CN113866810A - Method, system, electronic equipment and storage medium for train positioning and navigation - Google Patents

Method, system, electronic equipment and storage medium for train positioning and navigation Download PDF

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CN113866810A
CN113866810A CN202111135061.5A CN202111135061A CN113866810A CN 113866810 A CN113866810 A CN 113866810A CN 202111135061 A CN202111135061 A CN 202111135061A CN 113866810 A CN113866810 A CN 113866810A
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train
positioning
time
expressed
signal
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马钰昕
石孝文
韩熠
苏阿峰
刘螺辉
朱林贤
黄辉
王美茜
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Casco Signal Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
    • G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations

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  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
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  • Automation & Control Theory (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)
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Abstract

本发明公开了一种列车定位导航的方法、系统、电子设备及存储介质，所述方法包括：获取GNSS信号，当GNSS信号的信号强度不符合预设标准时；获取列车行驶过程中，列车基于时间序列的位置指纹库；获取第一SINS信号；采用DTW算法从位置指纹库中得到列车的位置信息，将列车的位置信息与第一SINS信号结合后，采用卡尔曼滤波修正第一SINS信号的累计误差偏移值，得到列车的最佳估算位置。本发明具有精度高、可靠性高、准确度高等优点。

Figure 202111135061

The invention discloses a method, system, electronic device and storage medium for train positioning and navigation. The method includes: acquiring a GNSS signal, when the signal strength of the GNSS signal does not meet a preset standard; The location fingerprint database of the sequence; obtain the first SINS signal; use the DTW algorithm to obtain the location information of the train from the location fingerprint database, combine the location information of the train with the first SINS signal, and use Kalman filtering to correct the accumulation of the first SINS signal Error offset value to get the best estimated position of the train. The invention has the advantages of high precision, high reliability and high accuracy.

Figure 202111135061

Description

Method, system, electronic equipment and storage medium for train positioning and navigation
Technical Field
The invention relates to the technical field of rail transit train positioning, in particular to a train positioning and navigation method, a train positioning and navigation system, electronic equipment and a storage medium.
Background
The existing subway train positioning equipment mainly comprises a digital audio track circuit, a cross induction loop, an inquiry transponder, an axle counter, a speed measurement sensor, an accelerometer, a doppler radar, a leaky cable, a crack waveguide tube and the like, and the train positioning technology mainly comprises a Wireless Local Area Network (WLAN for short), a Long Term Evolution (LTE) System, a Global Navigation Satellite System (GNSS) for short and the like. However, the existing train positioning equipment has the problems of discontinuous train-ground information transmission, low data transmission rate, accumulated positioning result errors, high investment cost, inconvenient maintenance and the like; an inertial navigation positioning system (SINS) does not need to be additionally laid with positioning equipment, and a self coordinate system and a terrestrial coordinate system are unified to finish high-precision positioning. However, inertial navigation has accumulated errors, and the accumulated errors need to be eliminated by being additionally combined with a satellite positioning system. However, the train satellite technology is susceptible to interference, occupies scarce spectrum resources, and the like, and the function of the satellite positioning technology in tunnel positioning is limited by the absorption of electromagnetic waves by tunnel building materials and the electromagnetic wave shielding effect of a steel structure. In view of the above, the existing Train positioning technology restricts the development of a Communication Based Train Control System (CBTC), and therefore a new positioning technology is needed to supplement or replace the existing Train positioning technology.
Disclosure of Invention
In order to solve the above problems, the present invention provides a method, a system, an electronic device, and a storage medium for train positioning and navigation, which combine positioning with a GNSS signal and an SINS signal, or obtain position information of a train from a position fingerprint database by using a Dynamic Time Warping (DTW) algorithm, combine the position information with the SINS signal, and finally correct an accumulated error deviation value of the SINS by using kalman filtering to obtain an optimal positioning result, thereby implementing positioning and navigation of the train.
In order to achieve the purpose, the invention is realized by the following technical scheme: a method of train location navigation, comprising:
GNSS signals are acquired.
And when the signal intensity of the GNSS signal does not meet the preset standard.
And acquiring a position fingerprint database of the train based on the time sequence in the running process of the train.
A first SINS signal is acquired.
And obtaining the position information of the train from the position fingerprint database by adopting a DTW algorithm, and correcting the accumulated error deviation value of the first SINS signal by adopting Kalman filtering after combining the position information of the train and the first SINS signal to obtain the optimal estimated position of the train.
Optionally, the method for train positioning and navigation further includes: and when the signal intensity of the GNSS signal meets a preset standard.
A second SINS signal is acquired.
And after the GNSS signal and the second SINS signal are combined and positioned, correcting the accumulated error deviation value of the first SINS signal by adopting the Kalman filtering to obtain the optimal estimated position of the train.
Optionally, the method for train positioning and navigation further includes: when the number of satellites participating in positioning calibration in the GNSS sensor is at least three, the signal intensity of the GNSS signal meets a preset standard.
When the number of satellites participating in positioning calibration in the GNSS sensor is less than three, the signal intensity of the GNSS signal does not meet a preset standard.
Optionally, the step of acquiring the location fingerprint database includes:
and dividing the track in the running process of the train into a plurality of sections, and numbering each section. I.e. the driving interval is divided into sections, thereby reducing the time taken for each search.
And establishing a plane two-dimensional coordinate system in each section, and dividing each plane two-dimensional coordinate system into n grids at equal intervals.
On each square, a vehicle Access Unit (TAU) is used for acquiring Reference Signal Receiving Power (RSRP) data and Physical Cell Identifier (PCI) data of an LTE system, and the RSRP data and the PCI data are recorded in the corresponding square to establish the position fingerprint database. That is, the data collected by the TAU at each time point is a PRSP value and a PCI value, and a pair of data is formed and recorded in the square grid, and a corresponding fingerprint database is established.
Optionally, before the step of obtaining the location information of the train from the location fingerprint database by executing the DTW algorithm, the method includes:
and obtaining the section number nearest to the current position of the train by using Inertial sensor (Inertial Measurement Unit, IMU for short) positioning analysis in the SINS.
Acquiring first PCI data and first RSRP data of an LTE system on the train at the current moment by using the TAU, and searching m second PCI data with the same value as the first PCI data in the PCI data acquired in a time point before the current moment by using the first PCI data at the current moment as an index.
And combining the first RSRP data at the current moment with m second RSRP data corresponding to m second PCI data by using the TAU to obtain the fingerprint of the train at the current moment, wherein the m second PCI data and the m second RSRP data are in one-to-one correspondence.
Optionally, the step of obtaining the location information of the train from the location fingerprint database by using a DTW algorithm includes:
and matching the fingerprints with all time sequence fingerprints in the position fingerprint database by adopting the DTW algorithm, and outputting a plurality of confidence scores.
And finding out a time sequence position point corresponding to the lowest confidence score from the confidence scores, wherein the time sequence position point is the position information of the train.
