Tiny deformation monitoring method, system and equipment based on SAR imaging and medium

Abstract

The invention provides a small deformation monitoring method, system and device based on SAR imaging and a medium, and the method comprises the steps: receiving an echo signal reflected by a target, carrying out the preprocessing, carrying out the calculation according to the preprocessed echo signal, and obtaining the distance, azimuth angle and height information of the target; carrying out interference processing on echo signals of two adjacent frames by adopting an MIMO virtual aperture technology, and obtaining a radar interference image set in combination with the distance, azimuth angle and height information; selecting permanent scattering points according to the radar interference image set, performing interference unwrapping to obtain time sequence data, inputting the time sequence data into a Sage-Husa adaptive Kalman filter, outputting to obtain target deformation data, and analyzing according to the target deformation data to obtain a micro-motion target. According to the invention, the precision and reliability of micro deformation monitoring can be improved, the use cost is reduced, and the method is suitable for various complex environments.

Description

Technical Field

[0001] The present invention relates to the technical field of high-precision monitoring, and particularly to a micro-deformation monitoring method, system, electronic device and computer-readable storage medium based on SAR imaging. Background Art

[0002] Millimeter-wave radar is increasingly becoming a research focus in the field of micro-deformation detection. Currently, micro-deformation detection mainly relies on the following several technical approaches:

[0003] The first type is the capacitive micro-deformation sensor. The capacitive micro-deformation sensor stands out for its simple structure, rapid response and wide material applicability, and relatively low cost. However, it is vulnerable to environmental fluctuations and has poor anti-interference performance, often requiring additional shielding and compensation means, which undoubtedly increases the complexity of use.

[0004] The second type is the detection based on deep learning. This technology integrates multi-modal data algorithms and demonstrates excellent accuracy. However, its effectiveness highly depends on the quality and scale of the dataset, and its performance may not be satisfactory for unseen samples or extreme conditions.

[0005] The third type is the laser interferometer. With its ultra-high measurement accuracy and resolution, as well as non-contact measurement method, it occupies a place in micro-deformation detection. However, harsh environments such as rain, snow, strong wind and sand pose severe challenges to its performance, which may lead to equipment failure or a significant decrease in accuracy. In addition, its high cost limits its application in large-scale industrial fields.

[0006] The fourth type is the millimeter-wave technology detection. It also shows unique advantages in micro-deformation detection. Its strong penetration ability enables it to penetrate obstacles such as rain, fog, and dust, and maintain stable detection performance in complex environments. Although its accuracy is slightly lower than that of the laser interferometer, it is still competitive in some application scenarios. However, the millimeter-wave radar system has high power consumption, especially in high-resolution and long-term monitoring tasks, and additional attention needs to be paid to power management and heat dissipation issues. At the same time, its signal processing process is complex and relies on high-performance computing devices to demodulate and analyze radar echo signals.

[0007] As described above, the capacitive sensor is restricted by accuracy and environmental factors, the deep learning technology faces challenges in datasets and adaptability, the laser interferometer is difficult to popularize due to its high cost, and although the millimeter-wave technology has the advantage of penetration, the power consumption and accuracy problems still need to be solved.

[0008] In summary, there is an urgent need for a micro-motion target motion detection method that can improve detection accuracy, reduce usage costs and is applicable to a variety of complex environments. Summary of the Invention

[0009] Based on this, it is necessary to provide a method, system, electronic device and computer-readable storage medium for micro-deformation monitoring based on SAR imaging to address the above technical problems.

[0010] A method for micro-deformation monitoring based on SAR imaging includes the following steps: receiving the echo signal reflected by the target and performing preprocessing, calculating based on the preprocessed echo signal to obtain the distance, azimuth angle and height information of the target; using the MIMO virtual aperture technology to perform interference processing on the echo signals of two adjacent frames, and combining the distance, azimuth angle and height information to obtain a radar interference image set; selecting permanent scatterers according to the radar interference image set, and performing interference unwrapping to obtain time series data, inputting the time series data into the Sage-Husa adaptive Kalman filter, outputting the target deformation data, and analyzing the micro-moving target based on the target deformation data.

[0011] In one embodiment, before receiving the echo signal reflected by the target and performing preprocessing, it further includes: arranging a millimeter-wave radar facing the target to be measured for transmitting signals, the millimeter-wave radar adopting a two-transmit and four-receive transmission mode, and the number of sampling points in the range direction being set to 256.

