Millimeter wave radar-based bridge deformation monitoring method and system

Abstract

The invention provides a millimeter wave radar-based bridge deformation monitoring method and system, and the method comprises the steps: obtaining a transmitting signal and an echo signal of a radar, and carrying out the preprocessing of the signals, and obtaining an intermediate frequency signal; processing the intermediate frequency signal by adopting matched filtering and a zoomFFT algorithm to obtain target distance information; processing the intermediate frequency signal by adopting a MUSIC algorithm to obtain target angle information; and obtaining a target distance angle unit by combining the target distance information and the target angle information, and obtaining target deformation information by adopting phase difference and phase unwrapping. According to the invention, the interference of noise and background can be avoided, the deformation displacement of the target area can be accurately calculated, and the real-time monitoring and analysis of the deformation state of the bridge can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of deformation monitoring, and particularly to a bridge deformation monitoring method and system based on millimeter-wave radar. Background Art

[0002] With the long-term use of bridges, factors such as construction defects, material aging, natural environment, and load effects gradually reduce the reliability of bridge structures. Therefore, it is crucial to monitor and warn the health status of bridges.

[0003] Traditional monitoring methods have many disadvantages, including the need for a large amount of manpower, material resources, and financial resources, the impact on normal traffic operation, long cycle, poor real-time performance, etc. These limitations make it impossible to effectively apply them to the health monitoring and warning of large bridges. Since the deformation amount of the target to be measured is very small, generally between sub-millimeters and centimeters, for micro-deformation monitoring technology, common monitoring methods need to use a variety of instruments to measure the tiny changes of the target to judge the health status of the target. For example, displacement gauges are used to measure the dynamic deflection of bridges under load, accelerometers are used to monitor the vibration of high-rise buildings, GPS or leveling measurement methods are used to monitor the deformation of the surface of mining areas, and three-dimensional laser scanning technology is used to monitor the deformation of landslides and buildings, etc.

[0004] However, there are some problems with the above traditional measurement methods. Among them, displacement gauges and accelerometers require contact measurement, which is difficult to install and has low efficiency; GPS and leveling measurement require a large amount of work, and it is difficult to keep the measuring points for a long time. The error of traditional GPS monitoring in bridge deflection detection can reach ±3 mm, and a reference station needs to be set up on the bridge deck; while the accuracy of three-dimensional laser scanning technology decreases as the target distance increases, and it is greatly affected by the weather.

[0005] Therefore, there is an urgent need for a bridge deformation monitoring method that can accurately calculate and real-time monitor the deformation amount of bridges. Summary of the Invention

[0006] Based on this, it is necessary to provide a bridge deformation monitoring method and system based on millimeter-wave radar for the above technical problems.

[0007] A bridge deformation monitoring method based on millimeter-wave radar includes the following steps: obtaining the transmitted signal and echo signal of the radar, and preprocessing to obtain an intermediate frequency signal; processing the intermediate frequency signal by using a matched filter and a zoomFFT algorithm to obtain target distance information; processing the intermediate frequency signal by using a MUSIC algorithm to obtain target angle information; combining the target distance information and the target angle information to obtain a target distance-angle unit, and using phase difference and phase unwrapping to obtain target deformation information.

[0008] In one embodiment, acquiring the transmitted signal and echo signal of the radar, and preprocessing to obtain an intermediate-frequency signal includes:

[0009] Acquiring the transmitted signal of the radar, which is:

[0010] x T (t) = A T cos[2πf c t + πKt 2 + φ];

[0011] In the formula, x T (t) is the transmitted signal, A T is the amplitude of the transmitted signal, K is the modulation slope, is the phase noise, f c is the starting frequency;

[0012] The received echo signal is:

[0013]

[0014] In the formula, x R (t) is the echo signal, A R is the amplitude of the echo signal, τ represents the time delay;

[0015] Mixing the transmitted signal and the echo signal, performing quadrature demodulation and low-pass filtering to obtain the intermediate-frequency signal, which is:

[0016]

[0017] In the formula, X IF (t) is the intermediate-frequency signal, A IF represents the amplitude of the intermediate-frequency signal, f IF represents the frequency of the intermediate-frequency signal, j represents the imaginary number;

[0018] Among them, is the phase information of the intermediate-frequency signal, which is:

[0019]

[0020] In the formula, represents the phase difference.

