CN113064186A - Intelligent terminal rapid dynamic positioning system and method based on dual-system satellite difference - Google Patents
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- 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
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Abstract
This patent has realized a quick dynamic positioning system of intelligent terminal and method based on dual system satellite difference, and this patent technique includes: the pseudo-range differential positioning algorithm is realized in the intelligent terminal; the realization of TCP/IP communication and edge calculation between the intelligent terminal and the positioning base station; realizing rapid relative positioning of the intelligent terminal; designing a multi-thread intelligent terminal positioning system; this patent can increase single point location's accuracy and real-time nature by a wide margin through using above technique to realize intelligent terminal's quick dynamic positioning.
Description
Technical Field
The invention relates to a GPS and BDS dual-system positioning technology, a pseudo-range differential single-point algorithm, a rapid relative positioning algorithm, protocol communication, a multi-thread architecture and Android platform-based intelligent terminal software system development, in particular to a dual-system satellite differential-based intelligent terminal rapid dynamic positioning system and method.
Background
In 2016, Google opened the raw GNSS observation data interface of the Android platform. Because the open time of the original data is short, based on the differential positioning and dynamic relative positioning of the intelligent terminal, relevant research is already carried out abroad, while the research on the aspect is very little in China, and the research only stays in the data analysis stage at present. However, no matter the current express delivery, the shared trip, or the future, the dynamic relative positioning of the unmanned automatic vehicle, the unmanned cluster, and the like, the intelligent terminal participating in the positioning has higher requirements on the position of the rapid, high-precision and motion state. The pseudo-range differential positioning mode is considered in the method, because the mathematical model of the pseudo-range differential is simple, the whole-cycle ambiguity does not need to be calculated, and the precision meets the requirements of most intelligent terminal users.
Therefore, the intelligent terminal rapid dynamic positioning method based on the dual-system satellite difference is applied to achieve rapid dynamic positioning of the intelligent terminal based on the dual-system satellite difference.
Disclosure of Invention
The invention discloses an intelligent terminal rapid dynamic positioning system and method based on dual-system satellite difference, which uses the effective difference of the positioning data of an intelligent terminal and a base station and the relative positioning between a plurality of intelligent terminal devices to solve the problems of low positioning speed and low precision of the intelligent terminal.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a quick dynamic positioning system of an intelligent terminal based on dual-system satellite difference is characterized by comprising a dual-satellite system data reading and processing module, a positioning base station and edge calculation module, a private protocol communication module, a difference positioning module and a multithreading processing module; the double-satellite system data reading and processing module is used for obtaining GNSS original data such as pseudo range, ephemeris, initial positioning estimation value and the like, and performing filtering smoothing pretreatment; the positioning base station and edge calculation module is used for obtaining data such as pseudo-range, ephemeris and initial positioning estimation value, completing edge calculation and reducing the calculation pressure of the intelligent terminal; the private protocol communication module is used for realizing the secret real-time communication among all the nodes; the differential positioning module obtains a positioning result with improved precision by calculating a single-point positioning result and base station correction data through a differential algorithm, and can also carry out dynamic position calculation based on the result; the multithreading processing module is used for preprocessing the satellite position by means of multithreading concurrency advantages to achieve asynchronism between the ephemeris and the observation epoch, and provides a data calculation method of ephemeris junction, so that the positioning speed and the system stability are improved.
A quick dynamic positioning method of an intelligent terminal based on dual-system satellite difference is characterized by comprising the following steps:
step 2, in a dynamic environment, performing rapid relative positioning based on the calculated absolute position;
and 3, designing a positioning system applied to the intelligent terminal, and realizing high-speed and accurate position resolving by means of pseudo-range Kalman filtering, an SAT viewModel structure and a multi-thread program architecture.
As a further improvement of the invention, the method comprises the following specific steps:
when the intelligent terminal communicates with the base station, interaction of data streams is performed after a monitoring socket binding port is created, wherein the interaction comprises single satellite ephemeris data matched by the base station, error correction obtained by edge calculation of the base station and the like. After receiving the data, the data is firstly recorded into a buffer area, and when the bit is read, the obtained data stream is split through a frame head and a frame tail specified by a private protocol.
In the Android terminal development, GNSS raw data are obtained from a GnssMeasurement class and a gnssClock class, and a pseudo range is calculated:
ρ＝(tRx-tTx)·c
GNSSweek is the time from the zero point of 1/6 th of 1980 to the GPS week of each system, and the calculation method of different GNSS systems is different, so that the tRx calculation mode of different systems is also different.
