1. A rail transit deformation monitoring method integrating InSAR and GNSS is characterized by comprising the following steps:

based on historical InSAR monitoring data of a monitoring area, selecting a layout place of a GNSS-InSAR common antenna, and laying the GNSS-InSAR common antenna, wherein the GNSS-InSAR common antenna comprises a GNSS antenna and an InSAR corner reflector, monitoring points of which are common points, the GNSS antenna is used for collecting the GNSS data of the monitoring points, and the InSAR corner reflector is used for collecting the InSAR data of the monitoring points;

performing interference processing on the InSAR data according to an SBAS-InSAR method, inverting the atmospheric delay of the InSAR based on the GNSS data, and correcting the atmospheric delay error of the InSAR data through the atmospheric delay of the InSAR;

and acquiring the three-dimensional deformation rate of the monitoring point through the fusion of the interference result of the InSAR data and the time-space domain of the GNSS data.

2. The track traffic deformation monitoring method integrating the InSAR and the GNSS as claimed in claim 1, wherein the selecting of the layout place of the GNSS-InSAR common antenna based on the historical InSAR monitoring data of the monitoring area comprises the steps of: and obtaining a subsidence area of the monitoring area by a D-InSAR two-rail method, grading the subsidence area by the historical monitoring data, and selecting a layout place of the GNSS-InSAR common antenna based on the graded grades.

3. The track traffic deformation monitoring method of integrated InSAR and GNSS according to claim 1, wherein the interferometric processing of the InSAR data according to SBAS-InSAR method, and based on the atmospheric delay of the inversion of the GNSS data to InSAR, the atmospheric delay error of the InSAR data is corrected by the atmospheric delay to InSAR, comprising the steps of:

acquiring an interferogram from the InSAR data according to the SBAS-InSAR method;

selecting coherent points from the interferogram by a correlation coefficient;

phase unwrapping the coherent point by an LAMBDA phase unwrapping method;

constructing an interference linear model containing a linear deformation rate based on the coherent points after phase unwrapping, and separating the phase of the interference linear model through singular value decomposition of the interference linear model;

inverting atmospheric delay to InSAR based on the GNSS data, and separating atmospheric phase through the atmospheric delay to InSAR;

and converting the deformation phase in the interference pattern subjected to the atmospheric phase separation into a deformation quantity.

4. The method for monitoring deformation of track traffic integrating InSAR and GNSS as claimed in claim 3, wherein said atmospheric delay of InSAR based on said GNSS data inversion comprises the steps of:

solving tropospheric delays of the GNSS from the GNSS data;

and acquiring the atmospheric delay of InSAR from the troposphere delay of the GNSS according to a double difference method.

5. The method for monitoring deformation of track traffic based on InSAR and GNSS as claimed in claim 4, wherein said double difference method is an inter-station time domain double interpolation method of difference between time domains.

6. The method for monitoring deformation of track traffic integrating InSAR and GNSS as claimed in claim 3, wherein said separating atmospheric phase by atmospheric delay of said pair of InSAR comprises the steps of: interpolating the atmospheric delay of the pair of InSAR into the coherence point based on an inverse distance weighted interpolation method.

7. The method for monitoring deformation of track traffic integrating the InSAR and the GNSS according to claim 1, wherein the GNSS-InSAR conformal antenna is a fourteen frequency point GNSS-InSAR corner reflector conformal antenna of a four system, and the fourteen frequency point GNSS-InSAR corner reflector conformal antenna of the four system comprises: the antenna comprises a connecting column, a common structural part, a GNSS antenna and InSAR corner reflectors, wherein the GNSS antenna is arranged at the top end of the connecting column, the I nSAR corner reflectors are connected with the connecting column through the common structural part, the InSAR corner reflectors comprise four triangular pyramid three-surface corner reflectors, and the common structural part is vertically connected with the connecting column.

8. The utility model provides a track traffic deformation monitoring devices who synthesizes InSAR and GNSS which characterized in that includes:

the data monitoring unit is used for selecting a layout place of a GNSS-InSAR common antenna based on historical InSAR monitoring data of a monitoring area and laying the GNSS-InSAR common antenna, wherein the GNSS-InSAR common antenna comprises a GNSS antenna and an InSAR corner reflector, monitoring points of which are common points, the GNSS antenna is used for collecting the GNSS data of the monitoring points, and the InSAR corner reflector is used for collecting the InSAR data of the monitoring points;

the data processing unit is used for carrying out interference processing on the InSAR data according to an SBAS-InSAR method, inverting the atmospheric delay of the InSAR based on the GNSS data and correcting the atmospheric delay error of the InSAR data through the atmospheric delay of the InSAR;

and the data fusion unit is used for acquiring the three-dimensional deformation rate of the monitoring point through the time-space domain fusion of the interference result of the InSAR data and the GNSS data.

9. A track traffic deformation monitoring equipment integrating InSAR and GNSS is characterized by comprising: at least one control processor and a memory for communicative connection with the at least one control processor; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform the method for integrated InSAR and GNSS rail transit deformation monitoring of any of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions for causing a computer to perform the method for integrated InSAR and GNSS rail transit deformation monitoring according to any of claims 1 to 7.

Background

Ground settlement caused by factors such as underground water extraction and artificial construction is always a great potential safety hazard of rail transit, and the rail transit is required to be deformed by adopting a monitoring means which has higher spatial sampling density, higher data measurement precision and shorter monitoring period. Monitoring costs also need to be considered.