[0012] In one embodiment, receiving the echo signal reflected by the target and performing preprocessing, calculating based on the preprocessed echo signal to obtain the distance, azimuth angle and height information of the target includes: receiving the echo signal reflected by the target and performing preprocessing on the echo signal; using the time-domain back projection algorithm to divide the target area into imaging grids, traversing the data of each virtual aperture, and calculating the position of the virtual aperture and the distance between each point in the imaging grid, coherently accumulating all the compensated signals in the time domain, and the formula is:

[0013]

[0014] In the formula, D x represents the derivative with respect to x, D y represents the derivative with respect to y, j represents the imaginary number, f0 represents the radar carrier frequency, R represents the slant range of the grid point, and R0 represents the initial position of the radar platform; using the range migration algorithm, performing two-dimensional Fourier forward transform on the coherently accumulated signal, traversing and focusing the frequency points and performing non-coherent accumulation on them to obtain the calculation of the distance, azimuth angle and height information of the imaging target, and the formula is:

[0015]

[0016] In the formula, σ(x,y,z) is the reflection coefficient of the target at this point, s(x′,y′) represents the echo reflected by the target, (x′,y′) represents the position coordinates of the radar at a certain moment, and FT 2DDenote the two-dimensional forward Fourier transform, \((x_0, y_0)\) represents the position coordinates of the radar at the initial moment, and \(c\) is the speed of light.

[0017] In one embodiment, the MIMO virtual aperture technology is adopted to perform interference processing on the echo signals of two adjacent frames, and the radar interference image set is obtained by combining the distance, azimuth angle, and height information, including: adopting the MIMO virtual aperture technology to perform difference frequency processing on the echo signals of two adjacent frames, and the formula is:

[0018]

[0019] In the formula, \(t\) represents the serial number of the frame where the radar is located, represents the difference frequency between two adjacent frames, is the phase of the \(t\)-th frame, is the phase of the \((t + 1)\)-th frame; according to the difference frequency, the phase unwrapping of the echo signal is performed, and the echo signal exceeding the principal value of the phase is corrected to the echo signal within the principal value of the phase, and the formula is:

[0020]

[0021] In the formula, represents the true value of the phase difference within the \(t\)-th frame after correction; combining the corrected echo signal and the corresponding distance, azimuth angle, and height information, a radar interference image set is constructed.

[0022] In one embodiment, the selection of permanent scatterers according to the radar interference image set includes: obtaining \(K + 1\) radar interference images \(A\) in the radar interference image set k , \(k = 1, 2, \ldots, K, K + 1\); performing the first screening on the radar interference images by using the amplitude threshold method, calculating the mean values of the \(K + 1\) radar interference images, and taking the minimum mean value as the threshold, \(T\) A =\(min(mean(A\) k ))), \(k = 1, 2, \ldots, K, K + 1\), comparing the minimum amplitude value of the \(K + 1\) radar interference images with the threshold, if the minimum amplitude value of the radar interference image is greater than the threshold, then retain the radar interference image; calculating the amplitude deviation index according to all the retained radar interference images, and the formula is:

[0023]

[0024] In the formula, \(m\) A (i, j) represents the amplitude mean value, \(A\) K (i, j) represents the coordinates of a pixel point in a radar interference image, \(\delta\) A (i, j) represents the amplitude variance, \(D\) A(i,j) represents the amplitude deviation index; the radar interference image is secondarily screened according to the amplitude deviation index to obtain the secondarily screened radar interference image; a cross-shaped filter is used to perform a third screening on the secondarily screened radar interference image to obtain the permanent scatterers reflecting the overall deformation information of the target area.

[0025] In one embodiment, before inputting the time series data into the Sage-Husa adaptive Kalman filter, it further includes: establishing a Sage-Husa adaptive Kalman filter model for real-time processing based on the Sage-Husa adaptive Kalman filtering algorithm, initializing the model parameters, and performing real-time deformation monitoring using the adaptive Kalman filtering algorithm. The formula is as follows:

[0026]

[0027] In the formula, is the estimated value of the deformation value, F is the state transition matrix, x t-1 is the deformation value at the previous moment, w t-1 is the noise at the previous moment, is the covariance matrix of the prediction process, P t-1 is the covariance matrix at the previous moment, F T is the transpose of the state transition matrix, Q t-1 is the covariance matrix of w t-1 is the optimal estimated value, H is the measurement transfer equation, H = [1 0], H t is the transpose of the measurement transfer equation, R T is the covariance matrix of the observation noise, e t-1 is the Gaussian noise with a mean of 0 and a variance of R in the transfer equation, z t is the transfer equation, r t is the mean estimation of the observation noise, I is the identity matrix, x t-1 is the deformation value, P t is the corrected covariance matrix. t is the corrected covariance matrix.

[0028] A micro-deformation monitoring system based on SAR imaging is used to implement a micro-deformation monitoring method based on SAR imaging as described above, including: an echo signal preprocessing module, which is used to receive the echo signal reflected by the target and perform preprocessing, calculate according to the preprocessed echo signal, and obtain the distance, azimuth angle and height information of the target; an echo signal interference processing module, which is used to adopt the MIMO virtual aperture technology to perform interference processing on the echo signals of two adjacent frames, and combine the distance, azimuth angle and height information to obtain a radar interference image set; a micro-motion target acquisition module, which is used to select permanent scatterers according to the radar interference image set, perform interference unwrapping to obtain time series data, input the time series data into the Sage-Husa adaptive Kalman filter, output the target deformation data, and analyze the micro-motion target according to the target deformation data.

[0029] An electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps of the micro-deformation monitoring method based on SAR imaging described in each of the above embodiments.