Optionally, the step of correcting the accumulated error deviation value of the first SINS signal by using kalman filtering to obtain the optimal estimated position of the train includes: a prediction phase and an update phase.
The step of predicting the phase comprises: and estimating the size of the train position at the current moment according to the posterior state estimation value of the train position at the last moment, thereby obtaining the prior state estimation value at the t moment.
The step of the update phase comprises: and correcting the estimated value of the prediction stage by using the measured value at the current moment to obtain the estimated value of the posterior state at the current moment.
Optionally, the step of obtaining the section number closest to the current position of the train by utilizing IMU positioning analysis includes:
converting the acceleration of the three-dimensional space of the train acquired by the IMU from a self coordinate system to a global coordinate system:
ag＝Rab+G
G＝[0,0,-g]T
Figure BDA0003281994760000041
wherein: a isgExpressed as acceleration in a global coordinate system; a isbExpressed as self-acceleration measured by the IMU; g is expressed as a gravity acceleration matrix; c represents cos; s represents sin; gamma, psi and theta represent three coordinate axes and a global coordinate systemThe included angle of (a).
And calculating to obtain the position of the train at the time t:
Figure BDA0003281994760000042
wherein: v. ofkExpressed as the train speed measured by the IMU at time k;
Figure BDA0003281994760000043
expressed as the acceleration in the global coordinate system at time k; k is expressed as the number of samples of the IMU data; Δ T is expressed as a sampling period; v. ofkExpressed as the instantaneous velocity of the kth sample point; x is the number oftDenoted as time t; instantaneous coordinates of the train in two-dimensional/three-dimensional coordinates; x is the number oft-1Expressed as the instantaneous coordinates of the train in two/three dimensional coordinates at time t-1.
And combining the position of the train at the time t with the positioning result of the last time point to obtain the section nearest to the current position of the train and the number of the section.
Optionally, the prediction phase includes:
using the Kalman filtering to estimate the posterior state of the train position at t-1
Figure BDA0003281994760000044
To predict the prior state estimation value of the train position at the time t
Figure BDA0003281994760000045
Figure BDA0003281994760000046
Wherein: a is expressed as a train motion state transition matrix in a Kalman filter.
Estimating covariance E of the train position posteriori by t-1 timet-1And white Gaussian noise beta to predict prior estimated covariance at time t
Figure BDA0003281994760000047
Figure BDA0003281994760000048
Wherein: a. theTRepresented as a transpose of the train motion state transition matrix.
Optionally, the update phase includes:
calculating the Kalman gain Kt：
Figure BDA0003281994760000049
Wherein: c is expressed as a state variable to measurement conversion matrix; r is expressed as a measurement noise covariance; cTExpressed as the transpose of the state variables to the measured conversion matrix.
Performing Kalman filtering state correction updating, and calculating the posterior state estimation value of the train position at the time t
Figure BDA0003281994760000051
Figure BDA0003281994760000052
Wherein: y istExpressed as an observed value.
In order to further estimate the iteration of the optimal train position at the time t +1 and perform the updating operation, the posterior estimation covariance E at the time t needs to be updatedt：
Figure BDA0003281994760000053
Wherein: i denotes an identity matrix.
Optionally, the train positioning and navigation system includes:
the GNSS sensor 100 is configured to acquire a GNSS signal.
And the judging module is connected with the GNSS sensor 100 and is configured to judge whether the signal intensity of the GNSS signal meets a preset standard, so as to obtain a judgment result.
The storage module 400 is used for storing a location fingerprint database.
The TAU vehicle access unit 500 is connected to the storage module 400, and the TAU vehicle access unit 500 is configured to, when the determination result indicates that the signal strength of the GNSS signal does not meet a preset standard, obtain the time-series-based location fingerprint database of the train and a fingerprint corresponding to the train at the current time.
And a DTW algorithm processor 600 respectively connected to the TAU vehicle access unit 500 and the storage module 400, wherein the DTW algorithm processor 600 is configured to obtain the location information of the train from the location fingerprint database.
And the IMU inertial sensor 200 is configured to acquire the first SINS signal and acquire a segment number closest to the current location of the train.
A first combined positioning module, respectively connected to the IMU inertial sensor 200 and the DTW algorithm processor 600, for combined positioning of the position information of the train and the first SINS signal.
And a kalman filter 300, which is respectively connected to the first combined positioning module, the DTW algorithm processor 600 and the IMU inertial sensor 200, and configured to correct an accumulated error deviation value of the received first SINS signal to obtain an optimal estimated position of the train.
Optionally, the train positioning and navigation system further includes:
the IMU inertial sensor 200 is further configured to acquire a second SINS signal when the determination result is that the signal strength of the GNSS signal meets a preset standard.
A second combined positioning module, respectively connected to the IMU inertial sensor 200 and the GNSS sensor 100, for combined positioning of the GNSS signal and the first SINS signal.
The kalman filter 300 is further connected to the second combined positioning module, and the kalman filter 300 is further configured to correct an accumulated error deviation value of the received second SINS signal to obtain the optimal estimated position of the train.
Optionally, the judgment module has the following judgment criteria:
when the number of satellites participating in positioning calibration in the GNSS sensor 100 is at least three, the signal strength of the GNSS signal meets a preset standard.
When the number of satellites participating in positioning calibration in the GNSS sensor 100 is less than three, the signal strength of the GNSS signal does not meet a preset standard.
Optionally, the train positioning and navigation system further includes: and a dividing module connected with the TAU vehicle-mounted access unit 500.
The dividing module is used for dividing the track in the running process of the train into a plurality of sections and numbering each section. I.e. the driving interval is divided into sections, thereby reducing the time taken for each search.
The dividing module is further used for establishing a plane two-dimensional coordinate system in the service range of each section, and dividing each plane two-dimensional coordinate system into n grids at equal intervals.
On each square, the TAU vehicle-mounted access unit 500 is further configured to acquire RSRP data and PCI data of an LTE system, record the RSRP data and the PCI data in the corresponding square to establish the location fingerprint database, and input the location fingerprint database into the storage module. That is, the data collected by the TAU car access unit 500 at each time point is a PRSP value and a PCI value, which form a pair of data, and the data is recorded in a square grid to establish a corresponding fingerprint database.
Optionally, the IMU inertial sensor 200 is further configured to perform positioning analysis to obtain the section number closest to the current position of the train.
Optionally, the TAU vehicle-mounted access unit 500 is further configured to acquire first PCI data and first RSRP data of an LTE system on the train at the current time, and search m pieces of second PCI data having the same value as the first PCI data in PCI data acquired in a time point before the current time by using the first PCI data at the current time as an index.
Optionally, the TAU vehicle-mounted access unit 500 is further configured to combine the first RSRP data at the current time with m second RSRP data corresponding to m second PCI data to obtain a fingerprint of the train at the current time, where the m second PCI data and the m second RSRP data are in a one-to-one correspondence relationship.
