ISAR-RID imaging enhancement method and device, equipment and storage medium

Abstract

The invention relates to an ISAR-RID imaging enhancement method and device, equipment and a storage medium. The method comprises the following steps: performing RID imaging according to accumulated echo data corresponding to an RDA image; performing position coding on the ISAR image to obtain a position code of the ISAR image; the position codes are used for indicating positions of different pixel points in the ISAR image; and inputting three frames of continuous RID images and position codes into the trained ISAR image enhancement network to obtain an enhanced intermediate frame RID image. Compared with a traditional large-rotation-angle imaging scheme, the ISAR-RID imaging enhancement method based on deep learning solves the problem that imaging is inaccurate due to the fact that traditional imaging algorithms such as PFA and SP are low in calculation efficiency and inaccurate in signal reconstruction, a new efficient solution is provided for ISAR super-resolution imaging, and the method has great significance in subsequent target recognition and target classification.

Description

Technical Field

[0001] The present application relates to the field of radar technology, and in particular to an ISAR-RID imaging enhancement method, device, equipment and storage medium. Background Art

[0002] ISAR is a radar technology that uses data processing to convert the relative motion between the radar carrier and the target into a longer antenna aperture to replace the smaller real antenna aperture. It is often used in missile defense, target classification, and environmental monitoring. The ISAR system transmits a signal, receives the target echo, and processes the signal to obtain a two-dimensional image of the target in azimuth and range. However, when imaging at large angles, azimuth defocusing will occur, resulting in poor imaging effects. Therefore, how to improve the resolution of ISAR imaging and solve the azimuth defocusing under large-angle imaging conditions is a key problem that needs to be solved urgently.

[0003] ISAR-RID imaging has a good focusing effect, but the resolution is low. At present, the mainstream algorithms for large-angle ISAR imaging are PFA (Polar Format Algorithm) and SP (Subspace Pursuit) algorithms. PFA uses polar coordinate format to store observation data in frequency space, and then converts polar coordinate data into plane rectangular coordinate data through two-dimensional interpolation to obtain high-resolution images, but interpolation usually affects computational efficiency. The SP algorithm divides the imaging target into several small areas to avoid the MTRC phenomenon during large-angle imaging, and finally obtains a high-resolution image of the entire target by splicing each sub-image, which has good computational speed and reconstruction probability. However, the signal amplitude often fluctuates randomly. In each backtracking of the SP algorithm, the strong signal component is always retained, which will cause the strong signal component to block the weak signal component, and the signal component with relatively small amplitude cannot be correctly reconstructed, resulting in reconstruction errors; at the same time, false reconstruction signals will be generated near signals with relatively large amplitudes.

[0004] As the observation time accumulates, the imaging angle increases, and the ISAR azimuth resolution increases. However, the problem is that if the observation angle increases too much, the scattering points of the target will shift, that is, the migration of the cross-range unit (MTRC), causing one scattering point to shift to the position of other scattering points, thus affecting the imaging. Not only will the image be distorted, but in severe cases, the image may also be completely defocused, so classic ISAR imaging algorithms (such as RDA) may be invalid. Summary of the invention

[0005] Based on this, it is necessary to provide an ISAR-RID imaging enhancement method, device, equipment and storage medium to address the defocus problem of large-angle imaging.

[0006] An ISAR-RID imaging enhancement method, the method comprising:

[0007] RID imaging is performed based on the accumulated echo data corresponding to the RDA image to obtain continuous frame RID images.

[0008] The ISAR image is position-coded to obtain the position code of the ISAR image; the position code is used to indicate the positions of different pixel points in the ISAR image.

[0009] The three consecutive RID images and the position code are input into a trained ISAR image enhancement network to obtain an enhanced intermediate frame RID image; the ISAR image enhancement network includes multiple convolution modules, the input of the first convolution module is the result of splicing the position code and the three consecutive RID images in the channel dimension, the input of the second to the last convolution module is the result of splicing the output of the previous convolution module and the position code in the channel dimension, and the last convolution module outputs an enhanced intermediate frame RID image.