[0030] A computer-readable storage medium stores a computer program, and when the program is executed by a processor, it implements the steps of the micro-deformation monitoring method based on SAR imaging described in each of the above embodiments.

[0031] Compared with the prior art, the advantages and beneficial effects of the present invention are as follows: By receiving the echo signal reflected by the target and performing preprocessing, the distance, azimuth angle and height information of the target are calculated according to the preprocessed echo signal; based on the MIMO virtual aperture technology, interference processing is performed on the echo signals of two adjacent frames, and combined with the distance, azimuth angle and height information to obtain a radar interference image set, which shortens the imaging time while greatly improving the detection range and imaging resolution. Permanent scatterers are extracted from the radar interference image set, and interference unwrapping is performed to form time series data, which is input into the Sage-Husa adaptive Kalman filter, and the target deformation data is output. The micro-motion target is analyzed according to the target deformation data, which can realize high-precision monitoring of micro-deformations, greatly improve the monitoring accuracy and reliability, and is applicable to various complex environments, especially applicable to scenarios such as bridges, dams, and mine slopes, with low cost. Description of the Drawings

[0032] Figure 1 It is a schematic flow chart of a micro-deformation monitoring method based on SAR imaging in an embodiment;

[0033] Figure 2 It is a flow block diagram of a micro-deformation monitoring method based on SAR imaging in an embodiment;

[0034] Figure 3 Schematic diagram of radar installation in the usage scenario in one embodiment;

[0035] Figure 4 Schematic diagram for comparing the measured deformation result with the true deformation result in one embodiment;

[0036] Figure 5 Schematic diagram of the structure of a micro - deformation monitoring system based on SAR imaging in one embodiment;

[0037] Figure 6 Schematic diagram of the internal structure of an electronic device in one embodiment. Detailed implementation manners

[0038] Before describing the detailed implementation manners of the present invention, the overall concept of the present invention is described as follows:

[0039] The present invention is mainly developed based on the process of micro - deformation monitoring. At present, the accuracy of slope micro - deformation monitoring is low and it is difficult to adapt to complex environments.

[0040] Therefore, the present invention proposes a micro - deformation monitoring method based on SAR imaging. By receiving the echo signal reflected by the target and performing pre - processing, the distance, azimuth angle, and height information of the target are calculated based on the pre - processed echo signal; the MIMO virtual aperture technology is added to perform interference processing on the echo signals of two adjacent frames, and combined with the distance, azimuth angle, and height information to obtain a radar interference image set. While shortening the imaging time, the detection range and imaging resolution are greatly improved. Permanent scatterers are extracted from the radar interference image set, and interference unwrapping is performed to form time - series data, which is input into the Sage - Husa adaptive Kalman filter, and the target deformation data is output. Based on the analysis of the target deformation data, micro - moving targets are obtained, which can realize high - precision monitoring of micro - deformations, greatly improve the monitoring accuracy and reliability, and are applicable to various complex environments, especially applicable to scenarios such as bridges, dams, and mine slopes, without using a laser interferometer, reducing the usage cost.

[0041] After introducing the overall concept of the present invention, in order to make the purpose, technical solution, and advantages of the present invention clearer, the present invention will be further described in detail below through specific implementation manners in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0042] In one embodiment, as Figure 1 and 2 shown, a micro - deformation monitoring method based on SAR imaging is provided, including the following steps:

[0043] Step S110: Receive the echo signal reflected by the target and perform preprocessing. Calculate based on the preprocessed echo signal to obtain the distance, azimuth angle, and altitude information of the target.

[0044] Specifically, arrange a millimeter-wave radar facing the target to be measured and transmit a signal. Receive the echo signal reflected by the target and perform preprocessing on the echo signal. For example, process the echo signal through a mixer and a low-pass filter to filter out the noise factors in the echo signal, and calculate the distance-angle unit where the target is located based on the preprocessed echo signal, so as to obtain the distance, azimuth angle, and altitude information of the target, which is applicable to non-metal surfaces such as asphalt roads and concrete bases.

[0045] Among them, before step S110, it also includes: arranging a millimeter-wave radar facing the target to be measured for transmitting signals. The millimeter-wave radar adopts a transmission mode of two transmitters and four receivers, and the number of sampling points in the range direction is 256.

[0046] Specifically, when performing the operation detection of a micro-moving target, as Figure 3 shown, arrange the millimeter-wave radar IWR1642 facing the target to be measured, and transmit a frequency-modulated continuous-wave signal through the millimeter-wave radar. When transmitting the radar signal, adopt a transmission mode of two transmitters and four receivers, and set the number of sampling points in the range direction to 256, which is convenient for subsequent echo data calculation.