Optionally, the DTW algorithm processor 600 is specifically configured to match the fingerprint with all time sequence fingerprints in the location fingerprint database by using a built-in DTW algorithm, and output a plurality of confidence scores.
The DTW algorithm processor 600 is further configured to find a time sequence position point corresponding to the lowest confidence score from the confidence scores, where the time sequence position point is the position information of the train.
Optionally, the kalman filter 300 includes: a prediction module and an update module.
The prediction module is used for estimating the size of the train position at the current moment according to the posterior state estimation value of the train position at the previous moment, so that the prior state estimation value at the t moment is obtained.
The updating module is used for correcting the estimation value of the prediction stage by using the measurement value at the current moment to obtain the estimation value of the posterior state at the current moment.
Optionally, the IMU inertial sensor 200 includes: the IMU inertial sensor comprises a conversion module and a calculation module, wherein the conversion module is connected with the calculation module and is arranged inside the IMU inertial sensor 200;
the conversion module is configured to convert the acceleration of the three-dimensional space of the train acquired by the IMU inertial sensor 200 from a self coordinate system to a global coordinate system:
ag＝Rab+G
G＝[0,0,-g]T
Figure BDA0003281994760000071
wherein: a isgExpressed as acceleration in a global coordinate system; a isbExpressed as self-acceleration measured by the IMU; g is expressed as a gravity acceleration matrix; c represents cos; s represents sin; gamma, psi and theta represent the included angles between the three coordinate axes and the global coordinate system.
The calculation module calculates the position of the train at the time t by using the following formula:
Figure BDA0003281994760000072
wherein: v. ofkExpressed as the train speed measured by the IMU inertial sensor 200 at time k;
Figure BDA0003281994760000073
expressed as the acceleration in the global coordinate system at time k; k represents the number of samples of IMU inertial sensor 200 data; Δ T is expressed as a sampling period; v. ofkExpressed as the instantaneous velocity of the kth sample point; x is the number oftDenoted as time t; instantaneous coordinates of the train in two-dimensional/three-dimensional coordinates; x is the number oft-1Expressed as the instantaneous coordinates of the train in two/three dimensional coordinates at time t-1.
The calculation module is further configured to combine the position of the train at the time t with the positioning result at the previous time point to obtain the section closest to the current position of the train and the number of the section.
Optionally, the prediction module is further specifically configured to estimate a posterior state of the train according to the train position at the t-1 time
Figure BDA0003281994760000081
To predict the prior state estimation value of the train position at the time t
Figure BDA0003281994760000082
Figure BDA0003281994760000083
Wherein: a is represented as a train motion state transition matrix in the kalman filter 300.
The prediction module is specifically further configured to estimate the covariance E of the train position a posteriori at the time t-1t-1And white Gaussian noise beta to predict prior estimated covariance at time t
Figure BDA0003281994760000084
Figure BDA0003281994760000085
Wherein: a. theTRepresented as a transpose of the train motion state transition matrix.
Optionally, the update module is further specifically configured to calculate a kalman gain Kt：
Figure BDA0003281994760000086
Wherein: c is expressed as a state variable to measurement conversion matrix; r is expressed as a measurement noise covariance; cTExpressed as the transpose of the state variables to the measured conversion matrix.
The updating module is specifically used for performing Kalman filtering state correction updating and calculating the posterior state estimation value of the train position at the moment t
Figure BDA0003281994760000087
Figure BDA0003281994760000088
Wherein: y istExpressed as an observed value.
The updating module is specifically further configured to update the posterior estimated covariance E at time ttAnd the iteration of the optimal train position at the time of t +1 is further estimated, and the updating operation is carried out:
Figure BDA0003281994760000089
wherein: i denotes an identity matrix.
Optionally, an electronic device includes a processor and a memory, where the memory stores a computer program, and the computer program, when executed by the processor, implements the method of any one of the methods of train positioning and navigation.
Optionally, a readable storage medium, in which a computer program is stored, and when being executed by a processor, the computer program implements the method of any one of the methods for train positioning and navigation.
The invention has at least one of the following advantages:
1) according to the invention, a position fingerprint database based on a time sequence is established by using data of an LTE system, when the GNSS signal is weak, the LTE system and an SINS signal are fused, and a method of obtaining the train position by Kalman filtering is adopted, so that the positioning and navigation of a train are finally realized, and the problem of the positioning and navigation of the train when the GNSS signal is weak is solved.
2) The whole system is modularized, a track along the way of the train in operation is divided into a plurality of sections, each section is divided into a two-dimensional plane coordinate system, and time sequence position fingerprint data are collected for points on each track in the sections. Data can be acquired for a plurality of sections at the same time, and the data acquisition efficiency is effectively improved; meanwhile, large-scale positioning can be realized according to IMU information, a service base station closest to a train at present is judged, the section number is confirmed, the positioning range is narrowed to be close to the determined base station, the running time of the whole DTW algorithm is shortened, the positioning efficiency is enhanced, and the problems that the construction amount of the whole time sequence fingerprint database is large and the positioning time is long are solved.
3) Because the DTW is less influenced by noise and is matched with the overall change trend, even if the surrounding environment changes, the data of a certain acquisition point generates larger noise, but the influence on the algorithm accuracy is small, the requirement of high-precision train positioning can be met, and the problem that the train positioning precision is influenced due to the fact that the noise of a certain point is larger in the position fingerprint is solved.
4) According to the invention, the TAU is used for collecting position fingerprint data, the vehicle-mounted IMU is used for collecting train acceleration, speed and relative distance data, the existing LTE-M base station is used for providing RSRP, no positioning equipment needs to be additionally arranged independently, the cost is effectively reduced, the positioning precision of the train can be ensured, and the problems that the train positioning cost is high and the positioning equipment needs to be additionally laid are solved.
Drawings
FIG. 1 is a flow chart of a method of train location navigation in one embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a train positioning and navigation system according to an embodiment of the present invention;
the reference numbers in the figures illustrate: 100. a GNSS sensor; 200. an IMU inertial sensor; 300. a Kalman filter; 400. a storage module; 500. a TAU vehicle-mounted access unit; 600. a DTW algorithm processor.
Detailed Description
The method, system, electronic device and storage medium for train positioning and navigation according to the present invention are described in detail below with reference to the accompanying drawings and the detailed description, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that, the letters in the present embodiment, such as "a", "B", "C", "W", "P", "X", "Y", "M", etc., are only for convenience of describing the present embodiment, and should not be construed as limiting the present invention.
As shown in fig. 1, a method for train positioning and navigation includes:
and step S1, acquiring GNSS signals by utilizing the GNSS sensor.
And when the signal intensity of the GNSS signal does not meet the preset standard.
In this embodiment, when the number of satellites participating in positioning calibration in the GNSS sensor is less than three, the signal strength of the GNSS signal does not meet a preset standard.
And step S2, acquiring a position fingerprint database of the train based on the time sequence in the running process of the train by utilizing the TAU.
