Bridge deformation monitoring method based on navigation satellite signals

Abstract

The invention provides a bridge deformation monitoring method based on a navigation satellite signal, and relates to the technical field of bridge deformation monitoring, and the method comprises the following steps: obtaining a GNSS signal of a to-be-detected bridge, and processing the GNSS signal; sAR imaging is carried out on the processed GNSS signal, and a corresponding SAR imaging result is obtained; processing the imaging result to obtain a continuous deformation measurement result; according to the invention, low-cost and high-temporal-spatial-resolution measurement of bridge deformation can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge deformation monitoring, and more specifically, to a bridge deformation monitoring method based on navigation satellite signals. Background Art

[0002] At present, bridges are the throat of highways and railways and play a crucial role in social economy and life. To ensure the safe operation of existing bridges, the work of bridge deformation monitoring and maintenance management is gradually increasing, so it is extremely important to study high-performance automatic monitoring methods.

[0003] However, current bridge deformation measurements are mainly divided into contact type and non-contact type. Contact type measurements include level measurement, differential GNSS measurement, etc. The measurement accuracy is relatively high, but the level requires manual operation, and the layout and maintenance costs of differential GNSS are high. Therefore, the observation area is limited, and the time or space continuity is poor. Non-contact type measurements include digital close-range photogrammetry, ground-based radar measurement, spaceborne synthetic aperture radar interferometry (InSAR), etc. The information obtained by photogrammetry is intuitive, but it requires on-site survey by personnel, is affected by light, the measurement accuracy decreases with the increase of distance, and the spatio-temporal resolution is low. The ground-based radar measurement has high accuracy, but the monitoring range is small and the equipment is relatively expensive. Spaceborne InSAR is not limited by light and weather conditions, can obtain surface information under all-day and all-weather conditions, has a large coverage range, high spatial resolution, and high measurement accuracy. However, the number of spaceborne satellites is small, the revisit period is long, and the time resolution is low.

[0004] Therefore, how to provide a bridge deformation monitoring method with low cost, high accuracy, and good spatio-temporal continuity is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a bridge deformation monitoring method based on navigation satellite signals, which can achieve low-cost and high spatio-temporal resolution measurement of bridge deformation.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A bridge deformation monitoring method based on navigation satellite signals, comprising the following steps:

[0008] Obtain the GNSS signals of the bridge to be measured and process the GNSS signals;

[0009] Perform SAR imaging on the processed GNSS signals to obtain corresponding SAR imaging results;

[0010] Process the imaging results to obtain continuous deformation measurement results.

[0011] Preferably, the specific process of processing the GNSS signal includes:

[0012] Dividing the GNSS signal into direct signals and reflected signals, and constructing a reference signal according to the direct signals and the reflected signals;

[0013] Performing range compression and NH code elimination processing on the reference signal in sequence to obtain a corresponding signal processing result.

[0014] Preferably, the specific process of obtaining a corresponding SAR imaging result includes:

[0015] Obtaining an imaging area corresponding to the bridge to be measured, and performing grid division on the imaging area to obtain a corresponding plurality of grid points;

[0016] Calculating a first time delay value corresponding to the reflection-direct path difference between the navigation satellite and each grid point, and finding a second time delay value corresponding to the first time delay value from the range compression result of the reference signal for back projection;

[0017] Calculating the back projection result to obtain a final SAR imaging result.

[0018] Preferably, the specific process of calculating the back projection result to obtain a final SAR imaging result includes:

[0019] Constructing a phase compensation factor according to the echo-direct path time delay difference of the grid points;

[0020] Calculating phase compensation according to the phase compensation factor, and performing coherent accumulation on the phase compensation result to obtain a final SAR imaging result.

[0021] Preferably, the specific process of obtaining continuous deformation measurement results includes:

[0022] Preprocessing the SAR imaging result;

[0023] Processing the preprocessed SAR imaging result to obtain a corresponding true phase value;

[0024] Performing elevation deformation inversion on the true phase value to obtain continuous deformation measurement results.

[0025] Preferably, the specific process of obtaining a true phase value includes:

[0026] Performing cross-correlation image registration on the preprocessed SAR imaging result to obtain an image registration offset;

[0027] Processing the SAR imaging result according to the image registration offset to generate registered master and slave images;

[0028] Process the master-slave images to obtain corresponding interference amplitude maps and interference phase maps;

[0029] Filter the interference amplitude map and the interference phase map and perform phase unwrapping to obtain corresponding true phase values.