[0010] In one embodiment, performing RID imaging according to the accumulated echo data corresponding to the RDA image to obtain multiple frames of continuous RID images includes:

[0011] The RID imaging technology is used for the accumulated echo data corresponding to the defocused RDA image to capture RID images of different frames and obtain multiple frames of continuous RID images.

[0012] In one of the embodiments, the position coding includes: radius coding and angle coding.

[0013] The radius code is used to display the distance from a pixel point in the ISAR image to the center of the image.

[0014] The angle code is used to mark the angle between each pixel point in the ISAR image and the positive half axis of the x-axis.

[0015] In one embodiment, the last convolution module includes a convolution layer, and the other convolution modules include convolution layers and Relu activation functions; three frames of continuous RID images and the position code are input into the trained ISAR image enhancement network to obtain an enhanced intermediate frame RID image, including:

[0016] The position code and three frames of continuous RID images are spliced ​​in the channel dimension and input into the first convolution module. After being processed by the convolution layer and then activated by the Relu activation function, the first convolution feature is obtained.

[0017] The first convolution feature and the position code are concatenated in the channel dimension and input into two convolution modules to obtain a second convolution feature.

[0018] For the third convolution module to the last convolution module, the input is the result of concatenating the output of the previous convolution module and the position code in the channel dimension; the output of the last convolution module is the enhanced intermediate frame RID image.

[0019] In one embodiment, three consecutive RID images and the position code are input into a trained ISAR image enhancement network to obtain an enhanced intermediate RID image, and the step also includes:

[0020] A plurality of sample label pairs for training the network are generated, and the label pairs and the position encoding of the ISAR images are used as training samples; the label pairs include three consecutive RID images and an ideal RID image of an intermediate frame.

[0021] Construct an ISAR image enhancement network.

[0022] The loss function for constructing the ISAR image enhancement network is:

[0023]

[0024] in, is the loss function, is the i-th predicted value, y (i) is the i-th true value, and m is the total number of pixels.

[0025] The ISAR image enhancement network is trained according to the training samples and the unbalanced loss function to obtain a trained ISAR image enhancement network.

[0026] In one embodiment, the tag pair generating step includes:

[0027] The ideal coordinates of the target in the RID image are convolved with the Gaussian kernel function to obtain the ideal RID image, and the ideal RID image is used as the label.

[0028] In one of the embodiments, the method further comprises: selecting mean square error and peak signal-to-noise ratio to evaluate the enhanced intermediate frame RID image.

[0029] An ISAR-RID imaging enhancement device, comprising:

[0030] The ISAR-RID imaging module is used to perform RID imaging based on the accumulated echo data corresponding to the RDA image to obtain continuous frame RID images.

[0031] The position coding module is used to perform position coding on the ISAR image to obtain the position coding of the ISAR image; the position coding is used to indicate the positions of different pixel points in the ISAR image.

[0032] The ISAR-RID image enhancement module is used to input three consecutive RID images and the position code into a trained ISAR image enhancement network to obtain an enhanced intermediate frame RID image; the ISAR image enhancement network includes multiple convolution modules, the input of the first convolution module is the result of splicing the position code and the three consecutive RID images in the channel dimension, the input of the second to the last convolution module is the result of splicing the output of the previous convolution module and the position code in the channel dimension, and the last convolution module outputs an enhanced intermediate frame RID image.

[0033] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0034] RID imaging is performed based on the accumulated echo data corresponding to the RDA image to obtain continuous frame RID images.

[0035] The ISAR image is position-coded to obtain the position code of the ISAR image; the position code is used to indicate the positions of different pixel points in the ISAR image.

[0036] The three consecutive RID images and the position code are input into a trained ISAR image enhancement network to obtain an enhanced intermediate frame RID image; the ISAR image enhancement network includes multiple convolution modules, the input of the first convolution module is the result of splicing the position code and the three consecutive RID images in the channel dimension, the input of the second to the last convolution module is the result of splicing the output of the previous convolution module and the position code in the channel dimension, and the last convolution module outputs an enhanced intermediate frame RID image.