In this embodiment, the step S2 includes:
and S21, dividing the track in the running process of the train into a plurality of sections, and numbering each section to construct a modular positioning system. I.e. the driving interval is divided into sections, thereby reducing the time taken for each search.
Step S22, establishing a plane two-dimensional coordinate system in the service range of each section, and equally dividing each plane two-dimensional coordinate system into n grids.
Step S23, collecting RSRP data and PCI data of the LTE system based on the time sequence on each square by using the TAU, and recording the RSRP data and the PCI data in the corresponding square to establish the location fingerprint database, thereby constructing a complete training database. That is, the data collected by the TAU at each time point is a PRSP value and a PCI value, and a pair of data is formed and recorded in the square grid, and a corresponding fingerprint database is established. Wherein the TAU performs acquisition of RSRP data and PCI data for LTE 10 times per second, but performs positioning calculation immediately for each acquisition 1 time.
In the step S2, the train operation positioning process is modularized, so that the line data of different sections can be acquired respectively, and the acquisition efficiency is improved.
In this embodiment, in each segment, W × L acquisition points are divided, and the TAU is used to acquire the RSRP data and the PCI data of the LTE system based on the time sequence 1 time each time point, so as to obtain location point data and the intensity of the RSRP corresponding to each location point, and obtain the corresponding time-series location fingerprint database:
Figure BDA0003281994760000111
Figure BDA0003281994760000112
wherein: p represents a location point; p is a radical of00Represents the position of point 00; by analogy in the following way,
Figure BDA0003281994760000113
indicates the position of the WL-th point; reference Signal Received Power (RSRP) strength I corresponding to each position point p00Representing the strength of Reference Signal Received Power (RSRP) corresponding to a 00 th point; by analogy in the following way,
Figure BDA0003281994760000114
expressed as the strength of the reference signal received power RSRP corresponding to the WL-th point. The above data is only a preferred embodiment of the step S2 of this embodiment, and should not be construed as a limitation to this embodiment.
Step S3, acquiring a first SINS signal using the IMU.
And step S4, obtaining the position information of the train from the position fingerprint database by adopting a DTW algorithm, combining the position information of the train with the first SINS signal, and correcting the accumulated error deviation value of the first SINS signal by adopting Kalman filtering to obtain the optimal estimated position of the train.
In this embodiment, before the step of obtaining the location information of the train from the location fingerprint database by executing the DTW algorithm, the step S4 further includes:
and step S41, obtaining the section number nearest to the current position of the train by utilizing the IMU positioning analysis.
In step S41, the data of the IMU is used to perform large-scale navigation, which shortens the search time of the fingerprint database.
In this embodiment, the step S41 includes:
step S411, converting the acceleration of the three-dimensional space of the train acquired by the IMU from a self coordinate system to a global coordinate system:
ag＝Rab+G (3)
G＝[0,0,-g]T (4)
Figure BDA0003281994760000121
wherein: a isgExpressed as acceleration in a global coordinate system; a isbExpressed as self-acceleration measured by the IMU; g is expressed as a gravity acceleration matrix; c represents cos; s represents sin; gamma, psi and theta represent the included angles between the three coordinate axes and the global coordinate system.
Step S412, calculating the position of the train at the time t:
Figure BDA0003281994760000122
wherein: v. ofkExpressed as the train speed measured by the IMU at time k;
Figure BDA0003281994760000123
expressed as the acceleration in the global coordinate system at time k; k is expressed as the number of samples of the IMU data; Δ T is expressed as a sampling period; v. ofkExpressed as the instantaneous velocity of the kth sample point; x is the number oftDenoted as time t; instantaneous coordinates of the train in two-dimensional/three-dimensional coordinates; x is the number oft-1Expressed as the instantaneous coordinates of the train in two/three dimensional coordinates at time t-1.
And step S413, combining the position of the train at the time t with the positioning result of the previous time point, and obtaining the section nearest to the current position of the train and the number of the section.
Step S42, in a train positioning stage, acquiring first PCI data and first RSRP data of the LTE system on the train at the current time by using the TAU, and searching for m pieces of second PCI data having the same value as the first PCI data from the PCI data acquired in a time point before the current time by using the first PCI data at the current time as an index. And combining the first RSRP data at the current moment with m second RSRP data corresponding to m second PCI data by using the TAU to obtain the fingerprint of the train at the current moment, wherein the m second PCI data and the m second RSRP data are in one-to-one correspondence.
In step S42, taking the value of m as 9 shows a preferred embodiment: at present, the actual time point of the train is 11.1 seconds, and the collected LTE data: PCI is 1, RSRP is-10; however, the above embodiment is only a preferred embodiment of the present invention, and should not be construed as a limitation to the present invention, i acquire LTE data at 9 time points of 1.1 second, 1.2 seconds, 1.3 seconds, 1.5 seconds, 10.1 seconds, 10.2 seconds, 10.3 seconds, 10.4 seconds, and 10.9 seconds, where PCI is 1, RSRP is 1, 2, 3, 4, 5, 6, 7, 8, and 9, and i then combine these 10 RSRP values into one fingerprint.
In this embodiment, the step S42 employs the LTE sequence, which can effectively avoid interference on the positioning result caused by a large noise at a single point, so that the noise at multiple points is regarded as a complete whole, and the positioning accuracy can be effectively improved.
In this embodiment, the step S4 further includes:
and step S43, matching the fingerprints with all time sequence fingerprints in the position fingerprint database by adopting the DTW algorithm, and outputting a plurality of confidence scores.
And step S44, finding out a time sequence position point corresponding to the confidence score which is the lowest from the confidence scores, wherein the time sequence position point is the position information of the train.
In this embodiment, a Dynamic Time Warping (DTW) algorithm is used to fit a Time sequence, so as to effectively reduce the influence of bursty noise on a positioning result.
With continued reference to fig. 1, the method for train positioning and navigation further includes: and when the signal intensity of the GNSS signal meets a preset standard.
In this embodiment, when the number of satellites participating in positioning calibration in the GNSS sensor is at least three, the signal strength of the GNSS signal meets a preset standard.
And step S5, acquiring a second SINS signal by utilizing the IMU.
And step S6, the GNSS signal and the second SINS signal are combined and positioned, and Kalman filtering is used to obtain the best estimation position.
In this embodiment, the first SINS signal or the second SINS signal includes: acceleration information, speed information, and position information of the train.
In this embodiment, by using the kalman filter in the step S4 and the step S6, the state of the dynamic system can be estimated from a series of data with measurement noise under the condition that the measurement variance is known, and the data acquired on site can be updated and processed in real time, so that the positioning accuracy is improved by integrating the GNSS satellite positioning result and the strapdown inertial navigation system SINS information acquired by the IMU, and integrating the train positioning result obtained by the DTW algorithm and the information acquired by the IMU, and the system state is optimally estimated, thereby effectively reducing the influence of noise on the positioning result, overcoming the accumulated error offset of the IMU of the SINS, improving the correction accuracy of errors, and meeting the requirement of high positioning accuracy of an actual train.