[0030] The present invention also provides a bridge deformation monitoring system based on navigation satellite signals, including:

[0031] An acquisition module, configured to acquire GNSS signals of a bridge to be measured and process the GNSS signals;

[0032] An imaging module, configured to perform SAR imaging on the processed GNSS signals to obtain corresponding SAR imaging results;

[0033] A processing module, configured to process the imaging results to obtain continuous deformation measurement results.

[0034] As can be seen from the above technical solutions, compared with the prior art, the present invention discloses a bridge deformation monitoring method and system based on navigation satellite signals, having the following beneficial effects:

[0035] (1) The present invention combines GNSS-R and InSAR technologies, uses navigation satellites as external radiation sources, introduces differential interferometry on the basis of GNSS-R SAR imaging, and obtains high-precision monitoring results for bridge deformation problems;

[0036] (2) The present invention uses GNSS satellites as signal transmitters, without the need for transmitters, which not only ensures the continuity and full coverage of signals, but also reduces the development cost, weight and power consumption of equipment. Description of the Drawings

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0038] Figure 1 It is the overall flowchart of a bridge deformation monitoring method based on navigation satellite signals provided by the present invention;

[0039] Figure 2 It is the geometric configuration diagram of the deformation monitoring process provided by the embodiment of the present invention;

[0040] Figure 3It is a structural principle block diagram of a bridge deformation monitoring system based on navigation satellite signals provided by an embodiment of the present invention. Detailed implementation manners

[0041] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0042] Refer to Figure 1 As shown, an embodiment of the present invention discloses a bridge deformation monitoring method based on navigation satellite signals, including the following steps:

[0043] Obtain the GNSS signals of the bridge to be measured and process the GNSS signals;

[0044] Perform SAR imaging on the processed GNSS signals to obtain corresponding SAR imaging results;

[0045] Process the imaging results to obtain continuous deformation measurement results.

[0046] Specifically, the GNSS signals of the bridge to be measured can be collected through a relevant hardware receiving platform and a data acquisition system. The hardware receiving platform is used to receive satellite signals and includes a direct antenna, a echo antenna, a data acquisition system, and a power supply device. The direct antenna is a GNSS full - right - hand circularly polarized antenna with a gain of 3 dBi; the echo antenna is a 4 - element left - hand circularly polarized antenna array with a gain of 13 dBi and a beam width of 38°.

[0047] The data acquisition system includes a down - conversion system and a digital intermediate - frequency storage system. The acquisition device reserves 4 RF input interfaces, with frequency points covering GPS L1 / L5 and Beidou B1 / B2 / B3, a sampling frequency of 62 MHz, and 8 - bit quantization, so as to obtain a digital signal with an intermediate frequency of 15.55 MHz for subsequent processing.

[0048] The power supply device is placed on the ground and supplies power to the acquisition system through a power cord.

[0049] After selecting the detection object, use the built - in hardware receiving platform to collect data; where the GNSS satellite is used as the signal transmitter, and the receiving platform is used as the signal receiver to collect GNSS direct and reflected signals.

[0050] In a specific embodiment, the specific process of processing the GNSS signals includes:

[0051] Divide the GNSS signal into direct signals and reflected signals, and construct a reference signal based on the direct signals and the reflected signals;

[0052] Perform range compression and NH code cancellation processing on the reference signal in sequence to obtain the corresponding signal processing result.

[0053] Specifically, constructing a reference signal based on the direct signal and the reflected signal, the specific expression is:

[0054]

[0055] In the formula, c Q (t) is the CA code of the quadrature branch, f IF represents the intermediate frequency, f d (u) is the Doppler frequency at azimuth time u, f L5 is the carrier frequency of the GPS L5 signal, τ d (u) represents the time delay of the satellite signal through the direct wave path, τ de and respectively represent the code time delay and phase error caused by atmospheric interference during the propagation of the signal in the direct path.

[0056] The reference signal serves as a range-matching filter to complete range compression. First, Fourier transform the two signals in the range direction to the frequency domain, multiply them by complex conjugates, and then perform an inverse Fourier transform. The specific expression of the obtained range compression result is:

[0057]

[0058] In the formula, σ T represents the target backscattering coefficient, p CA (t - Δτ(u)) represents the cross-correlation result of the time-synchronized echo signal and the reference signal, h Q represents the NH code of the quadrature branch, Δτ(u) represents the time delay difference between the echo path and the direct path, represents the atmospheric phase of the target-receiver distance history of the echo, Δf(u) represents the frequency difference between the direct signal and the echo signal; τ r (u) represents the signal reflection path time delay, τ r (u) represents the time delay of the satellite signal through the reflection path, S(u) represents the satellite position vector, c is the speed of light. Since the GNSS-RSAR imaging scene is usually within 1 km, the Doppler of the satellite relative to the receiver and the Doppler of the satellite relative to the target in the scene have a very small difference, and it can be considered that Δf(u) ≈ 0.