[0021] In one embodiment, the modulation slope satisfies 0.1 GHz / μs ≤ K ≤ 10 GHz / μs.

[0022] In one embodiment, processing the intermediate-frequency signal by using a matched filter and a zoom FFT algorithm to obtain target distance information includes:

[0023] The received signal contains a target signal and noise, which is expressed as:

[0024] r(t) = s(t) + n(t);

[0025] Wherein, s(t) is the true received signal, n(t) represents noise, and r(t) is the received signal;

[0026] The matched filtering is obtained according to the true received signal, which is:

[0027] h(t) = s(T - t);

[0028] Wherein, T is the signal duration;

[0029] By convolving the received signal with the matched filtering, the output y(t) of the matched filter is obtained, which is:

[0030]

[0031] The matched filter satisfies:

[0032]

[0033] When h(t) = s(T - t), the SNR reaches the maximum value;

[0034] The final output signal y[n] is:

[0035]

[0036] The zoom FFT algorithm is used to perform complex analytic band-pass filtering on the target frequency band, and the frequency band x(n) to be refined is shifted to the baseband. The formula is:

[0037]

[0038] Wherein, f0 is the center frequency of the frequency band to be refined, T s is the sampling interval, and f s is the sampling frequency;

[0039] The high-frequency components are filtered out through the low-pass filter h(n) with a cut-off frequency of B / 2:

[0040] z(n) = y(n) * h(n);

[0041] Wherein, * is the convolution operation, the frequency band [-B / 2, B / 2] is retained, and decimation is performed according to the factor D = f s / B:

[0042] w(m) = z(D · m);

[0043] Set the new sampling frequency f' s = B, and satisfy the Nyquist sampling theorem, and perform M-point FFT on the decimated signal w(m):

[0044]

[0045] The corresponding physical frequency is:

[0046]

[0047] The corresponding frequency resolution is:

[0048]

[0049] The target distance information is calculated based on the physical frequency, and the formula is:

[0050]

[0051] In the formula, B is the bandwidth, M is the number of FFT points, R represents the target distance, and c is the speed of light.

[0052] In one embodiment, the MUSIC algorithm is used to process the intermediate frequency signal to obtain the target angle information, including:

[0053] The MUSIC algorithm is used to calculate the intermediate frequency signal to construct a pseudo-spectrum function, and the formula is:

[0054]

[0055] In the formula, a(θ) is the steering vector matrix, is the noise subspace eigenvector matrix, a T (θ) is the transpose of the steering vector matrix, is the transpose of the noise subspace eigenvector matrix;

[0056] All angles are traversed and the corresponding spectral values are calculated according to the pseudo-spectrum function, the spectral value peaks are compared, and the angle corresponding to the spectral value peak is used as the target angle information.

[0057] In one embodiment, the target distance-angle unit is obtained by combining the target distance information and the target angle information, and the target deformation information is obtained by using phase difference and phase unwrapping, including:

[0058] Generate a target distance-angle unit according to the target distance information and the target angle information;

[0059] Perform difference frequency processing on the target angle units of two adjacent frames, and the formula is:

[0060]

[0061] In the formula, t represents the serial number of the frame where the radar is located, represents the difference frequency between two adjacent frames, represents the phase at the t-th frame, represents the phase at the (t + 1)-th frame;

[0062] According to the said 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. The formula is:

[0063]

[0064] In the formula, represents the true value of the phase difference within the t-th frame after correction;

[0065] Then, use the phase unwrapping formula to obtain the phase change information. The formula is:

[0066] Calculate the moving distance of the target between two adjacent frames based on the corrected echo signal to obtain the target deformation information. The formula is:

[0067]

[0068] In the formula, Δr is the moving distance of the target between two adjacent frames, and λ is the signal wavelength.