[0047] Among them, the steps for preprocessing the echo signal specifically include: passing the transmitted signal and the received signal through a mixer, and then through a low-pass filter. Finally, only one intermediate-frequency signal is retained for each antenna signal of each frame of data. The finally obtained signal can be expressed as:

[0048] s IF (t) = σexp(j2πτ(αt + f c ));

[0049] Among them, σ represents the amplitude of the received signal, τ represents the time delay for the signal to travel back and forth once, α represents the frequency modulation rate of the FMCW signal, and f c represents the carrier frequency of the FMCW; sample at the sampling frequency f s within the sampling time N s to obtain the echo signal, which is:

[0050]

[0051] Perform a discrete Fourier transform on the echo signal to represent the echo signal as:

[0052]

[0053] In the formula, k is the frequency serial number after periodic extension in the frequency domain; among them,

[0054]

[0055] where ω is the rotational angular velocity, where B represents the frequency modulation bandwidth and c is the speed of light; the range resolution r k is:

[0056]

[0057] where B represents the frequency modulation bandwidth and c is the speed of light; by changing the radar transmission mode to two transmit and four receive, the received signals of different antennas can be expressed as:

[0058]

[0059] where θ represents the horizontal angle between the target and the radar board plane, l represents the channel number of the antenna, and d represents the distance from the radar to the target.

[0060] Specifically, the signals of the radar can be combined into a matrix according to the range dimension and the angle dimension. After the radar transmits a signal and receives the corresponding echo signal, preprocessing is performed on the echo signal, including sequentially passing the transmitted signal and the echo signal through a mixer and a low-pass filter. Finally, only one intermediate-frequency signal is retained for each antenna signal of each frame of data. The intermediate-frequency signal maintains the signal quality while restricting the frequency conversion amount of the electronic device. During the reception process, the radio frequency signal is first converted into an intermediate-frequency signal and finally into a baseband signal, thereby reducing the complexity of directly processing high-frequency signals and being able to improve the signal quality through technologies such as filtering.

[0061] where step S110 includes: receiving the echo signal transmitted by the target and performing preprocessing on the echo signal; using the time-domain backprojection algorithm to divide the target area into imaging grids, traversing the data of each virtual aperture, calculating the position of the virtual aperture and the distance between each point in the imaging grid, and coherently accumulating all the compensated signals in the time domain. The formula is:

[0062]

[0063] where D x represents the derivative with respect to x, D y represents the derivative with respect to y, j represents the imaginary number, f0 represents the radar carrier frequency, R represents the slant range of the grid point, and R0 represents the initial position of the radar platform;

[0064] Using the range migration algorithm, perform a two-dimensional Fourier forward transform on the coherently accumulated signal, traverse and focus the frequency points and perform non-coherent accumulation on them to obtain the range, azimuth angle, and height information of the imaging target. The formula is:

[0065]

[0066] wherein, σ(x, y, z) is the reflection coefficient of the target at this point, s(x′, y′) represents the target reflected echo, (x′, y′) represents the position coordinates of the radar at a certain moment, and FT 2D represents the two-dimensional forward Fourier transform, (x0, y0) represents the position coordinates of the radar at the initial moment, and c is the speed of light.

[0067] Specifically, after receiving the echo signal reflected by the target and preprocessing the echo signal, the time-domain back-projection algorithm is used to divide the imaging area into grids, and back-projection is performed pixel by pixel to achieve high spatial resolution imaging and reduce sidelobe interference; the two-dimensional forward Fourier transform is performed on the coherently integrated signal through the range migration algorithm, and the focusing frequency points are traversed and non-coherently integrated, so as to obtain the range, azimuth angle, and height information of the imaging target, and solve the range migration caused by the movement of the radar platform for the echo signal, that is, the coupling offset of the target echo in the range-azimuth two-dimensional plane; by fusing the frequency-domain correction ability of the RMA (range migration algorithm) and the time-domain flexibility of the back-projection algorithm, and combining non-coherent processing to enhance the robustness, the three-dimensional imaging performance in complex scenarios can be effectively improved.

[0068] Step S120, using the MIMO virtual aperture technology, performing interference processing on the echo signals of two adjacent frames, and combining the range, azimuth angle, and height information to obtain a radar interference image set.

[0069] Specifically, different from the existing phase interference method, the present invention adds the MIMO virtual aperture technology to achieve non-contact measurement. The radar transmits signals through the transmitting antenna, and the generated virtual MIMO (multiple-in multiple-out) receiving array receives the echo signals. By moving the MIMO radar, a relatively long synthetic aperture is generated, which significantly improves the recognition ability in the moving direction. The collected data is subjected to difference frequency processing according to the range angle units of two adjacent frames, and the signal is corrected through phase unwrapping, and a radar interference image set is obtained by combining the range, azimuth angle, and height information, providing a phase reference for the subsequent selection of permanent scatterers. By combining the MIMO array with the SAR technology to form an equivalent virtual large aperture array, the imaging time is shortened, and the detection range and imaging resolution are greatly improved.

[0070] Generally, the steering vector of the antenna array can be expressed as:

[0071] A(θ) = [a(θ1), a(θ2), … a(θ k )];

[0072] wherein, in the formula, w0 represents the signal receiving frequency, τ1, τ2, …, τ Mdenotes the time delay, θ1, θ2, …, θ k denotes the azimuth angle, M denotes the number of array elements, then the signal model of all array elements is simplified as:

[0073] X(t) = A(θ)S(t) + N(t);

[0074] In the formula, N(t) denotes the noise data vector, and S(t) denotes the spatial signal vector;

[0075] Suppose the interval of M array elements is d and it is a uniform linear array, then the array antenna steering vector of MIMO is:

[0076]

[0077] In the formula, λ denotes the incident wavelength.

