WO2021237804A1 - 基于北斗高精度定位的基础设施结构变形监测方法 - Google Patents

基于北斗高精度定位的基础设施结构变形监测方法 Download PDF

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WO2021237804A1
WO2021237804A1 PCT/CN2020/095134 CN2020095134W WO2021237804A1 WO 2021237804 A1 WO2021237804 A1 WO 2021237804A1 CN 2020095134 W CN2020095134 W CN 2020095134W WO 2021237804 A1 WO2021237804 A1 WO 2021237804A1
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beidou
data
time
real
processing
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周俊华
梁晓东
雷孟飞
熊用
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Hunan Lianzhi Technology Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B7/00—Measuring arrangements characterised by the use of electric or magnetic techniques
    • G01B7/16—Measuring arrangements characterised by the use of electric or magnetic techniques for measuring the deformation in a solid, e.g. by resistance strain gauge
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/43—Determining position using carrier phase measurements, e.g. kinematic positioning; using long or short baseline interferometry
    • G01S19/44—Carrier phase ambiguity resolution; Floating ambiguity; LAMBDA [Least-squares AMBiguity Decorrelation Adjustment] method

Definitions

  • the invention relates to the technical field of Beidou high-precision positioning, in particular to a method for monitoring the deformation of infrastructure structures based on the Beidou high-precision positioning.
  • BeiDou Navigation Satellite System is a global navigation satellite system (Global Navigation Satellite System, GNSS) developed by China. It is one of the four major satellite navigation systems in the world recognized by the United Nations. The remaining three are US global positioning systems. System (Global Positioning System, GPS), Russian GLONASS system (GLONASS) and EU Galileo system (Galileo).
  • GPS Global Positioning System
  • GLONASS Russian GLONASS system
  • EU Galileo Galileo
  • the Beidou satellite navigation system has now completed the third-generation basic network layout. It has been transformed from a regional navigation and positioning system to a global navigation and positioning system, which can effectively provide users around the world with high-precision real-time positioning service.
  • Beidou-based high-precision positioning technology has been widely used in the field of deformation monitoring and safe operation of infrastructure structures (high-rise buildings, dams, mountains, bridges, electrical towers, etc.).
  • the Beidou high-precision positioning technology can realize high-precision deformation monitoring and safety early warning of high-risk infrastructure structures.
  • the method is: 1) Install Beidou signal antennas and receiver terminals on structural objects with potential displacement risks, as a Beidou monitoring station, to maintain real-time reception of Beidou satellite signals; 2) At the same time, at another location, usually at a distance of less than 10km Install another Taipei Dou terminal in a stable fixed position with a relatively stable and better observation environment as a reference site while maintaining real-time reception of Beidou satellite signals; 3) The two terminals will transmit the collected data through wired and wireless transmissions, etc. The data transmission method transmits the data back to the computer or data platform; 4) The three-dimensional displacement changes of the Beidou monitoring station relative to the reference station can be obtained through professional data processing algorithms and software processing in the computer.
  • the conventional data processing method is to adopt the post-delay data processing method, that is, first use the Beidou equipment to collect a period of data, such as 30 minutes, 1 hour, 2 hours, 3 hours, and then pass This long section of raw data is processed by professional software. Since a large amount of original observation data can be accumulated for a long period of time, and most short-term noise and interference, such as instrument noise, environmental noise, signal multipath, etc., can be eliminated, accurate displacement deformation can be obtained.
  • This Beidou data solution method has a rigorous theoretical foundation and positioning results, has strong independence and versatility, and is suitable for high-precision positioning requirements in various fields. But there are also obvious disadvantages:
  • the purpose of the present invention is to provide an infrastructure structure deformation monitoring method based on Beidou high-precision positioning, so as to solve the technical problem that the existing Beidou positioning technology is not suitable for the slow and small deformation monitoring of the infrastructure structure.
  • the present invention provides a method for monitoring the deformation of infrastructure structures based on Beidou high-precision positioning, including four steps: Beidou satellite signal data acquisition, wireless network communication and data analysis, Beidou real-time processing and Beidou post-processing.
  • Beidou real-time processing is to use Kalman filter to update and estimate real-time parameters to obtain real-time solution results
  • Beidou post-processing is to use double Kalman filter processing for real-time solution results and historical result data.
  • the specific method for collecting Beidou satellite signal data is: select at least two Beidou receivers, both devices are set to 15s sampling rate, and the output of Beidou observation data and ephemeris data and the corresponding 485 output port are set, and the format is It is: RTCM3.03;
  • the antenna feeder is used to connect the Beidou antenna and the Beidou receiver, one of which is used as a reference site, installed and deployed in an open environment, which can effectively receive Beidou satellite signals; the other is used as a deformation monitoring site, installed and deployed at On infrastructure structures that need to be monitored, such as dam bodies, building roofs, etc.; requirements: the distance between the reference site and the deformation monitoring site is not more than 10km.
  • the specific method of wireless network communication and data analysis is: use two 485 serial communication lines to connect to the data output ports of the two Taipei receivers, and plug the other end of the split screen into two configured wireless communication terminals 4G DTU ports , And the DTU is plugged into a 4G traffic card, and the configuration is successful to send the background IP address; the above operation is successful, the data collected by Beidou will be received on the platform in real time; the background TCP/IP monitoring port and data analysis software will be activated, and the software will be real-time Receive Beidou collected data and complete real-time data analysis to generate Beidou ephemeris data and observation data.
  • the specific method of data analysis is: read RTCM data, search for the message header D300, extract the length of the message, perform CRC integrity check, obtain the complete RTCM message, analyze the message header information, and determine whether the Beidou message is the case. If yes, analyze the corresponding For the content of the message, delete the parsed message and obtain new data, otherwise directly delete the parsed message and obtain new data.
  • the specific method of Beidou real-time processing is to receive the observation data and ephemeris data analyzed by Beidou in real time, and perform quality audit and control on the analyzed Beidou observation data, and form the content of the Kalman filter equation after removing the abnormal data.
  • Complete the real-time update of the Kalman filter by epoch output the solution result to the Beidou post-processing step, and store it locally at the same time.
  • the observation data is collected by the Beidou receiver and transmitted to the platform via a wireless network, and the platform completes data analysis according to the RTCM3.03 protocol, thereby obtaining Beidou observation data in a custom format;
  • the Beidou observation data of the present invention is mainly Refers to dual-frequency observation data, which is about to receive two frequency observation signals from each satellite, thereby generating redundant observation values, which greatly facilitates data quality control and achieves the purpose of improving the accuracy of the solution.
  • the ephemeris data refers to the broadcast orbit information of the Beidou satellite, and its accuracy is within 5m, which can be generated and received internally by the Beidou receiver, or downloaded from other enterprises and research institutions through the web, and can also be obtained at the same time
  • the precision ephemeris products released by research institutes can have orbital accuracy within 5cm; although the accuracy of Beidou broadcast ephemeris and precision ephemeris are far from each other, because the method of the present invention adopts the double difference of two stations and two stars Therefore, the orbit error will not have a big impact on the final calculation result, so special instructions and processing are not given; the multi-source Beidou ephemeris data obtained from the outside can be merged through the software, and then the standard orbit integration method can be used.
  • the cycle slip detection is carried out by adopting the Total Electron Content Change Rate (TECR) of the ionosphere to realize quality audit and control.