For the GPS system:
tRx＝tRxGNSS-weekNumberNanos
for the BDS system:
tRx＝tRxGNSS-weekNumberNanos-14s
during specific implementation, a prediction equation is built depending on the obtained pseudo-range change rate, Kalman filtering is carried out through an observed value obtained every time, the pseudo-range value is corrected, a pseudo-range differential positioning model is built through obtained base station data, the influence of relativity errors, earth rotation effects, troposphere correction and elevation angles and direction angles is solved by adopting triple iteration difference, and high-precision multimode differential positioning is carried out on an intelligent terminal.
Step 2, in a dynamic environment, performing rapid relative positioning based on the calculated absolute position;
firstly, calculating the absolute position of one station by using a static pseudo-range difference result, and constructing a double-difference observed quantity between an inter-satellite and an intelligent terminal by using the position, the speed and the acceleration of the other mobile station as state quantities to construct an error equation of the observed quantity; solving the baseline vector, checking the standard deviation after setting a threshold range, and taking the baseline vector without the coarse error for correction iteration; and calculating the relative position, converting the relative position into a station center coordinate system, and finally obtaining a relative positioning result.
Step 3, the method is applied to the design of a positioning system of an intelligent terminal, and high-speed and accurate position resolving is realized by means of pseudo-range Kalman filtering, an SAT viewModel structure and a multi-thread program architecture;
in order to improve the positioning speed and solve the problem of data loss at the ephemeris junction, including pseudo-range differential positioning of a static position and rapid relative positioning of multiple intelligent terminals, a multi-thread system design and ephemeris junction data calculation method is provided. One thread is always in the process of processing ephemeris data acquired from a base station, because the ephemeris data is acquired every 15 minutes, data matching processing is performed by taking 7 minutes and 30 seconds as a boundary, and ephemeris of 30 seconds after the acquired ephemeris data is predicted in advance is relied on, so that the influence of time delay caused by processing the base station data on the positioning speed is avoided, a coarse error is detected when a data packet is analyzed, and the resolving accuracy is improved.
In addition, N threads are started according to the number of the received satellites for processing the read GNSS original data, each group of observation values is subjected to data processing by an independent thread to solve ephemeris, a database established in the first thread is asynchronously called to obtain a more accurate satellite position, when the data are processed, pseudo-range Kalman filtering is adopted, a system model is formed by means of the obtained pseudo-range change rate and received noise for prediction, Kalman filtering is performed by combining a new observation value obtained in each step, and the observation value formula is as follows:
in the formula:
is the pseudo-range rate of the GPS satellites,
the pseudo range rate is obtained by the mobile terminal through a getsegerundregrametermeterpersperssecond () method.
Forward reckoning state variables and error covariance through time updating, performing measurement updating by means of observed quantity, calculating Kalman gain, and updating and estimating observation variables and error covariance; thereby using the observed quantity to calibrate the predicted value, the algorithm formula is as follows: and a prediction part:
in the formula: k represents a current epoch; k-1 represents the last epoch (k, k-1) tableShowing a predicted value of a previous epoch to a current epoch;
the best estimate (state vector) for the last epoch;
the predicted value of the current epoch is obtained;
a Jacobian matrix (state transition matrix) for state vector derivation for the motion model; u. ofk-1The selected control input is generally set as a zero matrix B as a conversion matrix thereof; pk-1Is a corresponding covariance matrix; qk-1Is a process noise covariance matrix.
The correction part:
in the formula: kkIs a Kalman filter gain matrix; hkA Jacobian matrix (observation matrix) for state vector partial derivation for the observation model; rkA covariance matrix of observation noise; zkIs an observation vector; and I is a unit array.
According to the motion state of the carrier, the state parameters of the current epoch can be estimated by using an extended Kalman filter. When the carrier is static, only 4 state parameters of coordinates and receiver clock difference need to be estimated; when the carrier moves, 10 state parameters such as coordinates, speed, acceleration, receiver clock error and the like need to be estimated.
The epoch data obtained by the N threads is stored in the SAT View Model, so that memory leakage and data loss caused by equipment state adjustment are prevented, and asynchronous callback is realized.
And monitoring the change of the epoch data in the SAT View Model in the main thread, performing position calculation in real time and displaying the position calculation in a map. And by means of a multi-thread program architecture, the situation of partial blockage in data processing is eliminated, and the positioning precision is ensured and the positioning speed is improved.
As a further improvement, the method utilizes the Android system to perform positioning calculation on the externally opened original positioning data after 2016.