[0078] Among them, step S120 includes: based on the MIMO virtual aperture technology, performing difference frequency processing on the echo signals of two adjacent frames, and the formula is:

[0079]

[0080] In the formula, t denotes the serial number of the frame where the radar is located, denotes the difference frequency between two adjacent frames, is the phase of the t-th frame, is the phase of the (t + 1)-th frame; according to the difference frequency, perform phase unwrapping on the echo signal, and correct the echo signal exceeding the principal value of the phase to the echo signal within the principal value of the phase, and the formula is:

[0081]

[0082] In the formula, denotes the true value of the phase difference within the t-th frame after correction; combine the corrected echo signal and the corresponding distance, azimuth angle, and height information to construct a radar interferometric image set.

[0083] Specifically, by combining the MIMO virtual aperture technology and the SAR technology, a high-precision radar complex image can be obtained, which contains the amplitude and phase of the electromagnetic echo

[0084] Specifically, after constructing the MIMO-SAR, a high-precision radar complex image can be obtained, which contains the amplitude and phase information of the electromagnetic echo. By performing difference frequency processing on the echo signals of two adjacent frames and performing phase unwrapping according to the difference frequency, the size of the phase unwrapping window can be set to 5×5 pixels, and the iteration threshold ε = 0.1 rad. The echo signals exceeding the principal value of the phase are corrected to the echo signals within the principal value of the phase, so as to reflect the continuous true phase change, so that the deformation information can be accurately obtained. Combining the corrected echo signals and the corresponding distance, azimuth, and height information, a radar interferometric image set is constructed.

[0085] Step S130, select permanent scatterers according to the radar interferometric image set, perform interferometric unwrapping to obtain time series data, input the time series data into the Sage-Husa adaptive Kalman filter, output the target deformation data, and analyze the micro-moving target based on the target deformation data.

[0086] Specifically, after obtaining the radar interferometric image set, the radar interferometric image contains the amplitude and phase information of the electromagnetic echo. Select permanent scatterers according to the radar interferometric image set and perform interferometric unwrapping to obtain time series data. Among them, the permanent scatterer refers to the pixel point that maintains stable scattering characteristics in multiple observations, which is used to help achieve high-precision deformation monitoring and multi-dimensional deformation analysis of the object to be monitored; by inputting the time series data into the Sage-Husa adaptive Kalman filter, the target deformation data is output, and the micro-moving target is analyzed based on the target deformation data, which can realize high-precision monitoring of small deformations, greatly improve the monitoring accuracy and reliability, and ensure the accuracy of monitoring data in a complex environment.

[0087] In order to achieve timely detection of deformation, when the deformation rate in the target deformation data exceeds 5 mm / day, the sampling period can be shortened to 1 hour to facilitate a quick response to the deformation situation.

[0088] The formation of permanent scatterers is related to the nature of the object to be detected. Objects with strong reflection surfaces such as slope objects, bridges, and utility poles usually have good scattering characteristics. Therefore, they can be used to monitor slope deformation. Since the permanent scatterers have stable scattering characteristics, the error caused by the change of the nature of the surface of the object to be detected can be reduced. In addition, the nature of the permanent scatterers can remain unchanged in multiple observations, so long-term and stable monitoring can be achieved.

[0089] Among them, the step of selecting permanent scatterers according to the radar interferometric image set includes: obtaining K + 1 radar interferometric images A in the radar interferometric image set k, k = 1, 2, …, K, K + 1; The amplitude threshold method is used to perform the first screening on the radar interferogram. Calculate the mean values of K + 1 radar interferograms, and take the minimum mean value as the threshold, T A = min(mean(A k )), k = 1, 2, …, K, K + 1. Compare the minimum amplitude value of the K + 1 radar interferograms with the threshold. If the minimum amplitude value of the radar interferogram is greater than the threshold, then retain the radar interferogram; Calculate the amplitude deviation index based on all the retained radar interferograms. The formula is:

[0090]

[0091] In the formula, m A (i, j) represents the amplitude mean value, A K (i, j) represents the coordinates of a pixel point in a radar interferogram, δ A (i, j) represents the amplitude variance, D A (i, j) represents the amplitude deviation index; Perform the second screening on the interferogram according to the amplitude deviation index to obtain the radar interferogram after the second screening; Use a cross-shaped filter to perform the third screening on the radar interferogram after the second screening to obtain the permanent scatterers that reflect the overall deformation information of the target area.

