal data sets respectively, and then obtains fused data through a decision layer fusion module as input of an imaging optimization module to generate a final detection result. The dielectric constant of the object to be imaged is accurately reconstructed by jointly extracting the scattered field information in the three single-modal data sets and optimizing the reconstruction of the imaging optimization module. The specific steps are as follows:

[0057] Step 1: Set the phase of the phase controller 1.2 in each planar vortex electromagnetic wave transmitting module 1 to ,in, is the orbital angular momentum, n is the number of array antenna units 1.1 in each plane vortex electromagnetic wave transmitting module 1, and initialization ; By making the array antenna unit 1.1 have The equal phase difference of the signal enables the required phase delay to be achieved when the signal is fed to each array antenna element 1.1, thereby generating the required modal number. Plane vortex electromagnetic waves.

[0058] Step 2: The signal generator 1.4 in the j-th planar vortex electromagnetic wave transmitting module 1 generates an electromagnetic wave signal of a preset frequency, which in this embodiment is set to 400 MHz, and sends it to the j-th phase controller 1.2, which applies a predetermined frequency to the electromagnetic wave signal. The phase shift is obtained by the j-th power divider 1.3, and the j-th power divider 1.3 distributes the shifted electromagnetic wave signal to each array antenna unit 1.1 to form At the same time, the jth reflector 1.5 is used to enhance the radiation power of the jth plane vortex electromagnetic wave in the emission direction, thereby forming a uniformly surrounded area with the object to be imaged as the center. Planar vortex electromagnetic wave incident source; j∈[1, ]; In this embodiment, the test scene is arranged as follows Figure 1 As shown, The distance between the incident source and the center of the object to be imaged is set to 4 meters, with equal angles. The arrangement is carried out in a manner of sequentially emitting plane vortex electromagnetic waves to the object to be imaged, thereby obtaining an echo signal containing internal scattering information of the object to be imaged. In this embodiment, =32.

[0059] Step 3: The jth incident source emits toward the center of the object to be imaged, and after being scattered and reflected by the object to be imaged, generates the jth echo signal;

[0060] Step 4: Center the object to be imaged. The radiation units 2.1 in the antenna receiving modules 2 are evenly arranged in a circle. The distance between the antenna receiving module 2 and the center of the object to be imaged is set to 4 meters, with equal angles. The j-th echo signal is received and sent to each phase and amplitude controller 2.2. Each phase and amplitude controller 2.2 adjusts the phase and amplitude of the received echo signal, and obtains The adjusted echo information; this The echo information is generated by a transmitting source after emitting a plane vortex electromagnetic wave. The antenna receiving modules 2 simultaneously receive the scattered information in all directions of the object to be imaged;

[0061] Each low noise amplifier 2.3 performs noise reduction amplification on the respective adjusted echo signal to obtain After the amplified echo signal is transmitted to the corresponding vector signal processor 2.4, it is extracted The amplitude and phase information of the echo signal can be extracted After the echo signal emitted by the incident source, we get amplitude and phase information; after the jth signal source is transmitted The antenna receiving module 2 obtains The amplitude and phase information is taken as the jth column of the image, thus forming a scattered field matrix.

[0062] Step 5: Add the scattered field matrix The amplitude and phase information are normalized to [0, 255], and the normalized amplitude is assigned to the red channel of the RGB image, the normalized phase information is assigned to the green channel of the RGB image, and the blue channel of the RGB image is padded with 0 to obtain a scattering field image, such as Figure 3b As shown in the example in; the real dielectric constant distribution map of the object to be imaged corresponding to the scattered field image is used as the real label, such as Figure 3a As shown in the example in , a labeled image sample is obtained, and then different objects to be imaged are used to construct A single-modal image dataset , and Any i-th image in is denoted as , The corresponding true label is recorded as ; i∈[1,n], n is the number of images in the unimodal image dataset; This example uses the MNIST dataset to train and evaluate the model. The MNIST dataset is a handwritten digit dataset that contains 10 categories (digits 0 to 9) of handwritten digit images. 600 images are selected for each digit, totaling 6000 images to form the dataset label. 4000 images (400 images per category) are selected for training, 1000 images (100 images per category) are used for verification, and another 1000 images (100 images per category) are used for testing. The resolution of each image is adjusted to , to correspond to the real and imaginary parts of the dielectric constant distribution after the object to be imaged is discretized. In this embodiment, , the image content is a single handwritten number, the digital part corresponds to the relative dielectric constant of the object to be imaged, the real part is set to 2.0, the background part corresponds to the air, the real part of the relative dielectric constant is set to 1.0, the imaginary parts of the digital part and the background part are set to zero, and the actual size of the object to be imaged is .