[0037] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0038] RID imaging is performed based on the accumulated echo data corresponding to the RDA image to obtain continuous frame RID images.

[0039] The ISAR image is position-coded to obtain the position code of the ISAR image; the position code is used to indicate the positions of different pixel points in the ISAR image.

[0040] The three consecutive RID images and the position code are input into a trained ISAR image enhancement network to obtain an enhanced intermediate frame RID image; the ISAR image enhancement network includes multiple convolution modules, the input of the first convolution module is the result of splicing the position code and the three consecutive RID images in the channel dimension, the input of the second to the last convolution module is the result of splicing the output of the previous convolution module and the position code in the channel dimension, and the last convolution module outputs an enhanced intermediate frame RID image.

[0041] The above-mentioned ISAR-RID imaging enhancement method, device, equipment and storage medium, the method includes: performing RID imaging according to the accumulated echo data corresponding to the RDA image to obtain continuous frame RID images; performing position encoding on the ISAR image to obtain the position encoding of the ISAR image; the position encoding is used to indicate the position of different pixel points in the ISAR image; three frames of continuous RID images and the position encoding are input into the trained ISAR image enhancement network to obtain an enhanced intermediate frame RID image. Compared with the traditional large-angle imaging scheme, the ISAR-RID imaging enhancement method based on deep learning solves the problem of low computational efficiency and inaccurate imaging caused by inaccurate reconstructed signals of traditional imaging algorithms such as PFA and SP. This method provides a new and efficient solution for ISAR super-resolution imaging, which is of great significance for subsequent target recognition and target classification. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is an overall flow chart of an ISAR-RID imaging enhancement method in one embodiment;

[0043] Figure 2 A schematic diagram of a flow chart of an ISAR-RID imaging enhancement method in one embodiment;

[0044] Figure 3 A schematic diagram of an ISAR-RID image enhancement network structure in one embodiment;

[0045] Figure 4 For another embodiment, the radius is encoded;

[0046] Figure 5 Angle encoding in another embodiment;

[0047] Figure 6 is a structural block diagram of an ISAR-RID imaging enhancement device in one embodiment;

[0048] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0050] The ISAR-RID imaging enhancement method proposed in this application first uses the RID algorithm to process the ISAR echo and obtain the RID image. Then the real and imaginary parts of three consecutive RID images are input into the neural network. By training with the ideal RID image of the intermediate frame as the label, the network can learn the optimal mapping between input and output and finally generate a super-resolution ISAR image. The overall process of the ISAR-RID imaging enhancement method using the neural network is as follows: Figure 1 shown.

[0051] In one embodiment, Figure 2 As shown, an ISAR-RID imaging enhancement method is provided, the method comprising the following steps:

[0052] Step 100: performing RID imaging according to the accumulated echo data corresponding to the RDA image to obtain continuous frame RID images;

[0053] Specifically, in order to improve the resolution of ISAR images and prevent azimuth defocusing, RID imaging is performed on the accumulated echo data corresponding to the defocused RDA image. The processing of the RID (Range-Instantaneous Doppler) algorithm in the range direction is consistent with the RD algorithm (by transmitting a signal with a large time-width-bandwidth product to obtain high resolution in the range direction), and in the azimuth direction, the instantaneous Doppler distribution information of the target is obtained by using time-frequency transformation methods such as short-time Fourier transform, thereby obtaining a two-dimensional high-resolution image of the target.

[0054] By utilizing RID imaging technology, it is possible to capture RID images of different frames, which contain information about different scattering points of the target. These consecutive RID images are then input into the network for image enhancement, allowing the network to better learn and utilize target features. In order to achieve high Doppler resolution in RID images, the length of the time window should be increased as much as possible while ensuring that the RID image remains focused in azimuth. Therefore, the echo data corresponding to the defocused RDA imaging results are divided into three parts for RID imaging. Ensure that the RDA image corresponding to the total time covered by the three-frame window is just defocused, but the single-frame RID image is not defocused. Finally, three consecutive ISAR-RID images are simultaneously input into the network to enhance the image of the intermediate frame. Using this method, the defocus problem of RDA imaging can be solved while improving the resolution of the intermediate frame ISAR image and reducing the sidelobes.