[0092] Specifically, when selecting the permanent scatterers, obtain K + 1 radar interferograms A k from the radar interferogram set. Use the amplitude threshold method to perform the first screening on the radar interferogram. Calculate the mean values of K + 1 radar interferograms, and set the minimum mean value as the threshold. Traverse the minimum amplitude values of the K + 1 radar interferograms and compare them with the threshold. If the minimum amplitude value of the radar interferogram is greater than the threshold, then retain the radar interferogram; otherwise, exclude the radar interferogram, so as to exclude the points with strong reflection energy but insufficient stability.

[0093] The amplitude threshold method is simple to calculate and has strong practicability, but it does not consider the stable characteristics of the permanent scatterers. Therefore, it is also necessary to calculate its discrete index to select the pixel points with stable amplitude and phase. The amplitude and phase of the target pixel points have similar statistical characteristics and stability. Therefore, the phase stability can be inferred from the amplitude stability in the radar image pixels. Based on this characteristic of the permanent scatterers, the amplitude discrete index method is used for further screening. Calculate the amplitude deviation index based on the retained radar interferograms, and obtain the amplitude deviation threshold. For example, set the amplitude deviation threshold to 0.1. Perform the second screening on the radar interferogram according to the amplitude deviation index, exclude the radar interferograms with amplitude deviation index greater than or equal to the amplitude deviation threshold, and retain the radar interferograms with amplitude deviation index less than 0.1, so as to obtain the radar interferogram after the second screening.

[0094] Since the permanent scatterers are still too dense for the monitoring scenario, it is impossible to simply and effectively count all PS points and achieve continuous monitoring of deformation. Therefore, a cross filter is also needed to perform a third screening on the radar interferogram after the second screening to obtain the permanent scatterers that reflect the overall deformation information of the target area, so as to achieve the effect of sparse permanent scatterers and realize more continuous and effective deformation monitoring.

[0095] Before inputting the time series data into the Sage-Husa adaptive Kalman filter, it also includes: establishing a Sage-Husa adaptive Kalman filter model for real-time processing based on the Sage-Husa adaptive Kalman filtering algorithm, initializing the model parameters, and performing real-time deformation monitoring using the adaptive Kalman filtering algorithm. The formula is as follows:

[0096]

[0097] In the formula, is the estimated value of the deformation value, F is the state transition matrix, x t-1 is the deformation value at the previous moment, w t-1 is the noise at the previous moment, is the covariance matrix of the prediction process, P t-1 is the covariance matrix at the previous moment, F T is the transpose of the state transition matrix, Q t-1 is the covariance matrix of w t-1 is the optimal estimated value, H is the measurement transfer equation, H = [1 0], H t is the transpose of the measurement transfer equation, R T is the covariance matrix of the observation noise, e t-1 is the Gaussian noise with a mean of 0 and a variance of R in the transfer equation, z t is the transfer equation, r t is the mean estimate of the possible observation noise, I is the identity matrix, x t-1 is the deformation value, P t is the covariance matrix for correction. t is the covariance matrix for correction.

[0098] Specifically, the Sage-Husa Adaptive Kalman Filter (SHAKF) is a method that can suppress the divergence phenomenon of the standard Kalman filter. It introduces the idea of dynamic noise statistical estimation in the prediction correction process of the Kalman filter. It not only continuously corrects the prediction value using the measurement value, but also estimates and corrects the unknown system model parameters, noise statistical parameters, etc. This method can better adapt to the dynamic changes and uncertainties of the system, and improve the stability and robustness of the Kalman filter.

[0099] In this embodiment, by receiving the echo signal reflected by the target and performing preprocessing, the distance, azimuth, and altitude information of the target are calculated based on the preprocessed echo signal; based on the MIMO virtual aperture technology, the echo signals of two adjacent frames are interfered, and combined with the distance, azimuth, and altitude information to obtain a radar interference image set, which shortens the imaging time while greatly improving the detection range and imaging resolution. Permanent scatterers are extracted from the radar interference image set and unwrapped to form time-series data, which is input into the Sage-Husa adaptive Kalman filter, and the target deformation data is output. The micro-motion target is analyzed based on the target deformation data, enabling high-precision monitoring of minute deformations, greatly improving the monitoring accuracy and reliability, and being applicable to various complex environments, especially scenarios such as bridges, dams, and mine slopes, without the need to use a laser interferometer, reducing the usage cost.

[0100] In one embodiment, the monitoring accuracy of the above method is verified. Under 30 sampling periods, the corner reflector is fixed, and the comparison between the true deformation value and the measured deformation value and the measurement error caused only by external noise and the system's own error are plotted using a curve for more intuitive comparison. The comparison between the measured deformation result and the true deformation result is as Figure 4 shown. It can be seen from the figure that the difference between the measured deformation value obtained by using the method of the present invention and the actual deformation value is small, close to the true deformation value, that is, the present invention can achieve high-precision monitoring of minute deformations of the slope with high reliability.

[0101] In addition, compared with the LiDAR (Light Laser Detection and Ranging) technology, the reliability of deformation monitoring of the present invention is increased by 42% in rainy and foggy weather. After testing on a certain mine slope, compared with the InSAR (Interferometric Synthetic Aperture Radar) technology, the monitoring accuracy is increased by 37% (the RMSE (Root Mean Square Error) is reduced from 1.2 cm to 0.76 cm).

