A method for monitoring bridge deflection by integrating GNSS / accelerometer and MEMS-IMU

Abstract

The present invention discloses a method for monitoring bridge deflection by integrating GNSS / accelerometer and MEMS-IMU. The method can reasonably weight the observation value of the inertial system according to the hard constraint of MEMS-IMU heading attitude and the output of high-precision attitude in a short time, take into account the deformation characteristics of the bridge, and fully exploit the non-complete constraint information such as quaternion digital model constraint and heading angle hard constraint, and use the constrained smooth variable structure filter to update and feedback correct the random drift error of the MEMS-IMU device, use the noise information and error information to force the estimated state to switch at the upper limit of the error by using the saturation term, and use the error new information to update in the upper and lower sliding mode surfaces, so as to suppress the multi-path error and other unknown or unmodeled errors in the bridge monitoring environment due to modeling residuals; construct the consistency monitoring index based on the state vector such as rate, and realize the reliable monitoring of GNSS / accelerometer and MEMS-IMU fusion.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge health monitoring, and in particular to a method for monitoring bridge deflection by integrating an integrated GNSS / accelerometer and a MEMS-IMU. Background Art

[0002] In the past 30 years, bridge construction in various regions has entered a period of rapid development, with more than 10,000 bridges started construction each year. The current prominent problem is that the total amount of bridge construction is large, the cycle is short, there are many hidden dangers, and the aging of bridges in service is accelerating. Therefore, long-term real-time and accurate monitoring of bridge structure health is of great significance to ensure the safe operation of super-large bridges.

[0003] In order to improve the frequency and accuracy of bridge monitoring using GNSS (Global Navigation Satellite System) technology, many scholars have proposed the use of multi-sensor integration methods. In recent years, the application of inertial measurement units in the field of deformation monitoring has also received attention. Zheng Rongcai et al. proposed the integration of multiple IMUs to measure the deformation of large ship decks. The results showed that it is feasible to use IMU (Inertial Measurement Unit) to monitor the deformation of ship decks. The layout of IMU has a great influence on deck deformation monitoring. Xu Peiliang et al. designed a platform integrating precise single-point positioning and IMU to monitor seismic waves, indicating that the integration of IMU and high-precision satellite positioning technology has the ability to monitor disasters such as seismic waves. Li Xingxing et al. tightly combined the raw data of GPS (Global Positioning System) and accelerometer and tested the seismic data, indicating that GPS / accelerometer integration can not only effectively increase the frequency of GPS monitoring, It also improves the reliability of ambiguity resolution; Chen Qijin and others applied GPS / IMU combined positioning to the monitoring of railway track unevenness, improving the monitoring efficiency; Geng Jianghui and others proposed the concept and prototype of "six-degree-of-freedom GNSS seismograph", integrating an inertial measurement unit into the GNSS baseband chip to improve the tracking quality of its carrier loop for high-dynamic satellite signals, and at the same time developed six-degree-of-freedom seismic data fusion theory and methods, improving the overall accuracy from 2cm to 2mm; Gao Zhouzheng and others proposed a tightly coupled model of BDS three-frequency precise single-point positioning assisted by INS and other multi-sensors, and applied it to the monitoring of railway track unevenness.

[0004] GNSS / IMU combined positioning has great potential in the field of engineering measurement. Whether it is used in experimental research on earthquake monitoring or track irregularity research for specific engineering applications, GNSS / IMU has shown great potential in the field of engineering measurement. At present, related studies mostly use navigation-grade IMU, which is expensive for bridge monitoring. MEMS-IMU (micro-electromechanical system-inertial measurement system) is relatively cheap, but the device has large random drift errors and is mostly used for dynamic measurement, and GNSS / IMU is mostly an integrated design. Reliable and consistent bridge deflection monitoring results are of great significance both for real-time operation monitoring of bridges and for long-term data mining for bridge auxiliary design. However, whether it is GNSS, GNSS / accelerometer integration, or GNSS / IMU integration in monitoring bridges, existing research focuses on verifying the effectiveness of integrated algorithms or improving integration accuracy, and there is little research on the consistency of integrated results. Summary of the invention

[0005] In view of the above problems, the present invention proposes a method for monitoring bridge deflection by integrating GNSS / accelerometer and MEMS-IMU.

