Real-time vehicle positioning method and system based on PPP-B2b

Abstract

A real-time vehicle positioning method and system based on PPP-B2b is disclosed. Based on satellite orbit and clock correction information acquired from PPP-B2b signals, and considering the impact of signal obstruction and delay on positioning results, the system provides real-time predictions of short-term orbit and clock correction information. It also monitors changes in correction values ​​in real time to determine whether the predictions need correction. Using real-time GNSS data streams and PPP-B2b real-time clock and ephemeris correction information, the system acquires the vehicle's real-time location information. The corresponding vehicle terminal includes a receiving module, a display terminal, an electronic map module, a voice prompt module, and a power supply module. This disclosure simultaneously solves the technical problems of existing real-time vehicle positioning systems relying on network environments and the susceptibility of B2b signals to obstruction, thereby achieving high-precision real-time vehicle positioning without network dependence, providing better location services for vehicle driving safety.

Description

Technical Field

[0001] This invention relates to the field of real-time positioning technology, and in particular to a real-time vehicle positioning system based on PPP-B2b and its implementation method. Background Technology

[0002] With the rapid development of Global Navigation Satellite System (GNSS), GNSS has been widely used in automotive navigation and positioning due to its advantages such as all-weather operation, high precision, small receiver size, full functionality, and ease of operation. Precise Point Positioning (PPP), first proposed in the late 1990s, has attracted widespread attention and has become a popular technology for precise positioning applications within GNSS due to its high accuracy, stable performance, and flexible computation. Since PPP positioning accuracy largely depends on the accuracy of satellite orbits and clock bias products, obtaining high-precision satellite orbits and clock biases has always been an important research topic in PPP positioning.

[0003] Currently, the Multi-GNSS Experiment (MGEX) initiated by the International GNSS Service (IGS) has provided accurate orbit and clock bias products for multiple GNSS users. Meanwhile, to address the issue of significant time delays in IGS's precise orbit products, which prevent their application in real-time PPP positioning, IGS launched the Real-Time Pilot Project (RTPP) in 2007. RTPP, based on the RTCM Maritime Service Radio Technical Committee (RTCM) and transmitted over the RTCM network via Internet Protocol (NTRIP), provides Real-Time Service (RTS) in State-Space Representation (SSR) format. The SSR includes orbit and clock bias corrections for broadcast ephemeris. Users receive broadcast ephemeris, precise orbit, and clock bias corrections in real time via IP and port, providing real-time and high-precision satellite orbit and clock bias for real-time PPP positioning. To date, more than ten domestic and international organizations, including GMV, CNES, and WHU, have provided SSR products, promoting the application of real-time PPP in high-precision positioning and vehicle navigation, among other fields. However, since IGS real-time precision orbit and clock correction data streams are broadcast over the network, poor network performance or unavailability can lead to the loss of SSR correction data, resulting in PPP solution interruptions. Therefore, a stable network environment is crucial for IGS real-time PPP broadcast over the network.

[0004] China has completed the construction of the third phase (BDS-3) of the BeiDou Navigation Satellite System (BDS), and officially began providing positioning, navigation, and timing services to global users at the end of July 2020. BDS-3 also provides PPP-B2b positioning services based on high-precision satellite clock bias and orbit correction information broadcast from GEO satellites. The ground control center continuously monitors all visible satellites on the BDS-3 GEO satellites and GNSS, generating pseudorange and carrier observation information, and collecting meteorological data for preprocessing. The preprocessed raw data is then verified and evaluated, analyzing the observation data, navigation messages, and satellite orbit and clock bias corrections. According to the protocol, enhanced information including corrections and other relevant parameters is generated and transmitted to the GEO satellites. This information is then broadcast in real-time via the GEO satellites' B2b signals at a data rate of 500 bps, aiming to provide users with real-time decimeter-level positioning results. This makes it possible to implement real-time vehicle navigation and positioning technology using PPP-B2b.