[0059] Binarize the output of the in-phase branch after stripping the carrier and CA code to obtain the data code, which is used to cancel the NH code in the range compression result.

[0060] In a specific embodiment, the specific process of obtaining the corresponding SAR imaging result includes:

[0061] Obtain the imaging area corresponding to the bridge to be measured, and perform grid division on the imaging area to obtain a corresponding plurality of grid points;

[0062] Calculate the first time delay value corresponding to the reflection-direct path difference between the navigation satellite and each grid point, and find the second time delay value corresponding to the first time delay value from the range compression result of the reference signal, and perform back projection;

[0063] Calculate the back projection result to obtain the final SAR imaging result.

[0064] In a specific embodiment, the specific process of calculating the back projection result to obtain the final SAR imaging result includes:

[0065] Construct a phase compensation factor according to the echo-direct path time delay difference of the grid points;

[0066] Calculate the phase compensation according to the phase compensation factor, and perform coherent accumulation on the phase compensation result to obtain the final SAR imaging result.

[0067] Specifically, the imaging area is divided into a two-dimensional grid (x i , y j ), calculate the time delay t(x i , y j ) corresponding to the reflection-direct path difference between the navigation satellite and each grid point, find the correlation value corresponding to the time delay in the range compression result, and perform back projection.

[0068] Assume that a certain grid pixel point Q is mapped from a strong point target T, and the back projection value of this point is:

[0069]

[0070] In the formula, λ L5 represents the GPS L5 signal wavelength.

[0071] Construct a phase compensation factor according to the echo-direct path time delay difference of the grid points (x i , y j ):

[0072]

[0073] The result after phase compensation for each azimuth time:

[0074]

[0075] After azimuth coherent accumulation, the final GNSS-R SAR imaging result is obtained:

[0076]

[0077] In a specific embodiment, the specific process of obtaining the true phase value includes:

[0078] Perform cross-correlation image registration on the preprocessed SAR imaging result to obtain the image registration offset;

[0079] Process the SAR imaging result according to the image registration offset to generate the registered master and slave images;

[0080] Process the master and slave images to obtain the corresponding interference amplitude image and interference phase image;

[0081] Filter and phase unwrap the interference amplitude image and interference phase image to obtain the corresponding true phase value.

[0082] Specifically, the specific process of preprocessing the original SAR imaging result can be to improve the range resolution, and then perform cross-correlation registration on the SAR image pair after resolution improvement to obtain the registration offset.

[0083] Let the two GNSS-R SAR master and slave image pairs before and after deformation obtained in the observation area be {I k ; k = 1, 2}, and any pixel point Q(m, n) in the SAR image can be expressed as:

[0084]

[0085] In the formula, A k represents the image amplitude, represents the image phase.

[0086]

[0087] Among them, S k represents the satellite equivalent position (taking the average position) vector within the synthetic aperture time of the k-th image, R represents the position vector of the receiver, T k represents the position vector of the target point in the space actually corresponding to the grid point Q, λ represents the wavelength of the carrier wave, represents the atmospheric phase, represents the phase difference between the receiver reflection channel and the direct path channel, represents the noise phase.

[0088] Further, assume that the target spatial position vector corresponding to the imaging grid point Q changes from T1 to T2, and the target elevation changes from H1 to H2. After performing interference processing on the interference image pair, that is, conjugate multiplication, the interference phase of the target point is obtained, and the specific expression is:

[0089]

[0090] In the formula,

[0091] In the formula, represents the phase at Q of the two SAR images, and S1 and S2 represent the satellite positions corresponding to the two imaging times. represents the atmospheric phase difference. represents the channel phase difference. represents the noise phase difference; represent the atmospheric phase, channel phase, and noise phase of the two SAR images respectively.