[0006] To achieve the purpose of the present invention, a method for monitoring bridge deflection by integrating GNSS / accelerometer and MEMS-IMU is provided, comprising the following steps:

[0007] S10, in the initialization stage of the fusion of the integrated GNSS / accelerometer and the MEMS-IMU, the initial position and velocity are given according to the actual position, the heading angle provided by the bridge coordinate system is used as a hard constraint, and the attitude of the bridge monitoring load system is calculated using the MEMS-IMU observation value. The geographic location deviation relationship between the MEMS-IMU and the integrated GNSS / accelerometer and the lever arm position error vector are obtained through the lever arm velocity error between them, thereby realizing the synchronization of the GNSS / accelerometer and the MEMS-IMU space lever arm;

[0008] S20, based on the MEMS-IMU observation value, the spatial arm value measured by the integrated GNSS / accelerometer and the MEMS-IMU observation value, convert the displacement and velocity values ​​integrated by the integrated GNSS / accelerometer and monitored by consistency to the MEMS-IMU carrier system, and fuse them with the MEMS-IMU observation value as a comprehensive observation value, and output the fused dynamic displacement of the bridge;

[0009] S30, reasonably determine the weights according to the hard constraints and comprehensive observation values, take into account the deformation characteristics of the bridge, and fully exploit the constraint information between the quaternion digital model constraint and the heading angle hard constraint, and use the constrained smooth variable structure filter to update and feedback correct the random drift error of the MEMS-IMU device, use the noise information and error information to use the saturation term at the upper limit of the error to force the estimation state to switch, and use the error new information to update in the upper and lower sliding surface to suppress the multi-path error and other unknown or unmodeled errors of the modeling residual in the bridge monitoring environment.

[0010] In one embodiment, the above-mentioned method for monitoring bridge deflection by integrating the integrated GNSS / accelerometer and MEMS-IMU further includes:

[0011] S40, obtain the rate and rate threshold through the rate detection algorithm. If the rate obtained by the filter exceeds the rate threshold, it is determined that there is inconsistency in the fusion of GNSS / accelerometer and MEMS-IMU. At the same time, the obtained test statistic is compared with the rate threshold to determine whether there is a gross error fault and issue an error alarm to ensure that the fusion result of GNSS / accelerometer and MEMS-IMU is reliable, thereby monitoring the dynamic deflection of the high-speed railway bridge.

[0012] In one embodiment, in step S10, during the initialization phase of the integrated GNSS / accelerometer and MEMS-IMU fusion, the main task is to synchronize the GNSS / accelerometer and the MEMS-IMU spatial arm, and the process is as follows:

[0013] Assume that the vector of MEMS-IMU relative to the center of the earth is R, the vector of the antenna phase center of the integrated GNSS / accelerometer relative to the center of the earth is r, and the vector of the antenna phase center relative to the MEMS-IMU is δl. The vector relationship between the three satisfies

[0014] r=R+δl (1)

[0015] Considering that the installation positions of the integrated GNSS / accelerometer and MEMS-IMU are generally relatively fixed, that is, the space arm δl is a constant vector in the carrier coordinate system (b system), the derivatives of both sides of the above equation relative to the earth coordinate system (e system) can be obtained:

[0016]

[0017] Among them, is the ground speed of the carrier, is the ground speed of the satellite antenna;

[0018] Project (2) to the navigation coordinate system and we get

[0019]

[0020] In the above formula, the speed subscript "en" is omitted. In practical applications, due to ω ie and ω en The influence of or The velocity error between the MEMS-IMU and the integrated GNSS / accelerometer is defined as the lever arm velocity error, which is:

[0021]