  • TECR Total Electron Content Change Rate
  • the total electron content (Ionospheric Total Electron Contents, TEC) of the i-1th epoch at the station can be calculated as:
  • i is the current observation time, where i is greater than or equal to 0; Indicates the total electron content of the ionosphere in the i-1th epoch; Respectively are the observation values of L1 and L2 of the i-1th epoch; f 1 and f 2 are the frequencies of the L1 and L2 carriers respectively; ⁇ 1 and ⁇ 2 mean the wavelengths on the two bands of L1 and L2 respectively; with Denote the ambiguity of the whole cycle on the two bands of L1 and L2 respectively; b r , b p are the frequency offset between the receiver and the satellite respectively. If the total electron content of the ionosphere calculated in formula (1) is calculated between adjacent epochs, the TECR can be obtained. The method is as follows:
  • GNSS cycle slip detection is often epoch-by-epoch detection. Assuming that all carrier observations before the i-th epoch have not had cycle slips or have been accurately repaired, the current i-th epoch can be calculated based on the clean observations of the first two epochs. Epochs The estimated value is:
  • the calculation formula (3) and estimation formula (4) can be used to obtain the calculated value at epoch i respectively And estimated value
  • the difference between the two is called TECR residual, and it is recorded as ⁇ TECR. Since the ionosphere changes slowly in a short period of time, it can be used as a test quantity for cycle slip. If the detection amount ⁇ TECR exceeds the set threshold, it is considered that a cycle slip has occurred in the i-th epoch.
  • its threshold is set to 0.15 (TECU/s) to accurately detect cycle slips.
  • the real-time calculation of Beidou data mainly adopts the Kalman filter estimation method.
  • the entire processing flow mainly includes: state equation formation, double-difference observation equation formation, Kalman filter update and parameter estimation, ambiguity fixation, and result output.
  • the Kalman filtering method is as follows:
  • the state vector and observations of the X k system at the current moment ⁇ k, k-1 is the state transition matrix from time t(t-1) to the state of the system at time t(t); w k is the system noise Vector; L k is the observation of the system at t(t); H k is the observation matrix of the observation equation, and v k is the residual.
  • the Kalman filtering method is used to estimate the parameters, that is, the accurate solution of the Beidou position, that is, the parameter X k in the formula. First, it is necessary to solve the corresponding state transition matrix ⁇ k,k-1 , the observation matrix H k and the corresponding noise sum Residual. The following explains how to obtain the above content:
  • the establishment of the state equation is mainly to obtain the state transition matrix ⁇ k, k-1 and the system noise vector w k in the formula. Since the deformation monitoring application of infrastructure structures is often static or quasi-static, that is, whether the change or displacement is slow, and the data sampling rate of the present invention is less than 15s, the data interval is very small, therefore, the state adopted by the present invention
  • the equation is drawn up as follows:
  • X k X k-1 , that is, the state change between epochs is zero.
  • the formation of the double-difference observation equation is mainly to obtain the observation matrix H k and the residual v k in the formula.
  • the present invention adopts the inter-station difference mode, and combines the Beidou observation data OBS analyzed by the monitoring station and the reference station, and combines the initial coordinates (X, Y, Z) of the two stations to form the residual v k .
  • the observation matrix H k is formed by combining the unit azimuth angles between the stations.
  • the aforementioned Kalman filtering method achieves the solution of Beidou coordinate parameters, but the essence of its solution is a floating-point solution, that is, the ambiguity parameter of the satellite is a floating-point number, which is inconsistent with the objective essential phenomenon, that is, the ambiguity must be an integer. Therefore, in order to obtain more precise and reliable position coordinates, a fixed solution of the ambiguity must be obtained.
  • the present invention uses the LAMBDA method to achieve a fixed solution of ambiguity. This method is mainly based on the above-mentioned floating-point solution, unifying and rounding the satellite ambiguity parameters involved in the solved parameter X k to integers, and ensuring its minimum The characteristics of variance. It belongs to the reprocessing of the Kalman filtering method.
  • the station coordinates (X, Y, Z) information in the ambiguity fixed solution parameter X k can be selected for output.
  • the specific method of Beidou post-processing is to calculate the results in real time according to the length of the set time period, and at the same time, query the locally stored historical result data according to the historical window size, and both data are smoothed by Kalman filtering to obtain the corresponding two
  • the Beidou coordinate value is then evaluated according to the weighted value, and the final Beidou post-processing result is obtained and stored and updated locally.
  • the Beidou post-processing step is started regularly.
  • the post-processing of Beidou specifically includes:
  • Kalman filter is used to filter the collected single epoch fixed solution results (X, Y, Z) in three directions, and the filter parameters and noise level can be based on actual conditions Set, generally the state equation noise is 1mm, and the measurement noise is set according to the actual situation. It can be set to 5mm-1cm.
  • the most reliable time period solution coordinate X time period ( X0, Y0, Z0).
  • the invention uses the Beidou high-precision positioning equipment terminal to collect Beidou raw data in real time, and then perform calculations through real-time and static post-processing algorithms and processing procedures, thereby realizing the realization of infrastructure structures (such as high-rise buildings, dams, mountains, bridges, etc.) , Electrical towers, etc.) for real-time, post-mortem deformation monitoring at the millimeter level.
  • the core technology lies in the use of Kalman filtering for real-time parameter estimation, which improves the real-time performance and achieves the Beidou real-time monitoring purpose; and adopts the Beidou post-processing method of dual-Kalman filtering. details as follows:
  • Beidou deformation monitoring for infrastructure structures has real-time and post-event modes, which meets the real-time requirements in special business scenarios, while ensuring the requirements for high precision and stronger adaptability;
  • Beidou's post-processing method avoids Beidou's professional algorithm and complexity, and has a simple and easy effect. At the same time, it can well eliminate long-period errors and noise interference, such as Sunday and half-week errors, which can reach higher Accuracy
  • the platform level is used for centralized management and calculation, it can be very easy to manage. Through a reasonable software architecture, tens of thousands of point data access and data traceability can be realized, meeting the needs of Beidou monitoring in a wide area; meeting the platform level A large number of Beidou data processing needs.
  • Figure 1 is an overall flow chart of the present invention
  • Figure 2 is a schematic diagram of the components of the Beidou receiver
  • Figure 3 is a schematic diagram of wireless communication for a single Beidou site
  • Figure 4 is a flow chart of Beidou raw data analysis
  • Figure 5 is a flowchart of Beidou real-time processing
  • Figure 6 is a flow chart of the dual Kalman smoothing and filtering method
  • Figures 7a and 7b are the real-time solution results and the post-calculation results of the experimental example, respectively.
  • the monitoring method of the present invention includes: Beidou receiver terminal and data collection, data transmission and analysis, Beidou real-time processing, and Beidou post-processing.
  • the Beidou receiver generally adopts the conventional survey Beidou receiver, and its component composition is shown in Figure 2.
  • the Beidou receiver contains two key components: the antenna device and the high-precision core board.
  • the antenna device ie Beidou antenna
  • the antenna device is mainly used to collect Beidou frequency signals in real time and send the frequency signals to the high-precision board, which is completed by the board.
  • the signal is processed to produce the original Beidou observation data required by the data processing of the present invention.
  • the present invention can usually adopt a dual-frequency receiver, set a 15s sampling rate, and can receive signals from multiple navigation systems such as Beidou, GPS, and GLONASS.
  • the dual-frequency receiver is mainly set to eliminate the error of the ionosphere, and at the same time, it can better realize the quality control processing of the present invention, thereby greatly improving the reliability and stability of the solution.
  • the Beidou receivers and antennas are required, one of which is used as a displacement monitoring station and the other is used as a position reference station.
  • the monitoring can be obtained by the data processing method of the present invention.
  • the displacement deformation of the station relative position reference station with millimeter-level accuracy. Since the Beidou receiver is a mature product that has been marketed and is more commonly used, and the present invention focuses on the Beidou algorithm and data processing, the Beidou receiver will not be elaborated too much.
  • the Beidou receiver has a variety of data transmission methods: RJ45 network cable port, 485 communication serial port, SD built-in memory card, Bluetooth.
  • the above four methods can reliably obtain the original data generated by the Beidou receiver.
  • a structure often involves data collection and processing at multiple Beidou points.
