Positioning method based on RFID and inertial navigation system in long-distance tunnel scene

Abstract

The invention belongs to the technical field of navigation, and provides a positioning method based on RFID and an inertial navigation system in a long-distance tunnel scene, which comprises the following steps: deploying a plurality of first RFID tags in a tunnel at equal intervals, and writing an ID number of a corresponding position into each first RFID tag; an RFID reader and an inertial navigation system are installed on a rail car, the inertial navigation system calculates real-time position information and real-time speed information of the rail car through integration by continuously measuring real-time acceleration data of running of the rail car, and before the rail car enters a tunnel, initial positioning is conducted according to a GNSS, and initial position information and initial speed information are obtained; after the rail car enters the tunnel, switching to a combined positioning mode of the RFID and the inertial navigation system; and high-precision positioning is realized by adopting a Kalman filtering algorithm. According to the invention, seamless switching from GNSS to RFID and INS is realized, and the positioning continuity and accuracy of the rail car before and after entering the tunnel are ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of navigation, and particularly relates to a positioning method based on RFID and inertial navigation system in a long-distance tunnel scenario. Background Art

[0002] Most of the subway track areas are underground tunnels where satellite communication signals cannot cover or penetrate. Multiple engineering vehicles and various professional operators are simultaneously constructing in the narrow and poorly visible track areas, and there are great potential safety hazards in the mutually influencing construction arrangements. The positioning technology of rail vehicles is the key to ensuring the normal operation of the rail vehicle control system, and it is crucial for the construction and operation and maintenance safety in the track area. With the continuous growth of rail transportation demand, especially in areas prone to accidents such as track areas with many cross-operations in post-station projects, the requirements for the control and management of rail vehicles are also increasing, and there is a need for high-precision positioning of rail vehicle personnel and other movable devices.

[0003] The Global Positioning and Navigation System (GPS) can provide vehicles with high-precision three-dimensional position, speed, and time information all-weather, and the error does not accumulate over time. In outdoor situations with good satellite visibility conditions, positioning results with meter-level or even sub-meter-level accuracy can be obtained using the Global Navigation Satellite System. However, using the GNSS system to position rail vehicles highly depends on the number of visible satellites. In areas with fewer or completely missing visible satellites, such as in densely built-up areas, mountainous areas, and tunnels, there is a large error between the positioning result and the actual position of the vehicle. However, these scenarios are usually high-incidence areas of safety accidents during the construction process, and the positioning accuracy often directly affects the control, dispatching of rail vehicles and the life and property safety of relevant personnel. Therefore, in these scenarios with poor or even completely missing satellite signals, it is very important to find a high-dynamic, real-time, and high-precision positioning technology suitable for long-distance tunnels. Summary of the Invention

[0004] In view of the problems existing in the above background art, the present invention provides a positioning method based on RFID and inertial navigation system in a long-distance tunnel scenario. The specific technical solution is as follows: A positioning method based on RFID and inertial navigation system in a long-distance tunnel scenario, comprising the following steps: Step S1: Deploy a plurality of first RFID tags at equal intervals in the tunnel, and write the ID number corresponding to the position inside each first RFID tag. Deploy a corresponding plurality of second RFID tags at short intervals on the same side of the plurality of first RFID tags; Step S2: Install an RFID reader and an inertial navigation system on the rail vehicle. The inertial navigation system calculates the real-time position information and real-time speed information of the rail vehicle by continuously measuring the real-time acceleration data of the rail vehicle's travel through integration; Step S3: Before the rail vehicle enters the tunnel, perform initial positioning based on GNSS to obtain the initial position information and initial speed information; Step S4: After the rail vehicle enters the tunnel, switch to the combined positioning mode of RFID and inertial navigation system; Step S5: When the rail vehicle passes the first RFID tag, obtain the accurate position information provided by the first RFID tag; Step S6: Measure the time difference of the rail vehicle within a short period through the first RFID tag and the second RFID tag, so as to obtain the accurate speed information of the rail vehicle; Step S7: Adopt the Kalman filtering algorithm to fuse the real-time position information, the accurate position information, the real-time speed information and the accurate speed information respectively to obtain the high-precision positioning information of the rail vehicle.