As a further improvement, the invention uses the positioning base station to complete the acquisition of original data, match satellite ephemeris and calculate a correction value, and provides edge calculation for the intelligent terminal.
Compared with the prior art, the invention has the beneficial effects that:
1. the TCP/IP protocol with short service time, high performance and good encryption is used for carrying out socket communication with the base station end, and can be used for packing the correction data of the base station and the matched satellite data according to a self-defined transmission protocol to carry out real-time communication between the mobile end and the base station; the method can also be used for information exchange among users who agree to share the position information, and effective information is provided for dynamic position calculation;
2. constructing double-difference observed quantities between the satellites and the intelligent terminal, and constructing an error equation of the observed quantities; solving the baseline vector, checking the standard deviation, and taking the baseline vector without the coarse error for correction iteration;
3. by means of an original GNSS data multithreading processing architecture and by means of multithreading concurrency advantages, independent threads are provided for each satellite, preprocessing and differential iteration of satellite positions are achieved, and asynchronization of ephemeris and observation epochs is achieved; in order to solve the temporary loss of data at the ephemeris junction, a data calculation method of the ephemeris junction is provided, and the positioning speed and the system stability are improved;
4. the integration of functions such as a differential positioning module, a multithreading system module and a base station communication module is completed, a multithreading processing framework of GNSS data is embedded, the human-computer interaction experience is improved, the operation pressure is reduced, the cost is reduced, and the high efficiency and the portability are realized.
Drawings
FIG. 1 is a process of an intelligent terminal acquiring navigation message data of a base station;
FIG. 2 is a schematic diagram of a method for obtaining GNSS data from Android;
FIG. 3 is a schematic diagram of an implementation of pseudorange differential positioning;
FIG. 4 is a schematic diagram of a fast relative positioning procedure in a multi-station scenario;
FIG. 5 is a multi-threaded pseudorange Kalman filtering system architecture.
Detailed Description
The present invention will be further illustrated with reference to the accompanying drawings and specific embodiments, which are to be understood as merely illustrative of the invention and not as limiting the scope of the invention.
As shown in the figure, the invention provides an intelligent terminal rapid dynamic positioning system based on dual-system satellite difference, which comprises a dual-satellite system data reading and processing module, a positioning base station and edge calculation module, a private protocol communication module, a difference positioning module and a multithreading processing module; the double-satellite system data reading and processing module is used for obtaining GNSS original data such as pseudo range, ephemeris, initial positioning estimation value and the like, and performing filtering smoothing pretreatment; the positioning base station and edge calculation module is used for obtaining data such as pseudo-range, ephemeris and initial positioning estimation value, completing edge calculation and reducing the calculation pressure of the intelligent terminal; the private protocol communication module is used for realizing the secret real-time communication among all the nodes; the differential positioning module obtains a positioning result with improved precision by calculating a single-point positioning result and base station correction data through a differential algorithm, and can also carry out dynamic position calculation based on the result; the multithreading processing module is used for preprocessing the satellite position by means of multithreading concurrency advantages to achieve asynchronism between the ephemeris and the observation epoch, and provides a data calculation method of ephemeris junction, so that the positioning speed and the system stability are improved.
In this example, a method for fast and dynamically positioning an intelligent terminal based on dual-system satellite difference is disclosed, which comprises the following specific steps:
when the intelligent terminal communicates with the base station, interaction of data streams is performed after a monitoring socket binding port is created, wherein the interaction comprises single satellite ephemeris data matched by the base station, error correction obtained by edge calculation of the base station and the like. After receiving the data, the data is firstly recorded into a buffer area, and when the bit is read, the obtained data stream is split through a frame head and a frame tail specified by a private protocol. As in fig. 1.
In the Android terminal development, GNSS raw data is acquired from GnssMeasurement class and gnsscock class as shown in fig. 2, and pseudo-range is calculated:
ρ＝(tRx-tTx)·c
GNSSweek is the time from the zero point of 1/6 th of 1980 to the GPS week of each system, and the calculation method of different GNSS systems is different, so that the tRx calculation mode of different systems is also different.
For the GPS system:
tRx＝tRxGNSS-weekNumberNanos
for the BDS system:
tRx＝tRxGNSS-weekNumberNanos-14s
in specific implementation, a prediction equation is built depending on the obtained pseudo-range change rate, Kalman filtering is performed through an observed value obtained every time, the pseudo-range value is corrected, a pseudo-range differential positioning model is built through obtained base station data, the influence of relativity errors, earth rotation effects, troposphere correction and elevation angles and direction angles is solved by adopting triple iteration difference, and high-precision multimode differential positioning is performed on an intelligent terminal, as shown in fig. 3.