  • the present invention adopts wireless communication mode to realize the centralized and unified management of Beidou sites and Beidou data on the cloud platform, which can be completed efficiently Computational work of Beidou.
  • the communication method shown in Figure 3 is adopted for the data of a single Beidou site.
  • the Beidou real-time raw data is forwarded to wireless communication equipment, such as 4G DTU communication terminals, through the 485 communication serial port built into the Beidou receiver.
  • the wireless communication device relies on its built-in wireless network card traffic to forward data to the network platform in real time, realizing online data processing.
  • the present invention adopts the DGPS data general format established by the International Maritime Radio Technical Committee: RTCM3.03. Because Beidou data uses the above-mentioned protocol for transmission, the received RTCM3.03 must be analyzed before Beidou data processing.
  • the content of the present invention requires Beidou observation data, Beidou ephemeris and observation data of other navigation satellite systems. The data analysis process and method of the present invention are shown in FIG. 4.
  • the Beidou observation data OBS and Beidou ephemeris data NAV have been obtained.
  • the observation data determines the time interval of the data according to the sampling rate of the Beidou receiver, usually: 1s, 5s, 15s, 30s.
  • the final data collection quantity is determined according to the length of observation time. Because the Beidou receiver adopts collection data and real-time data transmission according to the settings, the platform can complete online real-time calculation after receiving the data.
  • Beidou real-time calculation processing is the core and key of Beidou data processing, which directly determines the accuracy of the accuracy and the availability of data.
  • the invention realizes a Beidou online real-time calculation method, adopts a dual-frequency double-difference mode, and adopts a Kalman filtering method to realize real-time update and estimation of parameters.
  • This mode can avoid the delay of the traditional Beidou calculation method, and can realize real-time data transmission, reception, and calculation processing according to the receiver's sampling rate and data transmission frequency, which greatly improves the timeliness of monitoring. At the same time, strict quality control procedures have been implemented to ensure accuracy.
  • the real-time processing flow is shown in Figure 5.
  • Beidou observation data To realize the online real-time calculation of Beidou, it must have two basic data content: Beidou observation data and Beidou ephemeris data.
  • the Beidou ephemeris data refers to the broadcast orbit information of the Beidou satellite. Its accuracy is within 5m. It can be generated and received internally by the Beidou receiver, or downloaded from other companies and research institutions through the web, and can also be obtained from research institutions.
  • the precision ephemeris products released can have an orbit accuracy within 5cm. Although the accuracy of Beidou broadcast ephemeris and precision ephemeris are far from each other, because the method of the present invention adopts a two-station two-satellite double difference method, its orbit error will not have a big impact on the final solution result. , So no special instructions and treatment are given.
  • the multi-source Beidou ephemeris data obtained from the outside is merged by software, and then the coordinate value of the Beidou satellite in the CGCS2000 coordinate system can be obtained by using the standard orbit integration method to prepare for the next step of forming the double difference observation value.
  • the Beidou observation data of the present invention is collected and generated by the Beidou receiver and transmitted to the platform through the wireless network.
  • the platform completes data analysis according to the RTCM3.03 protocol to obtain Beidou observation data in a custom format.
  • the Beidou observation data of the present invention mainly refers to dual-frequency observation data, which is about to receive two frequency observation signals of each satellite, thereby generating redundant observation data, which can greatly facilitate data quality control and achieve the purpose of improving solution accuracy.
  • the total electron content (Ionospheric Total Electron Contents, TEC) of the i-1th epoch at the station can be calculated as:
  • i is the current observation time, where i is greater than or equal to 0; Indicates the total electron content of the ionosphere in the i-1th epoch; Respectively are the observation values of L1 and L2 of the i-1th epoch; f 1 and f 2 are the frequencies of the L1 and L2 carriers respectively; ⁇ 1 and ⁇ 2 mean the wavelengths on the two bands of L1 and L2 respectively; with Denote the ambiguity of the whole cycle on the two bands of L1 and L2 respectively; b r , b p are the frequency offset between the receiver and the satellite respectively. If the total electron content of the ionosphere calculated in formula (1) is calculated between adjacent epochs, the TECR can be obtained. The method is as follows:
  • GNSS cycle slip detection is often epoch-by-epoch detection. Assuming that all carrier observations before the i-th epoch have not had cycle slips or have been accurately repaired, the current i-th epoch can be calculated based on the clean observations of the first two epochs. Epochs Estimated value is:
  • the calculation formula (3) and estimation formula (4) can be used to obtain the calculated value at epoch i respectively And estimated value
  • the difference between the two is called TECR residual, and it is recorded as ⁇ TECR. Since the ionosphere changes slowly in a short period of time, it can be used as a test quantity for cycle slip. If the detection amount ⁇ TECR exceeds the set threshold, it is considered that a cycle slip has occurred in the i-th epoch.
  • its threshold is set to 0.15 (TECU/s) to accurately detect cycle slips.
  • the cycle slip value can be accurately detected, and then the abnormal observation data can be marked or cleared, thereby ensuring the reliability of subsequent calculations.
  • the quality control module also needs to check the data integrity and noise of the overall Beidou observation data, and participate in the next step of the calculation for the qualified observation data. Otherwise, iterative quality control or even abandon the calculation of this data.
  • the real-time calculation of Beidou data mainly adopts the Kalman filter estimation method.
  • the entire processing flow mainly includes: state equation formation, double-difference observation equation formation, Kalman filter update and parameter estimation, ambiguity fixation and result output.
  • the Kalman filtering method is as follows:
  • the state vector and observations of the X k system at the current moment ⁇ k, k-1 is the state transition matrix from time t(t-1) to the state of the system at time t(t); w k is the system noise Vector; L k is the observation of the system at t(t); H k is the observation matrix of the observation equation, and v k is the residual.
  • the Kalman filtering method is used to estimate the parameters, that is, the accurate solution of the Beidou position, that is, the parameter X k in the formula. First, it is necessary to solve the corresponding state transition matrix ⁇ k,k-1 , the observation matrix H k and the corresponding noise sum Residual. The following explains how to obtain the above content:
  • the establishment of the state equation is mainly to obtain the state transition matrix ⁇ k, k-1 and the system noise vector w k in the formula. Since the deformation monitoring application of infrastructure structures is often static or quasi-static, that is, whether the change or displacement is slow, and the data sampling rate of the present invention is less than 15s, the data interval is very small, therefore, the state adopted by the present invention
  • the equation is drawn up as follows:
  • X k X k-1 , that is, the state change between epochs is zero.
  • the formation of the observation equation is mainly to obtain the observation matrix H k and the residual v k in the formula.
  • the present invention adopts the inter-station difference mode, and combines the Beidou observation data OBS analyzed by the monitoring station and the reference station, and combines the initial coordinates (X, Y, Z) of the two stations to form the residual v k .
  • the observation matrix H k is formed by combining the unit azimuth angles between the stations.
  • the aforementioned Kalman filtering method achieves the solution of Beidou coordinate parameters, but the essence of its solution is a floating-point solution, that is, the ambiguity parameter of the satellite is a floating-point number, which is inconsistent with the objective essential phenomenon, that is, the ambiguity must be an integer. Therefore, in order to obtain more precise and reliable position coordinates, a fixed solution of the ambiguity must be obtained.
  • the present invention uses the LAMBDA method to achieve a fixed solution of ambiguity. This method is mainly based on the above-mentioned floating-point solution, unifying and rounding the satellite ambiguity parameters involved in the solved parameter X k to integers, and ensuring its minimum The characteristics of variance. It belongs to the reprocessing of the Kalman filtering method.
  • the station coordinates (X, Y, Z) information in the ambiguity fixed solution parameter X k can be selected for output.
  • the Beidou receiver outputs data stably according to a fixed sampling rate, and transmits it to the data processing platform through a wireless network, and uses the Kalman filtering method for parameter estimation, it can achieve real-time update and retain historical state information, and its real-time solution results It can achieve mm-cm accuracy, is relatively reliable, and can serve for real-time deformation monitoring.