Synthetic Aperture Radar Interferometry (InSAR) technology and Global Navigation Satellite System (GNSS) have advantages and disadvantages in the aspect of rail transit deformation monitoring application. Such as: the principle of utilizing InSAR technology to provide large-area and high-precision monitoring results and effectively determining a settlement area with low cost is that the elevation change of the earth surface is extracted by utilizing the relation between phase difference and space distance difference contained in complex data obtained by two observations of a synthetic aperture radar, but the measurement precision is influenced by factors such as atmospheric delay, coherence and the like. In some cases, mm-level accuracy cannot be achieved. The GNSS technology can obtain accurate deformation measurement data with high time resolution and high precision of a plurality of fixed positions. However, the GNSS measurement observation point density is low, and the repeated observation period is long. If higher measurement accuracy is to be obtained, high-density monitoring points need to be distributed, and the construction cost of the monitoring points is inevitably high.

The applicant found in the study that: considering the complementarity of the two technologies, on one hand, the large-area deformation monitoring result of the InSAR has a better guiding function on the arrangement of the monitoring position and the monitoring density of the GNSS station; on the other hand, the GNSS can also correct the influence of the atmospheric delay on the InSAR result, so that the accuracy of the result is improved. Therefore, how to fuse the two technologies of the InSAR and the GNSS, the effective unification of the high time resolution and the high plane position precision of the GNSS technology and the high spatial resolution and the high elevation deformation precision of the InSAR technology is realized, and the high-precision rail transit deformation monitoring is a problem to be solved urgently.

Disclosure of Invention

The present invention is directed to at least solving the problems of the prior art. Therefore, the invention provides a rail transit deformation monitoring method, a rail transit deformation monitoring device and rail transit deformation monitoring equipment integrating InSAR and GNSS. And fusing InSAR data with high spatial sampling rate and GNSS data with high spatial and temporal resolution as monitoring results.

The invention provides a track traffic deformation monitoring method integrating InSAR and GNSS, which comprises the following steps:

based on historical InSAR monitoring data of a monitoring area, selecting a layout place of a GNSS-InSAR common antenna, and laying the GNSS-InSAR common antenna, wherein the GNSS-InSAR common antenna comprises a GNSS antenna and an InSAR corner reflector, monitoring points of which are common points, the GNSS antenna is used for collecting the GNSS data of the monitoring points, and the InSAR corner reflector is used for collecting the InSAR data of the monitoring points;

performing interference processing on the InSAR data according to an SBAS-InSAR method, inverting the atmospheric delay of the InSAR based on the GNSS data, and correcting the atmospheric delay error of the InSAR data through the atmospheric delay of the InSAR;

and acquiring the three-dimensional deformation rate of the monitoring point through the fusion of the interference result of the InSAR data and the time-space domain of the GNSS data.

According to the embodiment of the invention, at least the following technical effects are achieved:

the method utilizes InSAR and GNSS technologies, breaks through the limitation of single technology application, selects the layout place of a GNSS-InSAR common antenna based on the historical monitoring result of an InSAR area, reasonably arranges the layout control of GNSS sites, utilizes the GNSS to invert atmospheric delay to correct InSAR atmospheric error, effectively solves the error generated by atmospheric delay in the InSAR monitoring technology, and fuses InSAR data with high spatial sampling rate and GNSS data with high space-time resolution as the monitoring result. The deformation monitoring method which is controllable in cost, high in three-dimensional monitoring precision and high in space-time resolution is provided for rail transit monitoring.

In a second aspect of the present invention, there is provided a rail transit deformation monitoring device integrating InSAR and GNSS, including:

the data monitoring unit is used for selecting a layout place of a GNSS-InSAR common antenna based on historical InSAR monitoring data of a monitoring area and laying the GNSS-InSAR common antenna, wherein the GNSS-InSAR common antenna comprises a GNSS antenna and an InSAR corner reflector, monitoring points of which are common points, the GNSS antenna is used for collecting the GNSS data of the monitoring points, and the InSAR corner reflector is used for collecting the InSAR data of the monitoring points;

the data processing unit is used for carrying out interference processing on the InSAR data according to an SBAS-InSAR method, inverting the atmospheric delay of the InSAR based on the GNSS data and correcting the atmospheric delay error of the InSAR data through the atmospheric delay of the InSAR;

and the data fusion unit is used for acquiring the three-dimensional deformation rate of the monitoring point through the time-space domain fusion of the interference result of the InSAR data and the GNSS data.

According to the embodiment of the invention, at least the following technical effects are achieved:

the device utilizes InSAR and GNSS technologies, breaks through the limitation of single technology application, selects the layout place of a GNSS-InSAR common antenna based on the historical monitoring result of an InSAR area, reasonably arranges the layout control of GNSS sites, utilizes the GNSS to invert atmospheric delay and correct InSAR atmospheric error, effectively solves the error generated by atmospheric delay in the InSAR monitoring technology, and fuses InSAR data with high spatial sampling rate and GNSS data with high spatial and temporal resolution as the monitoring result. The deformation monitoring method which is controllable in cost, high in three-dimensional monitoring precision and high in space-time resolution is provided for rail transit monitoring.

Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.

Drawings

The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:

fig. 1 is a schematic flow chart of a rail transit deformation monitoring method by integrating InSAR and GNSS according to a first embodiment of the present invention;

fig. 2 is a schematic flow chart of a rail transit deformation monitoring method by integrating an InSAR and a GNSS according to a second embodiment of the present invention;

fig. 3 is a flowchart of a rail transit deformation monitoring method by integrating InSAR and GNSS according to a second embodiment of the present invention;

fig. 4 is a flow chart of a D-InSAR two-rail method according to a second embodiment of the present invention;

FIG. 5 is a diagram illustrating an external shape of a GNSS-InSAR co-mode antenna according to a second embodiment of the present invention;

FIG. 6 is a block diagram of a process of correcting InSAR atmospheric delay errors by GNSS inversion atmospheric delay according to a second embodiment of the present invention;

fig. 7 is a flowchart of a flow of performing interference processing on InSAR data by using an SBAS-InSAR method according to a second embodiment of the present invention.