[0055] Step 102: Position coding is performed on the ISAR image to obtain the position coding of the ISAR image; the position coding is used to indicate the positions of different pixel points in the ISAR image.

[0056] Specifically, for ISAR images, the greater the distance from the center of the scene, the greater the degree of defocus, and the defocus in the azimuth direction is more serious than the defocus in the range direction, that is, the defocus degrees in the horizontal and vertical directions of the ISAR image are different. In view of these two characteristics, a position encoding can be designed to indicate the position of the pixel points in the ISAR image, so that the neural network designed subsequently can perform better feature learning on the ISAR image, thereby enabling the neural network to perform better feature learning on the ISAR image.

[0057] Considering the different characteristics of the middle and edge parts of ISAR images, radius coding and angle coding are designed for ISAR images to indicate the positions of different pixels in ISAR images.

[0058] Step 104: input the three consecutive RID images and the position code into the trained ISAR image enhancement network to obtain an enhanced intermediate frame RID image; the ISAR image enhancement network includes multiple convolution modules, the input of the first convolution module is the result of splicing the position code and the three consecutive RID images in the channel dimension, the input of the second to the last convolution module is the result of splicing the output of the previous convolution module and the position code in the channel dimension, and the last convolution module outputs the enhanced intermediate frame RID image.

[0059] Specifically, considering the different characteristics of the middle and edge parts of the ISAR image, a position encoding is designed for the ISAR image to indicate the positions of different pixels in the ISAR image. The total number of input channels of the network is 10, including the real and imaginary parts of three consecutive RID images received by the network, and four channels for position encoding. The ISAR-RID image enhancement network is as follows: Figure 3 shown.

[0060] In the above-mentioned ISAR-RID imaging enhancement method, the method includes: performing RID imaging according to the accumulated echo data corresponding to the RDA image to obtain continuous frame RID images; performing position encoding on the ISAR image to obtain the position encoding of the ISAR image; the position encoding is used to indicate the positions of different pixel points in the ISAR image; and inputting three frames of continuous RID images and the position encoding into the trained ISAR image enhancement network to obtain an enhanced intermediate frame RID image. Compared with the traditional large-angle imaging scheme, the ISAR-RID imaging enhancement method based on deep learning solves the problem of low computational efficiency and inaccurate imaging caused by inaccurate reconstructed signals of traditional imaging algorithms such as PFA and SP. This method provides a new and efficient solution for ISAR super-resolution imaging, which is of great significance for subsequent target recognition and target classification.

[0061] In one embodiment, step 100 includes: using RID imaging technology on the accumulated echo data corresponding to the defocused RDA image, capturing RID images of different frames, and obtaining multiple frames of continuous RID images.

[0062] In one embodiment, the position coding includes: radius coding and angle coding; the radius coding is used to display the distance from the pixel point in the ISAR image to the center of the image; the angle coding is used to mark the angle between each pixel point in the ISAR image and the positive half axis of the x-axis.

[0063] Specifically, the position coding of ISAR images includes radius coding and angle coding. The radius coding is as follows: Figure 4 As shown, the angle encoding is Figure 5 As shown. The radius encoding shows the distance from the pixel to the center of the image. In addition, complementary radius encoding is required to prevent the network from using the radius as a weight during training. It is added to the radius corresponding to the radius encoding at the same position as the maximum radius value. The angle encoding marks the angle between each pixel and the positive half axis of the x-axis. Similarly, the corresponding complementary angle encoding needs to be set.