[0069] A bridge deformation monitoring system based on millimeter-wave radar, which is used to implement a bridge deformation monitoring method based on millimeter-wave radar as described above, includes: a signal preprocessing module, which is used to obtain the transmitted signal and echo signal of the radar and preprocess to obtain an intermediate frequency signal; a target distance information acquisition module, which is used to process the intermediate frequency signal by using a matched filtering and zoom FFT algorithm to obtain target distance information; a target angle information acquisition module, which is used to process the intermediate frequency signal by using a MUSIC algorithm to obtain target angle information; a target deformation information acquisition module, which combines the target distance information and target angle information to obtain a target distance-angle unit, and uses phase difference and phase unwrapping to obtain target deformation information.

[0070] Compared with the prior art, the advantages and beneficial effects of the present invention are as follows: By acquiring the transmitted signal and echo signal of the radar, preprocessing to obtain the intermediate frequency signal, using the matched filtering and zoom FFT algorithms to process the intermediate frequency signal to obtain the target distance information. Zoom FFT can perform high-resolution spectral analysis on the frequency band of interest, while matched filtering further filters out the echo signals related to the target height by constructing a filter highly matched with the characteristics of the target signal, achieving precise capture of the target signal. Using the MUSIC algorithm to process the intermediate frequency signal to obtain the target angle information, it can accurately estimate the position information of the target under the background of noise and interference, further improving the accuracy and stability of target positioning, providing a solid spatial positioning basis for deformation monitoring. Combining the target distance information and target angle information to obtain the target distance-angle unit, and using phase difference and phase unwrapping to obtain the target deformation information. The phase difference technology can sensitively capture the phase change caused by small deformations, while phase unwrapping solves the ambiguity problem that may be caused by the periodicity of phase information, thereby accurately calculating the deformation displacement of the target area and realizing real-time monitoring and analysis of the deformation state. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 FIG. is a schematic flowchart of a bridge deformation monitoring method based on a millimeter-wave radar in an embodiment;

[0072] Figure 2 FIG. is a schematic framework diagram of a bridge deformation monitoring method based on a millimeter-wave radar in an embodiment;

[0073] Figure 3 FIG. is a schematic radar installation diagram of a usage scenario in an embodiment;

[0074] Figure 4 FIG. is a schematic radar installation diagram in an ideal usage scenario in an embodiment;

[0075] Figure 5 FIG. is a schematic structural diagram of a bridge deformation monitoring system based on a millimeter-wave radar in an embodiment;

[0076] Figure 6 FIG. is a schematic UI interface diagram of a bridge deformation monitoring system based on a millimeter-wave radar in an embodiment;

[0077] Figure 7 FIG. is a schematic experimental result diagram of an experiment using a bridge deformation monitoring system based on a millimeter-wave radar in an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0078] Before describing the specific embodiments of the present invention, the overall concept of the present invention is described as follows:

[0079] The present invention is mainly developed for the process of bridge deformation monitoring. Currently, the accuracy of bridge deformation monitoring is insufficient and it is difficult to achieve real-time monitoring.

[0080] Therefore, the present invention proposes a bridge deformation monitoring method based on millimeter-wave radar. By acquiring the transmitted signal and echo signal of the radar, the intermediate-frequency signal is obtained through preprocessing. The matching filtering and zoomFFT algorithms are used to process the intermediate-frequency signal to obtain the target distance information. ZoomFFT can perform high-resolution spectral analysis on the frequency band of interest, while matching filtering further screens out the echo signals highly relevant to the target by constructing a filter highly matching the characteristics of the target signal, achieving precise capture of the target signal. The MUSIC algorithm is used to process the intermediate-frequency signal to obtain the target angle information, which can accurately estimate the position information of the target under the background of noise and interference, further improving the accuracy and stability of target positioning, providing a solid spatial positioning basis for deformation monitoring. Combining the target distance information and target angle information to obtain the target distance-angle unit, and using phase difference and phase unwrapping to obtain the target deformation information. The phase difference technology can sensitively capture the phase change caused by tiny deformation, while phase unwrapping solves the ambiguity problem that may be caused by the periodicity of phase information, thereby accurately calculating the deformation displacement of the target area and realizing real-time monitoring and analysis of the deformation state. It is applicable to large bridge structures such as concrete girder bridges and cable-stayed bridges with non-metallic surfaces such as asphalt pavements and concrete bridge decks.