  • the present invention adopts the Kalman filtering method to achieve real-time solution of coordinates
  • the Kalman filtering requires that the observation data also have real-time characteristics, which will bring challenges to the quality control module.
  • the quality control of real-time data cannot achieve 100% "clean" data output in some special cases. Therefore, abnormal results are prone to occur in real-time processing.
  • the solution result after the event is usually used as the Beidou monitoring data processing method.
  • This method has a rigorous theoretical basis, and the positioning results have strong independence and versatility, but this method has strong professionalism, complexity, and high development, maintenance and upgrade costs.
  • the present invention proposes a Beidou post-processing method that is easy to implement and does not involve Beidou professionalism: a double-Kalman smoothing filtering method. The processing flow is shown in Figure 6.
  • (B) Receive and store the single epoch solution result and parameter estimation process information processed by Beidou in real time, including the fixed solution Ratio value, satellite altitude angle, satellite signal-to-noise ratio, and number of satellites. Start to perform the next operation until the set time period length is reached;
  • Kalman filter is used to filter the collected single epoch fixed solution results (X, Y, Z) in three directions, and the filter parameters and noise level can be based on actual conditions Set, generally the state equation noise is 1mm, and the measurement noise is set according to the actual situation, which can be set to 5mm-1cm.
  • the most reliable period solution coordinate X period (X0, Y0, Z0) in the period will be obtained.
  • This method has the following characteristics: it avoids the internal professional algorithms and data processing of Beidou, greatly reducing its complexity and professionalism. At the same time, the weighted average based on historical results can greatly reduce short-period noise errors, including Sundays and half weeks. The daily periodic error has obvious advantages over the accuracy of real-time results.
  • the present invention has verified the effectiveness and reliability of its method through a large number of experiments and actual projects; the following takes the real-time and post-event calculation results of Beidou data collected by actual projects as an example to illustrate:
  • the receiver is set with a sampling rate of 15s, the period length is set to 10 minutes, and data has been continuously collected for 2 days.
  • the real-time solution result is shown in Figure 7a, and the post-event solution result is shown in Figure 7b. It can be found by comparison that the The real-time calculation accuracy can reach the millimeter level, which can meet the accuracy of most infrastructure structure safety monitoring; at the same time, the accuracy of the post-smoothing method described in the present invention is further improved, especially the elevation direction is significantly improved.

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Abstract

本发明提供了一种基于北斗高精度定位的基础设施结构变形监测方法，包括北斗卫星信号数据采集、无线网络通信及数据解析、北斗实时处理和北斗事后处理四个步骤，北斗实时处理是采用kalman滤波进行参数的实时更新与估计得到实时解算结果，北斗事后处理是将实时解算结果与历史结果数据采用双kalman滤波处理。本发明利用北斗高精度定位设备终端，可实时采集北斗原始数据，然后通过实时、静态后处理算法及加工流程进行解算，从而实现对基础设施结构物（如高层建筑、大坝、山体、桥梁、电塔等）进行实时、事后毫米级别的变形监测。

Description

基于北斗高精度定位的基础设施结构变形监测方法 技术领域
本发明涉及北斗高精度定位技术领域，特别地，涉及一种基于北斗高精度定位的基础设施结构变形监测方法。
背景技术
北斗卫星导航系统(BeiDou Navigation Satellite System，BDS)是中国自行研制的全球卫星导航系统(Global Navigation Satellite System，GNSS)，是联合国承认的全球四大卫星导航系统之一，其余三个是美国全球定位系统(Global Positioning System，GPS)、俄罗斯格洛纳斯系统(GLONASS)和欧盟伽利略系统(Galileo)。北斗卫星导航系统经过第一代、第二代的发展，目前完成了第三代的基本布网，已经从区域级导航定位系统转变为全球导航定位系统，可有效为全球的用户提供高精度实时定位服务。
