A high-resolution imaging method for through-wall radar targets based on conditional diffusion model

Abstract

The present invention provides a through-wall radar target high-resolution imaging method based on a conditional diffusion model. By establishing a Markov process of forward diffusion and reverse sampling, designing a noise reduction network, and using radar image information to control the iterative generation of high-resolution optical images, the method breaks through the resolution limitations of traditional algorithms of through-wall radar systems, effectively restores the target's shape and contour information, enhances the recognizability of the results, and facilitates subsequent operation and use of the imaging results. In other words, compared with other imaging methods, the present invention can perform high-resolution imaging of targets in shielded spaces and scenes, restore the target's shape and contour information, improve and break through the resolution of traditional through-wall radar imaging methods, and provide intuitive and recognizable imaging results.

Description

Technical Field

[0001] The present invention belongs to the technical field of radar signal processing and through-wall radar imaging, and in particular relates to a through-wall radar target high-resolution imaging method based on a conditional diffusion model. Background Art

[0002] With the acceleration of global urbanization, urban building layouts and structures are becoming increasingly complex, building density is increasing, and some urban interiors are severely obscured, making it easy for targets to hide, posing a serious threat and posing a significant security risk. Currently, there are a variety of practical and effective target detection methods, including infrared signals, X-rays, and ultrasonic detection. However, each has different limitations in real-world scenarios. For example, while X-rays have strong penetrating properties, they pose a significant radiation hazard to the human body; ultrasonic detection has high accuracy and sensitivity but is highly susceptible to ambient temperature noise; and infrared signals have strong anti-interference capabilities but have almost no penetrating power. Therefore, research on solutions for efficiently detecting targets that penetrate walls is of great significance.

[0003] Through-the-wall radar imaging is a non-contact, non-destructive electromagnetic sensing technology with numerous advantages, including strong penetration, robust anti-interference capabilities, and a wide detection range. By leveraging the penetrating properties of the transmitted signal, it receives the target's echo signal after it passes through an obstacle, analyzes it, displays it, or forms an image, and obtains information about the target within the obstructed area, enabling target location and tracking. This technology has broad application prospects in disaster relief, military reconnaissance, and medical exploration.

[0004] Currently, many high-resolution algorithms have been proposed in the field of through-wall radar imaging. However, for complex extended targets in actual scenarios, performance limitations such as radar aperture and bandwidth result in very little improvement in target imaging resolution. Imaging results are mostly unrecognizable spots that lose the target's outline, greatly reducing the usability of the imaging results. High-resolution through-wall radar imaging technology remains a major challenge in this field. Summary of the Invention

[0005] To address the problem of low through-wall radar imaging resolution and missing target shape and contour information, which makes identification difficult, the present invention provides a through-wall radar target high-resolution imaging method based on a conditional diffusion model. By establishing a Markov process of forward diffusion and reverse sampling, designing a noise reduction network, and using radar image information to control the iterative generation of high-resolution optical images, the method breaks through the resolution limitations of traditional through-wall radar system algorithms, effectively restores the target's shape and contour information, enhances the recognizability of the results, and facilitates subsequent operation and use of the imaging results.

[0006] A method for high-resolution imaging of through-wall radar targets based on a conditional diffusion model comprises the following steps:

[0007] S1: Acquire a 3D radar image of the target behind the wall I radar and target depth image I optical ;

[0008] S2: target depth image I optical Add noise moment by moment until the target depth image I optical Transformed into a target noise image x that conforms to a random Gaussian distribution T , where T is the target depth image I optical The total number of noise addition moments required to transform into the target noise image;

[0009] S3: 3D radar image I radar , time T, target noise image x T Input the trained noise prediction network to obtain the predicted noise;

[0010] S4: Use the predicted noise to predict the target noise image x T Perform reverse denoising to obtain the target noise image required for reverse denoising at the next moment;

[0011] S5: Decrement the current time by 1 to get the next time, and then convert the 3D radar image I radar , at the next moment, the latest target noise image is input into the trained noise prediction network to obtain the predicted noise required for the next reverse denoising;

[0012] S6: re-execute steps S4 to S5 using the latest predicted noise and the latest target noise image until T decreases to 0. The target noise image obtained at the iteration time 0 is the final image of the target to be measured.