[0064] In one embodiment, the last convolution module includes a convolution layer, and the other convolution modules include convolution layers and Relu activation functions; step 104 includes: splicing the position code and three frames of continuous RID images in the channel dimension and inputting them into the first convolution module, processing them through the convolution layer and then activating them through the Relu activation function to obtain a first convolution feature; splicing the first convolution feature and the position code in the channel dimension and inputting them into the second convolution module to obtain a second convolution feature; for the third convolution module to the last convolution module, the input is the result of splicing the output of the previous convolution module and the position code in the channel dimension; the output of the last convolution module is an enhanced intermediate frame RID image.

[0065] In one embodiment, the following steps are also included before step 104:

[0066] Step 200: Generate multiple groups of sample label pairs for training the network, and use the label pairs and the position encoding of the ISAR image as training samples; the label pairs include three consecutive RID images and an ideal RID image of the intermediate frame.

[0067] Step 202: construct an ISAR image enhancement network.

[0068] Step 204: The loss function of the ISAR image enhancement network is constructed as:

[0069]

[0070] in, is the loss function, is the i-th predicted value, y (i) is the i-th true value, and m is the total number of pixels.

[0071] Specifically, in machine learning, the loss function plays a vital role, which can quantify the difference between the predicted value and the true value. The smaller the loss value, the more accurate the model's prediction. In the deep learning-based radar imaging method, an end-to-end training system is used to minimize the loss function value between the predicted image and the real image to achieve accurate prediction of the model.

[0072] However, during training, the imbalance between the number of pixels occupied by the target and the number of pixels occupied by the non-target area may cause the network to be more inclined to regress the non-target area rather than the target itself. The existing loss function cannot optimize the parameters of the network for this feature of ISAR images, and therefore cannot obtain better training results. Therefore, it is crucial to design a suitable loss function for ISAR images to help the network learn the correct features and achieve faster convergence and better prediction results.

[0073] There is a similar imbalance problem in the field of object detection. In order to solve the problem of class imbalance in the field of object detection, Focal loss is used to adjust the ratio of positive and negative samples based on the cross entropy loss function. The formula of the cross entropy loss function is as follows:

[0074]

[0075] The Focal loss formula is as follows:

[0076]

[0077] Among them, α is a factor used to balance the ratio of positive samples and negative samples, y specifies the category of the true value, and y'∈[0,1] is the model's estimated probability for the category y=1. γ is used to adjust the ratio of sample weight reduction. When γ is 0, Focalloss degenerates into a cross entropy loss function. When γ increases, the influence of the adjustment factor also increases. In the field of image regression, MSE is the most commonly used loss function, and its formula is:

[0078]

[0079] in is the predicted value, y (i) is the true value, and m is the total number of pixels. However, the MSE loss function uniformly weights all pixels in the image and cannot solve the imbalance problem faced by ISAR images as mentioned above.

[0080] Combining the concept of focal loss with the MSE loss function can solve the imbalance problem mentioned above. First, the values ​​of the pixels in the label are normalized, and then the loss function is weighted according to these normalized values. Pixels corresponding to the target are assigned higher weights, while pixels at non-target locations are assigned lower weights. This approach allows the network to focus more on accurately reconstructing the target area during training.

[0081] The improved loss function is shown in formula (1).

[0082] In order to evaluate the experimental ISAR imaging enhancement effect, mean square error (MSE) and peak signal-to-noise ratio (PSNR) are selected as measurement indicators.

[0083] Step 206: The ISAR image enhancement network is trained according to the training samples and the loss function to obtain a trained ISAR image enhancement network.

[0084] In one embodiment, the label pair generating step includes: convolving the ideal coordinates of the target in the RID image with the Gaussian kernel function to obtain an ideal RID image, and using the ideal RID image as the label.

[0085] In one of the embodiments, the method further comprises: selecting a mean square error and a peak signal-to-noise ratio to evaluate the enhanced intermediate frame RID image.

[0086] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0087] In a specific embodiment, Figure 1 As shown, an ISAR-RID imaging enhancement method is provided, comprising the following steps:

[0088] Step S1 generates sample label pairs for training the network.

[0089] 3000 sets of sample label pairs are generated as training sets, and 1000 sets of sample label pairs are generated as test sets. The image size in the dataset is 256×256.