[0102] As Figure 5 shown, a micro-deformation monitoring system 50 based on SAR imaging is provided for implementing a method for micro-deformation monitoring based on SAR imaging as described above, including:

[0103] An echo signal preprocessing module 51, configured to receive the echo signal reflected by the target and perform preprocessing, and calculate based on the preprocessed echo signal to obtain the distance, azimuth, and altitude information of the target;

[0104] The echo signal interference processing module 52 is used to perform interference processing on the echo signals of two adjacent frames by using the MIMO virtual aperture technology, and combine the range, azimuth, and altitude information to obtain a radar interference image set;

[0105] The micro-motion target acquisition module 53 is used to select permanent scatterers according to the radar interference image set, perform interference phase unwrapping to obtain time-series data, input the time-series data into the Sage-Husa adaptive Kalman filter, output the target deformation data, and analyze the micro-motion target based on the target deformation data.

[0106] In this embodiment, by using the above system for monitoring the small deformations of scenarios such as bridges, dams, and mine slopes, the monitoring accuracy and reliability can be greatly improved, and the usage cost can be reduced.

[0107] The present invention also has a powerful all-weather monitoring ability, without being affected by adverse weather conditions. Whether it is heavy rain, thick fog, strong wind, or night environment, the millimeter-wave radar can work stably, ensuring the continuity and accuracy of the monitoring data. It can be calculated in real time, which is particularly important for application scenarios such as slope safety monitoring and landslide warning that require long-term and continuous detection.

[0108] The present invention adopts a non-contact monitoring method, avoiding the physical influence and interference of traditional contact sensors on the monitored object, and is particularly suitable for areas that are not suitable for direct contact, such as mountain slopes, improving the safety and applicability of the monitoring.

[0109] In addition, the present invention can be applied to the surface deformation monitoring of dams or other slopes, and is particularly suitable for industrial application fields such as slope safety monitoring, landslide warning, and other applications that require high sensitivity and high-precision deformation monitoring, improving the accuracy of the millimeter-wave radar for target deformation monitoring and having wide application value.

[0110] In one embodiment, an electronic device is provided. The device can be a server, and its internal structure diagram can be as Figure 6 shown. The device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the device is used to provide computing and control capabilities. The memory of the device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the device is used to store configuration templates and can also be used to store target web page data. The network interface of the device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it realizes the method for monitoring small deformations based on SAR imaging.

[0111] Those skilled in the art can understand, Figure 6The structure shown is only a block diagram of some of the structures related to the solution of this application, and does not constitute a limitation on the devices to which the solution of this application is applied. Specific devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0112] In one embodiment, a computer-readable storage medium may also be provided. The storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer is caused to execute the method as described in the foregoing embodiment. The computer may be a part of the SAR imaging-based micro-deformation monitoring system mentioned above.

[0113] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0114] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present invention can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. Optionally, they can be implemented by program codes executable by the computing device. Thus, they can be stored in a computer storage medium (ROM / RAM, magnetic disk, optical disk) and executed by the computing device. And in some cases, the steps shown or described can be executed in a different order than here, or they can be separately made into individual integrated circuit modules, or multiple modules or steps among them can be made into a single integrated circuit module to implement. Therefore, the present invention is not limited to any specific combination of hardware and software.

[0115] The above content is a further detailed description of the present invention in combination with specific implementation manners. It cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention belongs, without departing from the concept of the present invention, several simple deductions or substitutions can still be made, and all should be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for monitoring minute deformation based on SAR imaging, characterized in that, Including the following steps: Receiving the echo signal reflected by the target and performing preprocessing, calculating according to the preprocessed echo signal, and obtaining the distance, azimuth angle, and altitude information of the target; Adopting the MIMO virtual aperture technology to perform interference processing on the echo signals of two adjacent frames, and combining the distance, azimuth angle, and altitude information to obtain a radar interference image set; Selecting permanent scatterers according to the radar interference image set, performing interference phase unwrapping to obtain time series data, inputting the time series data into a Sage-Husa adaptive Kalman filter, outputting target deformation data, and analyzing the micro-motion target according to the target deformation data.

2. The method for monitoring minute deformation based on SAR imaging according to claim 1, characterized in that, Before receiving the echo signal reflected by the target and performing preprocessing, it further includes: Arranging a millimeter-wave radar facing the target to be measured for transmitting signals. The millimeter-wave radar adopts a transmitting mode of two transmitters and four receivers, and the number of sampling points in the range direction is set to 256.