[0013] Furthermore, in step S2, the target noise image x that conforms to the random Gaussian distribution T The method to obtain is:

[0014]

[0015] Among them, x0 is the target depth image I optical ,∈ T is the random noise sampled from the standard Gaussian distribution at the Tth noise addition moment, is the variance variable at the Tth noise-added moment, and α k Represents the random noise ∈ sampled at the kth noise adding moment k The noise variance β k The difference between α and 1, and α k =1-β k , k=1,…,T;

[0016] At the same time, the target intermediate noise image x at any noise adding time t t The method to obtain is:

[0017]

[0018] Among them, ∈ t is the random noise sampled from the standard Gaussian distribution at the t-th noise addition moment, t=1,…,T-1, is the variance variable at the tth noise adding moment, and α i Represents the random noise ∈ sampled at the i-th noise adding moment i The noise variance β i The difference between α and 1, and α i =1-β i , i=1,…,t.

[0019] Furthermore, in step S4, a method for acquiring any target noise image required for reverse noise reduction at the next moment is:

[0020] Get the mean value μ of the target noise image required for reverse noise reduction at the next moment θ (x t ,t,c):

[0021]

[0022] Among them, t represents the current reverse noise reduction moment, f θ (x t ,t,c) represents the latest predicted noise at the current reverse denoising time t, and c represents the three-dimensional radar image I radar , θ represents the network parameter set of the prediction network; x t represents the latest target noise image obtained at the current reverse denoising time t; is the variance variable at the t-th noise addition moment corresponding to the t-th reverse noise reduction moment, and α i Represents the random noise ∈ sampled at the i-th noise adding moment i The noise variance β i The difference between α and 1, and α i =1-βi ， i=1,…,t;

[0023] Fit the mean to μ θ (x t ,t,c), the variance conforms to (1-α t )I image is used as the target noise image x required for reverse denoising at the next moment t-1 t-1 .

[0024] Furthermore, in step S3, the training method of the noise prediction network is:

[0025] S31: Collect 3D radar images and target depth images of different targets behind the wall;

[0026] S32: Acquire a target noise image of each target based on the target depth image of each target;

[0027] S33: Inputting the total number of noise addition moments T required to convert the three-dimensional radar image of each target and the target depth image of each target into the target noise image and the target noise image of each target into the trained noise prediction network to obtain predicted noise;

[0028] S34: Construct the loss function L based on the predicted noise as follows:

[0029]

[0030] Among them, the reverse noise reduction time t=1,…,T, represents the predicted noise obtained at the reverse denoising time t, and c represents the three-dimensional radar image I radar , θ represents the network parameter set of the prediction network; ∈ t represents the random noise sampled from the standard Gaussian distribution at the tth noise adding moment in the noise adding process of converting the target depth image into the target noise image; x0 represents the target depth image without noise; x t represents the target noise image obtained at any reverse denoising time t, and is the variance variable at the tth noise adding moment, and α i Represents the random noise ∈ sampled at the i-th noise adding moment i The noise variance β i The difference between α and 1, and α i =1-β i , i=1,…,t; Indicates finding the p-norm; Indicates obtaining random noise ∈ t As a variable, change the random noise ∈ t The different values ​​of expectations; E x,c Indicates expectation of acquisition expectations;

[0031] S35: Determine whether the loss function L is less than a set threshold. If yes, obtain the final noise prediction network; if not, proceed to step S36;

[0032] S36: changing the network parameter values ​​in the network parameter set θ of the noise prediction network, and then re-executing steps S33 to S35 using the noise prediction network after the network parameters are changed.

[0033] Furthermore, in step S1, the three-dimensional radar image I of the target to be measured radar The method to obtain is:

[0034] Assume that there is a wall with a thickness of d and a dielectric constant of ∈ in the x-axis direction, the antenna and the target to be measured are respectively in the positive and negative directions of the y-axis, and the space outside the wall is free space;

[0035] The radar operates in synthetic aperture mode, using N transceiver antenna units with a spacing of d1 to transmit a stepped-frequency continuous wave signal containing M frequency points. Assuming that there are Q targets to be measured on the other side of the wall, the frequency domain echo signal S(m,n) received by the nth transceiver antenna unit at the mth frequency point is:

[0036]