基于北斗的高精度定位技术已经广泛运用于基础设施结构物(高层建筑、大坝、山体、桥梁、电塔等)的变形监测与安全运营领域。利用北斗高精度定位技术可以实现对高危的基础设施结构物进行高精度形变监测与安全预警。其方法为：1)在具有潜在位移风险的结构物体上安装北斗信号天线和接收机终端，作为北斗监测站点，保持实时接收北斗卫星信号；2)同时，在另外一处，通常在距离小于10km之内的比较稳定、观测环境较优的稳定固定位置安装另外一台北斗终端，作为基准站点，同时保持实时接收北斗卫星信号；3)两台终端将采集的数据通过有线、无线传输等多种数据传输方式将数据传回电脑或者数据平台；4)在电脑中通过专业的数据处理算法和软件处理即可获取北斗监测站点相对基准站点的三维位移变化。为保障结构物的安全运营及人民的生命财产安全，工程应用中通常要求变形监测精度为毫米(mm)级，特殊结构物场景下还需要北斗解算结果具有实时性，以便更快的做出安全和处理决策。由于工程应用对精度和实时性要求非常苛刻，现有的技术和方法存在缺点和不适用。
而且，由于基础设施结构物的变形往往是缓慢和微小的，因此，要求北斗形变监测精度必须达到毫米级别，这对北斗的硬件设备、数据处理流程的准确性及解算算法先进性提出了苛刻要求。
为了达到这一个关键精度指标要求，常规的数据处理方式都是采用的事后延迟数据处理方式，即先利用北斗设备采集一段数据，如30分钟、1小时、2小时、3小时不等，然后通过这一长段的原始数据进行专业软件加工处理。由于长段时间内可以累积大量的原始观测数据，且可以消除大部分的短期噪声和干扰，如仪器噪声、环境噪声、信号多路径等，因此可以得 到准确的位移形变量。这种北斗数据解算方法具有严谨的理论基础和定位结果，具有较强的独立性和通用性，其适合各个领域的高精度定位要求。但是也存在明显缺点：
1)算法非常复杂，且缺乏灵活性，必须由专业人员进行实现、维护和升级；
2)上述事后处理模式具有严重的时间延迟性，无法满足特殊基础设施形变监测对实时性的要求；
3)由于必须积累较长时间的观测数据，因此单个观测点的计算量非常大，无法满足对于平台级的大量北斗数据处理需求；
4)仍然无法消除具有周日、半周日等大于处理时段长度的周期性误差；
5)对原始数据质量要求较高，在环境恶劣的情况下，其精度降低到厘米级别(1-5cm)；
6)虽然定位结果具有较强的独立性，但是没有充分利用结构物具有缓变、微小变形的特点。
技术问题
如何提高北斗变形监测的精度和实时性，达到实时监测目的。
技术解决方案
本发明目的在于提供一种基于北斗高精度定位的基础设施结构变形监测方法，以解决现有北斗定位技术不适用于基础设施结构缓慢和微小变形监测的技术问题。
为实现上述目的，本发明提供了一种基于北斗高精度定位的基础设施结构变形监测方法，包括北斗卫星信号数据采集、无线网络通信及数据解析、北斗实时处理和北斗事后处理四个步骤，其中，北斗实时处理是采用kalman滤波进行参数的实时更新与估计得到实时解算结果，北斗事后处理是将实时解算结果与历史结果数据采用双kalman滤波处理。
优选的，北斗卫星信号数据采集的具体方法是：选择最少两台北斗接收机，两台设备都设置为15s采样率，设置北斗观测数据和星历数据的输出及对应的485输出端口，其格式为：RTCM3.03；同时利用天线馈线将北斗天线与北斗接收机进行连接，其中一个作为基准站点，安装部署在空旷环境下，可有效接收北斗卫星信号；另外一个作为变形监测站点，安装部署在需监测的基础设施结构物上，比如大坝体、建筑楼顶等；要求：基准站点与变形监测站点的距离不大于10km。
优选的，无线网络通信及数据解析的具体方法是：利用两根485串口通信线连接分别连接两台北斗接收机的数据输出端口，另外一头分屏插入配置好的两台无线通信终端4G DTU端口，且该DTU分别插上4G流量卡，配置成功发送后台的IP地址；上述操作成功则会在平台实时接收北斗采集的数据；启动后台的TCP/IP监听端口及数据解析软件，该软件会实时接收北斗采集数据并完成实时数据解析，生成北斗星历数据和观测数据。
进一步优选的，数据解析的具体方法是：读取RTCM数据，寻找电文头D300，提取电文长度，进行CRC完整性检验，获得完整RTCM电文，解析电文头信息，判断是否北斗电文，是则解析对应电文内容，删除已解析电文，获取新数据，否则直接删除已解析电文，获取新数据。
优选的，北斗实时处理的具体方法是：实时接收北斗解析完的观测数据和星历数据，并对解析的北斗观测数据进行质量审核和控制，将异常数据进行剔除后组建kalman滤波方程的内容，逐历元完成kalman滤波的实时更新，将解算的结果输出到北斗事后处理步骤，同时，进行本地存储。
进一步优选的，观测数据是根据北斗接收机采集产生并通过无线网络传输至平台，由平台根据RTCM3.03协议完成数据解析，从而得到自定义格式的北斗观测数据；本发明的北斗观测数据主要是指双频观测数据，即将接受每颗卫星的两个频点观测信号，从而产生冗余观测值可大大利于数据质量控制，达到提高解算精度的目的。
进一步优选的，星历数据是指北斗卫星的广播轨道信息，其精度在5m之内，可由北斗接收机内部产生和接收获取，也可通过web从其他企业、研究机构下载获取，同时还可以获取研究机构所发布的精密星历产品，其轨道精度可在5cm之内；虽然北斗广播星历和精密星历精度相差很远，但是由于本发明所述方法采用的是两站两星的双差分的方式，因此其轨道误差不会对最终的解算结果产生大的影响，故不作特殊说明和处理；从外部获取的多源北斗星历数据通过软件进行合并，然后采用标准的轨道积分方法即可获取北斗卫星在CGCS2000坐标系统下的坐标值，以备下一步组建双差观测值。
进一步优选的，采用电离层总电子含量变化率(TECR)进行周跳探测，从而实现质量审核和控制，具体方法是：
根据测站的双频载波相位GNSS观测值可计算出测站在第i-1个历元的电离层总电子含量(Ionospheric Total Electron Contents，TEC)为：
Figure PCTCN2020095134-appb-000001
i为当前观测时间，其中i大于等于0；
Figure PCTCN2020095134-appb-000002
表示第i-1历元的电离层总电子含量；
Figure PCTCN2020095134-appb-000003
分别为第i-1个历元的L1、L2观测值；
Figure PCTCN2020095134-appb-000004
f 1、f 2分别为L1、L2载波的频率；λ 1和λ 2含义的分别是L1和L2两个波段上的波长；
Figure PCTCN2020095134-appb-000005
和
Figure PCTCN2020095134-appb-000006
分别表示L1和L2两个波段上的整周模糊度；b r、b p分别为接收机和卫星的频率间偏置。将式(1)中计 算的电离层总电子含量在相邻历元间做差，则可得到电离层总电子含量变化速率TECR，其方法如下：
Figure PCTCN2020095134-appb-000007
式(2)中
Figure PCTCN2020095134-appb-000008
表示在历元i处的电离层总电子含量；
Figure PCTCN2020095134-appb-000009
表示第i-1历元的电离层总电子含量；Δt为相邻两个历元的时间间隔。如果在第i个历元处未发生周跳，由式(1)、式(2)可得第i个历元处的电子含量变化率
Figure PCTCN2020095134-appb-000010
Figure PCTCN2020095134-appb-000011
GNSS周跳探测往往是逐个历元进行检测，假设第i个历元之前的所有载波观测值均未发生周跳或者已经准确修复，则根据前两个历元的干净观测值可计算当前第i个历元的
Figure PCTCN2020095134-appb-000012
估计值为：
Figure PCTCN2020095134-appb-000013
Figure PCTCN2020095134-appb-000014
为第i-1个历元处的电子含量变化率，其中
Figure PCTCN2020095134-appb-000015
为第i-1个历元时的TEC加速度，其计算表达式如下:
Figure PCTCN2020095134-appb-000016
如果在第i个历元处，载波相位观测值
Figure PCTCN2020095134-appb-000017
分别发生周跳，其周跳值为：