Detailed Description

Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.

The applicant researches and discovers that: the principle of the InSAR technology is that the relation between phase difference and space distance difference contained in complex data obtained by two observations of a synthetic aperture radar is utilized to extract elevation change of the earth surface, however, the InSAR technology is easily influenced by the atmosphere, so that the precision cannot reach the theoretical precision. A small-baseline-set-based multi-time-sequence synthetic aperture radar differential interferometry (SBAS-InSAR for short) technology is an extension of a D-InSAR technology, and the technology performs interference combination on SAR images through preset thresholds of a time baseline and a space baseline, so that the time sampling rate is high. And forming a phase regression analysis sequence by using a short baseline differential interference fringe pattern set, taking a mean value coherence coefficient as an index for coherent target identification, and separating phase components one by using a singular value decomposition algorithm to obtain each coherent target deformation sequence. The small base line highlights the superiority of the method in terms of time and visual angle, the SAR image requirement is reduced, the influence caused by elevation errors is reduced, and meanwhile, the operation efficiency is improved.

The GNSS technology can obtain accurate deformation measurement data with high time resolution and high precision of a plurality of fixed positions, and can provide the atmospheric delay amount, so that the InSAR atmospheric delay error can be effectively corrected by utilizing the GNSS technology. Meanwhile, the layout scheme of the GNSS sites is optimized by using the InSAR region monitoring result, and the monitoring cost is reduced.

The method comprehensively utilizes two technologies of InSAR and GNSS, breaks through the limitation of single technology application, optimizes the layout scheme of GNSS sites by utilizing the monitoring result of InSAR area, realizes the effective combination of GNSS and InSAR data based on a GNSS-InSAR corner reflector common antenna, solves the problem that the precision of InSAR technology is easily influenced by atmospheric delay effect by utilizing the GNSS data, and realizes the effective unification of the high time resolution and high plane position precision of GNSS technology and the high space resolution and elevation deformation precision of InSAR technology, thereby providing the track traffic deformation monitoring method integrating InSAR and GNSS.

The examples section;

referring to fig. 1, a first embodiment of the present invention provides a rail transit deformation monitoring method that integrates InSAR and GNSS, including the following steps:

step S101, based on historical InSAR monitoring data of a monitoring area, selecting a layout place of a GNSS-InSAR common antenna, and laying the GNSS-InSAR common antenna, wherein the GNSS-InSAR common antenna comprises a GNSS antenna and an InSAR corner reflector with monitoring points as common points and is respectively used for collecting the GNSS data and the InSAR data of the monitoring points.

As an optional implementation manner, in the step S101, based on the historical InSAR monitoring data of the monitoring area, the layout place of the GNSS-InSAR conformal antenna is selected, in which a subsidence area of the monitoring area is obtained by a D-InSAR two-rail method, then the historical monitoring data is used to classify the subsidence area, and the layout place of the GNSS-InSAR conformal antenna is selected based on the classified grade. Therefore, the position and the density of the GNSS monitoring station can be reasonably guided by using a large-area deformation detection result obtained by a D-InSAR two-rail method, the longitudinal annual settlement of the line and the horizontal two-side settlement trends of the line are comprehensively considered to comprehensively evaluate the line, and the influence grade is divided for each area of the line. Compared with deformation monitoring methods such as traditional levels, GPS (global positioning system), hydrostatic levels and the like, the D-InSAR has the characteristics of all-weather all-day-long time, high deformation sensitivity, continuous region, historical backtracking and the like, and historical settlement information of a specific region can be monitored and a future settlement region can be predicted through historical data of a satellite. By collecting historical SAR data of a monitoring area, the D-InSAR technology can effectively monitor the settlement range, the settlement amount and the settlement rate of the earth surface area along the railway, so that the influence level is divided for the settlement area, and the layout place of the GNSS and the InSAR corner reflector is determined.

In step S101, a GNSS-InSAR conformal antenna is arranged, and arranged antenna points are used as control points for coordinate transformation of GNSS and InSAR data and for correcting InSAR errors, and are also used as monitoring points for monitoring key parts of a measurement area. The SBAS-InSAR uses pixel points with relatively stable phases in a time sequence as research objects, so that the loss-of-correlation noise can be reduced to the minimum, however, the SBAS-InSAR technology needs a monitoring area with pixel points with relatively stable phases, and in order to ensure that enough monitoring points (PS for short) are provided on a track route to be monitored, an artificial installation corner reflector is needed to serve as the PS. According to the scheme, InSAR data and GNSS data need to be fused subsequently, and the GNSS-InSAR conformal antenna can well complete the fusion step, so that the corner reflector and the GNSS antenna are effectively combined, and the combination of the InSAR data and the GNSS data is facilitated.

As an optional implementation manner, the GNSS-InSAR conformal antenna is a fourteen-frequency GNSS-InSAR corner reflector conformal antenna of a four-system, and the fourteen-frequency GNSS-InSAR corner reflector conformal antenna of the four-system includes: the combined-type InSAR angle reflector comprises four triangular-cone-shaped three-face angle reflectors, InSAR signals of all incident angles can be reflected, and the reflection intensity of the InSAR angle reflector is far greater than that of surrounding objects. The common structural member is vertically connected with the connecting column, and the monitoring points of the GNSS antenna and the InSAR corner reflector can be ensured to be the common point through rigid vertical connection between the connecting column and the common structural member. The fourteen-frequency-point GNSS-InSAR corner reflector common antenna of the four-system has the following effects: by means of complementation of InSAR measurement and GNSS positioning measurement information, a more accurate reference value is obtained, monitoring precision is improved, and monitoring quality of integral differential settlement is improved.