Step 2, inDynamic statePerforming rapid relative positioning based on the calculated absolute position in the environment of (1);
the method comprises the steps of firstly, calculating the absolute position of a station by using a static pseudo-range difference result, and constructing double-difference observed quantities between inter-satellite and mobile stations, wherein single-difference subtraction between two stations at the same time is used as the station-to-satellite double-difference, so that the receiver clock difference can be further eliminated. And then constructing an error equation of the double-difference observed quantity, solving a baseline vector, performing standard deviation checking after setting a threshold range, removing a coarse error, receiving the baseline vector after judging that no error exists, performing correction iteration and circularly running the process, meanwhile, calculating a relative position by depending on the iterated certificate, converting the relative position into a station center coordinate system, and finally obtaining a relative positioning result, wherein the flow chart is shown in fig. 4.
Step 3, the method is applied to the design of a positioning system of an intelligent terminal, high-speed and accurate position resolving is realized by means of pseudo-range Kalman filtering, an SAT view Model structure and a multi-thread program framework, and the flow is shown in figure 5;
in order to improve the positioning speed and solve the problem of data loss at the ephemeris junction, including pseudo-range differential positioning of a static position and rapid relative positioning of multiple intelligent terminals, a multi-thread system design and ephemeris junction data calculation method is provided. One thread is always in the process of processing ephemeris data acquired from a base station, because the ephemeris data is acquired every 15 minutes, data matching processing is performed by taking 7 minutes and 30 seconds as a boundary, and ephemeris of 30 seconds after the acquired ephemeris data is predicted in advance is relied on, so that the influence of time delay caused by processing the base station data on the positioning speed is avoided, a coarse error is detected when a data packet is analyzed, and the resolving accuracy is improved.
In addition, N threads are started according to the number of the received satellites for processing the read GNSS original data, each group of observation values is subjected to data processing by an independent thread to solve ephemeris, a database established in the first thread is asynchronously called to obtain a more accurate satellite position, when the data are processed, pseudo-range Kalman filtering is adopted, a system model is formed by means of the obtained pseudo-range change rate and received noise for prediction, Kalman filtering is performed by combining a new observation value obtained in each step, and the observation value formula is as follows:
in the formula:
is the pseudo-range rate of the GPS satellites,
the pseudo range rate is obtained by the mobile terminal through a getpseudo range metemeters PerSecond () method。
Forward reckoning state variables and error covariance through time updating, performing measurement updating by means of observed quantity, calculating Kalman gain, and updating and estimating observation variables and error covariance; thereby using the observed quantity to calibrate the predicted value, the algorithm formula is as follows: and a prediction part:
in the formula: k represents a current epoch; k-1 represents a last epoch (k, k-1) represents a predicted value of the last epoch to the current epoch;
the best estimate (state vector) for the last epoch;
the predicted value of the current epoch is obtained;
a Jacobian matrix (state transition matrix) for state vector derivation for the motion model; u. ofk-1The selected control input is generally set as a zero matrix B as a conversion matrix thereof; pk-1Is a corresponding covariance matrix; qk-1Is a process noise covariance matrix.
The correction part:
in the formula: kkIs a Kalman filter gain matrix; hkA Jacobian matrix (observation matrix) for state vector partial derivation for the observation model; rkA covariance matrix of observation noise; zkIs an observation vector; and I is a unit array.
According to the motion state of the carrier, the state parameters of the current epoch can be estimated by using an extended Kalman filter. When the carrier is static, only 4 state parameters of coordinates and receiver clock difference need to be estimated; when the carrier moves, 10 state parameters such as coordinates, speed, acceleration, receiver clock error and the like need to be estimated.
The epoch data obtained by the N threads is stored in the SAT View Model, so that memory leakage and data loss caused by equipment state adjustment are prevented, and asynchronous callback is realized.
And monitoring the change of the epoch data in the SAT View Model in the main thread, performing position calculation in real time and displaying the position calculation in a map. And by means of a multi-thread program architecture, the situation of partial blockage in data processing is eliminated, and the positioning precision is ensured and the positioning speed is improved.
The technical means disclosed in the invention scheme are not limited to the technical means disclosed in the above embodiments, but also include the technical scheme formed by any combination of the above technical features.