[0081] After introducing the overall concept of the present invention, in order to make the purpose, technical solution and advantages of the present invention more clear and understandable, the following further details the present invention through specific embodiments in conjunction with the 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.

[0082] In one embodiment, as Figure 1 and Figure 2 shown, a bridge deformation monitoring method based on millimeter-wave radar is provided, including the following steps:

[0083] Step S110, acquire the transmitted signal and echo signal of the radar, and obtain the intermediate-frequency signal through preprocessing.

[0084] Specifically, when conducting bridge deformation monitoring, the millimeter-wave radar can use the IWR6843 product and place it facing the target to be measured. The arrangement method of the millimeter-wave radar is as Figure 3 shown, and the radar installation in the ideal use scenario is as Figure 4As shown, the radar emits a transmission signal and receives the corresponding echo signal. By preprocessing the transmission signal and the echo signal, the preprocessing includes determining parameters such as the radar coordinates and the center coordinates of the target area, or calculating the distances from the radar to each point within the target area and the incident angles, etc., so as to obtain the echo signals of different antennas after preprocessing.

[0085] Among them, step S110 includes: obtaining the transmission signal of the radar, which is:

[0086] x T (t) = A T cos[2πf c t + πKt 2 + φ];

[0087] In the formula, x T (t) is the transmission signal, A T is the amplitude of the transmission signal, K is the modulation slope, and the modulation slope K satisfies 0.1 GHz / μs ≤ K ≤ 10 GHz / μs, is the phase noise, f c is the starting frequency, and B is the bandwidth; the received echo signal is:

[0088]

[0089] In the formula, x R (t) is the echo signal, A R is the amplitude of the echo signal, and τ represents the time delay; mixing the transmission signal and the echo signal, performing quadrature demodulation and low-pass filtering to obtain the intermediate-frequency signal, which is:

[0090]

[0091] In the formula, X IF (t) is the intermediate-frequency signal, A IF represents the amplitude of the intermediate-frequency signal, f IF represents the frequency of the intermediate-frequency signal, and j represents the imaginary number; among them, is the phase information of the intermediate-frequency signal, which is:

[0092]

[0093] In the formula, represents the phase difference.

[0094] Specifically, after the millimeter-wave radar transmits a signal and receives the corresponding echo signal, preprocessing is performed on the transmitted signal and the echo signal, including passing the transmitted signal and the echo signal through a mixer and a low-pass filter in sequence. 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.

[0095] Multiply the transmitted signal and the received signal through a mixer, and use trigonometric identities to separate the high-frequency component (radio frequency) and the low-frequency component (intermediate frequency). After mixing, retain the difference-frequency signal (intermediate-frequency signal) and filter out the high-frequency component. The radar system can convert the high-frequency echo signal into a low-frequency intermediate-frequency signal, facilitating subsequent signal processing and information extraction.

[0096] Step S120: Process the intermediate-frequency signal using a matched filter and a zoom FFT algorithm to obtain a target distance signal.

[0097] Specifically, using zoom FFT and matched filtering, a target echo signal is obtained. Zoom FFT can perform high-resolution spectral analysis for the frequency band of interest, while matched filtering further screens out the echo signals highly correlated with the target by constructing a filter highly matched to the characteristics of the target signal, achieving precise capture of the target signal.

[0098] Among them, step S120 includes: The received signal contains a target signal and noise, expressed as:

[0099] r(t) = s(t) + n(t);

[0100] In the formula, s(t) is the true received signal, n(t) represents noise, usually Gaussian white noise, and r(t) is the received signal; a matched filter is obtained according to the true received signal, which is:

[0101] h(t) = s(T - t);

[0102] Among them, T is the signal duration; the output y(t) of the matched filter is obtained by convolving the received signal and the matched filter, which is:

[0103]

[0104] To maximize the signal-to-noise ratio, the matched filter satisfies:

[0105]

[0106] When h(t) = s(T - t), the SNR reaches its maximum value, indicating that the matched filter is the optimal linear filter and can maximize the output signal-to-noise ratio in a Gaussian noise environment; the final output signal y[n] is:

[0107]

[0108] The zoom FFT algorithm is used to perform complex analytic bandpass filtering on the target frequency band, and the frequency band x(n) to be refined is shifted to the baseband. The formula is:

[0109]

[0110] where f0 is the center frequency of the frequency band to be refined, T s is the sampling interval, and f s is the sampling frequency;

[0111] The high-frequency components are filtered out through a low-pass filter h(n) with a cut-off frequency of B / 2:

[0112] z(n) = y(n) * h(n);

[0113] where * is the convolution operation, the frequency band [-B / 2, B / 2] is retained, and downsampling is performed by a factor of D = f s / B:

[0114] w(m) = z(D · m);

[0115] Set the new sampling frequency f′ s = B, and satisfy the Nyquist sampling theorem. Perform an M-point FFT on the downsampled signal w(m):

[0116]

[0117] The corresponding physical frequency is:

[0118]

[0119] The corresponding frequency resolution is:

[0120]

[0121] The target distance information is calculated based on the physical frequency. The formula is:

[0122]

[0123] where B is the bandwidth, M is the number of FFT points, R represents the target distance, and c is the speed of light.

[0124] Specifically, since there is noise in the received signal, matched filtering can be used to maximize the signal-to-noise ratio (SNR) in the received signal, so as to accurately detect and extract the target signal in the presence of noise. By matching with the known signal template, the signal characteristics are enhanced, and the influence of noise on the signal is suppressed.

[0125] The zoom FFT method is adopted, that is, the refined FFT (Fast Fourier Transform) method. By pre-filtering and downsampling the specific frequency band, the target distance information is obtained. While maintaining the global spectrum analysis ability, the frequency resolution of the target frequency band is significantly improved. At the same time, the refined analysis of narrowband signals is realized, the calculation complexity is reduced, the hardware resources are saved, the noise interference can be effectively suppressed, and the detection reliability of the radar system for weak targets or dense targets is improved. The distance information extracted by zoom FFT provides a basis for optimizing the spatial resolution for the subsequent MUSIC algorithm.

[0126] Step S130: Process the intermediate frequency signal using the MUSIC algorithm to obtain the target angle information.

[0127] Specifically, different from the existing phase interferometer scheme, the present invention uses the MUSIC algorithm to realize non-contact measurement. Using the MUSIC (Multiple Signal Classification) algorithm, by utilizing the spatial characteristics of the intermediate frequency signal and constructing a pseudo-spectrum function, the target angle information can be accurately estimated under the background of noise and interference. At the same time, the spatial smoothing technology is adopted to suppress the coherent interference, further improving the accuracy and stability of target positioning, and providing a solid spatial positioning basis for deformation monitoring.

[0128] Among them, step S130 includes: calculating the intermediate frequency signal using the MUSIC algorithm and constructing a pseudo-spectrum function. The formula is:

[0129]

[0130] In the formula, a(θ) is the steering vector matrix, is the noise subspace eigenvector matrix, a T (θ) is the transpose of the steering vector matrix, is the transpose of the noise subspace eigenvector matrix; all angles are traversed and the corresponding spectral values are calculated according to the pseudo-spectrum function, the spectral value peaks are compared, and the angle corresponding to the spectral value peak is used as the target angle information.

[0131] Specifically, perform multi-snapshot sampling on the preprocessed intermediate-frequency signal, calculate the covariance matrix of the received data to characterize the statistical characteristics of the signal and noise, perform eigenvalue decomposition on the covariance matrix, arrange the eigenvalues in descending order, the eigenvectors corresponding to the first K larger eigenvalues form the signal subspace, and the remaining eigenvectors form the noise subspace. Utilize the orthogonality between the signal subspace and the noise subspace to construct a spatial pseudo-spectrum function. Through the orthogonality constraint between the noise subspace and the signal direction vector, targets with angle intervals much smaller than those of the traditional beamforming method can be resolved, which is applicable to dense multi-target scenarios and can adapt to background noise and interference signals to ensure the stability of angle estimation; search for the peak position of the pseudo-spectrum within the angle range, and each peak corresponds to the arrival angle of a target signal, thereby obtaining the target angle information and achieving high-precision positioning of the target angle.