[0005] In a preferred implementation manner, the inertial navigation system calculates the real-time position information and real-time speed information of the rail vehicle by integrating the continuously measured real-time acceleration data of the rail vehicle, specifically including: Step S201: Measure the acceleration information in the carrier coordinate system of the rail vehicle by using an accelerometer; Step S202: Use the quaternion method to convert the acceleration information in the carrier coordinate system into the inertial navigation coordinate system; Step S203: Integrate to calculate the real-time position information and real-time speed information of the rail vehicle; The expression of the quaternion method is:

[0006] where respectively represent the heading angle, roll angle and pitch angle of the carrier.

[0007] In a preferred implementation manner, the conversion of the acceleration information in the carrier coordinate system into the inertial navigation coordinate system by using the quaternion method specifically includes: Calculate the change of the current carrier coordinate system relative to the inertial navigation coordinate system, that is, use the attitude quaternion at the k-1 moment and The two gyroscope angle increment sampling outputs within the time period can be used to calculate the attitude quaternion at the current moment:

[0008] where q is the attitude quaternion. The first element is used to describe the angle size that the rigid body rotates around the rotation axis, and the remaining 3 elements are used to describe the direction of the rotation axis. Used to describe the attitude of the vehicle coordinate system relative to the inertial navigation coordinate system at time k-1, the symbol represents the multiplication symbol of attitude quaternions, represents the change in the attitude of the vehicle coordinate system at time k-1, expressed as:

[0009] is the rotation matrix of the attitude change of the vehicle coordinate system in the time period The gyroscope samples 2 times at equal intervals at time k-1, and the angular increments are respectively and According to the double-sample cone compensation algorithm, is expressed as:

[0010] represents the attitude quaternion of the attitude change from time k-1 to time k:

[0011] represents the rotation vector of the attitude change in the inertial navigation coordinate system time period [k-1, k]:

[0012] Among them, represents the projection of the angular velocity of the Earth-centered Earth-fixed coordinate system relative to the inertial coordinate system in the navigation system, ; represents the projection of the angular velocity of the navigation coordinate system relative to the Earth-centered Earth-fixed coordinate system in the navigation coordinate system, ; are respectively expressed as:

[0013]

[0014] Among them, is the latitude of the rail vehicle carrier, and are the eastward and northward speeds respectively, and represent the radius of the meridian circle and the radius of the prime vertical circle respectively.

[0015] In a preferred implementation manner, the integral calculates the real-time position information of the rail vehicle, including: The differential equation of the real-time position information of the rail vehicle in the inertial navigation coordinate system is

[0016] Among them, Indicates the three-axis velocity of the velocity vector of the origin of the vehicle coordinate system relative to the Earth-centered Earth-fixed coordinate system, m / s ; Indicates the non-gravitational acceleration measured by the accelerometer, ; Indicates the gravitational acceleration, ; Discretize the differential equation of the movement of the rail vehicle in the above inertial navigation coordinate system and adopt the double-sample velocity update algorithm to obtain:

[0017] Among them, Indicates the velocity vector at the current moment k in the inertial navigation coordinate system; Indicates the velocity vector at the (k - 1)th moment in the inertial navigation coordinate system; Indicates the velocity change caused by the specific force; Indicates the Coriolis integral term:

[0018] Among them, Indicates the gravitational acceleration vector in the inertial navigation coordinate system, is to the intermediate moment, Indicates the time difference between the current moment and the previous moment;

[0019] Among them, Indicates the identity matrix, Indicates the equivalent rotation vector of the inertial navigation coordinate system between two consecutive moments, Indicates the matrix cross product, Indicates the skew-symmetric matrix of, Indicates the transformation matrix from the vehicle coordinate system to the inertial navigation coordinate system, Indicates the specific force integral term in the vehicle coordinate system;

[0020] Among them, Indicates the angular velocity of the Earth's rotation, Indicates the velocity of the navigation coordinate system relative to the Earth-fixed coordinate system;

[0021] Among them, Indicates the velocity change caused by the current specific force measurement value, Indicates the angular increment measured by the current gyroscope, represents the angular increment measured by the gyroscope at the previous moment, represents the velocity change caused by the specific force measurement at the previous moment.