Figure PCTCN2020095134-appb-000018
Figure PCTCN2020095134-appb-000019
则由式(3)可得L1、L2两个频段上发生周跳项的值大小为：
Figure PCTCN2020095134-appb-000020
根据
Figure PCTCN2020095134-appb-000021
的计算公式(3)和估计公式(4)可分别求出历元i处的计算值
Figure PCTCN2020095134-appb-000022
和估计值
Figure PCTCN2020095134-appb-000023
两者之差称为TECR残差，记为ΔTECR。由于电离层短时间内变化缓慢，因此，可将其作为周跳的检验量。如果检测量ΔTECR超过设置的阈值，则认为第i个历元发生了周跳。通常其其阈值设置为0.15(TECU/s)即可准确探测周跳。
进一步优选的，北斗数据实时解算主要采用了kalman滤波估计方法，整个处理流程主要包含了：状态方程组建、双差观测方程组建、kalman滤波更新与参数估计、模糊度固定及结果输出几大部分。Kalman滤波方法如下：
X k＝Φ k,k-1X k-1+w k
L k＝H kX k+v k
其中式中，X k系统在当前时刻的状态向量和观测量；Φ k,k-1为从t(t-1)时刻到t(t)时刻系统状态的状态转移矩阵；w k为系统噪声向量；L k是系统在t(t)时刻的观测量；H k为观测方程的观测矩阵，v k为残差。本发明采用kalman滤波方法进行参数估计即北斗位置的精确求解，即公式中的参数X k，则首先需要求解出对应的状态转移矩阵Φ k,k-1、观测矩阵H k以及相应的噪声和残差。下面阐述如何获取以上内容：
(1)状态方程组建
状态方程组建主要就是获取式中状态转移矩阵Φ k,k-1和系统噪声向量w k。由于基础设施结构物的变形监测应用往往是静态或者准静态形式，即变化或者位移是否缓慢的场景，并且本发明的数据采样率<15s,其数据间隔很小，因此，本发明所采用的状态方程拟定如下：
X k＝X k-1，即历元之间的状态变化为零。
(2)双差观测方程组建
双差观测方程组建主要是获取式中的观测矩阵H k，残差v k。本发明采用的是站间差分模式，则通过监测站、基准站两个站点解析的北斗观测数据OBS进行组合，并结合两站的初始坐标(X、Y、Z)形成残差v k。其观测矩阵H k则是通过站星之间的单位方位角组合而成。
(3)Kalman滤波参数估计
在完成状态方程、双差观测方程组建之后，则所有内容都已经符合kalman的要求，通过一次求解即可完成X k参数的更新和估计，即包含北斗精确坐标的求解。由于kalman滤波是个实时滤波器，其北斗坐标的更新可以实现实时求解。
(4)模糊度固定
上述Kalman滤波方法实现的是对北斗坐标参数的求解，但是其解算的本质是浮点解，即卫星的模糊度参数是浮点数，则与客观本质现象不符合，即模糊度必须是整数。因此要想取得更高精度、可靠的位置坐标，则必须取得模糊度的固定解。本发明采用LAMBDA方法实现模糊度的固定求解，该方法主要是在上述浮点解的基础上，将求解的参数X k中涉及的卫星模糊度参数统一、归整到整数上，并且保证其最小方差的特性。属于kalman滤波方法的再次加工。
5、固定解输出
经过上述固定解的求解，即可选择将模糊度固定解参数X k中的站点坐标(X、Y、Z)信息进行输出。
优选的，北斗事后处理的具体方法是：根据设置时段长度实时解算结果，同时，根据历史窗口大小查询本地存储的历史结果数据，两个数据都采用kalman滤波进行平滑处理，得到相应的两个北斗坐标值，然后根据加权值进行评价，获取最终的北斗事后处理结果并进行本地存储和更新。
进一步优选的，根据设置的时段长度、历史窗口大小，定时启动北斗事后处理步骤。
进一步优选的，北斗事后处理的具体包括：
(A)必要参数设置：首先需要设置北斗事后处理的结果输出时段间隔，比如设置为30分钟、1小时输出一次事后结果；同时也设置对于异常结果的阈值和依据内部过程信息进行加权的权值信息，主要是为了实现异常结果的剔除；
(B)接收和存储北斗实时处理的单历元解算结果以及参数估计的过程信息，包括固定解的Ratio值、卫星高度角、卫星信噪比、卫星数量，直到达到设置的时段长度则开始执行下一步操作；
(C)当达到设置的时段长度后，则采用kalman滤波对所采集的单历元固定解算结果(X、Y、Z)三个方向进行滤波，其滤波的参数和噪声大小可以根据实际情况设定，一般其状态方程噪声取1mm，测量噪声根据实际情况设定，可设置为5mm-1cm，经过kalman滤波平滑后，将会得出该时段内最可靠的时段解算坐标X 时段＝(X0、Y0、Z0)。
(D)同时，根据设定的历史结果窗口长度从历史结果数据库中取出最新的对应窗口大小的历史数据，然后同样采用kalman滤波进行平滑，得到历史坐标X 历史平滑＝(X1、Y1、Z1)。
(E)将得到的小时段内结果X 时段和历史结果X 历史平滑进行加权平均，得到最终的北斗事后处理结果X 最终＝(X、Y、Z)，并将该结果更新存储到历史结果数据库中。
有益效果
本发明具有以下有益效果：
本发明利用北斗高精度定位设备终端，可实时采集北斗原始数据，然后通过实时、静态后处理算法及加工流程进行解算，从而实现对基础设施结构物(如高层建筑、大坝、山体、桥梁、电塔等)进行实时、事后毫米级别的变形监测。核心技术在于采用kalman滤波进行实时参数估计，提高了实时性，达到了北斗的实时监测目的；并采用双kalman滤波的北斗事后处理方法。具体如下：
1、针对基础设施结构物的北斗变形监测，具有实时、事后两种模式，满足了特殊业务场景下的实时性要求，同时保证了对高精度的要求，具有更强的适应性；
2、北斗事后处理方法避开了北斗专业算法和复杂性，具有简单易行的效果，同时，可以很好的消除长周期误差和噪声干扰，如周日、半周日的误差，可以达到更高的精度；
3、由于采用平台级进行集中管理和解算，可以非常便于管理，通过合理的软件架构可以实现上万级别的点数据接入且数据可溯源，满足广域范围的北斗监测需求；满足对于平台级的大量北斗数据处理需求。
除了上面所描述的目的、特征和优点之外，本发明还有其它的目的、特征和优点。下面将参照图，对本发明作进一步详细的说明。
附图说明
构成本申请的一部分的附图用来提供对本发明的进一步理解，本发明的示意性实施例及其说明用于解释本发明，并不构成对本发明的不当限定。在附图中：
图1是本发明的整体流程图；
图2是北斗接收机部件的组成示意图；
图3是单个北斗站点无线通信示意图；
图4是北斗原始数据解析流程图；
图5是北斗实时处理流程图；
图6是双kalman平滑滤波方法流程图；
图7a和图7b分别为试验例的实时解算结果和事后解算结果。
本发明的实施方式
以下结合附图对本发明的实施例进行详细说明，但是本发明可以根据权利要求限定和覆盖的多种不同方式实施。
实施例：
如图1所示，本发明的监测方法包括：北斗接收机终端及数据采集、数据传输与解析、北斗实时处理、北斗事后处理。
一、北斗接收机终端及数据采集
北斗接收机一般采用常规测量型北斗接收机，其部件组成如图2所示。
北斗接收机内部包含两大关键部件：天线装置和高精度核心板卡，天线装置(即北斗天线)，主要用于实时采集北斗频率信号，并将频率信号发送至高精度板卡，由板卡完成对信号的加工处理，从而产生本发明所述数据处理所需要的北斗原始观测数据。本发明在工程应用中通常可以采用双频接收机，设置15s采样率，且可以接收北斗、GPS、GLONASS多个导航系统信号。其中双频接收机设置主要是为了消除电离层的误差，同时，可更好的实现本发明所述的质量控制处理，从而大幅提高解算的可靠性和稳定性。在工程应用中，完成一个点位的三维位移监测，最少需要两个北斗接收机及天线，其中一个作为位移监测站点，另外一个 作为位置基准站点，通过本发明的数据处理方法，即可获取监测站点相对位置基准站点的毫米级精度的位移形变。由于北斗接收机是已经市场化且较为常用的成熟产品，且本发明重点在北斗算法和数据加工，因此，不对北斗接收机做过多阐述。
二、数据传输与解析
北斗接收机具有多种数据传输方式：RJ45网线口、485通信串口、SD内置内存卡、蓝牙。上述四种方式可以可靠的获取到北斗接收机产生的原始数据。但工程应用中，由于需要依靠多个站点对结构物的不同点位进行监测，因此，一个结构物往往涉及多个北斗点的数据采集与处理工作。为了实现对广域范围(全国、省级、市级)的基础设施结构进行在线实时、事后变形监测，本发明采用无线通信方式，实现云平台集中、统一管理北斗站点和北斗数据，可高效完成北斗的解算工作。对单个北斗站点的数据采用如图3所示的通信方式。