[0037] Among them, σ wall and σ q Represents the scattering coefficient of the wall and the qth target to be measured, q=1,2,…,Q,S noise (m,n) represents the noise signal of the nth transmitting and receiving antenna unit at the mth frequency point, f m is the frequency of the mth frequency point, τ wall represents the round-trip delay between N transmitting and receiving antenna units and the wall, τ q,n represents the round-trip delay between the nth antenna and the qth target to be measured;

[0038] Perform inverse Fourier transform on the frequency domain echo signals S(m,n) received by the N transceiver antenna units to obtain the time domain echo corresponding to each transceiver antenna unit;

[0039] Perform BP imaging on the time domain echo corresponding to each transceiver antenna unit to obtain the BP imaging I of each transceiver antenna unit in the target area. n ;

[0040] The BP image I of each transmitting and receiving antenna unit in the target area n Perform coherent superposition to obtain a superimposed image

[0041] The phase coherence factor PCF is used to weight the superimposed image to obtain the three-dimensional radar image I of the target to be measured. radar =PCF·I BP .

[0042] Furthermore, the method for obtaining the phase coherence factor PCF is:

[0043] PCF=1-std(e -jφ )

[0044]

[0045] in, Indicates the calculated standard deviation; j indicates the imaginary part; φ l Indicates the phase of the echo signal reflected by the target to be measured to the lth transceiver antenna unit, the serial number of the transceiver antenna unit is l=0,1,…,N-1,φ n It represents the phase of the echo signal reflected by the target to be measured to the nth transceiver antenna unit, and φ represents the phase of the total echo signal reflected by the target to be measured to all transceiver antenna units.

[0046] Furthermore, in step S2, the target depth image I of the target to be measured is optical The method to obtain is:

[0047] According to the perspective principle of light, the optical depth camera is placed at the center of the radar's transceiver antenna array to obtain the same viewing angle as the radar and the same three-dimensional radar image I radar Matching target depth images I optical .

[0048] Beneficial effects:

[0049] The present invention provides a through-wall radar target high-resolution imaging method based on a conditional diffusion model. By establishing a Markov process of forward diffusion and reverse sampling, designing a noise reduction network, and using radar image information to control the iterative generation of high-resolution optical images, the method breaks through the resolution limitations of traditional algorithms of through-wall radar systems, effectively restores the target's shape and contour information, enhances the recognizability of the results, and facilitates subsequent operation and use of the imaging results. In other words, compared with other imaging methods, the present invention can perform high-resolution imaging of targets in shielded spaces and scenes, restore the target's shape and contour information, improve and break through the resolution of traditional through-wall radar imaging methods, and provide intuitive and recognizable imaging results. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a flow chart of an embodiment of the present invention;

[0051] Figure 2 It is a schematic diagram of the signal scenario in the present invention;

[0052] Figure 3 Schematic diagram of phase coherence factor weighting in the present invention;

[0053] Figure 4 It is the forward diffusion and reverse sampling process of the conditional noise reduction diffusion probability logic adopted by the present invention;

[0054] Figure 5 It is the noise reduction network hierarchical structure designed by the present invention;

[0055] Figure 6 It is the iterative high-resolution imaging process of the present invention;

[0056] Figure 7 It is a schematic diagram of a simulation scenario of the present invention;

[0057] Figure 8 Schematic diagram of the experimental scenario of the present invention, where (a) is the MIMO radar used, (b) is a schematic diagram of the radar array element positions, (c) is a schematic diagram of the non-wall penetration scenario, (d) is a schematic diagram of the wall penetration scenario, (e) is a schematic diagram of the radar wall-attached mode in the wall penetration scenario, and (f) is a schematic diagram of the target placement behind the wall;

[0058] Figure 9 Figure 1 is a simulation result diagram of the present invention, where (a) is a simulated target image, (b) is a 3D radar image, (c) is a radar cross-sectional image from the front and top views, (d) is a high-resolution image obtained by the method proposed in the present invention, and (e) is the target ground truth label.

[0059] Figure 10 The figures are the measured results of the present invention, where (a) is the measured target image, (b) is the three-dimensional radar imaging image, (c) is the radar cross-sectional image from the front and top views, (d) is the high-resolution imaging image of the method proposed in the present invention, and (e) is the target true value label. DETAILED DESCRIPTION

[0060] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0061] To overcome the problems of insufficient resolution and poor imaging identifiability in existing through-wall radar imaging algorithms, the present invention provides a through-wall radar target high-resolution imaging method based on the Conditional Denoising Diffusion Probabilistic Model (CDDPM). The basic idea is to fit the distribution characteristics of the target data domain under specified conditions through a forward denoising and backward denoising process based on a Markov chain.