[0022] In a preferred implementation, the integration calculates the real-time speed information of the rail vehicle, including: The latitude differential equation of the real-time position of the rail vehicle's movement is:

[0023] The longitude differential equation of the real-time position of the rail vehicle's movement is:

[0024] The elevation differential equation of the real-time position of the rail vehicle's movement is:

[0025] Wherein, represents the latitude differential equation, represents the longitude differential equation, represents the elevation differential equation, represents the northward velocity component, represents the eastward velocity component, represents the downward velocity component, represents the radius of curvature of the meridian, represents the radius of curvature of the prime vertical; The elevation update equation of the rail vehicle's movement position is:

[0026] The latitude update equation of the rail vehicle's movement position is:

[0027] The longitude update equation of the rail vehicle's movement position is:

[0028] Wherein,

[0029]

[0030]

[0031]

[0032] e is the first eccentricity of the earth ellipsoid model. For the CGCS2000 ellipsoid, , , .

[0033] In a preferred implementation, the precise position information provided by the first RFID tag is:

[0034] The precise speed information of the rail vehicle is:

[0035] where is the precise position information provided by the second RFID tag, is the time difference between the rail vehicle passing the first RFID tag and the second RFID tag within a short period of time.

[0036] In a preferred implementation, step S7 specifically includes: Establish the state vector of the inertial navigation system:

[0037] where represents the position error vector, represents the speed error vector, represents the attitude error vector, represents the zero bias vector of the three-axis gyroscope of the inertial sensor, the error vector of the three-axis gyroscope of the inertial sensor, the acceleration error vector of the inertial sensor; Establish the state change equation of the inertial navigation system at time k:

[0038] where represents the state transition matrix, represents the state noise vector, and represent the predicted state at the current time k and the predicted state at the previous time k - 1;

[0039] In the formula, represents the sub-matrix of the position error, represents the sub-matrix of the influence of the speed error on the position error, represents the sub-matrix of the influence of the position error on the speed error, represents the sub-matrix of the speed error, represents the sub-matrix of the influence of the attitude error on the speed error, represents the sub-matrix of the influence of the position error on the attitude error, represents the sub-matrix of the influence of the speed error on the attitude error, represents the sub-matrix of the attitude error; The observation equation of the inertial navigation system at time k is established as follows:

[0040]

[0041]

[0042] Wherein, represents the difference information of position and velocity, represents the observation matrix, contains the longitude, latitude, and elevation information in the e-system coordinate system, represents the noise vector; The state of the inertial navigation system at time k is estimated using the observation equation at time k-1 to obtain the state prediction equation, i.e., the one-step prediction equation:

[0043] The error is revised using the observation equation to obtain the state estimate at time k:

[0044] The Kalman filter gain is obtained:

[0045] The state prediction mean square error equation is defined:

[0046] The state estimate mean square error equation is defined:

[0047] Wherein, Q represents the prediction covariance, P represents the error prediction covariance, represents the observation noise covariance.

[0048] Compared with the prior art, the present invention has the following advantages: (1) The present invention creatively deploys an RFID system inside the tunnel and utilizes RFID tags and RFID readers to provide stable positioning information even in an environment with insufficient GNSS signals.

[0049] (2) The present invention installs two RFID tags at a short distance inside the tunnel and calculates the speed of the rail vehicle by using the time difference of a group of RFID tags through the RFID reader of the rail vehicle in a short time, which can effectively correct the error accumulated by the inertial navigation system over time, thereby improving the positioning accuracy.

[0050] (3) The present invention realizes seamless switching from GNSS to RFID and INS, ensuring the positioning continuity and accuracy of the rail vehicle before and after entering the tunnel. Description of the Drawings

[0051] Figure 1 It is a flowchart of the implementation of the positioning method based on RFID and inertial navigation system in a long - distance tunnel scenario. Specific Embodiments

[0052] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0053] An embodiment of the present invention discloses a positioning method based on RFID and inertial navigation system in a long - distance tunnel scenario, which is used to solve the problems existing in the prior art: As Figure 1 As shown, an embodiment of the present invention discloses a positioning method based on RFID and inertial navigation system in a long - distance tunnel scenario, including the following steps: Step S1: Deploy a plurality of first RFID tags at equal - distance intervals in the tunnel, write the ID number corresponding to the position inside each first RFID tag, and deploy a corresponding number of second RFID tags at a short interval on the same side of the plurality of first RFID tags; Step S2: Install an RFID reader and an inertial navigation system on the rail vehicle. The inertial navigation system calculates the real - time position information and real - time speed information of the rail vehicle by integrating the continuously measured real - time acceleration data of the rail vehicle's travel; Specifically, the inertial navigation system calculates the real - time position information and real - time speed information of the rail vehicle by integrating the continuously measured real - time acceleration data of the rail vehicle's travel, specifically including: Step S201: Measure the acceleration information in the carrier coordinate system of the rail vehicle using an accelerometer; Step S202: Convert the acceleration information in the carrier coordinate system to the inertial navigation coordinate system using the quaternion method; Step S203: Integrate to calculate the real - time position information and real - time speed information of the rail vehicle; The expression of the quaternion method is:

[0054] Wherein, They respectively represent the heading angle, roll angle, and pitch angle of the carrier; the quaternion method has the advantages of concise description, small computational amount, and no singularity compared with the direction cosine matrix. From the above formulas, it can be seen that if the initial attitude of the carrier and the quaternion are known, the attitude update of the object can be realized.