[0022] If you remember

[0023]

[0024] Then the geographical location deviation between them approximately satisfies the following relationship

[0025]

[0026] The arm position error vector between the MEMS-IMU and the integrated GNSS / accelerometer can be calculated from equations (5) and (6) as follows:

[0027]

[0028] where p GNSS =[L GNSS λ GNSS h GNSS ] T , L GNSS represents the latitude of the GNSS / accelerometer, λ GNSS Indicates the longitude of the GNSS / accelerometer, h GNSS Indicates the altitude of the GNSS / accelerometer, p INS =[L INS λ INS h INS ] T , L INS represents the latitude of MEMS-IMU, λ INS Indicates the longitude of MEMS-IMU, h INS represents the height of the MEMS-IMU, secL represents the secant value of INS latitude, R Nh R represents the principal curvature radius of the y-axis circle. Mh represents the principal radius of curvature of the meridian;

[0029] The error between the lever arms is estimated by the lever arm speed error and the geographical location deviation between them, thereby achieving synchronization of the spatial lever arms.

[0030] In one embodiment, the construction process of the smooth variable structure filter includes:

[0031] Assume that the system equation of the smooth variable structure filter is:

[0032]

[0033] x k is the system status; u k is the relative input of the system; w k is the system noise; z k is the measured value; v k is the measurement noise, f(x k ,u k ,w k ) represents a function, including the system state transfer matrix A and the input gain matrix B, h(x k ) includes a measurement transfer matrix H;

[0034] By deducing the covariance-based nonlinear system iteration equation, the state prediction equation is:

[0035]

[0036] Covariance prediction equation:

[0037]

[0038] Observation equation:

[0039]

[0040] Gain equation:

[0041]

[0042]

[0043]

[0044] in, is the linearized measurement matrix, H k+1 Positive constant matrix; A=|e z,k+1|k |+γ|e z,k|k |, γ is the memory or convergence speed factor, 0<γ<1;

[0045]

[0046] System state estimation equation:

[0047]

[0048] Covariance generalization equation:

[0049]

[0050] The error equation is:

[0051]

[0052] For nonlinear dynamic equations, linearization is required:

[0053]

[0054] Taking into account the deformation characteristics of the bridge, the modulus of the quaternion is set to 1 and the heading angle correction number is set to 0. This is used as constraint information to substitute into the observation equation (17) of the filter to construct a constrained smooth variable structure filter model, thereby realizing filter update and feedback correction to determine the smooth variable structure filter.

[0055] In one embodiment, the consistency monitoring indicator includes a rate; and the rate detection process includes:

[0056] The principle of the rate detection algorithm is to detect the growth rate of the detection quantity, which is achieved by adding a Kalman filter to the autonomous integrity extrapolation algorithm. The filter can detect the rate change of input and estimated noise.

[0057] The system state equation of the rate detection algorithm is:

[0058]

[0059] Among them, P and V represent the detected position and velocity respectively. V contains colored noise, and a represents acceleration; represents the correlation coefficient, W1, W2 and W3 represent the system noise, that is,

[0060]

[0061] The measurement matrix of the system can be expressed as:

[0062] H=[1 0 0] (21)

[0063] So the rate can be obtained as:

[0064]

[0065] Assume that the detected position P contains only a single slowly growing position error and other measurement errors are close to zero. When the system has no faults, it obeys a zero-mean Gaussian white noise distribution.

[0066] In one embodiment, the detection statistic defining the rate detection algorithm is

[0067]

[0068] Where σ0 is the standard deviation of V; P V is the covariance of the state variable V. When the system is operating normally, the detection statistic D follows the standard normal distribution;

[0069] Assume there are N observations, each with the same error probability, P FA is the sum of the false alarm rates of all observations, then the false alarm rate of a single observation is P FA / N; therefore, the threshold calculation formula based on the false alarm rate is as follows:

[0070]

[0071] Where: V D represents the rate threshold, and equation (24) shows that in the absence of faults, D is less than V D No alarm will occur when , so the detection threshold V D , by comparing D with V D The size of can be used to determine whether there is a gross error fault in the system, thereby determining whether to save the results of GNSS / accelerometer and IMU fusion.