In this embodiment, the step of correcting the accumulated error deviation value of the first SINS signal by using kalman filtering to obtain the optimal estimated position of the train includes: a prediction phase and an update phase.
The step of predicting the phase comprises: and estimating the size of the train position at the current moment according to the posterior state estimation value of the train position at the last moment, thereby obtaining the prior state estimation value at the t moment.
In this embodiment, the prediction phase includes:
using the Kalman filtering to estimate the posterior state of the train position at t-1
Figure BDA0003281994760000141
To predict the prior state estimation value of the train position at the time t
Figure BDA0003281994760000142
Figure BDA0003281994760000143
Wherein: a is expressed as a train motion state transition matrix in a Kalman filter.
In this embodiment, the state transition matrix a is actually a guess model of the target state transition. For example, in moving object tracking, the state transition matrix is often used to model the motion of the object, which may be uniform linear motion or uniform acceleration. When the state transition matrix does not conform to the state transition model of the target, the filtering may quickly diverge.
Estimating covariance E of the train position posteriori by t-1 timet-1And white Gaussian noise beta to predict prior estimated covariance at time t
Figure BDA0003281994760000144
Figure BDA0003281994760000145
Wherein: a. theTRepresented as a transpose of the train motion state transition matrix.
The step of the update phase comprises: and correcting the estimated value of the prediction stage by using the measured value at the current moment to obtain the estimated value of the posterior state at the current moment.
In this embodiment, the update phase includes:
calculating the Kalman gain Kt：
Figure BDA0003281994760000146
Wherein: c is a conversion matrix from the state variable to the measurement (observation), represents the relation connecting the state and the observation, is a linear relation in Kalman filtering, is responsible for converting the m-dimensional measurement value to the n-dimensional measurement value, and makes the m-dimensional measurement value accord with the mathematical form of the state variable, and is one of the preconditions of filtering; r is expressed as the measured noise covariance, and when the filter is actually realized, the measured noise covariance R can be generally observed and is a known condition of the filter; cTExpressed as the transpose of the state variables to the measured conversion matrix.
Performing Kalman filtering state correction updating, and calculating the posterior state estimation value of the train position at the time t
Figure BDA0003281994760000147
Figure BDA0003281994760000148
Wherein: y istDenoted as observations (measurements), are the filtered inputs.
In order to further estimate the iteration of the optimal train position at the time t +1 and perform the updating operation, the posterior estimation covariance E at the time t needs to be updatedt：
Figure BDA0003281994760000149
Wherein I represents an identity matrix.
In the present embodiment, the kalman filter can be divided into a time update equation and a measurement update equation. A time updating equation (namely a formula 7-8 in a prediction stage) is used for calculating a state variable prior estimation value and an error covariance prior estimation value at the current moment according to a state estimation value at the previous moment; the measurement update equation (i.e., equations 9-11 of the update phase) is responsible for combining the a priori estimates and the new measured variables to construct an improved a posteriori estimate. The time update equation and the measurement update equation are also referred to as a prediction equation and a correction equation. The kalman algorithm is therefore a recursive prediction-correction method.
As shown in fig. 2, a train positioning and navigation system includes:
the GNSS sensor 100 is configured to acquire a GNSS signal.
And the judging module is connected with the GNSS sensor 100 and is configured to judge whether the signal intensity of the GNSS signal meets a preset standard, so as to obtain a judgment result.
When the signal intensity of the GNSS signal does not meet the preset standard.
The storage module 400 is used for storing a location fingerprint database.
The TAU vehicle access unit 500 is connected to the storage module 400, and the TAU vehicle access unit 500 is configured to, when the determination result indicates that the signal strength of the GNSS signal does not meet a preset standard, obtain the time-series-based location fingerprint database of the train and a fingerprint corresponding to the train at the current time.
And a DTW algorithm processor 600 respectively connected to the TAU vehicle access unit 500 and the storage module 400, wherein the DTW algorithm processor 600 is configured to obtain the location information of the train from the location fingerprint database.
And the IMU inertial sensor 200 is configured to acquire the first SINS signal and acquire a segment number closest to the current location of the train.
A first combined positioning module, respectively connected to the IMU inertial sensor 200 and the DTW algorithm processor 600, for combined positioning of the position information of the train and the first SINS signal.
And a kalman filter 300, which is respectively connected to the first combined positioning module, the DTW algorithm processor 600 and the IMU inertial sensor 200, and configured to correct an accumulated error deviation value of the received first SINS signal to obtain an optimal estimated position of the train.
With continued reference to fig. 2, the train positioning and navigation system further includes:
the IMU inertial sensor 200 is further configured to acquire a second SINS signal when the determination result is that the signal strength of the GNSS signal meets a preset standard.
A second combined positioning module, respectively connected to the IMU inertial sensor 200 and the GNSS sensor 100, for combined positioning of the GNSS signal and the first SINS signal.
The kalman filter 300 is further connected to the second combined positioning module, and the kalman filter 300 is further configured to correct an accumulated error deviation value of the received second SINS signal to obtain the optimal estimated position of the train.
In this embodiment, the first SINS signal or the second SINS signal includes: acceleration information, speed information, and position information of the train.
In this embodiment, the judgment criteria of the judgment module are:
when the number of satellites participating in positioning calibration in the GNSS sensor 100 is at least three, the signal strength of the GNSS signal meets a preset standard.
When the number of satellites participating in positioning calibration in the GNSS sensor 100 is less than three, the signal strength of the GNSS signal does not meet a preset standard.
In this embodiment, the GNSS sensor 100, the IMU inertial sensor 200, the kalman filter 300, the TAU on-board access unit 500, and the DTW algorithm processor 600 are integrally installed on the train.
In this embodiment, the train positioning and navigation system further includes: and a dividing module connected with the TAU vehicle-mounted access unit 500.
The dividing module is used for dividing the track in the running process of the train into a plurality of sections, and numbering each section so as to construct a modular positioning system. I.e. the driving interval is divided into sections, thereby reducing the time taken for each search.
The dividing module is further configured to establish a planar two-dimensional coordinate system within the service range of each serving base station, and divide each planar two-dimensional coordinate system into n grids at equal intervals.
On each square, the TAU vehicle-mounted access unit 500 is further configured to collect RSRP data and PCI data of an LTE system, record the RSRP data and the PCI data in the corresponding square to establish the location fingerprint database, thereby constructing a complete training database, and input the location fingerprint database into the storage module. That is, the data collected by the TAU car access unit 500 at each time point is a PRSP value and a PCI value, which form a pair of data, and the data is recorded in a square grid to establish a corresponding fingerprint database.