[0092] When the slant range between the receiver position R and the target point T (usually more than 50 m) is much larger than the target deformation, without considering the random phase and its interference error, the interference phase of the target point can be expressed as:

[0093]

[0094] where the two parts of the obtained results represent the deformation phase and the terrain phase

[0095]

[0096] In the formula, Φ ST represents the unit vector in the line-of-sight direction between the main image imaging satellite position vector S1 and the target point position vector T1; similarly, Φ RT represents the unit vector in the line-of-sight direction between the receiver position vector R and the target point position vector T1; B = S2 - S1 is the difference vector of the satellite equivalent position (taking the average position) within the synthetic aperture time of the main and auxiliary images, that is, the baseline, and B = |S2 - S1| is the baseline magnitude.

[0097] The deformation phase of the target point is caused by the change in the target point position between the main and auxiliary images; the terrain phase is due to the difference between the imaging plane and the actual terrain and the non-zero spatial baseline; the magnitude of the terrain phase is closely related to the spatial baseline B.

[0098] The interference phase of the SAR image pair based on the reflected signal of the navigation satellite can ultimately be expressed as consisting of two parts, namely the deformable phase and the topographic phase that can be modeled, and three parts that cannot be modeled, namely the atmospheric delay phase, the noise phase, and the inter-channel phase error:

[0099]

[0100] The Goldstein filtering algorithm is used to filter out the phase noise, that is, the original interferogram is subjected to a fast Fourier transform, and the power spectrum after the transformation is smoothed, so as to achieve the purpose of filtering the interference phase diagram. First, the phase fringe diagram is divided into overlapping phase blocks according to the fringe size, and then it is transformed into the frequency domain. The power spectrum S(u,v) of each phase block is convolved with the rectangular smoothing window W(u,v) to obtain Then, the filtering result Z(u,v) is derived from the power spectrum through a non-linear operation:

[0101]

[0102] where the exponent α is the filtering parameter, and its value range is [0,1]. After Z(u,v) is multiplied by the power spectrum S(u,v) and then subjected to an inverse Fourier transform, the filtered interferogram phase block is obtained:

[0103]

[0104] Based on this interference phase, phase unwrapping is performed to obtain the true phase.

[0105] In a grid with a size of M×N, let and φ represent the unwrapped and wrapped phases respectively, then there is:

[0106]

[0107] In the formula, n is an integer and φ(i,j) ∈ (-π,π].

[0108] The discrete partial derivative of the wrapped phase is used as an estimate of the discrete partial derivative of the true phase:

[0109]

[0110] In the formula, represent the phase gradients in two directions respectively, and n1 and n2 are integers.

[0111] Thus, the phase unwrapping problem can be converted into finding the residual of the discrete partial derivative:

[0112]

[0113]

[0114] The sum of the absolute values of all K1(i,j) and K2(i,j) should be as small as possible, that is:

[0115]

[0116] Where C1(i,j) and C2(i,j) are weighted coefficients and satisfy the following constraints:

[0117]

[0118] Where K1 and K2 represent the residuals of the discrete partial derivatives in two directions, and Ψ1 and Ψ2 represent the discrete partial derivatives of the winding phase in two directions.

[0119] It is transformed into a linear minimum problem by the following formula:

[0120]

[0121] Then the conditional minimum value and constraint conditions are transformed into formula (23):

[0122]

[0123] In the formula, It is defined according to formula (22).

[0124] The phase unwrapping algorithm based on network planning calculates the weighted matrix from the correlation coefficient and other parameters as the cost of the minimum cost flow problem; the residual value calculated from the entangled phase matrix is used as the degree of the minimum cost flow problem. Thus, the residual of the discrete partial derivative and the phase gradient are derived and Calculate the true phase.

[0125] In a specific embodiment, the specific process of obtaining continuous deformation measurement results includes:

[0126] Preprocess the SAR imaging results;

[0127] Process the preprocessed SAR imaging results to obtain the corresponding real phase value;

[0128] The elevation deformation inversion is performed on the true phase value to obtain continuous deformation measurement results.

[0129] For details, see Figure 2 The geometric model of the along-track measurement is shown in Figure 1. Deformation measurement requires that the target position before deformation is accurately known, which can be obtained by accumulating the initial elevation and the results of previous deformation measurements. The specific expression of the final interference phase is:

[0130]

[0131] Convert the interference phase into the range difference information between the satellite, receiver and the target before and after deformation:

[0132]

[0133] ΔL = |S2 - T1| - |S2 - T2| + |R - T1| - |R - T2|

[0134]

[0135] In the formula, represents the elevation of the target before deformation, represents the elevation of the target after deformation.

[0136] If the target only deforms in the elevation direction, then according to the above distance equation and the position information of satellite S2, receiver and the target T1 before deformation, the elevation deformation of the target can be solved.