[0072] In the above-mentioned method for monitoring bridge deflection by integrating GNSS / accelerometer and MEMS-IMU, in the initialization stage of the integration of integrated GNSS / accelerometer and MEMS-IMU, the initial position and velocity are given according to the actual position, the heading angle provided by the bridge coordinate system is used as a hard constraint, and the MEMS-IMU observation value is used to calculate the attitude of the bridge monitoring load system; in the data fusion stage, according to the accurately measured spatial arm values ​​of the MEMS-IMU and the integrated GNSS / accelerometer, the real-time attitude of the MEMS-IMU is used to convert the displacement and velocity values ​​integrated by the integrated GNSS / accelerometer and monitored through consistency to the MEMS-IMU load system, and as the observation value, it is fused with the MEMS-IMU observation value to output the fused dynamic displacement of the bridge. According to the hard constraints of MEMS-IMU heading and attitude and the output of high-precision attitude in a short time, the inertial system observation values ​​are reasonably weighted, taking into account the deformation characteristics of the bridge, and fully exploring the non-complete constraint information such as quaternion digital-analog constraints and heading angle hard constraints. The constrained smooth variable structure filter is used to update and feedback correct the random drift error of the MEMS-IMU device. The noise information and error information are used to force the estimated state to switch at the upper limit of the error (sliding surface) using the saturation term, and the error new information is used to update on the upper and lower sliding surfaces to suppress the residual multipath error and other unknown or unmodeled errors in the bridge monitoring environment. The consistency monitoring index based on state vectors such as rate is constructed to realize reliable monitoring of the fusion of GNSS / accelerometer and MEMS-IMU. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 is a flow chart of a method for monitoring bridge deflection by integrating a GNSS / accelerometer and a MEMS-IMU in one embodiment;

[0074] Figure 2 It is a flow chart of a method for monitoring bridge deflection by integrating an integrated GNSS / accelerometer and MEMS-IMU according to another embodiment. DETAILED DESCRIPTION

[0075] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0076] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0077] refer to Figure 1 As shown, Figure 1 The flowchart of a method for monitoring bridge deflection by integrating an integrated GNSS / accelerometer and MEMS-IMU according to an embodiment includes the following steps:

[0078] S10, in the initialization stage of the fusion of the integrated GNSS / accelerometer and the MEMS-IMU, the initial position and velocity are given according to the actual position, the heading angle provided by the bridge coordinate system is used as a hard constraint, and the attitude of the bridge monitoring load system is calculated using the MEMS-IMU observation value. The geographic location deviation relationship between the MEMS-IMU and the integrated GNSS / accelerometer and the lever arm position error vector are obtained through the lever arm velocity error between them, thereby realizing the synchronization of the GNSS / accelerometer and the MEMS-IMU space lever arm;

[0079] S20, based on the MEMS-IMU observation value, the spatial arm value measured by the integrated GNSS / accelerometer and the MEMS-IMU observation value, convert the displacement and velocity values ​​integrated by the integrated GNSS / accelerometer and monitored by consistency to the MEMS-IMU carrier system, and fuse them with the MEMS-IMU observation value as a comprehensive observation value, and output the fused dynamic displacement of the bridge;

[0080] S30, reasonably determine the weights according to the hard constraints and comprehensive observation values, take into account the deformation characteristics of the bridge, and fully exploit the constraint information between the quaternion digital model constraint and the heading angle hard constraint, and use the constrained smooth variable structure filter to update and feedback correct the random drift error of the MEMS-IMU device, use the noise information and error information to use the saturation term at the upper limit of the error to force the estimation state to switch, and use the error new information to update in the upper and lower sliding surface to suppress the multi-path error and other unknown or unmodeled errors of the modeling residual in the bridge monitoring environment.