In this embodiment, the dividing module divides W × L acquisition points in each segment, and the TAU vehicle-mounted access unit 500 is further configured to acquire the RSRP data and the PCI data of the LTE system based on the time sequence 1 time at each time point, obtain location point data and the intensity of the RSRP corresponding to each location point, and obtain the corresponding time-series location fingerprint database:
Figure BDA0003281994760000171
Figure BDA0003281994760000172
wherein: p represents a location point; p is a radical of00Represents the position of point 00; by analogy in the following way,
Figure BDA0003281994760000173
indicates the position of the WL-th point; reference Signal Received Power (RSRP) strength I corresponding to each position point p00Representing the strength of Reference Signal Received Power (RSRP) corresponding to a 00 th point; by analogy in the following way,
Figure BDA0003281994760000174
expressed as the strength of the reference signal received power RSRP corresponding to the WL-th point. The above data is only a preferred embodiment of the present embodiment, and should not be construed as limiting the present embodiment.
In this embodiment, the IMU inertial sensor 200 is also used for location analysis to obtain the section number closest to the current position of the train.
In this embodiment, the IMU inertial sensor 200 includes: the IMU inertial sensor comprises a conversion module and a calculation module, wherein the conversion module is connected with the calculation module and is arranged inside the IMU inertial sensor 200.
The conversion module is configured to convert the acceleration of the three-dimensional space of the train acquired by the IMU inertial sensor 200 from a self coordinate system to a global coordinate system:
ag＝Rab+G (14)
G＝[0,0,-g]T (15)
Figure BDA0003281994760000175
wherein: a isgExpressed as acceleration in a global coordinate system; a isbExpressed as self-acceleration measured by the IMU; g is expressed as a gravity acceleration matrix; c represents cos; s represents sin; gamma, psi and theta represent the included angles between the three coordinate axes and the global coordinate system.
The calculation module calculates the position of the train at the time t by using the following formula:
Figure BDA0003281994760000181
wherein: v. ofkExpressed as the train speed measured by the IMU at time k;
Figure BDA0003281994760000182
expressed as the acceleration in the global coordinate system at time k; k is expressed as the number of samples of the IMU data; Δ T is expressed as a sampling period; v. ofkExpressed as the instantaneous velocity of the kth sample point; x is the number oftDenoted as time t; instantaneous coordinates of the train in two-dimensional/three-dimensional coordinates; x is the number oft-1Expressed as the instantaneous coordinates of the train in two/three dimensional coordinates at time t-1.
The calculation module is further configured to combine the position of the train at the time t with the positioning result at the previous time point to obtain the section closest to the current position of the train and the number of the section.
In this embodiment, the TAU vehicle-mounted access unit 500 is further configured to acquire first PCI data and first RSRP data of an LTE system on the train at the current time, and search for m pieces of second PCI data having the same value as the first PCI data from the PCI data acquired in a time point before the current time by using the first PCI data at the current time as an index.
In this embodiment, the TAU vehicle-mounted access unit 500 is further configured to combine the first RSRP data at the current time with m second RSRP data corresponding to m second PCI data to obtain a fingerprint of the train at the current time, where the m second PCI data and the m second RSRP data are in a one-to-one correspondence relationship. Wherein m is 9, but 9 is a preferred embodiment and should not be construed as limiting the invention.
In this embodiment, the DTW algorithm processor 600 is specifically configured to match the fingerprint with all time sequence fingerprints in the location fingerprint database by using a built-in DTW algorithm, and output a plurality of confidence scores.
The DTW algorithm processor 600 is further configured to find a time sequence position point corresponding to the lowest confidence score from the confidence scores, where the time sequence position point is the position information of the train.
In this embodiment, a Dynamic Time Warping (DTW) algorithm is used to fit a Time sequence, so as to effectively reduce the influence of bursty noise on a positioning result.
In the present embodiment, the kalman filter 300 includes: a prediction module and an update module.
The prediction module is used for estimating the size of the train position at the current moment according to the posterior estimation value of the train position at the previous moment, so that the prior estimation value at the t moment is obtained.
In the present embodiment, it is preferred that,the prediction module is specifically used for estimating the posterior state of the train according to the position of the train at the t-1 moment
Figure BDA0003281994760000183
To predict the prior state estimation value of the train position at the time t
Figure BDA0003281994760000184
Figure BDA0003281994760000191
Wherein: a is represented as a train motion state transition matrix in the kalman filter 300.
The prediction module is specifically further configured to estimate the covariance E of the train position a posteriori at the time t-1t-1And white Gaussian noise beta to predict prior estimated covariance at time t
Figure BDA0003281994760000192
Figure BDA0003281994760000193
Wherein: a. theTRepresented as a transpose of the train motion state transition matrix.
The updating module is used for correcting the estimation value of the prediction stage by using the measurement value at the current moment to obtain the estimation value of the posterior state at the current moment.
In this embodiment, the update module is specifically further configured to calculate a kalman gain Kt：
Figure BDA0003281994760000194
Wherein: c is expressed as a state variable to measurement conversion matrix; r is expressed as a measurement noise covariance; cTExpressed as the transpose of the state variables to the measured conversion matrix.
The updating module is specifically used for performing Kalman filtering state correction updating and calculating the posterior state estimation value of the train position at the moment t
Figure BDA0003281994760000195
Figure BDA0003281994760000196
Wherein: y istExpressed as an observed value.
The updating module is specifically further configured to update the posterior estimated covariance E at time ttAnd the iteration of the optimal train position at the time of t +1 is further estimated, and the updating operation is carried out:
Figure BDA0003281994760000197
wherein: i denotes an identity matrix.
In this embodiment, an electronic device includes a processor and a memory, where the memory stores a computer program, and the computer program, when executed by the processor, implements the method of any one of the methods of train positioning and navigation.
In this embodiment, a readable storage medium has a computer program stored therein, and when the computer program is executed by a processor, the method of any one of the methods of train positioning and navigation is realized.
In summary, the method, the system, the electronic device and the storage medium for train positioning and navigation provided by the invention adopt a redundant structure, when the GNSS sensor has 3 or more satellites participating in positioning calibration, the GNSS/SINS combined positioning is adopted, and kalman filtering is used to obtain an optimal positioning/navigation result; when the GNSS sensor has less than 3 satellites participating in positioning calibration, the DTW algorithm is adopted to obtain the position of the train from the time sequence-based RSRP position fingerprint database, then the train is combined with the SINS, and the Kalman filtering is utilized to correct the SINS accumulated error deviation value to obtain the optimal positioning/navigation result.
It is to be noted that the drawings are in a very simplified form and are all used in a non-precise scale for the purpose of facilitating and distinctly aiding in the description of the embodiments of the present invention. To make the objects, features and advantages of the present invention comprehensible, reference is made to the accompanying drawings. It should be understood that the structures, ratios, sizes, and the like shown in the drawings and described in the specification are only used for matching with the disclosure of the specification, so as to be understood and read by those skilled in the art, and are not used to limit the implementation conditions of the present invention, so that the present invention has no technical significance, and any structural modification, ratio relationship change or size adjustment should still fall within the scope of the present invention without affecting the efficacy and the achievable purpose of the present invention.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
It should be noted that the apparatuses and methods disclosed in the embodiments herein can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments herein. In this regard, each block in the flowchart or block diagrams may represent a module, a program, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments herein may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.