[0062] Specifically, such as Figure 1 As shown, the through-wall radar target high-resolution imaging method of the present invention includes the following steps:

[0063] S1: Acquire a 3D radar image of the target behind the wall I radar and target depth image I optical ;

[0064] Among them, the three-dimensional radar image I of the target to be measured radar The method to obtain is:

[0065] S11: If Figure 2 As shown in the figure, it is assumed that there is a wall with a thickness of d and a dielectric constant of ∈ in the x-axis direction, the antenna and the target to be measured are respectively in the positive and negative directions of the y-axis, and the space outside the wall is free space;

[0066] S12: The radar operates in synthetic aperture mode, using N transceiver antenna elements with a spacing of d1 to transmit a stepped frequency continuous wave (SFCW) signal with M frequency points. Assuming there are Q targets to be detected on the other side of the wall, the frequency domain echo signal S(m,n) received by the nth transceiver antenna element at the mth frequency point is:

[0067]

[0068] Among them, σ wall and σ q Represents the scattering coefficient of the wall and the qth target to be measured, q=1,2,…,Q,S noise (m,n) represents the noise signal of the nth transmitting and receiving antenna unit at the mth frequency point, f m is the frequency of the mth frequency point, τ wall represents the round-trip delay between N transmitting and receiving antenna units and the wall, τ q,n represents the round-trip delay between the nth antenna and the qth target to be measured;

[0069] S13: Perform inverse Fourier transform on the frequency domain echo signals S(m,n) received by the N transceiver antenna units to obtain the time domain echo corresponding to each transceiver antenna unit;

[0070] S14: Perform BP imaging on the time domain echo corresponding to each transceiver antenna unit, and obtain the BP imaging I of each transceiver antenna unit in the target area. n ;

[0071] S15: BP imaging of each transceiver antenna unit in the target area I n Perform coherent superposition to obtain a superimposed image

[0072] S16: To improve image quality, reduce the grating lobe energy, such as Figure 3 As shown in the figure, the phase coherence factor PCF is used to weight the superimposed image to obtain the three-dimensional radar image I of the target to be measured. radar =PCF·I BP .

[0073] Among them, the calculation method of the phase coherence factor PCF is:

[0074] PCF=1-std(e -jφ )

[0075]

[0076] in, Indicates the calculated standard deviation; j indicates the imaginary part; φ l Indicates the phase of the echo signal reflected by the target to be measured to the lth transceiver antenna unit, the serial number of the transceiver antenna unit is l=0,1,…,N-1,φ n It represents the phase of the echo signal reflected by the target to be measured to the nth transceiver antenna unit, and φ represents the phase of the total echo signal reflected by the target to be measured to all transceiver antenna units.

[0077] Furthermore, the target depth image I of the target to be measured optical The method to obtain is:

[0078] According to the perspective principle of light, the optical depth camera is placed at the center of the radar's transceiver antenna array to obtain the same viewing angle as the radar and the same three-dimensional radar image I radar Matching target depth images I optical .

[0079] S2: target depth image I optical Add noise moment by moment until the target depth image I optical Transformed into a target noise image x that conforms to a random Gaussian distribution T , where T is the target depth image I optical The total number of noise addition moments required to transform into the target noise image;

[0080] It should be noted that the target noise image x that conforms to the random Gaussian distribution T The method to obtain is:

[0081]

[0082] Among them, x0 is the target depth image I optical ,∈ T is the random noise sampled from the standard Gaussian distribution at the Tth noise addition moment, is the variance variable at the Tth noise-added moment, and α k Represents the random noise ∈ sampled at the kth noise adding moment k The noise variance β k The difference between α and 1, and α k =1-β k , k=1,…,T;

[0083] Based on this, the present invention can also give the target intermediate noise image x at any noise adding time t t The method to obtain is:

[0084]

[0085] Among them, ∈ t is the random noise sampled from the standard Gaussian distribution at the t-th noise addition moment, where t=1,…,T-1, is the variance variable at the tth noise adding moment, and α i Represents the random noise ∈ sampled at the i-th noise adding moment i The noise variance β i The difference between α and 1, and α i =1-β i , i=1,…,t.