Claims (7)
1. The utility model provides a quick dynamic positioning system of intelligent terminal based on dual system satellite difference which characterized in that: the system comprises a double-satellite system data reading and processing module, a positioning base station and edge calculation module, a private protocol communication module, a differential positioning module and a multithreading processing module;
the double-satellite system data reading and processing module is used for obtaining GNSS original data such as pseudo range, ephemeris, initial positioning estimation value and the like, and performing filtering smoothing pretreatment;
the positioning base station and edge calculation module is used for obtaining data such as pseudo-range, ephemeris and initial positioning estimation value, completing edge calculation and reducing the calculation pressure of the intelligent terminal;
the private protocol communication module is used for realizing the secret real-time communication among all the nodes;
the differential positioning module obtains a positioning result with improved precision by calculating a single-point positioning result and base station correction data through a differential algorithm, and can also carry out dynamic position calculation based on the result;
the multithreading processing module is used for preprocessing the satellite position by means of multithreading concurrency advantages to achieve asynchronism between the ephemeris and the observation epoch, and provides a data calculation method of ephemeris junction, so that the positioning speed and the system stability are improved.
2. The intelligent terminal rapid dynamic positioning method based on dual-system satellite difference as claimed in claim 1, wherein: by introducing a pseudo-range calculation mode and a satellite time system conversion mode which are matched with respective protocols, and simultaneously using a Beidou satellite system and a GPS satellite system, the independent positioning and hybrid optimized positioning of the two systems are realized; filtering abnormal satellite data through a filtering smoothing algorithm, wherein the prediction and correction equation is as follows:
in the formula: k represents a current epoch; k-1 represents a last epoch (k, k-1) represents a predicted value of the last epoch to the current epoch;
is the best estimate of the last epoch;
the predicted value of the current epoch is obtained;
solving a Jacobian matrix of the state vector partial derivatives for the motion model; u. ofk-1The selected control input is generally set as a zero matrix B as a conversion matrix thereof; pk-1Is a corresponding covariance matrix; qk-1Is a process noise covariance matrix;
in the formula: kkIs a Kalman filter gain matrix; hkSolving a Jacobian matrix of the state vector partial derivatives for the observation model; rkFor observing noiseAn acoustic covariance matrix; zkIs an observation vector; and I is a unit array.
3. The intelligent terminal rapid dynamic positioning method based on dual-system satellite difference as claimed in claim 2, characterized in that: and (3) using the positioning base station to complete the acquisition of original data, match the satellite ephemeris and calculate a correction value, and providing edge calculation for the intelligent terminal.
4. The intelligent terminal rapid dynamic positioning method based on dual-system satellite difference as claimed in claim 2, characterized in that: the method uses a TCP/IP protocol to carry out socket communication with a base station end, and can be used for packing the correction data of the base station and the matched satellite data according to a self-defined transmission protocol and carrying out real-time communication between a mobile end and the base station; the method can also be used for information exchange among users who agree to share the position information, and provides effective information for dynamic position calculation.
5. The intelligent terminal rapid dynamic positioning method based on dual-system satellite difference as claimed in claim 2, characterized in that: constructing double-difference observed quantities between the satellites and the intelligent terminal, and constructing an error equation of the observed quantities; and solving the baseline vector, checking the standard deviation, and taking the baseline vector without the coarse error for correction iteration.
6. The intelligent terminal rapid dynamic positioning method based on dual-system satellite difference as claimed in claim 2, characterized in that: by means of a multi-thread processing architecture of GNSS data, independent threads are provided for each satellite by means of multi-thread concurrency advantages, preprocessing and differential iteration of the satellite positions are achieved, and asynchronization of ephemeris and observation epochs is achieved; in order to solve the temporary lack of data at the ephemeris junction, a data calculation method of the ephemeris junction is provided.
7. The intelligent terminal rapid dynamic positioning method based on dual-system satellite difference as claimed in claim 2, characterized in that: and the integration of functions of a differential positioning module, a multithreading system module, a base station communication module and the like is completed, and a multithreading processing architecture of GNSS data is embedded.
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| CN114593750A (en) * | 2022-03-08 | 2022-06-07 | 长沙学院 | Attitude measurement and calibration method for single-satellite pseudo range |
| CN117607906A (en) * | 2023-11-24 | 2024-02-27 | 中交一公局厦门工程有限公司 | Quick static measurement lofting system based on Beidou/UWB cloud data processing |
| CN117607906B (en) * | 2023-11-24 | 2024-05-31 | 中交一公局厦门工程有限公司 | Quick static measurement lofting system based on Beidou/UWB cloud data processing |
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