3. The method for monitoring minute deformations based on SAR imaging according to claim 1, wherein Receiving the echo signal reflected by the target and performing preprocessing, calculating according to the preprocessed echo signal, and obtaining the distance, azimuth angle, and altitude information of the target, including: Receiving the echo signal reflected by the target and performing preprocessing on the echo signal; Using the time-domain back-projection algorithm to divide the target area into imaging grids, traversing the data of each virtual aperture, calculating the position of the virtual aperture and the distance between each point in the imaging grid, and coherently accumulating all compensated signals in the time domain. The formula is: where D x denotes the derivative with respect to x, D y denotes the derivative with respect to y, j denotes the imaginary number, f0 denotes the radar carrier frequency, R denotes the slant range of the grid point, and R0 denotes the initial position of the radar platform; Using the range migration algorithm to perform a two-dimensional Fourier forward transform on the coherently accumulated signal, traversing and focusing the frequency points and performing non-coherent accumulation on them to obtain the calculation of the distance, azimuth angle, and altitude information of the imaging target. The formula is: Where, σ(x, y, z) is the reflection coefficient of the target at this point, s(x′, y′) represents the target reflected echo, (x′, y′) represents the position coordinates of the radar at a certain moment, and FT 2D represents the two-dimensional forward Fourier transform, (x0, y0) represents the position coordinates of the radar at the initial moment, and c is the speed of light.

4. The method for monitoring minute deformation based on SAR imaging according to claim 1, wherein, Adopting the MIMO virtual aperture technology to perform interference processing on the echo signals of two adjacent frames, and combining the distance, azimuth angle, and altitude information to obtain a radar interference image set, including: Adopting the MIMO virtual aperture technology to perform difference frequency processing on the echo signals of two adjacent frames. The formula is: where \(t\) represents the sequence number of the frame where the radar is located, represents the difference frequency between two adjacent frames, is the phase of the \(t\)-th frame, is the phase of the \((t + 1)\)-th frame; Performing phase unwrapping on the echo signal according to the difference frequency, and correcting the echo signal exceeding the phase principal value to the echo signal within the phase principal value. The formula is: Wherein, represents the true value of the phase difference within the t-th frame after correction; Combining the corrected echo signal and the corresponding distance, azimuth angle, and altitude information to construct a radar interference image set.

5. The method for monitoring minute deformation based on SAR imaging according to claim 1, wherein Selecting permanent scatterers according to the radar interference image set, including: Obtain K + 1 radar interferometric images A from the radar interferometric image set k , where k = 1, 2, …, K, K + 1; The amplitude threshold method is used to perform the first screening on the radar interferogram, calculate the mean value of K + 1 radar interferograms, and take the minimum mean value as the threshold, T A = min(mean(A k ))), k = 1, 2, …, K, K + 1. Compare the minimum amplitude value of the K + 1 radar interferograms with the threshold. If the minimum amplitude value of the radar interferogram is greater than the threshold, then retain the radar interferogram; Calculating the amplitude deviation index according to all retained radar interference images. The formula is: where m A (i, j) represents the mean amplitude, A K (i, j) represents the coordinates of a pixel in a radar interferogram, δ A (i, j) represents the amplitude variance, D A (i, j) represents the amplitude deviation index; Performing secondary screening on the radar interference image according to the amplitude deviation index to obtain the radar interference image after secondary screening; Using a cross-shaped filter to perform tertiary screening on the radar interference image after secondary screening to obtain permanent scatterers reflecting the overall deformation information of the target area.

6. The method for monitoring minute deformation based on SAR imaging according to claim 1, wherein Before inputting the time series data into the Sage-Husa adaptive Kalman filter, it further includes: Based on the Sage-Husa adaptive Kalman filtering algorithm, establishing a real-time processing Sage-Husa adaptive Kalman filter model, initializing the model parameters, and performing real-time deformation monitoring using the adaptive Kalman filtering algorithm. The formula is: Wherein, is the estimated value of the deformation value, F is the state transition matrix, and x t-1 is the deformation value at the previous moment, and w t-1 is the noise at the previous moment, is the covariance matrix of the prediction process, and P t-1 is the covariance matrix at the previous moment, and F T is the transpose of the state transition matrix, and Q t-1 is the covariance matrix of w t-1 and K t is the optimal estimated value, H is the measurement transfer equation, H = [1 0], and H T is the transpose of the measurement transfer equation, and R t-1 is the covariance matrix of the observation noise, and e t is the Gaussian noise with a mean of 0 and a variance of R in the transfer equation, and z t is the transfer equation, and r t-1 is the mean estimation of the observation noise, I is the identity matrix, and x t is the deformation value, and P t is the corrected covariance matrix.

7. A micro-deformation monitoring system based on SAR imaging, characterized in that, A method for realizing a micro deformation monitoring method based on SAR imaging as described in any one of claims 1-6, including: An echo signal preprocessing module, configured to receive an echo signal reflected by a target and perform preprocessing, calculate based on the preprocessed echo signal, and obtain distance, azimuth angle, and height information of the target; An echo signal interference processing module, configured to adopt MIMO virtual aperture technology to perform interference processing on echo signals of two adjacent frames, and obtain a radar interference image set in combination with the distance, azimuth angle, and height information; A micro motion target acquisition module, configured to select permanent scatterers according to the radar interference image set, perform interference phase unwrapping to obtain time series data, input the time series data into a Sage-Husa adaptive Kalman filter, output target deformation data, and analyze the target deformation data to obtain micro motion targets.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, the steps of the method described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 6 are implemented.

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