[0090] Step S2 performs ISAR image position encoding.

[0091] For ISAR images, the greater the distance from the center of the scene, the greater the degree of defocus, and the defocus in the azimuth direction is more serious than the defocus in the range direction, that is, the defocus degrees in the horizontal and vertical directions of ISAR images are different. In view of these two characteristics, a position encoding is designed to indicate the position of the pixel points in the ISAR image, so that the neural network designed later can better learn the features of the ISAR image.

[0092] ISAR image position coding includes radius coding and angle coding. The radius coding shows the distance from the pixel to the center of the image. In addition, complementary radius coding is required to prevent the network from using the radius as a weight during training. It is added to the radius corresponding to the radius coding at the same position as the maximum radius value. Angle coding marks the angle between each pixel and the positive half axis of the x-axis. Similarly, the corresponding complementary angle coding needs to be set.

[0093] Step S3 designs the neural network structure.

[0094] Considering the different characteristics of the middle and edge parts of the ISAR image, a position encoding is designed for the ISAR image to indicate the positions of different pixels in the ISAR image. The total number of input channels of the network is 10, including the real and imaginary parts of three consecutive RID images received by the network, and four channels for position encoding. In addition, the position encoding is concatenated on the channel dimension after each convolutional layer. The designed ISAR-RID image enhancement network is shown in Figure 2. Figure 3 shown.

[0095] Step S4 designs a loss function as shown in formula (1) according to the characteristics of the ISAR image.

[0096] Step S5 evaluates the ISAR imaging enhancement performance.

[0097] Mean square error (MSE) and peak signal-to-noise ratio (PSNR) were chosen to evaluate the predicted images for experiments with ideal images.

[0098] MSE calculates the mean square error of the pixel values ​​between the ideal image and the predicted image. Through the above operation, it can evaluate the degree of difference between the two images and is the main method for objectively evaluating image quality. It is given by the following formula:

[0099]

[0100] Where I represents an ideal radar image of size m×n, and P represents the image predicted by the network. m and n represent the number of pixels in the horizontal and vertical directions of the image, respectively. The smaller the value of MSE, the better the regression effect of the network on the image.

[0101] PSNR is the ratio of the maximum pixel value to the noise intensity, which is mainly used to measure the ability of the algorithm to remove noise. The higher the PNSR value, the better the noise suppression performance of the algorithm. The formula of PSNR is as follows:

[0102]

[0103] Preferably, a stepped frequency signal with a frequency range of 8.8-9.2 GHz, a step length of 5 MHz, and a step number of 80 is selected as the ISAR radar transmission signal, and the imaging scene of the point target is designed to be (-8, 8) meters in the range direction and (-6, 6) meters in the azimuth direction. Among them, the range direction refers to the axis parallel to the propagation direction of the radar to the target, and the azimuth direction refers to the axis perpendicular to the range direction. In this scene, a maximum of 200 point targets are randomly generated. The target rotation speed is set to 37.497° / s. The window length of STFT is 158, and noverlap is 79. The range of observation angles corresponding to each frame of the RID image is 6 degrees, and the observation angle rotates 3 degrees between adjacent frames. Each group of sample label pairs includes three consecutive RID images and an ideal RID image of the intermediate frame. The ideal coordinates of the target in the RID image are convolved with the Gaussian kernel function to obtain the ideal RID image, that is, the label. Its expression is as follows:

[0104] I(x,y)=∫∫T(u,v)·h(xu,yv)dudv (7)

[0105] Where T(u,v) is the target function, I(x,y) represents the synthetic reconstructed image of the target, and h(x,y) is the PSF of the ISAR imaging system. It should be noted that the Gaussian kernel function is selected as the point spread function (PSF) because it reduces the side lobes in the label image compared to the sinc function.

[0106] Based on the above data, the proposed deep learning method is used to enhance ISAR-RID imaging, and the imaging indicators under different signal-to-noise ratio environments are finally obtained as shown in Table 1. Table 1 shows that the proposed method can effectively enhance the ISAR imaging effect.