[0132] Step S140: Combine the target distance information and the target angle information to obtain the target distance-angle unit, and use phase difference and phase unwrapping to obtain the target deformation information.

[0133] Specifically, after determining the target distance information and the target angle information, extract the target echo signal through the range dimension and angle dimension information, perform difference frequency processing on the target distance-angle units of two adjacent frames, and perform signal correction through phase unwrapping to obtain the target deformation information.

[0134] Among them, step S140 includes: generating the target distance-angle unit according to the target distance information and the target angle information; performing difference frequency processing according to the target distance information and the target angle information of two adjacent frames, and the formula is:

[0135]

[0136] In the formula, t represents the serial number of the frame where the radar is located, represents the difference frequency between two adjacent frames, represents the phase at the t-th frame, represents the phase at the (t + 1)-th frame; perform phase unwrapping on the echo signal according to the difference frequency, and correct the echo signal exceeding the phase principal value to the echo signal within the phase principal value, and the formula is:

[0137]

[0138] In the formula, represents the true value of the phase difference within the t-th frame after correction. When changes continuously, linear unwrapping is enabled, when there is a mutation, least squares fitting unwrapping is enabled; obtain the phase change information using the phase unwrapping formula, and the formula is:

[0139]

[0140] Calculate the moving distance of the target between two adjacent frames based on the corrected echo signal to obtain the target deformation information. The formula is as follows:

[0141]

[0142] In the formula, Δr is the moving distance of the target between two adjacent frames, and λ is the signal wavelength.

[0143] Specifically, convert the processed echo signal into phase information, and use the phase difference technique and the phase unwrapping algorithm. The phase difference technique can sensitively capture the phase changes caused by small deformations, while the phase unwrapping solves the ambiguity problem that may be caused by the periodicity of the phase information, so as to accurately calculate the deformation displacement of the target area and realize the real-time monitoring and analysis of the deformation state.

[0144] In this embodiment, by acquiring the transmitted signal and echo signal of the radar, preprocess to obtain the intermediate frequency signal, and use the matched filtering and zoomFFT algorithms to process the intermediate frequency signal to obtain the target distance information. ZoomFFT can perform high-resolution spectral analysis for the frequency band of interest, while matched filtering further screens out the echo signals highly related to the target by constructing a filter highly matched with the target signal characteristics, realizing the accurate capture of the target signal. Use the MUSIC algorithm to process the intermediate frequency signal to obtain the target angle information, which can accurately estimate the position information of the target under the background of noise and interference, further improving the accuracy and stability of target positioning, providing a solid spatial positioning basis for deformation monitoring. Combine the target distance information and the target angle information to obtain the target distance-angle unit, and use the phase difference and phase unwrapping to obtain the target deformation information. The phase difference technique can sensitively capture the phase changes caused by small deformations, while the phase unwrapping solves the ambiguity problem that may be caused by the periodicity of the phase information, so as to accurately calculate the deformation displacement of the target area and realize the real-time monitoring and analysis of the deformation state.

[0145] In one embodiment, the above method can achieve all-weather monitoring without being affected by bad weather conditions. Whether it is heavy rain, fog, strong wind or night environment, the millimeter-wave radar can work stably to ensure the continuity and accuracy of the monitoring data. This is particularly important for application scenarios such as bridge monitoring and urban planning that require long-term and continuous monitoring. In addition, the above method adopts a non-contact monitoring method, avoiding the physical impact and interference of traditional contact sensors on the monitored object, and is particularly suitable for areas where direct contact is not suitable, such as bridge monitoring, improving the safety and applicability of detection. Compared with the traditional GPS monitoring, the root mean square error of the present invention is reduced by 47%; compared with the accelerometer monitoring, the low-frequency vibration capture ability is increased by 3 times; the robustness test under different weather conditions shows that the signal-to-noise ratio in rainy days is ≥20dB. As Figure 5As shown, a bridge deformation monitoring system 50 based on millimeter-wave radar is provided for implementing a bridge deformation monitoring method based on millimeter-wave radar as described above, including:

[0146] A signal preprocessing module 51, configured to obtain the transmitted signal and the echo signal of the radar, and preprocess to obtain an intermediate-frequency signal;

[0147] A target distance information acquisition module 52, configured to process the intermediate-frequency signal by using a matched filtering and zoomFFT algorithm to obtain target distance information;

[0148] A target angle information acquisition module 53, configured to process the intermediate-frequency signal by using a MUSIC algorithm to obtain target angle information;

[0149] A target deformation information acquisition module 54, combines the target distance information and the target angle information to obtain a target distance-angle unit, and uses phase difference and phase unwrapping to obtain target deformation information.

[0150] In one embodiment, the UI interface of the bridge deformation monitoring system based on millimeter-wave radar can be set as Figure 6 shown to achieve "real-time monitoring + low power consumption" bridge deformation monitoring.

[0151] In one embodiment, the above-mentioned bridge deformation monitoring system based on millimeter-wave radar is adopted to perform real-time displacement monitoring on a corner reflector through a precision guide rail. Micro-displacement monitoring experiments are carried out at test distances of 5 meters and 10 meters respectively for micro-displacements of 0.5 mm and 0.25 mm levels. The real experimental data is as Figure 7 shown. According to the experimental results, it can be seen that the system can perform micro-displacement monitoring at the 0.5 mm and 0.25 mm levels, achieving high-precision micro-displacement monitoring.

[0152] In addition, the millimeter-wave radar of the present invention adopts SoC technology to integrate the hardware into a single chip. The IWR6843 radar chip (TI) is selected to achieve a 20Hz update rate, reducing the signal loss and power supply requirements between discrete components; an FPGA architecture is adopted to accelerate the zoomFFT processing to improve efficiency; a continuous low-power modulation wave is adopted to avoid instantaneous high-power requirements and significantly reduce the peak power consumption; ranging and speed measurement are performed by comparing the frequency difference (instead of the time difference) between the transmitted wave and the echo wave, avoiding the requirements for high-speed ADCs and complex time synchronization circuits in traditional pulsed radars, reducing the processing power consumption, so that the monitoring system has the ability of low power consumption.

[0153] 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.

[0154] 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 from here, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module to implement. Therefore, the present invention is not limited to any specific combination of hardware and software.

[0155] 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 bridge deformation based on millimeter-wave radar, characterized in that, Including the following steps: Obtain the transmitted signal and echo signal of the radar, and preprocess to obtain the intermediate frequency signal; Process the intermediate frequency signal using the matched filtering and zoomFFT algorithms to obtain the target distance information; Process the intermediate frequency signal using the MUSIC algorithm to obtain the target angle information; Combine the target distance information and target angle information to obtain the target distance-angle unit, and use phase difference and phase unwrapping to obtain the target deformation information.

2. The method for monitoring bridge deformation based on millimeter-wave radar according to claim 1, wherein, The obtaining of the transmitted signal and echo signal of the radar and preprocessing to obtain the intermediate frequency signal includes: Obtain the transmitted signal of the radar, which is: x T (t) = A T cos[2πf c t + πKt 2 + φ]; where x T (t) is the transmitted signal, A T is the amplitude of the transmitted signal, K is the modulation slope, is the phase noise, f c is the starting frequency; The received echo signal, which is: where x R (t) is the echo signal, A R is the amplitude of the echo signal, and τ represents the time delay; Mix the transmitted signal and echo signal, perform quadrature demodulation and low-pass filtering to obtain the intermediate frequency signal, which is: where X IF (t) is the intermediate frequency signal, A IF represents the amplitude of the intermediate frequency signal, f IF represents the frequency of the intermediate frequency signal, and j represents the imaginary number; wherein, is the phase information of the intermediate frequency signal, and is: In the formula, represents the phase difference.

3. The method for monitoring bridge deformation based on millimeter-wave radar according to claim 2, wherein, The modulation slope satisfies 0.1 GHz / μs ≤ K ≤ 10 GHz / μs.