Claims (22)

1. A method for train positioning and navigation is characterized by comprising the following steps:
acquiring a GNSS signal;
when the signal strength of the GNSS signal does not meet a preset standard,
acquiring a position fingerprint database of a train based on a time sequence in the running process of the train;
acquiring a first SINS signal;
and obtaining the position information of the train from the position fingerprint database by adopting a DTW algorithm, and correcting the accumulated error deviation value of the first SINS signal by adopting Kalman filtering after combining the position information of the train and the first SINS signal to obtain the optimal estimated position of the train.
2. The method for train positioning navigation of claim 1, further comprising: when the signal intensity of the GNSS signal meets a preset standard;
acquiring a second SINS signal;
and after the GNSS signal and the second SINS signal are combined and positioned, correcting an accumulated error deviation value of the second SINS signal by adopting the Kalman filtering to obtain the optimal estimated position of the train.
3. The method of train positioning navigation according to claim 2, further comprising: when the number of satellites participating in positioning calibration in the GNSS sensor is at least three, the signal intensity of the GNSS signal accords with a preset standard;
when the number of satellites participating in positioning calibration in the GNSS sensor is less than three, the signal intensity of the GNSS signal does not meet a preset standard.
4. The method for train positioning navigation of claim 3, wherein the step of obtaining the location fingerprint library comprises:
dividing the track in the running process of the train into a plurality of sections, and numbering each section;
establishing a plane two-dimensional coordinate system in each section, and equally dividing each plane two-dimensional coordinate system into n grids;
and on each square, collecting RSRP data and PCI data of an LTE system by using a TAU, and recording the RSRP data and the PCI data in the corresponding square to establish the position fingerprint database.
5. The method for train positioning navigation of claim 4, wherein prior to the step of executing the DTW algorithm to obtain the location information of the train from the location fingerprint database, comprising:
obtaining the section number nearest to the current position of the train by utilizing IMU positioning analysis;
acquiring first PCI data and first RSRP data of an LTE system on the train at the current moment by using the TAU, and searching m pieces of second PCI data which have the same value as the first PCI data in the PCI data acquired in a time point before the current moment by using the first PCI data at the current moment as an index;
and combining the first RSRP data at the current moment with m second RSRP data corresponding to m second PCI data to obtain the fingerprint of the train at the current moment, wherein the m second PCI data and the m second RSRP data are in one-to-one correspondence.
6. The method for train positioning and navigation according to claim 5, wherein the step of obtaining the position information of the train from the position fingerprint database by using the DTW algorithm comprises:
matching the fingerprints with all time sequence fingerprints in the position fingerprint database by adopting the DTW algorithm, and outputting a plurality of confidence scores;
and finding out a time sequence position point corresponding to the lowest confidence score from the confidence scores, wherein the time sequence position point is the position information of the train.
7. The method of train positioning and navigation as claimed in claim 6, wherein the step of using kalman filtering to modify the accumulated error offset value of the first SINS signal to obtain the optimal estimated position of the train comprises: a prediction stage and an update stage;
the step of predicting the phase comprises: estimating the size of the train position at the current moment according to the posterior state estimation value of the train position at the previous moment, thereby obtaining the prior state estimation value at the t moment;
the step of the update phase comprises: and correcting the estimated value of the prediction stage by using the measured value at the current moment to obtain the estimated value of the posterior state at the current moment.
8. The method of train location navigation of claim 7, wherein said step of using IMU location analysis to derive said segment number nearest said current location of the train comprises:
converting the acceleration of the three-dimensional space of the train acquired by the IMU from a self coordinate system to a global coordinate system:
ag＝Rab+G
G＝[0,0,-g]T
Figure FDA0003281994750000031
wherein: a isgExpressed as acceleration in a global coordinate system; a isbExpressed as self-acceleration measured by the IMU; g is expressed as a gravity acceleration matrix; c represents cos; s represents sin; gamma, psi and theta represent included angles between three coordinate axes of the device and a global coordinate system;
and calculating to obtain the position of the train at the time t:
Figure FDA0003281994750000032
wherein: v. ofkExpressed as the train speed measured by the IMU at time k;
Figure FDA0003281994750000033
expressed as the acceleration in the global coordinate system at time k; k is expressed as the number of samples of the IMU data; Δ T is expressed as a sampling period; v. ofkExpressed as the instantaneous velocity of the kth sample point; x is the number oftDenoted as time t; instantaneous coordinates of the train in two-dimensional/three-dimensional coordinates; x is the number oft-1Expressed as two-dimensional/three-dimensional at time t-1Instantaneous coordinates of the train under the coordinates;
and combining the position of the train at the time t with the positioning result of the last time point to obtain the section nearest to the current position of the train and the number of the section.
9. The method of train position location navigation of claim 8, wherein the prediction phase comprises:
using the Kalman filtering to estimate the posterior state of the train position at t-1
Figure FDA0003281994750000034
To predict the prior state estimation value of the train position at the time t
Figure FDA0003281994750000035
Figure FDA0003281994750000036
Wherein: a is expressed as a train motion state transition matrix in a Kalman filter;
estimating covariance E of the train position posteriori by t-1 timet-1And white Gaussian noise beta to predict prior estimated covariance at time t
Figure FDA0003281994750000037
Figure FDA0003281994750000038
Wherein: a. theTRepresented as a transpose of the train motion state transition matrix.
10. The method of train positioning navigation according to claim 9, characterized in that the update phase comprises:
calculating CarerMangan gain Kt：
Figure FDA0003281994750000039
Wherein: c is expressed as a state variable to measurement conversion matrix; r is expressed as a measurement noise covariance; cTExpressed as a transpose of the state variables to the measured transformation matrix;
performing Kalman filtering state correction updating, and calculating the posterior state estimation value of the train position at the time t
Figure FDA0003281994750000041
Figure FDA0003281994750000042
Wherein: y istExpressed as an observed value;
in order to further estimate the iteration of the optimal train position at the time t +1 and perform the updating operation, the posterior estimation covariance E at the time t needs to be updatedt：
Figure FDA0003281994750000043
Wherein: i denotes an identity matrix.