[0055] Specifically, the acceleration information in the carrier coordinate system is converted to the inertial navigation coordinate system by using the quaternion method, which specifically includes: Calculate the change of the current carrier coordinate system relative to the inertial navigation coordinate system, that is, use the attitude quaternion at time k - 1 and The two gyroscope angular increment sampling outputs within the time period can be used to calculate the attitude quaternion at the current moment:

[0056] where q is the attitude quaternion. The first element is used to describe the angle by which the rigid body rotates around the rotation axis, and the remaining 3 elements are used to describe the direction of the rotation axis, which can be used to describe the rotation or attitude transformation of the rigid body. is used to describe the attitude of the carrier coordinate system (b - frame) relative to the inertial navigation coordinate system (n - frame) at time k - 1, and the symbol represents the multiplication symbol of the attitude quaternion. represents the change in the attitude of the carrier coordinate system at time k - 1, which is expressed as:

[0057] is the rotation matrix of the attitude change of the carrier coordinate system within the time period The gyroscope is sampled twice at equal intervals at time k - 1, and the angular increments are respectively and , according to the dual - sample cone compensation algorithm, is expressed as:

[0058] represents the attitude quaternion of the attitude change from time k - 1 to time k:

[0059] represents the rotation vector of the attitude change within the inertial navigation coordinate system time period [k - 1, k]:

[0060] Among them, represents the projection of the angular velocity of the Earth - centered Earth - fixed coordinate system relative to the inertial coordinate system in the navigation system, ; Denotes the projection of the angular velocity of the navigation coordinate system relative to the Earth-centered inertial coordinate system in the navigation coordinate system. ; They are respectively expressed as:

[0061]

[0062] Among them, is the latitude of the rail vehicle carrier, and are the eastward and northward velocities respectively, and respectively represent the radius of the meridian circle and the radius of the prime vertical circle; Specifically, the integral calculates the real-time position information of the rail vehicle, including: The differential equation of the real-time position information of the rail vehicle in the inertial navigation coordinate system is

[0063] Among them, represents the three-axis velocity of the velocity vector of the origin of the carrier coordinate system relative to the Earth-centered inertial coordinate system, m / s ; represents the non-gravitational acceleration measured by the accelerometer, ; represents the gravitational acceleration, ; Discretize the above differential equation of the motion of the rail vehicle in the inertial navigation coordinate system, and adopt the double-sample velocity update algorithm to obtain:

[0064] Among them, represents the velocity vector at the current moment k in the inertial navigation coordinate system; represents the velocity vector at the (k - 1)th moment in the inertial navigation coordinate system; represents the velocity change caused by the specific force; represents the Coriolis integral term:

[0065] Among them, represents the gravitational acceleration vector in the inertial navigation coordinate system, is to the intermediate moment, represents the time difference between the current moment and the previous moment;

[0066] Among them, represents the identity matrix, Represents the equivalent rotation vector of the inertial navigation coordinate system between two consecutive moments, Represents the matrix cross product, Represents The skew-symmetric matrix of, Represents the transformation matrix from the vehicle coordinate system to the inertial navigation coordinate system, Represents the specific force integration term in the vehicle coordinate system;

[0067] Among them, Represents the angular velocity of the earth's rotation, Represents the velocity of the navigation coordinate system relative to the earth-fixed coordinate system;

[0068] Among them, Represents the velocity change caused by the specific force measurement value at the current moment, Represents the angular increment measured by the current gyroscope, Represents the angular increment measured by the gyroscope at the previous moment, Represents the velocity change caused by the specific force measurement at the previous moment.