4. The method for monitoring bridge deformation based on millimeter-wave radar according to claim 2, characterized in that The processing of the intermediate frequency signal using the matched filtering and zoomFFT algorithms to obtain the target distance information includes: The received signal contains the target signal and noise, expressed as: r(t) = s(t) + n(t); In the formula, s(t) is the true received signal, n(t) represents the noise, and r(t) is the received signal; Obtain the matched filtering according to the true received signal, which is: h(t) = s(T - t); Where, T is the signal duration; Convolve the received signal with the matched filtering to obtain the output y(t) of the matched filter, which is: The matched filter satisfies: When h(t) = s(T - t), the SNR reaches the maximum value; The final output signal y[n] is: Use the zoomFFT algorithm to perform complex analytic band-pass filtering on the target frequency band, and shift the frequency band x(n) to be refined to the baseband, and the formula is: where f0 is the center frequency of the frequency band to be refined, and T s is the sampling interval, and f s is the sampling frequency; Filter out the high-frequency components through the low-pass filter h(n) with the cut-off frequency B / 2: z(n) = y(n) * h(n); where * represents the convolution operation, the frequency band [-B / 2, B / 2] is retained, and downsampling is performed by a factor of D = f s / B: w(m) = z(D · m); Set a new sampling frequency f′ s = B, and satisfy the Nyquist sampling theorem, perform an M-point FFT on the downsampled signal w(m): The corresponding physical frequency is: The corresponding frequency resolution is: Calculate the target distance information according to the physical frequency, and the formula is: In the formula, B is the bandwidth, M is the number of FFT points, R represents the target distance, and c is the speed of light.

5. The method for monitoring bridge deformation based on millimeter-wave radar according to claim 1, characterized in that The processing of the intermediate frequency signal using the MUSIC algorithm to obtain the target angle information includes: Use the MUSIC algorithm to calculate the intermediate frequency signal and construct the pseudo-spectrum function, and the formula is: where \(a(\theta)\) is the steering vector matrix, is the noise subspace eigenvector matrix, \(a\) T (\(\theta\)) is the transpose of the steering vector matrix, is the transpose of the noise subspace eigenvector matrix; Traverse all angles and calculate the corresponding spectrum values according to the pseudo-spectrum function, compare the spectrum values to obtain the peak of the spectrum values, and take the angle corresponding to the peak of the spectrum values as the target angle information.

6. The method for monitoring bridge deformation based on millimeter-wave radar according to claim 1, characterized in that The combining of the target distance information and target angle information to obtain the target distance-angle unit and using phase difference and phase unwrapping to obtain the target deformation information includes: Generate the target distance-angle unit according to the target distance information and target angle information; Perform difference frequency processing on the target angle units of two adjacent frames, and the formula is: where \(t\) represents the serial number of the frame where the radar is located, represents the difference frequency between two adjacent frames, represents the phase at the \(t\)-th frame, represents the phase at the \((t + 1)\)-th frame; Perform phase unwrapping on the echo signal according to the difference frequency, and correct the echo signal exceeding the phase principal value to the echo signal within the phase principal value, and the formula is: In the formula, represents the true value of the phase difference within the t-th frame after correction; Obtain the phase change information using the phase unwrapping formula, and the formula is: Calculate the moving distance of the target between two adjacent frames according to the corrected echo signal to obtain the target deformation information, and the formula is: Where, Δr is the moving distance of the target between two adjacent frames, and λ is the signal wavelength.

7. A bridge deformation monitoring system based on millimeter-wave radar, characterized in that, A method for monitoring bridge deformation based on millimeter-wave radar according to any one of claims 1-6, comprising: A signal preprocessing module, configured to obtain a transmitted signal and a received echo signal of the radar, and preprocess to obtain an intermediate frequency signal; A target distance information acquisition module, configured to process the intermediate frequency signal by using a matched filtering and zoom FFT algorithm to obtain target distance information; A target angle information acquisition module, configured to process the intermediate frequency signal by using a MUSIC algorithm to obtain target angle information; A target deformation information acquisition module, which combines the target distance information and the target angle information to obtain a target distance-angle unit, and uses phase difference and phase unwrapping to obtain target deformation information.

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