[0081] In one embodiment, the above-mentioned method for monitoring bridge deflection by integrating the integrated GNSS / accelerometer and MEMS-IMU further includes:

[0082] S40, obtain the rate and rate threshold through the rate detection algorithm. If the rate obtained by the filter exceeds the rate threshold, it is determined that there is inconsistency in the fusion of GNSS / accelerometer and MEMS-IMU. At the same time, the obtained test statistic is compared with the rate threshold to determine whether there is a gross error fault and issue an error alarm to ensure that the fusion result of GNSS / accelerometer and MEMS-IMU is reliable, thereby monitoring the dynamic deflection of the high-speed railway bridge.

[0083] In one embodiment, in step S10, during the initialization phase of the integrated GNSS / accelerometer and MEMS-IMU fusion, the main task is to synchronize the GNSS / accelerometer and the MEMS-IMU spatial arm, and the process is as follows:

[0084] Assume that the vector of MEMS-IMU relative to the center of the earth is R, the vector of the antenna phase center of the integrated GNSS / accelerometer relative to the center of the earth is r, and the vector of the antenna phase center relative to the MEMS-IMU is δl. The vector relationship between the three satisfies

[0085] r=R+δl (1)

[0086] Considering that the installation positions of the integrated GNSS / accelerometer and MEMS-IMU are generally relatively fixed, that is, the space arm δl is a constant vector in the carrier coordinate system (b system), the derivatives of both sides of the above equation relative to the earth coordinate system (e system) can be obtained:

[0087]

[0088] Among them, is the ground speed of the carrier, is the ground speed of the satellite antenna; theoretically, due to the existence of the arm distance, the navigation coordinate systems defined by the two ground speeds are different, but the arm length is generally in the order of meters, and the difference between the two navigation coordinate systems is very small, so they can be considered to be parallel to each other.

[0089] Project (2) to the navigation coordinate system and we get

[0090]

[0091] In the above formula, the speed subscript "en" is omitted. In practical applications, due to ω ie and ω en The influence of or The velocity error between the MEMS-IMU and the integrated GNSS / accelerometer is defined as the lever arm velocity error, which is:

[0092]

[0093] If you remember

[0094]

[0095] Then the geographical location deviation between them approximately satisfies the following relationship

[0096]

[0097] The arm position error vector between the MEMS-IMU and the integrated GNSS / accelerometer can be calculated from equations (5) and (6) as follows:

[0098]

[0099] where p GNSS =[L GNSS λ GNSS h GNSS ] T , L GNSS represents the latitude of the GNSS / accelerometer, λ GNSS Indicates the longitude of the GNSS / accelerometer, h GNSS Indicates the altitude of the GNSS / accelerometer, p INS =[L INS λ INS h INS ] T , L INS represents the latitude of MEMS-IMU, λ INS Indicates the longitude of MEMS-IMU, h INS represents the height of the MEMS-IMU, secL represents the secant value of INS latitude, R Nh R represents the principal curvature radius of the y-axis circle. Mh represents the principal radius of curvature of the meridian;

[0100] The error between the lever arms is estimated by the lever arm speed error and the geographical location deviation between them, thereby achieving synchronization of the spatial lever arms.

[0101] In one embodiment, the construction process of the smooth variable structure filter includes:

[0102] Assume that the system equation of the smooth variable structure filter is:

[0103]

[0104] x k is the system status; u k is the relative input of the system; w k is the system noise; z k is the measured value; v k is the measurement noise, f(x k ,u k ,w k ) represents a function, including the system state transfer matrix A and the input gain matrix B, h(x k ) includes a measurement transfer matrix H;

[0105] By deducing the covariance-based nonlinear system iteration equation, the state prediction equation is:

[0106]

[0107] Covariance prediction equation:

[0108]

[0109] Observation equation:

[0110]

[0111] Gain equation:

[0112]

[0113]

[0114]