[0069] Specifically, the integral calculates the real-time velocity information of the rail vehicle, including: The latitude differential equation of the real-time position of the rail vehicle movement is:

[0070] The longitude differential equation of the real-time position of the rail vehicle movement is:

[0071] The elevation differential equation of the real-time position of the rail vehicle movement is:

[0072] Among them, Represents the latitude differential equation, Represents the longitude differential equation, Represents the elevation differential equation, Represents the northward velocity component, Represents the eastward velocity component, Represents the downward velocity component, Represents the radius of curvature of the meridian, Represents the radius of curvature of the prime vertical; The elevation update equation of the rail vehicle movement position is:

[0073] The latitude update equation of the rail vehicle movement position is:

[0074] The longitude update equation for the moving position of the rail vehicle is as follows:

[0075] Where,

[0076]

[0077]

[0078]

[0079] e is the first eccentricity of the earth ellipsoid model. For the CGCS2000 ellipsoid, , , .

[0080] Step S3: Before the rail vehicle enters the tunnel, perform initial positioning according to GNSS to obtain initial position information and initial velocity information; Step S4: After the rail vehicle enters the tunnel, switch to the combined positioning mode of RFID and inertial navigation system; Step S5: When the rail vehicle passes the first RFID tag, obtain the accurate position information provided by the first RFID tag; Step S6: Measure the time difference of the rail vehicle in a short time through the first RFID tag and the second RFID tag, so as to obtain the accurate velocity information of the rail vehicle; The accurate position information provided by the first RFID tag is:

[0081] The accurate velocity information of the rail vehicle is:

[0082] Where, is the accurate position information provided by the second RFID tag, is the time difference of the rail vehicle passing through the first RFID tag and the second RFID tag in a short time.

[0083] In the present invention, an RFID is arranged at a certain distance interval on the tunnel wall, and an RFID reader is deployed on the rail car carriage. The ID information of this location is written inside the tag. The RFID reader deployed on the rail car emits an activation signal, which is received by the tag and then reflected back to the RFID reader. The RFID reader then receives the backscattered signal of the RFID tag. When the rail car passes within the coverage range of the RFID, the RFID reader can read the ID of this location. The microcomputer can obtain the three-dimensional position information of the rail car and the attitude Euler angle information of this location (this information is measured in advance) by querying the ID of this location. There is a certain time delay during this period, and relevant research shows that the error caused by this time delay does not exceed 36 cm, and its positioning accuracy is high, the cost is low, and it is easy to arrange.

[0084] Step S7: Adopt the Kalman filtering algorithm to fuse the real-time position information, the accurate position information, the real-time speed information and the accurate speed information respectively to obtain the high-precision positioning information of the rail car.

[0085] In this embodiment, the step S7 specifically includes: Establish the state vector of the inertial navigation system:

[0086] Among them, represents the position error vector, represents the speed error vector, represents the attitude error vector, represents the zero bias vector of the three-axis gyroscope of the inertial sensor, the error vector of the three-axis gyroscope of the inertial sensor, the acceleration error vector of the inertial sensor; Establish the state change equation of the inertial navigation system at time k:

[0087] Among them, represents the state transition matrix, represents the state noise vector, and represent the predicted state at the current time k and the predicted state at the previous time k-1;

[0088] In the formula, represents the sub-matrix of the position error, represents the sub-matrix of the influence of the speed error on the position error, represents the sub-matrix of the influence of the position error on the speed error, represents the sub-matrix of the speed error, The sub - matrix representing the influence of attitude error on velocity error The sub - matrix representing the influence of position error on attitude error The sub - matrix representing the influence of velocity error on attitude error The sub - matrix representing attitude error; The observation equation of the inertial navigation system at time k is established as:

[0089]

[0090]

[0091] Wherein, Represents the difference information of position and velocity, Represents the observation matrix, Contains longitude, latitude, and elevation information in the e - system coordinate system, Represents the noise vector; The state of the inertial navigation system at time k is estimated using the observation equation at time k - 1 to obtain the state prediction equation, that is, the one - step prediction equation:

[0092] The error is revised using the observation equation to obtain the state estimate at time k:

[0093] The Kalman filter gain is obtained:

[0094] The state prediction mean - square error equation is defined:

[0095] The state estimate mean - square error equation is defined:

[0096] Wherein, Q represents the prediction covariance, P represents the error prediction covariance, Represents the observation noise covariance.

[0097] The present invention combines RFID with an inertial navigation system to solve the positioning problem of rail vehicles in environments with insufficient GNSS signal coverage such as long - distance tunnels. This fusion solution not only improves the accuracy and reliability of rail vehicle positioning, but also realizes seamless high - precision positioning in the full scenario through technical complementarity, effectively improving the efficiency of construction, operation and maintenance scheduling in the track area and transportation safety.