Step S102, interference processing is carried out on InSAR data according to an SBAS-InSAR method, atmospheric delay of the InSAR is inverted based on GNSS data, and atmospheric delay errors of the InSAR data are corrected through the atmospheric delay of the InSAR.

When the electromagnetic wave emitted by the InSAR passes through the atmosphere, the observation phase has an additional delay amount due to the fact that the change of the atmospheric refractive index causes extra increment on a signal propagation path or the slowing of the propagation speed. The atmospheric time-space distribution has large difference, rapid change, great uncertainty and no obvious regular characteristic, so the atmospheric delays of the SAR images acquired in different time in the same region are different, and the interference phase diagram is inevitably influenced. To this end, in step S102, the InSAR atmospheric delay error is corrected using GNSS inversion atmospheric delay.

As an optional implementation manner, a method based on a random process is firstly adopted to obtain troposphere estimation of GNSS, then a double differential algorithm is adopted to obtain atmospheric delay correction of InSAR from GNSS observation values, since the spatial resolution of atmospheric delay correction obtained by GNSS is lower than that of InSAR images, in order to obtain atmospheric delay correction in a wider area, the atmospheric delay correction obtained by GNSS is encrypted, and then the encrypted atmospheric delay correction is eliminated from an InSAR interferometric phase diagram, thereby achieving the effect of improving InSAR atmospheric delay error.

And S103, acquiring the three-dimensional deformation rate of the monitoring point through the time-space domain fusion of the interference result of the InSAR data and the GNSS data.

For rail transit, a high-precision, large-range and near-real-time means is needed to be adopted for deformation monitoring. Considering that the InSAR technology has a denser spatial sampling rate and is sensitive to vertical deformation, the GNSS has a higher spatial resolution and has higher observation accuracy in the horizontal direction. According to the complementarity of the InSAR monitoring result and the GNSS monitoring result, the InSAR monitoring result and the GNSS monitoring result are fused, and the high-precision and high-space-time resolution monitoring of the rail transit route is achieved.

According to the method, the InSAR and GNSS technologies are utilized, the limitation of single technology application is broken through, based on historical monitoring results of InSAR areas, the layout place of a GNSS-InSAR common antenna is selected, GNSS sites are reasonably arranged to be distributed and controlled, the GNSS is utilized to invert atmospheric delay to correct InSAR atmospheric errors, errors caused by atmospheric delay in the InSAR monitoring technology are effectively solved, and InSAR data with high spatial sampling rate and GNSS data with high spatial and temporal resolution are fused to serve as the monitoring results. The deformation monitoring method which is controllable in cost, high in three-dimensional monitoring precision and high in space-time resolution is provided for rail transit monitoring.

Referring to fig. 2 to 7, a second embodiment of the present invention provides a rail transit deformation monitoring method integrating InSAR and GNSS, including the following steps:

step S201, calculating a settlement area by a two-rail D-InSAR technology, and acquiring monitoring area distribution points.

And calculating the historical settlement amount of the specific area through historical archived data of the synthetic aperture radar in the settlement area, thereby predicting the settlement trend of the area. And (4) dividing influence levels on the subsidence area, and determining the layout place of the GNSS and InSAR corner reflectors. The method comprises the following specific steps: (1) collecting historical sentinel-1A (the sentinel-1A refers to a remote sensing satellite) data in a subsidence area, and selecting a deformation pair image with a short vertical baseline; (2) selecting a reference image, and accurately registering the other image with the reference image; (3) calculating a differential interference pattern by using the deformation pairs and the terrain phase simulated by using the high-resolution DEM; (4) phase unwrapping, calculating deformation quantity, and determining the range and position of the sedimentation funnel; (5) making deformation interpretation graphs, calculating deformation rate and the like. (6) Grading the influence on the settlement area; (7) and formulating a GNSS-InSAR monitoring station layout scheme according to the influence level of the specific area.

The formula for calculating the ground subsidence areas of different sections is as follows:

wherein v is₁、v₂Respectively representing the sedimentation rates of two points on the ground; t represents the settling time; d represents the square distance between two points on the ground.

The sedimentation curvature is strictly monitored in the area of more than 5mm/km, and the reason for sedimentation is analyzed and controlled. Therefore, when the GNSS control network is laid, the overall planning is firstly carried out according to the monitoring result of the InSAR, and then the gradual control and the local point laying are carried out. The GNSS monitoring stations can be flexibly arranged at relative positions, and are flexibly arranged beside the track route in the area with serious settlement at intervals of 2km according to the InSAR monitoring result. Meanwhile, a GNSS fixed station (CGPS) for 24-hour continuous observation is arranged in the main sinking area.

And S202, laying the GNSS-InSAR common mode antenna.

And laying the GNSS-InSAR common mode antenna according to the scheme designed in the step S201. The distributed antenna points are used as control points for GNSS and InSAR data coordinate conversion and InSAR error correction, and also used as monitoring points for monitoring key parts of a measuring area.

The utility model provides a fourteen frequency point GNSS-InSAR corner reflector common mode antennas of four systems, includes ground, spliced pole, common mode structure spare, GNSS antenna and InSAR corner reflector. The structure is as follows: the ground is mainly used for fixing the connecting column, and the GNSS receiving antenna is placed at the top end of the connecting column and used for receiving GNSS signals. The InSAR corner reflector is connected with the connecting column through a common structural member. The InSAR corner reflector integrally comprises four triangular conical three-sided corner reflectors, is made of aluminum materials, can reflect InSAR signals at all incident angles, and ensures that the reflection intensity of the InSAR signals is far greater than that of surrounding objects. The point of monitoring of the GNSS antenna and the InSAR corner reflector can be ensured to be a common point by a rigid vertical connection between the connecting column and the common structural member. The method has the advantages that more accurate reference values are obtained through the complementation of InSAR measurement and GNSS positioning measurement information, the monitoring precision is improved, and the monitoring quality of the integral differential settlement is further improved.