[0086] It should be noted that step S2 is essentially to establish a forward denoising process, which belongs to the diffusion process in the conditional denoising diffusion probability model, that is, to add noise to the optical data step by step using the definition of a Markov chain, where the Markov diffusion operator q(·) is expressed as follows:

[0087]

[0088] x0 represents the original high-resolution target depth image I optical , and preset a noise variance table β1,…,β with a value between 0 and 1 and monotonically increasing t , for the convenience of subsequent representation, define α t =1-β t , The diffusion operator is thus obtained as:

[0089]

[0090] The present invention uses t t=1,…,T represents the random noise sampled from the standard Gaussian distribution at each step, that is, a noise sampling is performed at each noise adding moment, and the random noise obtained by each sampling is independent of each other, then the noisy image at the tth noise adding moment can be expressed as

[0091]

[0092] Through the above recursive formula, the relationship between the image at any time and the initial time can be established as follows:

[0093]

[0094] The present invention uses this forward process to obtain a high-resolution target depth image I optical It is gradually submerged by noise and eventually transformed into a random Gaussian distribution.

[0095] S3: 3D radar image I radar , time T, target noise image x T Input the trained noise prediction network to obtain the predicted noise;

[0096] S4: Use the predicted noise to predict the target noise image x T Perform reverse denoising to obtain the target noise image required for reverse denoising at the next moment;

[0097] Furthermore, a method for acquiring a target noise image required for reverse noise reduction at any of the next moments is as follows:

[0098] Get the mean value μ of the target noise image required for reverse noise reduction at the next moment θ (x t ,t,c):

[0099]

[0100] Among them, t represents the current reverse noise reduction moment, f θ (x t ,t,c) represents the latest predicted noise at the current reverse denoising time t, and c represents the three-dimensional radar image I radar , θ represents the network parameter set of the prediction network; x t represents the latest target noise image obtained at the current reverse denoising time t; is the variance variable at the t-th noise addition moment corresponding to the t-th reverse noise reduction moment, and α i Represents the random noise ∈ sampled at the i-th noise adding moment i The noise variance β i The difference between α and 1, and α i =1-β i , i=1,…,t;

[0101] Fit the mean to μ θ (x t ,t,c), the variance conforms to (1-α t )I image is used as the target noise image x required for reverse denoising at the next moment t-1 t-1 .

[0102] S5: Decrement the current time by 1 to get the next time, and then convert the 3D radar image I radar, at the next moment, the latest target noise image is input into the trained noise prediction network to obtain the predicted noise required for the next reverse denoising;

[0103] S6: re-execute steps S4 to S5 using the latest predicted noise and the latest target noise image until T decreases to 0. The target noise image obtained at the iteration time 0 is the final image of the target to be measured.

[0104] For example, the target noise image x at the next moment after acquiring time T T-1 When the three-dimensional radar image I radar , time T, target noise image x T Input the trained noise prediction network to get the predicted noise f θ (x T ,T,c); Then, using the prediction noise f θ (x T ,T,c) for the target noise image x T Perform reverse denoising to obtain the target noise image x required for reverse denoising at the next moment T-1 ;

[0105] Then, the time T decreases by 1, and the three-dimensional radar image I radar , time T-1, target noise image x T-1 Input the trained noise prediction network to obtain the predicted noise f required for the next reverse denoising θ (x T-1 ,T-1,c); using the newly obtained prediction noise f θ (x T-1 ,T-1,c) for the target noise image x T-1 Perform reverse denoising to obtain the target noise image x required for the next reverse denoising T-2 ;

[0106] This process is repeated in this way until T decreases to 0, and the image obtained by the last reverse denoising is the final image of the target to be measured.

[0107] It can be seen that steps S3 to S6 of the present invention are actually establishing a reverse denoising process, which belongs to the sampling process in the conditional denoising diffusion probability model; Figure 4 As shown, the reverse process is also modeled by Markov chain, the purpose is to restore the three-dimensional radar image I under condition c radar ) under high-resolution image; Unlike the forward process, the reverse process needs to predict the noise of the forward noise, so the present invention defines a neural network f θ The noise of each step is predicted, θ represents the parameters of the network, so the operator of the reverse process is defined as

[0108]

[0109] Among them, p(x T ) is a sample from a standard Gaussian distribution. Similar to the forward process, the present invention can express the reverse process operator with mean and variance as follows:

[0110]