[0137] Since the premise of solving the deformation information is that the position of the target before deformation is known, and the position information needs to be obtained by accumulating the measurement results of previous deformations from the initial position, this will cause error accumulation.

[0138] Taking the elevation deformation Δh(k) as an example, construct the state variable as ΔH(k) = [Δh(k) Δh(k - 1)] T , then the state equation can be established as:

[0139] ΔH(k) = I·ΔH(k) + U(k) (26)

[0140] In the formula, U(k) = [u1(k) u2(k)] T is the state noise.

[0141] Taking the differential interferometric phase as the observation variable, establish the observation equation according to the system geometric structure:

[0142]

[0143] In the formula, v(k) is the observation noise.

[0144] Linearize the above formula to obtain the linear observation equation, and then the inversion deformation sequence can be processed by Kalman filtering to achieve the effect of suppressing divergence.

[0145] Finally, since the spatio-temporal sampling of the deformation satisfies the sampling theorem, based on the inversion results, sinc interpolation can be used to obtain the nearly spatio-temporally continuous deformation measurement results.

[0146] See Figure 3As shown in the figure, an embodiment of the present invention further provides a system for using the method for monitoring bridge deformation based on navigation satellite signals according to any one of the above embodiments, including:

[0147] An acquisition module, configured to acquire GNSS signals of a bridge to be measured and process the GNSS signals;

[0148] An imaging module, configured to perform SAR imaging on the processed GNSS signals to obtain a corresponding SAR imaging result;

[0149] A processing module, configured to process the imaging result to obtain a continuous deformation measurement result.

[0150] In this specification, the various embodiments are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple. For the relevant parts, reference can be made to the description in the method part.

[0151] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring bridge deformation based on navigation satellite signals, characterized in that, Including the following steps: Obtain the GNSS signal of the bridge to be measured and process the GNSS signal; Perform SAR imaging on the processed GNSS signal to obtain the corresponding SAR imaging result; Process the imaging result to obtain continuous deformation measurement results.

2. The bridge deformation monitoring method based on navigation satellite signals according to claim 1, characterized in that, The specific process of processing the GNSS signal includes: Divide the GNSS signal into direct signals and reflected signals, and construct a reference signal according to the direct signals and reflected signals; Perform range compression and NH code elimination processing on the reference signal in sequence to obtain the corresponding signal processing result.

3. The bridge deformation monitoring method based on navigation satellite signals according to claim 2, wherein, The specific process of obtaining the corresponding SAR imaging result includes: Obtain the imaging area corresponding to the bridge to be measured, and perform grid division on the imaging area to obtain corresponding multiple grid points; Calculate the first time delay value corresponding to the reflection-direct path difference between the navigation satellite and each grid point, and find the second time delay value corresponding to the first time delay value from the range compression result of the reference signal for back projection; Calculate the back projection result to obtain the final SAR imaging result.

4. A method for monitoring bridge deformation based on navigation satellite signals according to claim 3, characterized in that, The specific process of calculating the back projection result to obtain the final SAR imaging result includes: Construct a phase compensation factor according to the echo-direct path time delay difference of the grid points; Calculate phase compensation according to the phase compensation factor, and perform coherent accumulation on the phase compensation result to obtain the final SAR imaging result.

5. A method for monitoring bridge deformation based on navigation satellite signals according to claim 1, characterized in that, The specific process of obtaining continuous deformation measurement results includes: Preprocess the SAR imaging result; Process the preprocessed SAR imaging result to obtain the corresponding true phase value; Perform elevation deformation inversion on the true phase value to obtain continuous deformation measurement results.

6. The bridge deformation monitoring method based on navigation satellite signals according to claim 5, characterized in that, The specific process of obtaining the true phase value includes: Perform cross-correlation image registration on the preprocessed SAR imaging result to obtain the image registration offset; Process the SAR imaging result according to the image registration offset to generate the registered master-slave images; Process the master-slave images to obtain the corresponding interference amplitude image and interference phase image; Perform filtering and phase unwrapping on the interference amplitude image and interference phase image to obtain the corresponding true phase value.

7. A system using the method for monitoring bridge deformation based on navigation satellite signals according to any one of claims 1-6, characterized in that, Including: An acquisition module for acquiring the GNSS signal of the bridge to be measured and processing the GNSS signal; An imaging module for performing SAR imaging on the processed GNSS signal to obtain the corresponding SAR imaging result; A processing module for processing the imaging result to obtain continuous deformation measurement results.

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