When the device is installed, a battery pit is drilled in the foundation, a rechargeable battery is placed in the battery pit, a drain pipe for draining water and a threading pipe for routing are connected with the battery pit, and the rest areas are sealed by concrete. The ground of the battery pit is provided with the drain pipe for preventing the battery from being immersed by water. The side wall of the battery pit is provided with a threading pipe for wiring. The solar cell panel is arranged in a sunny mode to charge the rechargeable battery. The GNSS antenna receives the positioning information according to the time resolution, performs corresponding storage, and is connected with a computer through a data line to export the data.

And S203, collecting and resolving GNSS signals, and collecting SAR images.

Because the GNSS data is used to correct the atmospheric delay error of the InSAR data and the two data need to be fused, the GNSS signal acquisition is synchronized with the time of SAR image acquisition. The acquisition time period of the GNSS information can preferably be extended forward or backward by about 10 minutes during the acquisition of the SAR image. And resolving the GNSS signal to obtain the position information, the plane displacement and the vertical settlement information of the monitoring point.

And step S204, establishing a coordinate conversion relation between InSAR and GNSS.

In order to fuse InSAR data and data acquired by GNSS, it is necessary to unify their data into the same reference coordinate system. The step can be well completed by utilizing the GNSS-InSAR common mode antenna which is arranged in advance. Searching for a strong reflection point formed by the common mode antenna on the InSAR image, and acquiring the ground coordinates measured by the GNSS of the strong reflection point. Suppose the image coordinates of the feature point target are (I)_row，I_col) With ground coordinates of (G)_lat，G_lon). Under a unified projection relationship, the transformation of the two sets of coordinate systems can be expressed as:

wherein, a₁、a₂、b₁、b₂、c₁、c₂Is 6 elements of the transformation matrix. As can be seen from the equation, at least the coordinates of three feature points are required to find 6 parameters. When there are more characteristic point parameters, the least square adjustment can be used to obtain 6 parameters. And finally, converting the data such as the atmospheric delay correction obtained by the GNSS into data under the InSAR image coordinates by using the conversion matrix.

And S205, inverting the atmospheric delay by utilizing the GNSS data.

The troposphere delay is obtained by utilizing the GNSS, and the delay of the troposphere can be calculated while the earth surface deformation is monitored in real time by the continuous GNSS network. The GNSS zenith total delay measurement principle is that GNSS phase observation data are utilized, and zenith ionosphere delay and troposphere delay of an observation point are used as parameters to be resolved in adjustment. The pseudo-range observation equation of the dual-frequency GNSS is as follows:

L_1j＝ρ₁-d_trop-d_ion(f₁)+∈ (3)

L_2j＝ρ₁-d_trop-d_ion(f₂)+∈ (4)

wherein epsilon is other deviation and residual error terms; d_tropIs tropospheric delay; d_ion(f_k) Is a frequency f_kThe ionospheric delay of (a).

Due to the uncertainty in time and space of the troposphere, and in particular the moisture content thereof, it is difficult for a standard atmosphere model to accurately describe the actual meteorological conditions at a GNSS point for an observation period. When discussing the actual tropospheric zenith delay, it is common to combine the dry and wet delays of the tropospheric and substitute the zenith delay calculated by the standard model and the residual zenith delay as parameters into the pseudorange observation equation of the GNSS phase observation for solution together with other parameters. Typically, the unknown zenith residual delay is estimated at each point, every time period. Within a certain time period at each point, the atmospheric zenith residual delay is approximately considered to be constant. The tropospheric delay can be expressed as:

ΔD_trop＝ρ_wm_w(E)+ρ_dm_d(E) (5)

in the formula, ρ_w、ρ_dWet and dry delay components of the troposphere, respectively, are values estimated from models of the wet and dry delay components of the troposphere, m_w(E)、m_d(E) Which are projection functions of the wet and dry delay components, respectively, related to the propagation path elevation angle E.

The InSAR is used for obtaining the ground subsidence deformation information by carrying out differential processing on the InSAR interferogram. Therefore, only the relative atmospheric delays between two points on the SAR image and between two SAR images distort the deformation information acquired by InSAR. At the same time, the phase difference and the ground deformation are always relative to a fixed point on the image. Therefore, a double difference algorithm between sites and between time domains can be used to obtain the atmospheric delay correction for InSAR from GNSS observations.

Assuming that there are two sites a and B and two times j (primary SLC image) and k (secondary SLC image), their single difference is:

a double difference can be obtained by differentiating the two single differences:

there are two possible methods to do double differentiation: firstly, carrying out inter-site differential, and then carrying out inter-site time domain double-interpolation method (BSBE) of time domain differential; the other is a time domain inter-site double-interpolation method (BEBS) which firstly performs time domain difference and then performs inter-site difference. Considering that the BSBE differential energy interpolation generates a pair of products but the differential delay correction product, the product is only related to the SLC image, and as long as the SLC image forms the InSAR pair, the BS can freely form the next BE differential combination, so the embodiment of the method adopts the BSBE method which is more widely applied.

And S206, processing the InSAR image by using an SBAS-InSAR technology to obtain a small baseline set and generate a differential interferogram.