[0111] By using the noise prediction network f θ The noise is predicted and the mean of the reconstructed image is calculated, and then the reverse process can be iterated, wherein the predicted noise f θ (x t ,t,c) The reconstructed mean calculated is as follows:

[0112]

[0113] The following is a detailed introduction to the training method of the noise prediction network adopted by the present invention, which specifically includes the following steps:

[0114] S31: Collect 3D radar images and target depth images of different targets behind the wall;

[0115] S32: Acquire a target noise image of each target based on the target depth image of each target;

[0116] S33: Inputting the total number of noise addition moments T required to convert the three-dimensional radar image of each target and the target depth image of each target into the target noise image and the target noise image of each target into the trained noise prediction network to obtain predicted noise;

[0117] S34: Construct the loss function L based on the predicted noise as follows:

[0118]

[0119] Among them, the reverse noise reduction time t=1,…,T, represents the predicted noise obtained at the reverse denoising time t, and c represents the three-dimensional radar image I radar , θ represents the network parameter set of the prediction network; ∈ t represents the random noise sampled from the standard Gaussian distribution at the tth noise adding moment in the noise adding process of converting the target depth image into the target noise image; x0 represents the target depth image without noise; x t represents the target noise image obtained at any reverse denoising time t, and is the variance variable at the tth noise adding moment, and α i Represents the random noise ∈ sampled at the i-th noise adding momenti The noise variance β i The difference between α and 1, and α i =1-β i , i=1,…,t; Indicates finding the p-norm; Indicates obtaining random noise ∈ t As a variable, change the random noise ∈ t The different values ​​of expectations; E x,c Indicates expectation of acquisition Expected, x represents the target depth image;

[0120] S35: Determine whether the loss function L is less than a set threshold. If yes, obtain the final noise prediction network; if not, proceed to step S36;

[0121] S36: changing the network parameter values ​​in the network parameter set θ of the noise prediction network, and then re-executing steps S33 to S35 using the noise prediction network after the network parameters are changed.

[0122] It should be noted that if Figure 5 As shown, the noise prediction network of the present invention is a fully convolutional noise prediction network based on the self-attention mechanism. Specifically, a multi-layer two-dimensional convolution module is first used to extract features from radar data and noisy images and then perform feature splicing. The data is then reduced in dimension and restored through a multi-layer residual module, a downsampling module, and an upsampling module. At the same time, a self-attention mechanism module is added to the low-dimensional part to increase the processing weight of the effective part of the data. The network encodes random time samples and adds them to the residual module for fusion processing, and finally obtains a predicted noise output of the same size as the optical image.

[0123] like Figure 6 As shown, when the present invention implements high-resolution imaging through iterative noise reduction, it first samples a Gaussian white noise image from a normal distribution with zero mean and unit variance. The image at the current moment, the radar image as a condition, and the moment code are input into the noise prediction network to obtain the predicted noise output at the current moment. The image at the previous moment is calculated through parameterized reconstruction, and the previous steps are repeated to iteratively implement high-resolution imaging.

[0124] Below is Figure 7 Taking the application scenario of as an example, a through-wall radar target high-resolution imaging method based on the conditional diffusion model provided by the present invention is described in detail.

[0125] First, set the simulation parameters as shown in Table 1. The radar operates in synthetic aperture mode, transmits a stepped frequency signal from 1 GHz to 3.5 GHz, and sets a frequency point every 10 MHz. The antenna movement size is 1 m*1 m, and the antenna step is 10 cm.

[0126] Table 1 Simulation parameter settings

[0127]

[0128]

[0129] Construct a dataset containing tables, chairs, people, and RPGs in a simulation scenario.

[0130] The present invention lists Figure 8 In the experimental scenario shown in Table 2, the experimental parameters are set as shown. A 10-transmitter, 10-receiver MIMO array is used to transmit a stepped frequency signal from 1.7 GHz to 2.2 GHz. The array size is approximately 40 cm * 40 cm. The concrete brick wall is approximately 20 cm thick. In the wall-mounted mode, the target 2 meters behind the wall is illuminated.

[0131] Table 2 Experimental parameter settings

[0132]

[0133] A dataset containing tables, chairs, and people was constructed in an experimental setting and merged with the simulation dataset. Diffusion and sampling logic was established to divide the dataset into training and test sets. The designed network was trained using the training set to predict the noise at each step in the sampling process. The image at the previous moment was reconstructed through iterative parameterization, ultimately achieving high-resolution image reconstruction.