[0098] Those skilled in the art can make various corresponding changes and deformations according to the technical solutions and concepts described above, and all such changes and deformations should fall within the protection scope of the claims of the present invention.

Claims

1. A positioning method based on RFID and inertial navigation system in a long-distance tunnel scenario, characterized in that, It includes the following steps: Step S1: Deploy multiple first RFID tags at equal intervals in the tunnel, write the ID number corresponding to the position inside each first RFID tag, and deploy a corresponding number of second RFID tags at short intervals on the same side of the multiple first RFID tags; Step S2: Install an RFID reader and an inertial navigation system on the rail vehicle. The inertial navigation system calculates the real-time position information and real-time speed information of the rail vehicle by continuously measuring the real-time acceleration data of the rail vehicle's travel and integrating; Step S3: Before the rail vehicle enters the tunnel, perform initial positioning according to GNSS to obtain the initial position information and initial speed information; Step S4: After the rail vehicle enters the tunnel, switch to the combined positioning mode of RFID and inertial navigation system; Step S5: When the rail vehicle passes by the first RFID tag, obtain the accurate position information provided by the first RFID tag; Step S6: Measure the time difference of the rail vehicle within a short time through the first RFID tag and the second RFID tag, so as to obtain the accurate speed information of the rail vehicle; Step S7: Adopt the Kalman filtering algorithm to fuse the real-time position information, the accurate position information, the real-time speed information and the accurate speed information respectively to obtain the high-precision positioning information of the rail vehicle.

2. The positioning method based on RFID and inertial navigation system in a long-distance tunnel scenario according to claim 1, wherein the inertial navigation system calculates the real-time position information and real-time speed information of the rail vehicle by continuously measuring the real-time acceleration data of the rail vehicle's travel and integrating, specifically including: Step S201: Use an accelerometer to measure the acceleration information in the vehicle body coordinate system of the rail vehicle; Step S202: Use the quaternion method to convert the acceleration information in the vehicle body coordinate system into the inertial navigation coordinate system; Step S203: Integrate to calculate the real-time position information and real-time speed information of the rail vehicle; The expression of the quaternion method is: Among them, respectively represent the heading angle, roll angle and pitch angle of the carrier.

3. The positioning method based on RFID and inertial navigation system in a long-distance tunnel scenario according to claim 2, wherein the use of the quaternion method to convert the acceleration information in the vehicle body coordinate system into the inertial navigation coordinate system specifically includes: Calculate the change of the current vehicle coordinate system relative to the inertial navigation coordinate system, that is, use the attitude quaternion at time k-1 and The two gyroscope angular increment sampling outputs during the time period to calculate the attitude quaternion at the current moment: where q is the attitude quaternion, the first element is used to describe the angle by which the rigid body rotates about the rotation axis, and the remaining three elements are used to describe the direction of the rotation axis. is used to describe the attitude of the vehicle coordinate system relative to the inertial navigation coordinate system at time k - 1, and the symbol represents the multiplication symbol of attitude quaternions. represents the change in the attitude of the vehicle coordinate system at time k - 1, expressed as: For the rotation matrix of the attitude change within the time period of the vehicle coordinate system The gyroscope performs 2 equal-interval samplings at the k-1 moment respectively, and the angular increments are respectively and According to the double-sample cone compensation algorithm, It is expressed as: The attitude quaternion representing the attitude change from time k-1 to time k: Rotation vector representing the attitude change within the inertial navigation coordinate system time period [k - 1, k]: wherein, represents the projection of the angular velocity of the Earth-centered Earth-fixed coordinate system relative to the inertial coordinate system on the navigation coordinate system, ; represents the projection of the angular velocity of the navigation coordinate system relative to the Earth-centered Earth-fixed coordinate system on the navigation coordinate system, ; which are respectively expressed as: Among them, is the latitude of the rail vehicle carrier, and are the eastward and northward speeds respectively, and represent the radius of the meridian circle and the radius of the prime vertical respectively.