对于单个北斗站点，通过北斗接收机内置的485通信串口将北斗实时原始数据转发至无线通信设备，如4G DTU通信终端。由无线通信设备依靠其内置的无线网卡流量实时将数据转发至网络平台，实现数据的在线处理。
为了实现北斗数据的网络传输，必须明确北斗数据的传输协议，本发明采用的是由国际海事无线电技术委员会制定的DGPS数据通用格式：RTCM3.03。由于北斗数据采用上述协议进行传输，因此在北斗数据处理之前必须对接收到的RTCM3.03进行数据解析。本发明所涉及内容要求北斗观测数据、北斗星历及其它导航卫星系统的观测数据，本发明的数据解析流程和方法如图4所示。
操作说明：为简化流程，以单台北斗站点数据解析为例。平台实时接收北斗数据并缓存在电脑内存中以待下一步做处理，同时将数据存储在数据库中做永久性存档。启动软件程序进行TCP/IP网络监听，将每次接收到的网络数据按照二进制数据流形式读进内存，待达到设置的缓存字节数量时，如1024bytes，则将当前收到的数据作为一条北斗原始数据，由程序从数据开头遍历寻找D300的数据，如果没有该类字节信息，则表明该包数据无效，重新接收获取下次原始数据。如果存在对应头信息，则按位操作进行电文长度读取、CRC完整性检验等操作，如果通过则表明该条电文信息完整，然后将该电文按照RTCM 3.03协议进行解析，取出北斗的观测数据OBS、北斗星历数据NAV。如果CRC检验不合格，则说明该条电文已损坏，做无效电文处理，继续下批数据接收。如此循环往复，实现了北斗的实时接收、存储、解析北斗原始数据功能，为本发明所述的北斗数据实时、事后处理做准备。
三、北斗实时处理
在本发明上述的接收机数据采集、数据传输、数据解析已经准确完成，则已经获取到北斗的观测数据OBS、北斗星历数据NAV。该观测数据根据北斗接收机的采样率决定其数据的 时间间隔，通常为：1s、5s、15s、30s。同时，根据其观测时间长短决定最终的数据采集数量。由于北斗接收机是按照设置的采用了采集数据并实时传输数据，因此，平台在接收到数据即可完成在线实时解算。
北斗实时解算处理是北斗数据处理的核心与关键，直接决定精度的好坏和数据的可用性。本发明实现了一种北斗在线实时解算方法，采用了双频双差模式，并采用kalman滤波方法实现参数的实时更新与估计。该种模式可以避免传统北斗解算方法的延迟性，可以根据接收机的采样率和数据发送频率实现数据实时发送、接收、解算处理，大大提高监测的时效性。同时，做了严格的质量控制流程，精度有保障。其实时处理流程如图5所示。
要实现北斗的在线实时解算，则必须具有两部分基本数据内容：北斗观测数据和北斗星历数据。
1)北斗星历数据是指北斗卫星的广播轨道信息，其精度在5m之内，可由北斗接收机内部产生和接收获取，也可通过web从其他企业、研究机构下载获取，同时还可以获取研究机构所发布的精密星历产品，其轨道精度可在5cm之内。虽然北斗广播星历和精密星历精度相差很远，但是由于本发明所述方法采用的是两站两星的双差分的方式，因此其轨道误差不会对最终的解算结果产生大的影响，故不作特殊说明和处理。从外部获取的多源北斗星历数据通过软件进行合并，然后采用标准的轨道积分方法即可获取北斗卫星在CGCS2000坐标系统下的坐标值，以备下一步组建双差观测值。
2)本发明的北斗观测数据是上述根据北斗接收机采集产生并通过无线网络传输至平台，由平台根据RTCM3.03协议完成数据解析，从而得到自定义格式的北斗观测数据。本发明的北斗观测数据主要是指双频观测数据，即将接受每颗卫星的两个频点观测信号，从而产生冗余观测数据可大大利于数据质量控制，达到提高解算精度的目的。
3)质量控制是北斗获取高精度解算结果的前提和基础。没有“干净”的观测数据是不可能得到可靠结果的。北斗观测数据质量中包含了多路径、大气传播、硬件延迟、信号中断(周跳)等干扰和影响，由于信号中断导致的周跳现象其影响最为恶劣，会导致结果出现严重偏差甚至无法输出结果，因此，周跳的探测和处理是重中之重。针对周跳，本发明采用了电离层总电子含量变化率(TECR)进行周跳探测。
(一)TECR周跳探测方法：
根据测站的双频载波相位GNSS观测值可计算出测站在第i-1个历元的电离层总电子含量(Ionospheric Total Electron Contents,TEC)为：
Figure PCTCN2020095134-appb-000024
i为当前观测时间，其中i大于等于0；
Figure PCTCN2020095134-appb-000025
表示第i-1历元的电离层总电子含量；
Figure PCTCN2020095134-appb-000026
分别为第i-1个历元的L1、L2观测值；
Figure PCTCN2020095134-appb-000027
f 1、f 2分别为L1、L2载波的频率；λ 1和λ 2含义的分别是L1和L2两个波段上的波长；
Figure PCTCN2020095134-appb-000028
和
Figure PCTCN2020095134-appb-000029
分别表示L1和L2两个波段上的整周模糊度；b r、b p分别为接收机和卫星的频率间偏置。将式(1)中计算的电离层总电子含量在相邻历元间做差，则可得到电离层总电子含量变化速率TECR，其方法如下：
Figure PCTCN2020095134-appb-000030
式(2)中
Figure PCTCN2020095134-appb-000031
表示在历元i处的电离层总电子含量；
Figure PCTCN2020095134-appb-000032
表示第i-1历元的电离层总电子含量；Δt为相邻两个历元的时间间隔。如果在第i个历元处未发生周跳，由式(1)、式(2)可得第i个历元处的电子含量变化率
Figure PCTCN2020095134-appb-000033
Figure PCTCN2020095134-appb-000034
GNSS周跳探测往往是逐个历元进行检测，假设第i个历元之前的所有载波观测值均未发生周跳或者已经准确修复，则根据前两个历元的干净观测值可计算当前第i个历元的
Figure PCTCN2020095134-appb-000035
估计值为:
Figure PCTCN2020095134-appb-000036
Figure PCTCN2020095134-appb-000037
为第i-1个历元处的电子含量变化率，其中
Figure PCTCN2020095134-appb-000038
为第i-1个历元时的TEC加速度，其计算表达式如下:
Figure PCTCN2020095134-appb-000039
如果在第i个历元处，载波相位观测值
Figure PCTCN2020095134-appb-000040
分别发生周跳，其周跳值为：
Figure PCTCN2020095134-appb-000041
Figure PCTCN2020095134-appb-000042
则由式(3)可得L1、L2两个频段上发生周跳项的值大小为：
Figure PCTCN2020095134-appb-000043
根据
Figure PCTCN2020095134-appb-000044
的计算公式(3)和估计公式(4)可分别求出历元i处的计算值
Figure PCTCN2020095134-appb-000045
和估计值
Figure PCTCN2020095134-appb-000046
两者之差称为TECR残差，记为ΔTECR。由于电离层短时间内变化缓慢，因此，可将其作为周跳的检验量。如果检测量ΔTECR超过设置的阈值，则认为第i个历元发生了周跳。通常其其阈值设置为0.15(TECU/s)即可准确探测周跳。
通过对双频北斗观测数据进行质量控制，即可将周跳值准确进行探测，进而对异常观测数据进行标记或者清除，从而保证了后续解算的可靠性。
同时，质量控制模块还需要对整体的北斗观测数据的数据完整性、噪声进行检验，对于合格的观测数据则参与下一步解算，否则，迭代进行质量控制甚至放弃本次数据的解算。
4)北斗数据实时解算主要采用了kalman滤波估计方法，整个处理流程主要包含了：状态方程组建、双差观测方程组建、kalman滤波更新与参数估计、模糊度固定及结果输出几大部分。Kalman滤波方法如下：
X k＝Φ k,k-1X k-1+w k
L k＝H kX k+v k
其中式中，X k系统在当前时刻的状态向量和观测量；Φ k,k-1为从t(t-1)时刻到t(t)时刻系统状态的状态转移矩阵；w k为系统噪声向量；L k是系统在t(t)时刻的观测量；H k为观测方程的观测矩阵，v k为残差。本发明采用kalman滤波方法进行参数估计即北斗位置的精确求解，即公式中的参数X k，则首先需要求解出对应的状态转移矩阵Φ k,k-1、观测矩阵H k以及相应的噪声和残差。下面阐述如何获取以上内容：