[0134] The present invention selects some simulation and experimental results for analysis, and the simulation results are as follows: Figure 9 As shown in the figure, the target is a wooden table. (b) shows the 3D radar data obtained by BP imaging, and (c) is the cross-sectional view of the radar data from the front and top. Due to the low resolution of the radar data, the outline and type of the target are basically indistinguishable. (d) and (e) respectively show the high-resolution imaging results of the proposed method and the corresponding true value image, which greatly restores the true outline and texture information of the target and achieves high-resolution imaging of the target. The measured structure is as follows Figure 10 As shown in Figure 1, the target is an iron chair placed behind a 20 cm thick wall. (b) and (c) show the three-dimensional radar image and cross-sectional view obtained by BP imaging. Since the actual measurement is affected by many complex factors, such as the clutter of environmental multipath reflection, the imaging accuracy of the target drops sharply. Through the processing of the proposed method, as shown in (d), the target contour information is restored, achieving high-resolution imaging.

[0135] In summary, compared with other imaging methods, the present invention can improve the accuracy of through-wall radar imaging results, restore the target contour and texture information, and improve the target's recognizability.

[0136] Of course, the present invention may have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may of course make various corresponding changes and modifications based on the present invention, but these corresponding changes and modifications should all fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for high-resolution imaging of through-wall radar targets based on a conditional diffusion model, characterized in that: The following steps are involved: S1: Acquire a 3D radar image of the target behind the wall I radar and target depth image I optical ; S2: target depth image I optical Add noise moment by moment until the target depth image I optical Transformed into a target noise image x that conforms to a random Gaussian distribution T , where T is the target depth image I optical The total number of noise addition moments required to transform into the target noise image; S3: 3D radar image I radar , time T, target noise image x T Input the trained noise prediction network to obtain the predicted noise; S4: Use the predicted noise to predict the target noise image x T Perform reverse denoising to obtain the target noise image required for reverse denoising at the next moment; S5: Decrement the current time by 1 to get the next time, and then convert the 3D radar image I radar , at the next moment, the latest target noise image is input into the trained noise prediction network to obtain the predicted noise required for the next reverse denoising; S6: re-execute steps S4 to S5 using the latest predicted noise and the latest target noise image until T decreases to 0. The target noise image obtained at the iteration time 0 is the final image of the target to be measured.

2. The method for high-resolution through-wall radar target imaging based on a conditional diffusion model according to claim 1, wherein: In step S2, the target noise image x that conforms to the random Gaussian distribution T The method to obtain is: Among them, x0 is the target depth image I optical ,∈ T is the random noise sampled from the standard Gaussian distribution at the Tth noise addition moment, is the variance variable at the Tth noise-added moment, and α k Represents the random noise ∈ sampled at the kth noise adding moment k The noise variance β k The difference between α and 1, and α k =1-β k , k=1,…,T; At the same time, the target intermediate noise image x at any noise adding time t t The method to obtain is: Among them, ∈ t is the random noise sampled from the standard Gaussian distribution at the t-th noise addition moment, t=1,…,T-1, is the variance variable at the tth noise adding moment, and α i Represents the random noise ∈ sampled at the i-th noise adding moment i The noise variance β i The difference between α and 1, and α i =1-β i , i=1,…,t.

3. The method for high-resolution through-wall radar target imaging based on a conditional diffusion model according to claim 1, wherein: In step S4, a method for acquiring any target noise image required for reverse noise reduction at the next moment is: Get the mean value μ of the target noise image required for reverse noise reduction at the next moment θ (x t ,t,c): Among them, t represents the current reverse noise reduction moment, f θ (x t ,t,c) represents the latest predicted noise at the current reverse denoising time t, and c represents the three-dimensional radar image I radar , θ represents the network parameter set of the prediction network; x t represents the latest target noise image obtained at the current reverse denoising time t; is the variance variable at the t-th noise addition moment corresponding to the t-th reverse noise reduction moment, and α i Represents the random noise ∈ sampled at the i-th noise adding moment i The noise variance β i The difference between α and 1, and α i =1-β i , i=1,…,t; Fit the mean to μ θ (x t ,t,c), the variance conforms to (1-α t )I image is used as the target noise image x required for reverse denoising at the next moment t-1 t-1 .

