CN109781098B - Train positioning method and system - Google Patents
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Abstract
The invention provides a method and a system for positioning a train, which comprises the following steps: step 1, acquiring navigation measurement data and establishing a measurement value variable; step 2, establishing a system state quantity; step 3, initializing the filtering state of the filter; step 4, updating the time of the system state quantity; step 5, constructing an error value equation of the actual observed value and the predicted observed value; step 6 updates the state vector and variance of the filter. And finally, outputting the calculation result of the step 6 to a navigation computer. The GPS/INS combined navigation filtering method introduced into the sliding-mode observer not only can obtain accurate positioning information under the condition of good satellite signals, but also can provide a high-precision positioning result under the condition of abnormal satellite signals.
Description
Technical Field
The invention relates to the technical field of navigation, in particular to a method and a system for positioning a train.
Background
The development direction of train control systems is based on communication, and the speed and position of a train are important information of the train control system. Communication-based train positioning systems also require more accurate, reliable, lower cost train positioning systems. The existing train positioning methods mainly comprise the following steps: odometer, inquiry transponder, doppler radar, track circuit. The odometer is low in cost, the position is obtained through speed integration, and error accumulation exists; the Doppler radar is mainly used for measuring speed, the precision is high, but the measurement precision is greatly influenced by the speed of the vehicle, and the cost is high; a large number of transponders and track circuits are laid on the ground to eliminate positioning accumulated errors, and meanwhile, a corresponding receiving device needs to be added on a train, so that a large amount of cost is needed, ground equipment needs to be maintained regularly, the maintenance work amount is large, and the efficiency is low. With the establishment of the Beidou No. three basic system in China, the autonomously developed Beidou satellite navigation system in China starts to provide navigation positioning service for the whole world. The railway application of the satellite navigation system is developed, the seamless combined positioning technology based on the Beidou navigation is researched, and the method has great significance for a new generation of train control system in the future.
The precision of the satellite single-point positioning mode is easily influenced by the environment, the positioning signal is influenced under the conditions of shelter and multipath effect, the precision is low, the effect is poor, most positioning errors can be eliminated by adopting the carrier phase real-time difference technology, and the system precision is improved. However, the running environment of the motor train unit is variable, and the positioning accuracy is still reduced when some satellite signals in a tunnel, a valley and the like are unlocked. The inertial navigation is a navigation mode which is completely autonomous, independent of external signals and carries out dead reckoning according to self motion information, so that the satellite/inertial navigation combined navigation mode can combine the advantages of two navigation modes, overcomes the respective defects and is the current main navigation mode.
In general, Extended Kalman Filter (EKF) algorithm is adopted to realize the GPS/INS integrated navigation system data fusion. Extended kalman filtering is a functional approximation. Firstly, the target state is preliminarily estimated through a system model, and then the preliminary estimation is corrected by combining with the measured value to obtain the optimal estimation. The EKF performs a first-order Taylor series expansion on the nonlinear model around the state estimation value, thereby converting the nonlinear problem into linear processing. Because the EKF has the advantages of simplicity and easiness in implementation, the EKF is widely applied to the aspect of sensor data fusion.
The EKF algorithm also has some disadvantages. Firstly, the higher order term truncation error caused by linearization has an effect on the filtering accuracy, and a large estimation error is generated under the strong non-linear condition. In addition, the filtering accuracy is affected by uncertainty of system noise, disturbance of an error model and the like.
Disclosure of Invention
In order to solve the technical problems, the invention provides a train positioning method and a train positioning system, a GPS/INS combined navigation filtering method of a sliding-mode observer is introduced, and the positioning accuracy, reliability and safety of the system are improved.