[0063] Step 6: , and adjust the phase of the phase controller 1.2 in each plane vortex electromagnetic wave transmitting module 1, and then follow the process of steps 2 to 5 to construct A single-modal image dataset , and Any i-th image in is denoted as , The corresponding true label is recorded as ;

[0064] Step 7: , and adjust the phase of the phase controller 1.2 in the plane vortex electromagnetic wave transmitting module 1, and then follow the process of steps 2 to 5 to construct A single-modal image dataset , and Any i-th image in is denoted as , The corresponding true label is recorded as ;

[0065] Step 8: Construct an electromagnetic inverse scattering imaging network, including three preliminary imaging modules with the same structure but different parameters ( , and ), decision layer fusion module, imaging optimization module ;

[0066] Among them, the network structure of each preliminary imaging module is as follows Figure 2a As shown, they all include: a preliminary encoder and a preliminary decoder; an imaging optimization module It contains an optimized encoder and an optimized decoder. The network structure is as follows Figure 2b As shown;

[0067] Step 8.1: Input first preliminary imaging module The reconstructed image set is obtained by processing , used to construct the loss function , so that the first preliminary imaging module Perform training to obtain the first preliminary imaging module after training ;Will enter After that, the first preliminary reconstructed image set is obtained , and Any i-th image in is denoted as ,like Figure 3c As shown in the example;

[0068] Step 8.1.1. enter In, first go through After two convolutions, batch normalization, and pooling, it is processed by one convolution layer and one pooling layer (the number of convolution kernels in each convolution layer is 16, 64, and 128 respectively), and the size is obtained. Feature map ,in, for The number of pooling layers in the preliminary encoder, in this embodiment, , for The number of convolution kernels of the last convolution layer in the preliminary encoder is, in this embodiment, ; The convolution kernel sizes of all convolutional layers used in this embodiment are , the step length is ; Take 3, Take 2.

[0069] Step 8.1.2: enter In the preliminary decoder of , after 4 convolutions and upsampling processes (the number of convolution kernels in each convolution layer is 128, 64, 32, and 16 respectively), the output size of the last convolution layer is The reconstructed image ,in, is the discrete size of the object to be imaged. In this embodiment, . for The number of upsampling layers in the preliminary decoder of , and .

[0070] Step 8.1.3: Use formula (1) to construct the first preliminary imaging module The loss function :

[0071] (1)

[0072] Step 8.1.4: First preliminary imaging module Train and use the Adam optimizer with a learning rate minimize , until Until convergence, we get .

[0073] Step 8.2: Follow the process in step 8.1 and use Training the second preliminary imaging module , The initial network parameter settings and Keep it consistent and get the second preliminary imaging module after training ,Will enter Get the second preliminary reconstructed image set , and Any i-th image in is denoted as ,like Figure 3c As shown in the example;

[0074] Step 8.3: Follow the process in step 8.1 and use Training the third preliminary imaging module , The initial network parameter settings and Keep it consistent and get the third preliminary imaging module after training ,Will enter Get the third preliminary reconstructed image set , and Any i-th image in is denoted as ,like Figure 3c As shown in the example;

[0075] Step 8.4: , and The image in the decision layer fusion module is processed to obtain a multimodal fusion image set ,Will Any i-th fused image in is denoted as , The real part information of the preliminary reconstruction results of each preliminary imaging module is saved in :

[0076] The decision layer fusion module extracts , and After the reconstruction information of the red channel, the corresponding image is taken as the i-th multimodal fusion image The data of the three channels are obtained to obtain the i-th fused image ,and The size is ;

[0077] Step 8.5: Input imaging optimization module The optimized reconstructed image set is obtained by processing , and used to construct the loss function , thereby optimizing the input imaging module Perform training to obtain the trained imaging optimization module ;

[0078] Step 8.5.1. enter In the example, it is first processed by the optimized encoder, and then processed by a combination of two convolutions, batch normalization, and pooling, and then processed by a convolution layer and a pooling layer to obtain a size of Feature map ,in, To optimize the number of pooling layers in the encoder, in this embodiment, , To optimize the number of convolution kernels in the last convolutional layer in the encoder.

[0079] Step 8.5.2, feature map After being processed by the optimized decoder, it first goes through a combination of three convolutions and upsampling, and then the last convolution layer and a sigmoid activation function output size is The reconstructed image .