[0115] in, is the linearized measurement matrix, H k+1 Positive constant matrix; A=|e z,k+1|k |+γ|e z,k|k |, γ is the memory or convergence speed factor, 0<γ<1;

[0116]

[0117] System state estimation equation:

[0118]

[0119] Covariance generalization equation:

[0120]

[0121] The error equation is:

[0122]

[0123] For nonlinear dynamic equations, linearization is required:

[0124]

[0125] Taking into account the deformation characteristics of the bridge, the modulus of the quaternion is set to 1 and the heading angle correction number is set to 0. This is used as constraint information to substitute into the observation equation (17) of the filter to construct a constrained smooth variable structure filter model, thereby realizing filter update and feedback correction to determine the smooth variable structure filter.

[0126] In one embodiment, the consistency monitoring indicator includes a rate; and the rate detection process includes:

[0127] The principle of the rate detection algorithm is to detect the growth rate of the detection quantity, which is achieved by adding a Kalman filter to the autonomous integrity extrapolation algorithm. The filter can detect the rate change of input and estimated noise.

[0128] The system state equation of the rate detection algorithm is:

[0129]

[0130] Among them, P and V represent the detected position and velocity respectively. V contains colored noise, and a represents acceleration; represents the correlation coefficient, W1, W2 and W3 represent the system noise, that is,

[0131]

[0132] The measurement matrix of the system can be expressed as:

[0133] H=[1 0 0] (21)

[0134] So the rate can be obtained as:

[0135]

[0136] Assume that the detected position P contains only a single slowly growing position error and other measurement errors are close to zero. When the system has no faults, it obeys a zero-mean Gaussian white noise distribution.

[0137] In one embodiment, the detection statistic defining the rate detection algorithm is

[0138]

[0139] Where σ0 is the standard deviation of V; P V is the covariance of the state variable V. When the system is operating normally, the detection statistic D follows the standard normal distribution;

[0140] Assume there are N observations, each with the same error probability, P FA is the sum of the false alarm rates of all observations, then the false alarm rate of a single observation is P FA / N; therefore, the threshold calculation formula based on the false alarm rate is as follows:

[0141]

[0142] Where: V D represents the rate threshold, and equation (24) shows that in the absence of faults, D is less than V D No alarm will occur when , so the detection threshold V D , by comparing D with V D The size of can be used to determine whether there is a gross error fault in the system, thereby determining whether to save the results of GNSS / accelerometer and IMU fusion.

[0143] In one embodiment, the above-mentioned method of integrating GNSS / accelerometer and MEMS-IMU to monitor bridge deflection can also refer to Figure 2As shown in the figure, in the initialization stage of the fusion of integrated GNSS / accelerometer and MEMS-IMU, the initial position and velocity are given according to the actual position, the heading angle provided by the bridge coordinate system is used as a hard constraint, and the MEMS-IMU observation value is used to calculate the attitude of the bridge monitoring load system; in the data fusion stage, according to the accurately measured spatial arm values ​​of the MEMS-IMU and the integrated GNSS / accelerometer, the displacement and velocity values ​​integrated by the integrated GNSS / accelerometer and monitored through consistency are converted to the MEMS-IMU load system using the MEMS-IMU real-time attitude, and used as observation values ​​to be fused with the MEMS-IMU observation values ​​to output the fused dynamic displacement of the bridge. According to the hard constraints of MEMS-IMU heading and attitude and the output of high-precision attitude in a short time, the inertial system observation values ​​are reasonably weighted, taking into account the deformation characteristics of the bridge, and fully exploring the non-complete constraint information such as quaternion digital-analog constraints and heading angle hard constraints. The constrained smooth variable structure filter is used to update and feedback correct the random drift error of the MEMS-IMU device. The noise information and error information are used to force the estimated state to switch at the upper limit of the error (sliding surface) using the saturation term, and the error new information is used to update on the upper and lower sliding surfaces to suppress the residual multipath error and other unknown or unmodeled errors in the bridge monitoring environment. The consistency monitoring index based on state vectors such as rate is constructed to realize reliable monitoring of the fusion of GNSS / accelerometer and MEMS-IMU.