In order to achieve the purpose, the invention is concretely realized by the following technical scheme:
a method for train positioning comprises
Step (1), navigation measurement data are obtained, a measurement value variable u is established by taking the navigation measurement data as a basic variable,
wherein λ, L, h represents carrier longitude, latitude and altitude; vE,VN,VURepresenting the velocity of the carrier in the east, north and sky directions;
representing a roll angle, a pitch angle and a course angle of the carrier;
step (2), error data of the variables are obtained, a state parameter x is established according to the error data,
wherein, the delta lambda, the delta L and the delta h are longitude, latitude and height errors of the carrier, and the delta VE,δVN,δVUIs the velocity error in the east, north and sky directions of the carrier, phiE,φN,φUError of east, north and sky misalignment angle of carrierx,εy,εzIn order to realize the three-axis drift of the gyroscope fixedly connected with the carrier,
the drift of an accelerometer fixedly connected with the carrier on three axes is realized;
step (3), initializing the filtering state of the filter;
step (4), updating time of the data obtained in the step (1) and the step (2);
the estimation result at the time k +1 is:
the number of times of 'adjustment' of the sliding mode observer near the target value of the sliding mode surface is preset to be n, and at the moment, the state prediction covariance is as follows:
step (5) according to an observation equation yk＝hk(xk)+vkAnd equation of state xk+1＝fk(xk)+wkObtaining an error value between the actual observation and the predicted observation, wherein hk(xk) Representing the observation function as a non-linear discrete function representing the relationship between the observation value and the system state quantity, fk(xk) The state function of the system is shown, the nonlinear discrete function is also shown, the relation between state quantities at adjacent moments of the system is shown, and the subscript k represents the function corresponding to the moment k:
wherein, the difference value between the time observation value and the predicted value is a predicted state correction value at the moment of k + 1;
step (6), updating the state vector and the variance of the filter;
processing the data according to a formula to obtain a state vector and a variance of filtering at the moment k + 1:
wherein the content of the first and second substances,
for the observation equation about
The Jacobian matrix of (1); kk+1To extend the kalman filter gain, it can be expressed as the product of the variance and the sliding-mode gain:
and outputting a calculation result.
In the step (5), the measurement vector of the system is:
z＝[δvE δvN δvU δλ δL δh]T。
in the step (3), the filter state initialization formula is as follows:
in the step (6), the Jacobian matrix calculation process is as follows:
in the step (4), the constructed sliding-mode observer is as follows:
wherein F is a system matrix; g is a noise driving matrix; d is unknown and bounded input of the system, and according to the sliding mode observer theory, the system error is defined as follows:
selecting a sliding mode surface as a system error, and selecting a Lyapunov function as follows:
derived from the above formula
According to the sliding mode state observer theory, the following are provided:
where L represents the sliding mode observer gain.
Through the derivation process, the sliding-mode observer of the GPS/INS integrated navigation system is as follows:
wherein the content of the first and second substances,
is a systematic error; a is an adjustable parameter; sgn(s) is a sign function that can be expressed as:
further solving the systematic error derivative, the final estimated value of the unknown input d is:
a system applying the train positioning method comprises a processing chip capable of executing the train positioning method.
The method comprises the following steps: the system comprises a data acquisition module, a data fusion module, a satellite RTK positioning base station module, a data output module, a safety power supply module and a vehicle-mounted computer. The data acquisition module is electrically connected with the data fusion module, the data fusion module is electrically connected with the data output module, the data output module is electrically connected with the vehicle-mounted computer, and the safety power supply module is respectively connected with the data acquisition module and the data fusion module. The data output module is electrically connected, and the satellite RTK positioning base station module is communicated with the data acquisition module through a GPRS network.
The data acquisition module comprises a satellite data acquisition board card, an inertial navigation data acquisition board card and a positioning data analysis board card, wherein the satellite data acquisition board card has the functions of data collection and data processing, and the positioning data analysis board card has the functions of analyzing the satellite and the inertial navigation data and sending the analyzed satellite and inertial navigation data to the data fusion module.
The data fusion module has the functions of three parts, namely inertial navigation resolving, satellite/inertial navigation data fusion and positioning data correction;
the satellite RTK positioning base station module comprises a satellite antenna, a satellite data acquisition board card and a wireless transmission module;
the data acquisition module is electrically connected with the data fusion module through a connector and is connected with the data output module through serial communication, the data fusion module is electrically connected with the data output module through a connector and is connected with the data fusion module through serial communication, the RTK positioning base station is connected with the data acquisition module through wireless communication, the data output module is electrically connected with the vehicle-mounted computer through serial communication, and the data acquisition module, the data fusion module and the data output module are respectively electrically connected with the safety power supply module through a connector.