[0080] Step 8.5.3: Use formula (2) to construct an imaging optimization module The loss function :

[0081] (2)

[0082] Step 8.5.4: Optimize the imaging module Train and use the Adam optimizer with a learning rate minimize , until Until convergence, we get ,Through the decision-level fusion of the preliminary imaging results of the preliminary imaging modules obtained by training each single-modality dataset, the advantages of the preliminary imaging results of each single-modality dataset are fully utilized to obtain more accurate reconstruction results, such as Figure 3c as shown in the example.

[0083] Step 9: , , , decision layer fusion module and The trained electromagnetic inverse scattering imaging model is composed of Figure 2cAs shown, it is used to reconstruct any input scattering field image to be identified and obtain the prediction result of the dielectric constant distribution.

[0084] In this embodiment, an electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above method, and the processor is configured to execute the program stored in the memory.

[0085] In this embodiment, a computer-readable storage medium stores a computer program on the computer-readable storage medium, and the computer program executes the steps of the above method when executed by a processor.

[0086] In order to verify the important role of strategy-level fusion of the preliminary reconstruction results of the multimodal data set in the present invention, this example makes a qualitative comparison between the reconstruction results based on the traditional plane wave and the reconstruction results of the electromagnetic inverse scattering imaging model based on the multimodal data set, and uses the structural similarity index frequency distribution histogram to analyze the advantages and disadvantages of the reconstruction results of the two. Figure 3d They are frequency distribution diagrams of the structural similarity index of the deep learning algorithm based on traditional plane waves and the frequency distribution diagram of the structural similarity index of the present invention, respectively. The experimental results show that the imaging result of the present invention is better than the preliminary imaging results of each single-modal data set, and the SSIM frequency distribution histogram of the present invention is better than the SSIM frequency distribution histogram of the traditional plane wave. The experiment shows that the method proposed in the present invention can effectively improve the imaging accuracy of the deep learning electromagnetic inverse scattering imaging algorithm based on traditional plane waves.