[0144] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0145] It should be noted that the terms "first\second\third" involved in the embodiments of the present application are only used to distinguish similar objects, and do not represent a specific order for the objects. It is understandable that the specific order or sequence of "first\second\third" can be interchanged where permitted. It should be understood that the objects distinguished by "first\second\third" can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein.

[0146] The terms "including" and "having" and any variations thereof in the embodiments of the present application are intended to cover non-exclusive inclusions. For example, a process, method, device, product, or equipment that includes a series of steps or modules is not limited to the listed steps or modules, but may optionally include steps or modules that are not listed, or may optionally include other steps or modules that are inherent to these processes, methods, products, or equipment.

[0147] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for monitoring bridge deflection by integrating GNSS / accelerometer and MEMS-IMU, characterized in that: The steps include: S10, in the initialization stage of the fusion of the integrated GNSS / accelerometer and the MEMS-IMU, the initial position and velocity are given according to the actual position, the heading angle provided by the bridge coordinate system is used as a hard constraint, and the attitude of the bridge monitoring load system is calculated using the MEMS-IMU observation value. The geographic location deviation relationship between the MEMS-IMU and the integrated GNSS / accelerometer and the lever arm position error vector are obtained through the lever arm velocity error between them, thereby realizing the synchronization of the GNSS / accelerometer and the MEMS-IMU space lever arm; S20, based on the MEMS-IMU observation value, the spatial arm value measured by the integrated GNSS / accelerometer and the MEMS-IMU observation value, convert the displacement and velocity values ​​integrated by the integrated GNSS / accelerometer and monitored by consistency to the MEMS-IMU carrier system, and fuse them with the MEMS-IMU observation value as a comprehensive observation value, and output the fused dynamic displacement of the bridge; S30, reasonably determine the weights according to the hard constraints and the comprehensive observation values, take into account the deformation characteristics of the bridge, and fully explore the constraint information between the quaternion digital model constraint and the heading angle hard constraint, and use the constrained smooth variable structure filter to update and feedback correct the random drift error of the MEMS-IMU device, use the noise information and error information to force the estimation state to switch at the upper limit of the error using the saturation term, and use the error new information to update in the upper and lower sliding mode surfaces to suppress the multipath error and other unknown or unmodeled errors in the bridge monitoring environment. In step S10, during the initialization phase of the integrated GNSS / accelerometer and MEMS-IMU fusion, the main task is to synchronize the GNSS / accelerometer and the MEMS-IMU space arm, and the process is as follows: Assume that the vector of MEMS-IMU relative to the center of the earth is R, the vector of the antenna phase center of the integrated GNSS / accelerometer relative to the center of the earth is r, and the vector of the antenna phase center relative to the MEMS-IMU is δl. The vector relationship between the three satisfies r=R+δl (1) Considering that the installation positions of the integrated GNSS / accelerometer and MEMS-IMU are generally relatively fixed, that is, the space arm δl is a constant vector in the carrier coordinate system (b system), the derivatives of both sides of the above equation relative to the earth coordinate system (e system) can be obtained: Among them, is the ground speed of the carrier, is the ground speed of the satellite antenna; Project (2) to the navigation coordinate system and we get In the above formula, the speed subscript "en" is omitted. In practical applications, due to ω ie and ω en The influence of or The velocity error between the MEMS-IMU and the integrated GNSS / accelerometer is defined as the lever arm velocity error, which is: If you remember Then the geographical location deviation between them approximately satisfies the following relationship The arm position error vector between the MEMS-IMU and the integrated GNSS / accelerometer can be calculated from equations (5) and (6) as follows: where p GNSS =[L GNSS λ GNSS h GNSS ] T , L GNSS represents the latitude of the GNSS / accelerometer, λ GNSS Indicates the longitude of the GNSS / accelerometer, h GNSS Indicates the altitude of the GNSS / accelerometer, p INS =[L INS λ INS h INS ] T , L INS represents the latitude of MEMS-IMU, λ INS Indicates the longitude of MEMS-IMU, h INS represents the height of the MEMS-IMU, secL represents the secant value of INS latitude, R Nh R represents the principal curvature radius of the y-axis circle. Mh represents the principal radius of curvature of the meridian; The lever arm error estimation of the two is obtained through the lever arm speed error and the geographical position deviation between them, so as to achieve the synchronization of the space lever arms; The construction process of the smooth variable structure filter includes: Assume that the system equation of the smooth variable structure filter is: x k is the system status; u k is the relative input of the system; w k is the system noise; z k is the measured value; v k is the measurement noise, f(x k ,u k ,w k ) represents a function, including the system state transfer matrix A and the input gain matrix B, h(x k ) includes a measurement transfer matrix H; By deducing the covariance-based nonlinear system iteration equation, the state prediction equation is: Covariance prediction equation: Observation equation: Gain equation: in, is the linearized measurement matrix, H k+1 Positive constant matrix; A=|e z,k+1|k |+γ|e z,k|k |, γ is the memory or convergence speed factor, 0<γ<1; System state estimation equation: Covariance generalization equation: The error equation is: For nonlinear dynamic equations, linearization is required: Taking into account the deformation characteristics of the bridge, the modulus of the quaternion is set to 1 and the heading angle correction number is set to 0. This is used as constraint information to substitute into the observation equation (17) of the filter to construct a constrained smooth variable structure filter model, thereby realizing filter update and feedback correction to determine the smooth variable structure filter.