The inertial sensor is electrically connected with the satellite data acquisition board card by adopting an aviation plug;
the data acquisition module and the data fusion module are respectively provided with two identical modules which have the same functions and operate simultaneously, wherein the two data acquisition modules are respectively and electrically connected with the data fusion module by adopting a connector and adopting serial communication connection;
the data output module has the function of comparing and analyzing two groups of fusion data received at the same time, sending the fusion data to the vehicle-mounted computer if the fusion data are the same, and sending alarm information to the vehicle-mounted computer if the fusion data are different.
The GPS/INS combined navigation filtering method introduced into the sliding-mode observer not only can obtain accurate positioning information under the condition of good satellite signals, but also can provide a high-precision positioning result under the condition of abnormal satellite signals.
The extended Kalman filtering method introduced into the sliding-mode observer is suitable for nonlinear and non-stable signal processing, and high-order item truncation errors brought in the extended Kalman linearization process are effectively reduced.
The GPS/INS integrated navigation filtering method for the seamless train positioning system introduced by the invention introduces the sliding-mode observer to estimate the system model error and noise, introduces the estimated value into the extended Kalman filter, and improves the filtering precision and the tracking capability on the basis of not changing the original extended Kalman filtering performance.
Drawings
The invention is explained in more detail below with reference to the figures and examples.
FIG. 1 is a diagram of a GPS/INS integrated navigation observer.
FIG. 2 is a diagram of the SMO-EKF algorithm.
FIG. 3 is a flow chart of a GPS/INS motor train unit method incorporating a sliding-mode observer.
Detailed Description
The embodiment of the invention provides a train positioning method, which comprises the following steps
Step (1), navigation measurement data are obtained, a measurement value variable u is established by taking the navigation measurement data as a basic variable,
wherein λ, L, h represents carrier longitude, latitude and altitude; vE,VN,VURepresenting the velocity of the carrier in the east, north and sky directions;
representing a roll angle, a pitch angle and a course angle of the carrier;
step (2), error data of the variables are obtained, a state parameter x is established according to the error data,
wherein, the delta lambda, the delta L and the delta h are longitude, latitude and height errors of the carrier, and the delta VE,δVN,δVUIs the velocity error in the east, north and sky directions of the carrier, phiE,φN,φUError of east, north and sky misalignment angle of carrierx,εy,εzIn order to realize the three-axis drift of the gyroscope fixedly connected with the carrier,
the drift of an accelerometer fixedly connected with the carrier on three axes is realized;
step (3), initializing the filtering state of the filter;
step (4), updating time of the data obtained in the step (1) and the step (2);
the estimation result at the time k +1 is:
the number of times of 'adjustment' of the sliding mode observer near the target value of the sliding mode surface is preset to be n, and at the moment, the state prediction covariance is as follows:
step (5) according to an observation equation yk＝hk(xk)+vkAnd equation of state xk+1＝fk(xk)+wkObtaining an error value between the actual observation and the predicted observation, wherein hk(xk) Representing the observation function as a non-linear discrete function representing the relationship between the observation value and the system state quantity, fk(xk) The state function of the system is shown, the nonlinear discrete function is also shown, the relation between state quantities at adjacent moments of the system is shown, and the subscript k represents the function corresponding to the moment k: :
wherein, the difference value between the time observation value and the predicted value is a predicted state correction value at the moment of k + 1;
step (6), updating the state vector and the variance of the filter;
processing the data according to a formula to obtain a state vector and a variance of filtering at the moment k + 1:
wherein the content of the first and second substances,
for the observation equation about
The Jacobian matrix of (1); kk+1To extend the kalman filter gain, it can be expressed as the product of the variance and the sliding-mode gain:
and outputting a calculation result.
In the step (5), the measurement vector of the system is:
z＝[δvE δvN δvU δλ δL δh]T。
in the step (3), the filter state initialization formula is as follows:
in the step (6), the Jacobian matrix calculation process is as follows:
in the step (4), the constructed sliding-mode observer is as follows:
wherein F is a system matrix; g is a noise driving matrix; d is unknown and bounded input of the system, and according to the sliding mode observer theory, the system error is defined as follows:
selecting a sliding mode surface as a system error, and selecting a Lyapunov function as follows:
derived from the above formula
According to the sliding mode state observer theory, the following are provided:
where L represents the sliding mode observer gain.