[0107] Table 1. ISAR-RID enhanced imaging performance based on deep learning

[0108] Signal-to-noise ratio (dB) MSE PSNR(dB) Noise-free 0.0019 27.1167 -10 0.0019 27.1167 -20 0.0022 26.5856 -30 0.0025 26.1045 -40 0.0032 24.9574

[0109] In one embodiment, Figure 6 As shown, an ISAR-RID imaging enhancement device is provided, comprising: an ISAR-RID imaging module, a position encoding module and an ISAR-RID image enhancement module, wherein:

[0110] ISAR-RID imaging module, used to perform RID imaging based on the accumulated echo data corresponding to the RDA image to obtain continuous frame RID images;

[0111] A position coding module is used to perform position coding on the ISAR image to obtain the position coding of the ISAR image; the position coding is used to indicate the positions of different pixel points in the ISAR image;

[0112] The ISAR-RID image enhancement module is used to input three consecutive RID images and position codes into the trained ISAR image enhancement network to obtain an enhanced intermediate frame RID image. The ISAR image enhancement network includes multiple convolution modules. The input of the first convolution module is the result of splicing the position code and three consecutive RID images in the channel dimension. The input of the second to last convolution modules is the result of splicing the output of the previous convolution module and the position code in the channel dimension. The last convolution module outputs an enhanced intermediate frame RID image.

[0113] In one of the embodiments, the ISAR-RID imaging module is further used to use RID imaging technology on the accumulated echo data corresponding to the defocused RDA image to capture RID images of different frames to obtain multiple frames of continuous RID images.

[0114] In one of the embodiments, the position coding in the position coding module includes: radius coding and angle coding; the radius coding is used to display the distance from the pixel point in the ISAR image to the center of the image; the angle coding is used to mark the angle between each pixel point in the ISAR image and the positive half axis of the x-axis.

[0115] In one of the embodiments, the last convolution module includes a convolution layer, and the other convolution modules include convolution layers and a Relu activation function; the ISAR-RID image enhancement module is also used to splice the position code with three frames of continuous RID images in the channel dimension and input them into the first convolution module, and after being processed by the convolution layer and activated by the Relu activation function, a first convolution feature is obtained; the first convolution feature is spliced ​​with the position code in the channel dimension and input into the second convolution module to obtain a second convolution feature; for the third convolution module to the last convolution module, the input is the result of splicing the output of the previous convolution module with the position code in the channel dimension; the output of the last convolution module is an enhanced intermediate frame RID image.

[0116] In one embodiment, the ISAR-RID image enhancement module also includes an ISAR-RID image enhancement network training module before the ISAR-RID image enhancement module, which is used to generate multiple sets of sample label pairs for training the network, and the label pairs and the position encoding of the ISAR image are used as training samples; the label pairs include three consecutive RID images and an ideal RID image of the intermediate frame; an ISAR image enhancement network is constructed; and a loss function of the ISAR image enhancement network is constructed, and the loss function of the ISAR image enhancement network is shown in formula (1). The ISAR image enhancement network is trained according to the training samples and the loss function to obtain a trained ISAR image enhancement network.

[0117] In one embodiment, the label pair generation step in the ISAR-RID image enhancement network training module includes: convolving the ideal coordinates of the target in the RID image with the Gaussian kernel function to obtain an ideal RID image, and using the ideal RID image as a label.

[0118] In one of the embodiments, a performance evaluation module is further included, which is used to select mean square error and peak signal-to-noise ratio to evaluate the enhanced intermediate frame RID image.

[0119] For the specific definition of the ISAR-RID imaging enhancement device, please refer to the definition of the ISAR-RID imaging enhancement method above, which will not be repeated here. Each module in the above-mentioned ISAR-RID imaging enhancement device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0120] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an ISAR-RID imaging enhancement method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0121] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0122] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above method embodiment when executing the computer program.