And combining all SAR data obtained during monitoring into a plurality of sets, so that the baseline distance of the SAR images in the sets is small, and the baseline distance of the SAR images in the sets is large. And obtaining the surface deformation time sequence of each small set by using a least square method. Suppose there is a time series t₀…t_i…t_NAcquiring N +1 single-view complex SAR images, firstly setting a vertical baseline threshold, then grouping the SAR images with the vertical baseline being less than the threshold into a group, dividing the group into L groups, and then registering the groups with a proper main image, wherein each image can be at least registered with other N imagesForming an interference image pair in the images to form a short baseline subset, so that the short baseline subset consists of 2 or more images, performing differential interference processing on the images in each group, and finally obtaining M differential interference images from L groups of images, wherein if N is an odd number, the number of the differential interference images can be represented as:

with t₀The moment is the initial moment, a certain pixel x is at the moment t_i(i-0, 1, … N) relative to t₀Cumulative amount of distortion d (t) in the direction of line of sight_iAnd x) is the amount to be determined. Respectively using t_AAnd t_BTime (t)_BLater than t_A) The k (k is 1, …, M) th differential interference image is generated from the two acquired SAR images, and the observed quantity is the differential interference phase acquired by data processingThe phase at x in the differential interferogram can be expressed by the following equation:

where λ is the wavelength of the electromagnetic wave emitted by the satellite, d (t)_AX) and d (t)_BX) are respectively relative reference times t₀Accumulated deformation in LOS direction of phi (t)_AX) and phi (t)_BX) are each d (t)_AX) and d (t)_BAnd x) the phase of the deformation caused. The deformation of the N images can be estimated using the following linear model:

aΦ＝ΔΦ (11)

in the formula, phi [ N x 1 ]]A matrix formed by unknown deformation phases on the SAR image at the target point N moments; delta phi [ MX 1 ]]A matrix composed of phase values on the M differential interferograms; coefficient matrix A [ M × N ]]Each row corresponds to an interference image, each column corresponds to an SAR image at a moment, the column where the main image is located is +1, the auxiliary image is-1, and the rest are 0. If all data belong to a small baselineWhen the matrix is a subset, namely L is 1, M is more than or equal to N, A is an N-order matrix, and when M is N, the equation set has a fixed solution; when M is>When N, the system of equations is overdetermined, and its solution can be obtained by using least square as condition constraint and using matrix form to express itIs estimated value ofThe following were used:

Φ＝(A^TA)^-1A^TΔΦ (12)

however, in practice, the available data sets are usually distributed in several different subset matrices, so that a is now the case^TA becomes a reduced rank (i.e., singular matrix) matrix. If the data is assumed to come from L different small baseline subsets, A has a rank of N-L +1, where the solution to the system of equations is infinite, which requires a least squares solution in the sense of the minimum norm to be solved by the Singular Value Decomposition (SVD) method of the matrix.

And step S207, selecting coherent points by using the correlation coefficient.

Besides the GNSS-InSAR common antenna which is laid in advance can be used as a PS point, a point which has stable backward scattering signals and small loss coherence phenomenon in a short time interval in a monitoring area can also be used as a coherent point, and the point is called as a slow loss coherence filtering phase pixel point. Embodiments of the method select the coherence point by a correlation coefficient.

In the actual measurement process, because the pixels imaged by interference each time are not at the same spatial position, the theoretical value of the point is usually replaced by the overall interference coefficient in the peripheral window N of the pixel, and the overall interference coefficientThe expression is as follows:

because the SABS-InSAR algorithm utilizes a plurality of images to form interference contrast, the stability of a target in each interferogram and the stability of all interferogram series need to be considered, and when the coherent coefficients in the differential interferogram of a pixel simultaneously satisfy the following two equations, the pixel is selected as a coherent pixel:

in the formula, gamma_i(x) Is the coherence coefficient at x in the ith differential interferogram;the average coherence coefficient at x in all M differential interferograms;andrespectively corresponding threshold values.

And S208, carrying out coherent point phase unwrapping by adopting an LAMBDA phase unwrapping algorithm.

Because only partial coherent pixels are reserved in an interference pattern of the SBAS-InSAR, and the points are discrete in the distribution of spatial positions, the absolute value of phase difference between some adjacent coherent pixels cannot meet the condition of being less than pi, and an integral path of phase unwrapping is blocked, so that the discrete points need to be connected to form a complete network, and then the phase unwrapping processing is carried out. The method adopts the prior LAMBDA phase unwrapping algorithm to perform discrete coherent point phase unwrapping.

N PS points can be selected according to the selection principle of the PS points, and the PS grids are formed by using a certain net arrangement principle to arrange nets on the N PS points. Every two connected PS points form a PS point pair, and the line between the two PS points is called the baseline. Then for any stack of PS points, i and j will correspond to a time series of phase observations:andwhereinAndthe phases of points i and j on the mth interferogram, respectively, so that an interference model can be established:

wherein k is the phase integer ambiguity; beta is the phase elevation conversion coefficient, and delta H is the elevation correction; v is the sedimentation rate at the PS site; λ is the wavelength of the microwave emitted by the radar satellite; t is the time interval of two SAR images; epsilon is a random error, including errors caused by factors such as atmospheric delay and noise, and is omitted as a residual phase in the unwrapping model. The phase difference of point i with respect to point j can be expressed as:

in the above formula:

and respectively representing the interference phase difference, the phase integer ambiguity difference, the sedimentation velocity difference and the elevation correction factor difference of the ith point relative to the jth point in the mth interference diagram.

Writing equation (17) in matrix form:

order to

Equation (18) may be further abbreviated as:

y₁＝A₁*a+B₁*b (19)

in the formula (18), the phase ambiguity, the elevation correction and the sedimentation rate of the whole cycle are all the quantities to be solved; a total of m equations and m +2 unknowns, apparently the equations are rank deficient. Therefore, a pseudo observed value y needs to be added₂＝A₂*a+B₂B, together with (19), constitutes a new interference model:

y＝A*a+B*b (20)

wherein:y₂is a 2 × 1 pseudo observation vector; a. the₂Is a zero matrix of 2 x m; b is₂Is a 2 x 2 identity matrix.