2. The method for monitoring bridge deflection by integrating GNSS / accelerometer and MEMS-IMU according to claim 1, characterized in that: Also includes: S40, obtain the rate and rate threshold through the rate detection algorithm. If the rate obtained by the filter exceeds the rate threshold, it is determined that there is inconsistency in the fusion of GNSS / accelerometer and MEMS-IMU. At the same time, the obtained test statistic is compared with the rate threshold to determine whether there is a gross error fault and issue an error alarm to ensure that the fusion result of GNSS / accelerometer and MEMS-IMU is reliable, thereby monitoring the dynamic deflection of the high-speed railway bridge.

3. The method for monitoring bridge deflection by integrating GNSS / accelerometer and MEMS-IMU according to claim 1, characterized in that: The consistency monitoring indicator includes a rate; and the detection process of the rate includes: The principle of the rate detection algorithm is to detect the growth rate of the detection quantity, which is achieved by adding a Kalman filter to the autonomous integrity extrapolation algorithm; the filter can detect the rate change of the input and estimated noise; The system state equation of the rate detection algorithm is: Among them, P and V represent the detected position and velocity respectively, V contains colored noise, and a represents acceleration; represents the correlation coefficient, W1, W2 and W3 represent the system noise, that is, The measurement matrix of the system can be expressed as: H＝[1 0 0] (21) So the rate can be obtained as: Assume that the detected position P contains only a single slowly growing position error and other measurement errors are close to zero. When the system has no faults, it obeys a zero-mean Gaussian white noise distribution.

4. The method for monitoring bridge deflection by integrating GNSS / accelerometer and MEMS-IMU according to claim 2, characterized in that: Defines the detection statistic for the rate detection algorithm Where σ0 is the standard deviation of V; P V is the covariance of the state variable V. When the system is operating normally, the detection statistic D follows the standard normal distribution; Assume there are N observations, each with the same error probability, P FA is the sum of the false alarm rates of all observations, then the false alarm rate of a single observation is P FA / N; therefore, the threshold calculation formula based on the false alarm rate is as follows: Where: V D represents the rate threshold, and equation (24) shows that in the absence of faults, D is less than V D No alarm will occur when , so the detection threshold V D , by comparing D with V D The size of the error can be used to determine whether the system has a gross error fault, thereby determining whether to save the result of the fusion of GNSS / accelerometer and IMU.

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