Claims

1. An electromagnetic inverse scattering imaging method based on planar vortex electromagnetic waves, characterized in that: is applied to A planar vortex electromagnetic wave transmitting module (1), An antenna receiving module (2), an electromagnetic backscatter imaging network and a measurement system of an object to be imaged; each planar vortex electromagnetic wave transmitting module (1) comprises: a plurality of array antenna units (1.1), a phase controller (1.2), a power distributor (1.3), a signal generator (1.4), and a reflector (1.5); wherein the reflector (1.5) is installed on the front of the array antenna (1.1), and the signal generator (1.4) is connected to the power distributor (1.3) via a phase controller (1.2); each antenna receiving module (2) comprises: a radiation unit (2.1), a phase and amplitude controller (2.2), a low noise amplifier (2.3), a vector signal processor (2.4), and a signal synchronizer (2.5); the electromagnetic backscatter imaging method comprises the following steps: Step 1: Set the phase of the phase controller (1.2) in each planar vortex electromagnetic wave transmitting module (1) to ,in, is the orbital angular momentum, n is the number of array antenna units (1.1) in each planar vortex electromagnetic wave transmitting module (1), and the initialization ; Step 2: The signal generator (1.4) in the j-th planar vortex electromagnetic wave transmitting module (1) generates an electromagnetic wave signal of a preset frequency and sends it to the j-th phase controller (1.2), and the j-th phase controller (1.2) applies a predetermined frequency to the electromagnetic wave signal. The phase shift is obtained by performing phase shifting, and the shifted electromagnetic wave signal is sent to the j-th power divider (1.3), so that the j-th power divider (1.3) distributes the shifted electromagnetic wave signal to each array antenna unit (1.1) to form The jth single-mode plane vortex electromagnetic wave is emitted toward the center direction of the object to be imaged, and at the same time, the radiation power of the jth plane vortex electromagnetic wave in the emission direction is enhanced by using the jth reflector (1.5), thereby forming a uniformly surrounded area with the object to be imaged as the center. Planar vortex electromagnetic wave incident source; j∈[1,M]; Step 3, the jth incident source emits toward the center of the object to be imaged, and after being scattered and reflected by the object to be imaged, generates the jth echo signal; Step 4: Center the object to be imaged. The radiation units (2.1) in the antenna receiving modules (2) are evenly arranged in a circle, and simultaneously receive the jth echo signal and send it to each phase and amplitude controller (2.2). Each phase and amplitude controller (2.2) adjusts the phase and amplitude of the received echo signal, and obtains A conditioned echo signal; Each low noise amplifier (2.3) performs noise reduction amplification on the respective adjusted echo signal to obtain After the amplified echo signal is transmitted to the corresponding vector signal processor (2.4), it is extracted The amplitude and phase information of the echo signal is extracted After the echo signal emitted by the incident source, we get Amplitude and phase information; Step 5: The amplitude and phase information are normalized, and the normalized amplitude is assigned to the red channel of the RGB image, the normalized phase information is assigned to the green channel of the RGB image, and the blue channel of the RGB image is filled with 0 to obtain a scattering field image; the real dielectric constant distribution map of the object to be imaged corresponding to the scattering field image is used as the real label, so as to obtain a labeled image sample, and then different objects to be imaged are used to construct a scattering field image. A single-modal image dataset , and Any i-th image in is denoted as , The corresponding true label is recorded as ; i∈[1,n], n is the number of images in the single-modal image dataset; Step 6: After adjusting the phase of the phase controller (1.2) in each planar vortex electromagnetic wave transmitting module (1), the process of steps 2 to 5 is followed to construct A single-modal image dataset , and Any i-th image in is denoted as , The corresponding true label is recorded as ; Step 7: , and adjust the phase of the phase controller (1.2) in the plane vortex electromagnetic wave transmitting module (1), and then follow the process of steps 2 to 5 to construct A single-modal image dataset , and Any i-th image in is denoted as , The corresponding true label is recorded as ; Step 8: Construct an electromagnetic inverse scattering imaging network, including three preliminary imaging modules with the same structure but different parameters. , and , decision layer fusion module, imaging optimization module ; Each preliminary imaging module includes: a preliminary encoder and a preliminary decoder; the imaging optimization module Contains an optimized encoder and an optimized decoder; Step 8.1: Input first preliminary imaging module The reconstructed image set is obtained by processing , used to construct the loss function , so that the first preliminary imaging module Perform training to obtain the first preliminary imaging module after training ;Will enter After that, the first preliminary reconstructed image set is obtained , and Any i-th image in is denoted as ; Step 8.2: Follow the process in step 8.1 and use Training the second preliminary imaging module , get the second preliminary imaging module after training ,Will enter Get the second preliminary reconstructed image set , and Any i-th image in is denoted as ; Step 8.3: Follow the process in step 8.1 and use Training the third preliminary imaging module , get the third preliminary imaging module after training ,Will enter Get the third preliminary reconstructed image set , and Any i-th image in is denoted as ; Step 8.4: , and The image in the decision layer fusion module is processed to obtain a multimodal fusion image set ,Will Any i-th fused image in is denoted as , and its corresponding label is : The decision layer fusion module extracts , and After the reconstruction information of the red channel, the corresponding image is taken as the i-th multimodal fusion image The data of the three channels are obtained to obtain the i-th fused image ,and The size is ; Step 8.5: Input imaging optimization module The optimized reconstructed image set is obtained by processing , and used to construct the loss function , thereby optimizing the input imaging module Perform training to obtain the trained imaging optimization module ; Step 9: , , , decision layer fusion module and The trained electromagnetic inverse scattering imaging model is formed and used to reconstruct any input scattering field image to be identified to obtain the prediction result of the dielectric constant distribution.

2. The electromagnetic inverse scattering imaging method based on planar vortex electromagnetic waves according to claim 1, characterized in that: The step 8.1 comprises: Step 8.1.

1. enter In, first go through After the initial encoder processing, the size is Feature map ,in, for The number of pooling layers in the preliminary encoder, for The number of convolution kernels in the last convolution layer in the preliminary encoder; Step 8.1.2: enter The final convolutional layer outputs a size of The reconstructed image ,in, is the discrete size of the object to be imaged, for The number of upsampling layers in the preliminary decoder of , and ; Step 8.1.3: Use formula (1) to construct the first preliminary imaging module The loss function : (1) Step 8.1.4: First preliminary imaging module Train and minimize using the Adam optimizer , until Until convergence, we get .

3. The electromagnetic inverse scattering imaging method based on planar vortex electromagnetic waves according to claim 2, characterized in that: The step 8.5 comprises: Step 8.5.

1. enter After being processed by the optimized encoder, the size is Feature map ,in, To optimize the number of pooling layers in the encoder, To optimize the number of convolution kernels in the last convolution layer in the encoder; Step 8.5.2: The feature map After being processed by the optimized decoder, the final convolutional layer and a sigmoid activation function output size is The reconstructed image ; Step 8.5.3: Use formula (2) to construct an imaging optimization module The loss function : (2) Step 8.5.4: Optimize the imaging module Train and minimize using the Adam optimizer , until Until convergence, we get .

4. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the electromagnetic inverse scattering imaging method according to any one of claims 1 to 3, and the processor is configured to execute the program stored in the memory.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the electromagnetic inverse scattering imaging method according to any one of claims 1 to 3 are executed.

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