According to the least squares principle, it can be calculated:

wherein, C ═ AB]；Q_yIs a variance covariance matrix of the observations and the pseudo-observations. Thus obtainedIs a floating point solution to the phase integer ambiguity. Then, the floating solution is used as known data, and a fuzzy degree decorrelation method based on least square is used for searching to obtain fixed solution a/a of the phase integer ambiguity.

After obtaining the fixed { hacek over (a) }, the unwrapping phaseCan be expressed as:

at this time, the phase after unwrapping can be expressed again as:

the least square principle is utilized again to obtain:

wherein the content of the first and second substances,a covariance matrix representing the observed values.

And step S209, singular value decomposition.

The singular value decomposition is a key theoretical algorithm of SBAS inversion, firstly a corresponding model is established on a coherent point, then an equation set is established on the basis of a linear model, and a least square solution is given under the minimum norm by using a matrix singular value decomposition method. So by solving for the generalized inverse of matrix a to give a least squares solution of equation set a, matrix a can be decomposed into the following format by SVD decomposition:

A＝USV^T (25)

in the formula, U is an M × M orthogonal matrix formed by AA^TCharacteristic vector u of_iComposition is carried out; v is an N × N orthogonal matrix consisting of A^TA feature vector v_iComposition is carried out; s is an M × N diagonal matrix, and the diagonal elements are AA^TCharacteristic value λ of_i. General M>N, assuming rank of A is R, AA^TThe first R eigenvalues of (a) are not 0, and the last M-R eigenvalues are 0. Defining the pseudo-inverse matrix of A as A⁺Then, there are:

then the least squares phase estimate in the minimum norm sense is

And (3) obtaining an average phase velocity by using the phase, namely:

instead of the phase:

meanwhile, the phase contribution of the elevation error epsilon is considered, and a new matrix equation is obtained, namely:

Dv+C·ε＝ΔΦ (29)

wherein D is an M × (N-1) matrix. For the j-th row, the columns between the main and auxiliary image acquisition times have D (j, k) ═ t_k-t_k-1And the other D (j, k) ═ 0. C [ MX 1 ]]Is a matrix of coefficients related to the baseline distance. At this time, by applying SVD decomposition to the matrix D, a minimum norm solution of the velocity vector v can be obtained, and a high-pass error ∈ can be obtained.

And step S210, inverting the atmospheric phase in the separation interferogram based on the GNSS atmospheric delay.

According to the original method for separating the atmospheric phase in the SBAS-InSAR, after the low-frequency surface deformation and DEM elevation error of each coherent pixel are obtained, the low-frequency surface deformation and DEM elevation error are removed from M differential interferograms to obtain residual phases, wherein the residual phases comprise an atmospheric delay phase, a nonlinear deformation phase and phase loss coherent noise. The atmospheric delay is not correlated in time but strongly correlated in space, and thus exhibits a high-frequency characteristic in time series and a low-frequency characteristic in spatial distribution. The high-pass filtering on the time series from the residual phase is first carried out, and the result mainly comprises the atmospheric phase and the phase-loss correlation noise. On the basis of high-pass filtering, low-pass filtering on a spatial domain is carried out in the same differential interference pattern by combining all coherent pixels, so that the loss-correlated noise is filtered, and the residual phase is the atmospheric phase.

In addition, the method further adopts the atmosphere delay of GNSS inversion to correct the atmosphere delay error of the InSAR differential interference diagram. And (3) interpolating the atmosphere delay inverted by the GNSS site to the coherent point selected in the SBAS-InSAR processing by adopting an inverse distance ratio weighted interpolation method (IDW). IDW is a deterministic interpolation method based on the following model: each data point has a local effect which diminishes with increasing distance between the data point and the interpolated point and is negligible outside a certain range, the effect being symmetrical about the data point, the interpolated value at any point being the sum of the effects of the data points. To predict the value of an untested point, the IDW will utilize the measured values in the vicinity of the untested point. Observations closer to the untested points have more effect on the untested points than observations further away. Thus, IDW assumes that each observation point has a local effect that decreases with distance. Points closer to the predicted point are weighted more heavily than points further away from the predicted point.

The general formula of the GNSS acquired inverse distance weighted interpolation of atmospheric delays is:

in the formula (I), the compound is shown in the specification,is a coordinate of east lambda₀North and southInterpolated atmospheric delays of points. N represents the number of GNSS sites around the predicted point for interpolation; w is a_iRepresenting weights related to delays of GNSS acquisitions; of IDWThe weight decreases with distance for the interpolated position;is shown in position east lambda₀North of ChinaGNSS delay correction at (c). The weights are given by:

and the number of the first and second groups,

as the distance increases, the weight decreases to the power of P. d_i0Representing the distance between the projected point and the GNSS station. The power parameter P affects the weight of the GNSS value to the interpolation point, i.e., the weight of the observation point to the interpolation point decreases exponentially as the distance between the GNSS station and the interpolation point increases. By defining a higher power parameter, points that are closer together can be more emphasized, and the resulting surface will have more detail, but will not be smooth. In this embodiment of the method the power parameter P of the distance takes the value 2.

Step S211, the deformation phase is converted into a deformation amount.

After atmospheric phase separation is carried out, the residual components of the interferogram are nonlinear deformation phase and white noise, in order to further improve the estimation accuracy of the nonlinear deformation, low-pass filtering on a spatial domain is carried out on the residual phase, and the result after filtering is the estimated phase of the nonlinear deformation. The deformation phase is converted into a deformation quantity.

Wherein, Δ R_dIs the amount of deformation seen in the line of sight,to change phase。

And S212, carrying out geographic coding on the SBAS-InSAR deformation information.

The embodiment of the method adopts the existing Doppler (R-D) positioning model to convert the result in the radar coordinate system obtained by the previous processing into the process in the geographic coordinate system.

And S213, fusing the InSAR data and the GNSS data.