4. The method for high-resolution through-wall radar target imaging based on a conditional diffusion model according to claim 1, wherein: In step S3, the training method of the noise prediction network is: S31: Collect 3D radar images and target depth images of different targets behind the wall; S32: Acquire a target noise image of each target based on the target depth image of each target; S33: Inputting the total number of noise addition moments T required to convert the three-dimensional radar image of each target and the target depth image of each target into the target noise image and the target noise image of each target into the trained noise prediction network to obtain predicted noise; S34: Construct the loss function L based on the predicted noise as follows: Among them, the reverse noise reduction time t=1,…,T, represents the predicted noise obtained at the reverse denoising time t, and c represents the three-dimensional radar image I radar , θ represents the network parameter set of the prediction network; ∈ t represents the random noise sampled from the standard Gaussian distribution at the tth noise adding moment in the noise adding process of converting the target depth image into the target noise image; x0 represents the target depth image without noise; x t represents the target noise image obtained at any reverse denoising time t, and is the variance variable at the tth noise adding moment, and α i Represents the random noise ∈ sampled at the i-th noise adding moment i The noise variance β i The difference between α and 1, and α i =1-β i , i=1,…,t; Indicates finding the p-norm; Indicates obtaining random noise ∈ t As a variable, change the random noise ∈ t The different values ​​of expectations; E x,c Indicates expectation of acquisition expectations; S35: Determine whether the loss function L is less than a set threshold. If yes, obtain the final noise prediction network; if not, proceed to step S36; S36: changing the network parameter values ​​in the network parameter set θ of the noise prediction network, and then re-executing steps S33 to S35 using the noise prediction network after the network parameters are changed.

5. The method for high-resolution through-wall radar target imaging based on a conditional diffusion model according to claim 1, wherein: In step S1, the three-dimensional radar image I of the target to be measured radar The method to obtain is: Assume that there is a wall with a thickness of d and a dielectric constant of ∈ in the x-axis direction, the antenna and the target to be measured are respectively in the positive and negative directions of the y-axis, and the space outside the wall is free space; The radar operates in synthetic aperture mode, using N transceiver antenna units with a spacing of d1 to transmit a stepped-frequency continuous wave signal containing M frequency points. Assuming that there are Q targets to be measured on the other side of the wall, the frequency domain echo signal S(m,n) received by the nth transceiver antenna unit at the mth frequency point is: Among them, σ wall and σ q Represents the scattering coefficient of the wall and the qth target to be measured, q=1,2,…,Q,S noise (m,n) represents the noise signal of the nth transmitting and receiving antenna unit at the mth frequency point, f m is the frequency of the mth frequency point, τ wall represents the round-trip delay between N transmitting and receiving antenna units and the wall, τ q,n represents the round-trip delay between the nth antenna and the qth target to be measured; Perform inverse Fourier transform on the frequency domain echo signals S(m,n) received by the N transceiver antenna units to obtain the time domain echo corresponding to each transceiver antenna unit; Perform BP imaging on the time domain echo corresponding to each transceiver antenna unit to obtain the BP imaging I of each transceiver antenna unit in the target area. n ; The BP image I of each transmitting and receiving antenna unit in the target area n Perform coherent superposition to obtain a superimposed image The phase coherence factor PCF is used to weight the superimposed image to obtain the three-dimensional radar image I of the target to be measured. radar =PCF·I BP .

6. The method for high-resolution through-wall radar target imaging based on a conditional diffusion model according to claim 5, wherein: The method for obtaining the phase coherence factor PCF is: PCF=1-std(e -jφ ) in, Indicates the calculated standard deviation; j indicates the imaginary part; φ l Indicates the phase of the echo signal reflected by the target to be measured to the lth transceiver antenna unit, the serial number of the transceiver antenna unit is l=0,1,…,N-1,φ n It represents the phase of the echo signal reflected by the target to be measured to the nth transceiver antenna unit, and φ represents the phase of the total echo signal reflected by the target to be measured to all transceiver antenna units.

7. The method for high-resolution through-wall radar target imaging based on a conditional diffusion model according to claim 1, wherein: In step S2, the target depth image I of the target to be measured optical The method to obtain is: According to the perspective principle of light, the optical depth camera is placed at the center of the radar's transceiver antenna array to obtain the same viewing angle as the radar and the same three-dimensional radar image I radar Matching target depth images I optical .

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