Through the derivation process, the sliding-mode observer of the GPS/INS integrated navigation system is as follows:
wherein the content of the first and second substances,
is a systematic error; a is an adjustable parameter; sgn(s) is a sign function that can be expressed as:
further solving the systematic error derivative, the final estimated value of the unknown input d is:
a system applying the train positioning method comprises a processing chip capable of executing the train positioning method.
The method comprises the following steps: the system comprises a data acquisition module, a data fusion module, a satellite RTK positioning base station module, a data output module, a safety power supply module and a vehicle-mounted computer. The data acquisition module is electrically connected with the data fusion module, the data fusion module is electrically connected with the data output module, the data output module is electrically connected with the vehicle-mounted computer, and the safety power supply module is respectively connected with the data acquisition module and the data fusion module. The data output module is electrically connected, and the satellite RTK positioning base station module is communicated with the data acquisition module through a GPRS network.
The data acquisition module comprises a satellite data acquisition board card, an inertial navigation data acquisition board card and a positioning data analysis board card, wherein the satellite data acquisition board card has the functions of data collection and data processing, and the positioning data analysis board card has the functions of analyzing the satellite and the inertial navigation data and sending the analyzed satellite and inertial navigation data to the data fusion module.
The data fusion module has the functions of three parts, namely inertial navigation resolving, satellite/inertial navigation data fusion and positioning data correction;
the satellite RTK positioning base station module comprises a satellite antenna, a satellite data acquisition board card and a wireless transmission module;
the data acquisition module is electrically connected with the data fusion module through a connector and is connected with the data output module through serial communication, the data fusion module is electrically connected with the data output module through a connector and is connected with the data fusion module through serial communication, the RTK positioning base station is connected with the data acquisition module through wireless communication, the data output module is electrically connected with the vehicle-mounted computer through serial communication, and the data acquisition module, the data fusion module and the data output module are respectively electrically connected with the safety power supply module through a connector.
The inertial sensor is electrically connected with the satellite data acquisition board card by adopting an aviation plug;
the data acquisition module and the data fusion module are respectively provided with two identical modules which have the same functions and operate simultaneously, wherein the two data acquisition modules are respectively and electrically connected with the data fusion module by adopting a connector and adopting serial communication connection;
the data output module has the function of comparing and analyzing two groups of fusion data received at the same time, sending the fusion data to the vehicle-mounted computer if the fusion data are the same, and sending alarm information to the vehicle-mounted computer if the fusion data are different.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention as defined in the following claims. Any modification, equivalent replacement, or improvement made within the spirit and principle of the invention should be included in the protection scope of the invention.
Claims (9)
1. A method for train positioning comprises
Step (1), navigation measurement data are obtained, a measurement value variable u is established by taking the navigation measurement data as a basic variable,
wherein λ, L, h represents carrier longitude, latitude and altitude; vE,VN,VURepresenting the velocity of the carrier in the east, north and sky directions;
representing a roll angle, a pitch angle and a course angle of the carrier;
step (2), error data of the variables are obtained, a state parameter x is established according to the error data,
wherein, the delta lambda, the delta L and the delta h are longitude, latitude and height errors of the carrier, and the delta VE,δVN,δVUIs the velocity error in the east, north and sky directions of the carrier, phiE,φN,φUError of east, north and sky misalignment angle of carrierx,εy,εzIn order to realize the three-axis drift of the gyroscope fixedly connected with the carrier,
the drift of an accelerometer fixedly connected with the carrier on three axes is realized;
step (3), initializing the filtering state of the filter;
step (4), updating time of the data obtained in the step (1) and the step (2);
the estimation result at the time k +1 is:
the number of times of 'adjustment' of the sliding mode observer near the target value of the sliding mode surface is preset to be n, and at the moment, the state prediction covariance is as follows:
in the step (4), the constructed sliding-mode observer is as follows:
wherein F is a system matrix; g is a noise driving matrix; d is unknown and bounded input of the system, and according to the sliding mode observer theory, the system error is defined as follows:
selecting a sliding mode surface as a system error, and selecting a Lyapunov function as follows:
derived from the above formula
According to the sliding mode state observer theory, the following are provided:
wherein, L represents the gain of the sliding mode observer;
through the derivation process, the sliding-mode observer of the GPS/INS integrated navigation system is as follows:
wherein the content of the first and second substances,
is a systematic error; a is an adjustable parameter; sgn(s) is a sign function expressed as:
further solving the systematic error derivative to obtain a final estimated value of d as:
step (5) according to an observation equation yk＝hk(xk)+vkAnd equation of state xk+1＝fk(xk)+wkObtaining an error value between the actual observation and the predicted observation, wherein hk(xk) Representing the observation function as a non-linear discrete function representing the relationship between the observation value and the system state quantity, fk(xk) The state function of the system is shown, the nonlinear discrete function is also shown, the relation between state quantities at adjacent moments of the system is shown, and the subscript k represents the function corresponding to the moment k:
wherein, the difference value between the time observation value and the predicted value is a predicted state correction value at the moment of k + 1;
step (6), updating the state vector and the variance of the filter;
processing the data according to a formula to obtain a state vector and a variance of filtering at the moment k + 1:
wherein the content of the first and second substances,
for the observation equation about
The Jacobian matrix of (1); kk+1For extended kalman filter gain, expressed as the product of variance and sliding mode gain:
and outputting a calculation result.