4. The positioning method based on RFID and inertial navigation system in the long-distance tunnel scenario according to claim 3, wherein The integration to calculate the real-time position information of the rail vehicle includes: The differential equation of the real-time position of the rail vehicle in the inertial navigation coordinate system is Among them, represents the three-axis velocity of the velocity vector of the origin of the vehicle coordinate system relative to the Earth-centered Earth-fixed coordinate system, m / s ; represents the non-gravitational acceleration measured by the accelerometer, ; represents the gravitational acceleration, ; Discretize the differential equation of the rail vehicle's motion in the inertial navigation coordinate system above, and adopt the dual-sample velocity update algorithm to obtain: Among them, represents the velocity vector at the current moment k in the inertial navigation coordinate system; represents the velocity vector at the (k - 1)th moment in the inertial navigation coordinate system; represents the velocity change caused by specific force; represents the Coriolis integral term: Among them, represents the gravitational acceleration vector in the inertial navigation coordinate system, is to the intermediate moment, represents the time difference between the current moment and the previous moment; Among them, represents the identity matrix, represents the equivalent rotation vector of the inertial navigation coordinate system between two consecutive moments, represents the matrix cross product, represents the skew-symmetric matrix of, represents the transformation matrix from the vehicle coordinate system to the inertial navigation coordinate system, represents the specific force integration term in the vehicle coordinate system; Among them, represents the angular velocity of the Earth's rotation, represents the velocity of the navigation coordinate system relative to the Earth-fixed coordinate system; wherein, represents the velocity change caused by the specific force measurement value at the current moment, represents the angular increment measured by the current gyroscope, represents the angular increment measured by the gyroscope at the previous moment, represents the velocity change caused by the specific force measurement at the previous moment.

5. The positioning method based on RFID and inertial navigation system in the long-distance tunnel scenario according to claim 4, characterized in that, The integration to calculate the real-time speed information of the rail vehicle includes: The differential equation of the latitude of the real-time position of the rail vehicle's motion is: The differential equation of the longitude of the real-time position of the rail vehicle's motion is: The differential equation of the elevation of the real-time position of the rail vehicle's motion is: Among them, represents the latitude differential equation, represents the longitude differential equation, represents the altitude differential equation, represents the northward velocity component, represents the eastward velocity component, represents the downward velocity component, represents the radius of curvature of the meridian, represents the radius of curvature of the prime vertical; The elevation update equation of the rail vehicle's motion position is: The latitude update equation of the rail vehicle's motion position is: The longitude update equation of the rail vehicle's motion position is: wherein e is the first eccentricity of the earth ellipsoid model. For the CGCS2000 ellipsoid, , , .

6. The positioning method based on RFID and inertial navigation system in a long-distance tunnel scenario according to claim 1, wherein The precise position information provided by the first RFID tag is as follows: The precise speed information of the rail vehicle is as follows: Among them, Precise position information provided for the second RFID tag Is the time difference between the rail vehicle passing through the first RFID tag and the second RFID tag within a short period of time.

7. The positioning method based on RFID and inertial navigation system in a long-distance tunnel scenario according to claim 1, characterized in that The step S7 specifically includes: Establish the state vector of the inertial navigation system: Among them, represents the position error vector, represents the velocity error vector, represents the attitude error vector, represents the zero bias vector of the three-axis gyroscope of the inertial sensor, the error vector of the three-axis gyroscope of the inertial sensor, the acceleration error vector of the inertial sensor; Establish the state change equation of the inertial navigation system at time k: Among them, represents the state transition matrix, represents the state noise vector, and represents the predicted state at the current k-th moment and the predicted state at the previous k-1-th moment; In the formula, a sub-matrix representing the position error, a sub-matrix representing the influence of the velocity error on the position error, a sub-matrix representing the influence of the position error on the velocity error caused by the position error, a sub-matrix representing the velocity error, a sub-matrix representing the influence of the attitude error on the velocity error, a sub-matrix representing the influence of the position error on the attitude error, a sub-matrix representing the influence of the velocity error on the attitude error, a sub-matrix representing the attitude error; Establish the observation equation of the inertial navigation system at time k as: In the formula, represents the difference information of position and velocity, represents the observation matrix, including the longitude, latitude, and elevation information in the e-system coordinate system, represents the noise vector; Use the observation equation at time k-1 to estimate the state of the inertial navigation system at time k, and obtain the state prediction equation, that is, the one-step prediction equation: Use the observation equation to perform error correction to obtain the state estimate at time k: Calculate the Kalman filter gain: Define the state prediction mean square error equation: Define the state estimation mean square error equation: Wherein, Q represents the predicted covariance, P represents the error prediction covariance, represents the observation noise covariance.

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