[0123] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0124] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile 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. As an illustration and not limitation, RAM is available in many 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 (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0125] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0126] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. An ISAR-RID imaging enhancement method, characterized in that: The method comprises: Perform RID imaging according to the accumulated echo data corresponding to the RDA image to obtain continuous frame RID images; Performing position coding on the ISAR image to obtain the position coding of the ISAR image; the position coding is used to indicate the positions of different pixel points in the ISAR image; The three consecutive RID images and the position code are input into a trained ISAR image enhancement network to obtain an enhanced intermediate frame RID image; the ISAR image enhancement network includes multiple convolution modules, the input of the first convolution module is the result of splicing the position code and the three consecutive RID images in the channel dimension, the input of the second to the last convolution module is the result of splicing the output of the previous convolution module and the position code in the channel dimension, and the last convolution module outputs an enhanced intermediate frame RID image.

2. The method according to claim 1, characterized in that RID imaging is performed based on the accumulated echo data corresponding to the RDA image to obtain multiple frames of continuous RID images, including: The RID imaging technology is used for the accumulated echo data corresponding to the defocused RDA image to capture RID images of different frames and obtain multiple frames of continuous RID images.

3. The method according to claim 1, characterized in that The position coding includes: radius coding and angle coding; The radius code is used to display the distance from the pixel point to the center of the image in the ISAR image; The angle code is used to mark the angle between each pixel point in the ISAR image and the positive half axis of the x-axis.

4. The method according to claim 1, characterized in that: The last convolution module includes a convolution layer, and the other convolution modules include a convolution layer and a Relu activation function; Inputting three consecutive RID images and the position code into the trained ISAR image enhancement network to obtain an enhanced intermediate frame RID image, including: The position code and three frames of continuous RID images are spliced ​​in the channel dimension and input into the first convolution module. After being processed by the convolution layer and activated by the Relu activation function, the first convolution feature is obtained. The first convolution feature and the position code are concatenated in the channel dimension and input into two convolution modules to obtain a second convolution feature; For the third convolution module to the last convolution module, the input is the result of concatenating the output of the previous convolution module and the position code in the channel dimension; the output of the last convolution module is the enhanced intermediate frame RID image.

5. The method according to claim 1, characterized in that Inputting three consecutive RID images and the position code into the trained ISAR image enhancement network to obtain an enhanced intermediate frame RID image, the step also includes: Generate multiple groups of sample label pairs for training the network, and use the label pairs and the position encoding of the ISAR image as training samples; the label pairs include three consecutive RID images and an ideal RID image of an intermediate frame; Construct ISAR image enhancement network; The loss function for constructing the ISAR image enhancement network is: in, is the loss function, is the i-th predicted value, y (i) is the i-th true value, m is the total number of pixels; The ISAR image enhancement network is trained according to the training samples and the unbalanced loss function to obtain a trained ISAR image enhancement network.

6. The method according to claim 5, characterized in that The tag pair generating step comprises: The ideal coordinates of the target in the RID image are convolved with the Gaussian kernel function to obtain the ideal RID image, and the ideal RID image is used as the label.

7. The method according to claim 1, characterized in that The method further comprises: The mean square error and peak signal-to-noise ratio are selected to evaluate the enhanced intermediate frame RID image.

8. An ISAR-RID imaging enhancement device, characterized in that: The method comprises: ISAR-RID imaging module, used to perform RID imaging based on the accumulated echo data corresponding to the RDA image to obtain continuous frame RID images; A position coding module is used to perform position coding on the ISAR image to obtain the position coding of the ISAR image; the position coding is used to indicate the positions of different pixel points in the ISAR image; The ISAR-RID image enhancement module is used to input three consecutive RID images and the position code into a trained ISAR image enhancement network to obtain an enhanced intermediate frame RID image; the ISAR image enhancement network includes multiple convolution modules, the input of the first convolution module is the result of splicing the position code and the three consecutive RID images in the channel dimension, the input of the second to the last convolution module is the result of splicing the output of the previous convolution module and the position code in the channel dimension, and the last convolution module outputs an enhanced intermediate frame RID image.

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 method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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