11. A train positioning and navigation system, comprising:
a GNSS sensor (100) for acquiring GNSS signals;
the judging module is connected with the GNSS sensor (100) and used for judging whether the signal intensity of the GNSS signal meets a preset standard or not to obtain a judging result;
a storage module (400) for storing a library of location fingerprints;
the TAU vehicle-mounted access unit (500) is connected with the storage module (400), and the TAU vehicle-mounted access unit (500) is used for acquiring the position fingerprint database based on time sequence of the train and the fingerprint corresponding to the train at the current moment when the judgment result is that the signal intensity of the GNSS signal does not meet the preset standard;
the DTW algorithm processor (600) is respectively connected with the TAU vehicle-mounted access unit (500) and the storage module (400), and the DTW algorithm processor (600) is used for obtaining the position information of the train from the position fingerprint database;
the IMU inertial sensor (200) is used for acquiring a first SINS signal and acquiring a section number closest to the current position of the train;
a first combined positioning module, connected to the IMU inertial sensor (200) and the DTW algorithm processor (600), respectively, for combined positioning of the location information of the train with the first SINS signal;
and the Kalman filter (300) is respectively connected with the first combined positioning module, the DTW algorithm processor (600) and the IMU inertial sensor (200) and is used for correcting the accumulated error deviation value of the received first SINS signal to obtain the optimal estimated position of the train.
12. The train positioning and navigation system of claim 11, further comprising:
the IMU inertial sensor (200) is further configured to acquire a second SINS signal when the determination result is that the signal intensity of the GNSS signal meets a preset standard;
a second combined positioning module, connected to the IMU inertial sensor (200) and the GNSS sensor (100), respectively, for combined positioning of the GNSS signals and the first SINS signals;
the kalman filter (300) is further connected to the second combined positioning module, and the kalman filter (300) is further configured to correct an accumulated error deviation value of the received second SINS signal to obtain the optimal estimated position of the train.
13. The train positioning and navigation system of claim 12, wherein the determination criteria of the determination module are:
when the number of satellites participating in positioning calibration in the GNSS sensor (100) is at least three, the signal intensity of the GNSS signal accords with a preset standard;
when the number of satellites participating in positioning calibration in the GNSS sensor (100) is less than three, the signal intensity of the GNSS signal does not meet a preset standard.
14. The train positioning and navigation system of claim 13, further comprising: a partitioning module connected with the TAU onboard access unit (500);
the dividing module is used for dividing the track in the running process of the train into a plurality of sections and numbering each section;
the dividing module is further used for establishing a plane two-dimensional coordinate system in each section and dividing each plane two-dimensional coordinate system into n grids at equal intervals;
on each square, the TAU vehicle-mounted access unit (500) is further configured to acquire RSRP data and PCI data of an LTE system, record the RSRP data and the PCI data in the corresponding square to establish the location fingerprint database, and input the location fingerprint database into the storage module.
15. The train positioning and navigation system of claim 14,
the IMU inertial sensor (200) is also used for positioning and analyzing to obtain the section number closest to the current position of the train;
the TAU vehicle-mounted access unit (500) is further used for acquiring first PCI data and first RSRP data of an LTE system on the train at the current moment, taking the first PCI data at the current moment as an index, and searching m pieces of second PCI data which have the same value as the first PCI data in the PCI data acquired in a time point before the current moment;
the TAU vehicle-mounted access unit (500) is further configured to combine the first RSRP data at the current moment with m second RSRP data corresponding to m second PCI data to obtain a fingerprint of the train at the current moment, wherein the m second PCI data and the m second RSRP data are in a one-to-one correspondence relationship.
16. The train positioning and navigation system according to claim 15, wherein the DTW algorithm processor (600) is specifically configured to match the fingerprint with all time series fingerprints in the location fingerprint library using its built-in DTW algorithm, outputting a number of confidence scores;
the DTW algorithm processor (600) is further configured to find a time sequence position point corresponding to the lowest confidence score from the plurality of confidence scores, where the time sequence position point is the position information of the train.
17. The train position location navigation system of claim 16, wherein the kalman filter (300) comprises: a prediction module and an update module;
the prediction module is used for estimating the size of the train position at the current moment according to the posterior estimation value of the train position at the previous moment so as to obtain the prior estimation value at the t moment;
the updating module is used for correcting the estimation value of the prediction stage by using the measurement value at the current moment to obtain the posterior estimation value at the current moment.
18. The train positioning navigation system of claim 17, wherein the IMU inertial sensor (200) comprises: the conversion module and the calculation module are connected and are arranged inside the IMU inertial sensor (200);
the conversion module is used for converting the acceleration of the three-dimensional space of the train acquired by the IMU inertial sensor (200) from a self coordinate system into a global coordinate system:
ag＝Rab+G
G＝[0,0,-g]T
Figure FDA0003281994750000061
wherein: a isgExpressed as acceleration in a global coordinate system; a isbExpressed as self-acceleration measured by the IMU; g is expressed as a gravity acceleration matrix; c represents cos; s represents sin; gamma, psi and theta represent included angles between three coordinate axes of the device and a global coordinate system;
the calculation module calculates the position of the train at the time t by using the following formula:
Figure FDA0003281994750000071
wherein: v. ofkRepresenting the train speed measured by the inertial sensor (200) of the IMU at time k;
Figure FDA0003281994750000072
expressed as the acceleration in the global coordinate system at time k; k represents the number of samples of IMU inertial sensor (200) data; Δ T is expressed as a sampling period; v. ofkExpressed as the instantaneous velocity of the kth sample point; x is the number oftDenoted as time t; instantaneous coordinates of the train in two-dimensional/three-dimensional coordinates; x is the number oft-1Expressed as the instantaneous coordinates of the train in two-dimensional/three-dimensional coordinates at time t-1;
the calculation module is further configured to combine the position of the train at the time t with the positioning result at the previous time point to obtain the section closest to the current position of the train and the number of the section.
19. The train positioning and navigation system of claim 18, wherein the prediction module is further configured to estimate the a posteriori state based on the train position at time t-1
Figure FDA0003281994750000073
To predict a priori the train position at time tState estimation
Figure FDA0003281994750000074
Figure FDA0003281994750000075
Wherein: a is expressed as a train motion state transition matrix in a Kalman filter (300);
the prediction module is specifically further configured to estimate the covariance E of the train position a posteriori at the time t-1t-1And white Gaussian noise beta to predict prior estimated covariance at time t
Figure FDA0003281994750000076
Figure FDA0003281994750000077
Wherein: a. theTRepresented as a transpose of the train motion state transition matrix.
20. The train positioning and navigation system of claim 19, wherein the update module is further configured to calculate a kalman gain Kt：
Figure FDA0003281994750000078
Wherein: c is expressed as a state variable to measurement conversion matrix; r is expressed as a measurement noise covariance; cTExpressed as a transpose of the state variables to the measured transformation matrix;
the updating module is specifically used for performing Kalman filtering state correction updating and calculating the posterior state estimation value of the train position at the moment t
Figure FDA0003281994750000079
Figure FDA00032819947500000710
Wherein: y istExpressed as an observed value;
the updating module is specifically further configured to update the posterior estimated covariance E at time ttAnd the iteration of the optimal train position at the time of t +1 is further estimated, and the updating operation is carried out:
Figure FDA00032819947500000711
wherein: i denotes an identity matrix.
21. An electronic device comprising a processor and a memory, the memory having stored thereon a computer program which, when executed by the processor, implements the method of any of claims 1 to 10.
22. A readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method of any one of claims 1 to 10.
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