2. The method of claim 1, wherein the step of removing the metal oxide layer comprises removing the metal oxide layer from the metal oxide layer
In the step (5), the measurement vector of the system is:
z＝[δvE δvN δvU δλ δL δh]T。
3. the method of claim 1, wherein the step of removing the metal oxide layer comprises removing the metal oxide layer from the metal oxide layer
In the step (3), the filter state initialization formula is as follows:
4. the method of claim 1, wherein the step of removing the metal oxide layer comprises removing the metal oxide layer from the metal oxide layer
In the step (6), the Jacobian matrix calculation process is as follows:
5. a system applying the train positioning method of one of claims 1 to 4, comprising a processing chip capable of performing the train positioning method.
6. The system of claim 5,
the method comprises the following steps: the system comprises a data acquisition module, a data fusion module, a satellite RTK positioning base station module, a data output module, a safety power supply module and a vehicle-mounted computer; the data acquisition module is electrically connected with the data fusion module, the data fusion module is electrically connected with the data output module, the data output module is electrically connected with the vehicle-mounted computer, and the safety power supply module is respectively connected with the data acquisition module and the data fusion module; the data output module is electrically connected, and the satellite RTK positioning base station module is communicated with the data acquisition module through a GPRS network.
7. The system of claim 6,
the data acquisition module comprises a satellite data acquisition board card, an inertial navigation data acquisition board card and a positioning data analysis board card, wherein the satellite data acquisition board card has the functions of data collection and data processing, and the positioning data analysis board card has the functions of analyzing the satellite and the inertial navigation data and sending the analyzed satellite and inertial navigation data to the data fusion module.
8. The system of claim 7,
the data fusion module has the functions of three parts, namely inertial navigation resolving, satellite/inertial navigation data fusion and positioning data correction;
the satellite RTK positioning base station module comprises a satellite antenna, a satellite data acquisition board card and a wireless transmission module;
the data acquisition module is electrically connected with the data fusion module through a connector and is connected with the data output module through serial communication, the data fusion module is electrically connected with the data output module through a connector and is connected with the data fusion module through serial communication, the RTK positioning base station module is connected with the data acquisition module through wireless communication, the data output module is electrically connected with the vehicle-mounted computer through serial communication, and the data acquisition module, the data fusion module and the data output module are respectively electrically connected with the safety power supply module through a connector.
9. The system of claim 8,
the inertial sensor is electrically connected with the satellite data acquisition board card by adopting an aviation plug;
the data acquisition module and the data fusion module are respectively provided with two identical modules which have the same functions and operate simultaneously, wherein the two data acquisition modules are respectively and electrically connected with the data fusion module by adopting a connector and adopting serial communication connection;
the data output module has the function of comparing and analyzing two groups of fusion data received at the same time, sending the fusion data to the vehicle-mounted computer if the fusion data are the same, and sending alarm information to the vehicle-mounted computer if the fusion data are different.
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| CN111982126B (en) * | 2020-08-31 | 2023-03-17 | 郑州轻工业大学 | Design method of full-source BeiDou/SINS elastic state observer model |
| CN113739808A (en) * | 2021-09-03 | 2021-12-03 | 国网四川省电力公司电力科学研究院 | Edge processing method, device and equipment of combined navigation sensor |
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