For monitoring points where the GNSS-InSAR is distributed, the GNSS data and InSAR data results can be directly fused, and for other coherent points obtained by the SBAS-InSAR, interpolation is carried out on the GNSS data by using an interpolation method, a GNSS three-dimensional deformation field of a position corresponding to the coherent point is obtained, and then fusion is carried out.

First, a ground-flat rectangular coordinate system NVE is established. The X-axis and the Y-axis are fixed as an origin, the N-axis points to the north direction of the meridian, the V-axis is coincident with the normal line of the ellipsoid at the origin, and the E-axis points to the east direction. For a coherent point i in the mth interference pair in the SBAS-InSAR observation time sequence, converting a one-dimensional deformation result of the sight line of the coherent point i into three-dimensional deformation of the point in the e, n and v directions according to the projection vector of the SAR satellite

WhereinThe deformation quantity, t, of the coherent point i in the line of sight in the mth interference pair_mRepresenting the time interval of the mth interference pair,the vector matrix is projected for the unit of InSAR at point i.

For other coherent points where no common antenna is installed, a common kriging interpolation method is adopted for a three-dimensional deformation field obtained by GNSS to the same position as the SBAS-InSAR coherent point, and then:

whereinAnd obtaining the ENU direction three-dimensional deformation quantity at the coherent point i for GNSS interpolation.

And solving the three-dimensional deformation quantity of the coherent point by fusing InSAR observed quantity and interpolated GNSS observed quantity through a least square model.

L_4n×1＝A_4n×3X_3×1 (35)

For n InSAR-LOS directional observations and 3n GNSS interpolated observations,for the three-dimensional deformation rate to be solved, A_4n×3In order to design the matrix, specifically:

by solving equation (35), the three-dimensional deformation rate of all coherent points can be calculated. Thereby acquiring the relevant deformation information on the track traffic route.

The invention provides a rail transit deformation monitoring method integrating InSAR and GNSS technologies, which comprises the following steps: determining the layout place of the GNSS-InSAR common antenna by using a D-InSAR two-rail method; erecting a GNSS-InSAR common antenna; collecting and resolving GNSS signals, and collecting SAR images; establishing a coordinate conversion relation between the InSAR image and the GNSS; inverting the atmospheric delay by utilizing GNSS; processing the InSAR image by using an SBAS-InSAR technology to obtain a small baseline set and generate a differential interference map; selecting a coherent point by using the correlation coefficient; carrying out coherent point phase unwrapping by adopting an LAMBDA phase unwrapping algorithm; singular value decomposition; inverting the atmospheric phase in the separation interferogram based on the GNSS atmospheric delay; converting the deformation phase into a deformation quantity; SBAS-InSAR deformation information geocoding; and fusing InSAR data and GNSS data. The method adopts InSAR and GNSS technology to monitor the rail transit, makes a GNSS-InSAR common antenna layout scheme through the characteristics of high deformation sensitivity, continuous area, historical backtracking and the like of D-InSAR, corrects the InSAR atmospheric error by utilizing the GNSS inversion atmospheric delay, and fuses InSAR data with high spatial sampling rate and GNSS data with high space-time resolution as monitoring results. The deformation monitoring method which is controllable in cost, high in three-dimensional monitoring precision and high in space-time resolution is provided for rail transit monitoring.

A third embodiment of the present invention provides a track traffic deformation monitoring device that integrates InSAR and GNSS, including: a data monitoring unit, a data processing unit and a data fusion unit, wherein

The data monitoring unit is used for selecting a layout place of a GNSS-InSAR common antenna based on historical InSAR monitoring data of a monitoring area and laying the GNSS-InSAR common antenna, wherein the GNSS-InSAR common antenna comprises a GNSS antenna and an InSAR corner reflector with monitoring points as common points and is respectively used for collecting the GNSS data and the InSAR data of the monitoring points;

the data processing unit is used for carrying out interference processing on InSAR data according to an SBAS-InSAR method, carrying out atmospheric delay on the InSAR based on GNSS data inversion, and correcting the atmospheric delay error of the InSAR data through the atmospheric delay on the InSAR;

the data fusion unit is used for obtaining the three-dimensional deformation rate of the monitoring point through the time-space domain fusion of the interference result of the InSAR data and the GNSS data.

Since this embodiment and the first embodiment are based on the same inventive concept, the related contents of the first embodiment are also applicable to this embodiment of the apparatus, and are not described herein again.

In a fourth embodiment of the present invention, a track traffic deformation monitoring device integrating InSAR and GNSS is provided, and the device may be any type of intelligent terminal, such as a mobile phone, a tablet computer, a personal computer, and the like. Specifically, the apparatus includes: one or more control processors and memory, here exemplified by a control processor. The control processor and the memory may be connected by a bus or other means, here exemplified by a connection via a bus.

The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the track traffic deformation monitoring device integrating InSAR and GNSS in the embodiments of the present invention. The control processor implements the track traffic deformation monitoring method integrating the InSAR and the GNSS in the above method embodiment by operating the non-transitory software program, the instruction and the module stored in the memory.

The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes a memory remotely located from the control processor, and the remote memories may be connected to the track traffic deformation monitoring device of the integrated InSAR and GNSS via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The one or more modules are stored in the memory and when executed by the one or more control processors, perform the track traffic deformation monitoring method of the integrated InSAR and GNSS in the above embodiments.

The embodiment of the invention also provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are used by one or more control processors to execute the rail transit deformation monitoring method of the integrated InSAR and GNSS in the above embodiment.

Through the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be implemented by software plus a general hardware platform. Those skilled in the art will appreciate that all or part of the processes in the methods for implementing the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes in the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a Random Access Memory (RAM), or the like.

In the description herein, references to the description of the term "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples" or the like mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.