(1)状态方程组建
状态方程组建主要就是获取式中状态转移矩阵Φ k,k-1和系统噪声向量w k。由于基础设施结构物的变形监测应用往往是静态或者准静态形式，即变化或者位移是否缓慢的场景，并且本发明的数据采样率<15s,其数据间隔很小，因此，本发明所采用的状态方程拟定如下：
X k＝X k-1，即历元之间的状态变化为零。
(2)双差观测方程组建
观测方程组建主要是获取式中的观测矩阵H k，残差v k。本发明采用的是站间差分模式，则通过监测站、基准站两个站点解析的北斗观测数据OBS进行组合，并结合两站的初始坐标(X、Y、Z)形成残差v k。其观测矩阵H k则是通过站星之间的单位方位角组合而成。
(3)Kalman滤波参数估计
在完成状态方程、双差观测方程组建之后，则所有内容都已经符合kalman的要求，通过一次求解即可完成X k参数的更新和估计，即包含北斗精确坐标的求解。由于kalman滤波是个实时滤波器，其北斗坐标的更新可以实现实时求解。
(4)模糊度固定
上述Kalman滤波方法实现的是对北斗坐标参数的求解，但是其解算的本质是浮点解，即卫星的模糊度参数是浮点数，则与客观本质现象不符合，即模糊度必须是整数。因此要想取 得更高精度、可靠的位置坐标，则必须取得模糊度的固定解。本发明采用LAMBDA方法实现模糊度的固定求解，该方法主要是在上述浮点解的基础上，将求解的参数X k中涉及的卫星模糊度参数统一、归整到整数上，并且保证其最小方差的特性。属于kalman滤波方法的再次加工。
(5)固定解输出
经过上述固定解的求解，即可选择将模糊度固定解参数X k中的站点坐标(X、Y、Z)信息进行输出。
由于北斗接收机是按照固定采样率稳定输出数据，并通过无线网络传输至数据处理平台，且采用kalman滤波方法进行参数估计，可以达到实时更新，且保留了历史状态信息，其实时解算的结果可以达到mm-cm精度，比较可靠，可服务于实时变形监测。
四、北斗事后处理
虽然本发明采用kalman滤波方法实现了坐标的实时求解，但是kalman滤波中要求观测数据也具有实时性，而这会给质量控制模块带来挑战。实时数据的质量控制对于一些特殊的情况下无法做到100％的“干净”数据输出，因此，实时处理中容易出现异常结果。常规北斗解算方法中为了提高这种精度，通常采用事后时段解结果作为北斗监测数据处理手段。这种方法具有严谨的理论基础，以及定位结果具有较强的独立性和通用性，但是这种方法具有很强的专业性、复杂性，且开发、维护升级成本高。本发明提出一种易于实现且不涉及北斗专业性的北斗事后处理方法：双kalman平滑滤波方法。其处理流程如图6所示。
该方法步骤如下：
(A)必要参数设置。首先需要设置北斗事后处理的结果输出时段间隔，比如设置为30分钟、1小时输出一次事后结果。同时也设置对于异常结果的阈值和依据内部过程信息进行加权的权值信息，主要是为了实现异常结果的剔除；
(B)接收和存储北斗实时处理的单历元解算结果以及参数估计的过程信息，包括固定解的Ratio值、卫星高度角、卫星信噪比、卫星数量。直到达到设置的时段长度则开始执行下一步操作；
(C)当达到设置的时段长度后，则采用kalman滤波对所采集的单历元固定解算结果(X、Y、Z)三个方向进行滤波，其滤波的参数和噪声大小可以根据实际情况设定，一般其状态方程噪声取1mm，测量噪声根据实际情况设定，可设置为5mm-1cm。经过kalman滤波平滑后，将会得出该时段内最可靠的时段解算坐标X 时段＝(X0、Y0、Z0)。
(D)同时，根据设定的历史结果窗口长度从历史结果数据库中取出最新的对应窗口大小的历史数据，然后同样采用kalman滤波进行平滑，得到历史坐标X 历史平滑＝(X1、Y1、Z1)。
(E)将得到的小时段内结果X 时段和历史结果X 历史平滑进行加权平均，得到最终的北斗事后 处理结果X 最终＝(X、Y、Z)，并将该结果更新存储到历史结果数据库中。
该种方法具有以下特点：避免了北斗内部的专业算法和数据处理，大大降低了其复杂度和专业性，同时，基于历史结果的加权平均可以大大降低短周期的噪声误差，包括周日、半周日的周期性误差，较实时结果精度具有十分明显的优势。
试验例
本发明通过大量实验和实际项目验证了其方法的有效性和可靠性；下面以实际项目采集的北斗数据完成的实时、事后解算结果为例进行阐述：
接收机设置15s采样率，时段长度设置为10分钟，连续采集了2天数据，其实时解算结果如图7a所示，事后解算结果如图7b所示，通过对比可以发现，本发明的实时解算精度可以达到毫米级别，可以满足绝大多数基础设施结构安全监测的精度；同时，采用本发明所述的事后平滑方法，其精度得到进一步提升，尤其高程方向提升显著。
以上所述仅为本发明的优选实施例而已，并不用于限制本发明，对于本领域的技术人员来说，本发明可以有各种更改和变化。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (10)

  1. 一种基于北斗高精度定位的基础设施结构变形监测方法，包括北斗卫星信号数据采集、无线网络通信及数据解析、北斗实时处理和北斗事后处理四个步骤，其特征在于，北斗实时处理是采用kalman滤波进行参数的实时更新与估计得到实时解算结果，北斗事后处理是将实时解算结果与历史结果数据采用双kalman滤波处理。
  2. 根据权利要求1所述的监测方法，其特征在于，北斗卫星信号数据采集的具体方法是：选择最少两台北斗接收机，两台设备都设置为15s采样率，设置北斗观测数据和星历数据的输出及对应的485输出端口，其格式为：RTCM3.03；同时利用天线馈线将北斗天线与北斗接收机进行连接，其中一个作为基准站点；另外一个作为变形监测站点，安装部署在需监测的基础设施结构物上；要求：基准站点与变形监测站点的距离不大于10km。
  3. 根据权利要求1所述的监测方法，其特征在于，无线网络通信及数据解析的具体方法是：利用两根485串口通信线分别连接两台北斗接收机的数据输出端口，另外一头分屏插入配置好的两台无线通信终端4G DTU端口，且该DTU分别插上4G流量卡，配置成功发送后台的IP地址；上述操作成功则会在平台实时接收北斗采集的数据；启动后台的TCP/IP监听端口及数据解析软件，该软件实时接收北斗采集数据并完成实时数据解析，生成北斗星历数据和观测数据。
  4. 根据权利要求3所述的监测方法，其特征在于，数据解析的具体方法是：读取RTCM数据，寻找电文头D300，提取电文长度，进行CRC完整性检验，获得完整RTCM电文，解析电文头信息，判断是否北斗电文，是则解析对应电文内容，删除已解析电文，获取新数据，否则直接删除已解析电文，获取新数据。
  5. 根据权利要求1所述的监测方法，其特征在于，北斗实时处理的具体方法是：实时接收北斗解析完的观测数据和星历数据，并对解析的北斗观测数据进行质量审核和控制，将异常数据进行剔除后组建kalman滤波方程的内容，逐历元完成kalman滤波的实时更新，将解算的结果输出到北斗事后处理步骤，同时，进行本地存储。
  6. 根据权利要求5所述的监测方法，其特征在于，观测数据是根据北斗接收机采集产生并通过无线网络传输至平台，由平台根据RTCM3.03协议完成数据解析，从而得到自定义格式的北斗观测数据；北斗观测数据是指双频观测数据，即接受每颗卫星的两个频点观测信号，从而产生冗余观测数据可大大利于数据质量控制，达到提高解算精度的目的。
  7. 根据权利要求5所述的监测方法，其特征在于，星历数据是指北斗卫星的广播轨道信息，其精度在5m之内，由北斗接收机内部产生和接收获取，也能通过web从其他企业、研究机构下载获取，同时还能够获取研究机构所发布的精密星历产品，其轨道精度可在5cm之内； 从外部获取的多源北斗星历数据进行合并，然后采用标准的轨道积分方法即可获取北斗卫星在CGCS2000坐标系统下的坐标值，以备下一步组建双差观测值。
  8. 根据权利要求5所述的监测方法，其特征在于，采用电离层总电子含量变化率进行周跳探测，从而实现质量审核和控制。
  9. 根据权利要求1所述的监测方法，其特征在于，北斗数据实时解算主要采用了kalman滤波估计方法，整个处理流程主要包含：状态方程组建、双差观测方程组建、kalman滤波更新与参数估计、模糊度固定及结果输出几大部分。
  10. 根据权利要求1所述的监测方法，其特征在于，北斗事后处理的具体方法是：根据设置时段长度实时解算结果，同时，根据历史窗口大小查询本地存储的历史结果数据，两个数据都采用kalman滤波进行平滑处理，得到相应的两个北斗坐标值，然后根据加权值进行评价，获取最终的北斗事后处理结果并进行本地存储和更新。
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