[0005] The completion of BDS-3 and the provision of PPP-B2b positioning services effectively solve the problems of traditional real-time PPP positioning, such as network limitations and signal interference. Compared to traditional PPP real-time positioning services broadcast over the Internet based on the NTRIP protocol, PPP-B2b signals are broadcast using satellite-based precision product correction data, which is not affected by network environment factors. Therefore, compared to network-based correction data broadcasting methods, it has a wider coverage and more stable signal, enabling vehicle navigation to achieve real-time high-precision positioning even in areas with poor network signals. At the same time, PPP-B2b signals, broadcasting precision product correction data via satellite, are not affected by multipath effects and other factors. Therefore, compared to network-based correction data broadcasting methods, it has less signal noise and does not require further signal processing to extract useful information, enabling vehicle navigation to achieve real-time high-precision positioning even in urban environments with severe multipath effects.

[0006] However, in urban environments, GEO satellites may experience discontinuities in receiving orbit and clock correction information broadcast by GEO satellites due to obstruction from urban buildings. Taking the central Beijing area (116°E, 40°N) as an example, within the 73°E-135°E GEO band in my country, the maximum elevation angle for GEO satellites in the central Beijing area is 43.7°. Dense high-rise buildings severely reduce the visibility of GEO satellites, causing discontinuities in the reception of orbit and clock correction information, further affecting the continuity of real-time high-precision positioning. Summary of the Invention

[0007] This disclosure provides a real-time vehicle positioning system and its implementation method that fully considers the impact of the urban environment. Based on PPP-B2b signals, especially in urban environments, the system corrects satellite orbit and clock bias products to obtain real-time, high-precision vehicle location information.

[0008] The real-time vehicle positioning method based on PPP-B2b disclosed herein includes the following steps:

[0009] S1, the receiver module of the vehicle-mounted terminal performs interruption monitoring on the received satellite orbit and clock correction information; if an interruption occurs, then:

[0010] The dataset is selected from data collected within a certain continuous period before the interruption.

[0011] The correction data in the dataset is monitored for jumps. If a jump occurs, the data from the time of the jump to the time of the interruption is used as the new dataset.

[0012] Based on the obtained dataset, a fitting function is selected to predict and repair the satellite orbit and clock error correction information during the interruption period;

[0013] The predicted satellite orbit and clock error corrections are then incorporated into the satellite orbit and clock error information.

[0014] S2 corrects for other common errors;

[0015] S3 uses the Kalman filter algorithm to calculate the vehicle's position information in real time.

[0016] Furthermore, the method for determining the interruption in step S1 includes:

[0017] The received clock correction and orbit correction messages are decoded. If the interval between adjacent epochs of orbit correction exceeds 96 seconds, or the interval between adjacent epochs of clock correction exceeds 12 seconds, the correction information is considered to be interrupted.

[0018] Furthermore, in step S1, when the satellite orbit and clock error correction information is interrupted, the correction information of the 100 consecutive epochs before the interruption epoch is selected as the dataset.

[0019] Furthermore, in step S1, the method for detecting jumps includes the following steps:

[0020] For satellites with the same satellite number in the dataset, the difference between adjacent epochs is calculated to obtain a single difference sequence of correction information for each satellite.

[0021] The correction information single difference sequence was divided into two groups according to the GPS system and the BDS system;

[0022] Calculate the mean of the single differences in correction information of all satellites under the same system at the same epoch time;

[0023] When the average value of the single track difference exceeds the first threshold or the average value of the single clock difference exceeds the second threshold, the correction information is considered to have changed.

[0024] Furthermore, the first threshold is set to 0.3m, and the second threshold is set to 0.03m.

[0025] Further, in step S1, based on the acquired dataset, a fitting function is selected to predict and repair the satellite orbit and clock error correction information during the interruption period. This step specifically includes:

[0026] The total number of data corrections is collected and analyzed. Based on the different total numbers of data, a polynomial fitting model of different degrees is adaptively selected to predict the correction information for each satellite.

[0027] When the satellite correction information is received again, the prediction of the correction information will no longer be made;

[0028] If the correction data is interrupted for more than 30 minutes, the satellite is considered to be invisible in the current area and the prediction of correction data will no longer be made.

[0029] Furthermore, step S1, which involves correcting the predicted satellite orbit and clock bias values ​​into the satellite orbit and clock bias information, specifically includes:

[0030] The predicted satellite orbit correction is added to the satellite orbit information using the following formula:

[0031] X orbit =X broadcast -δX

[0032] In the formula, X orbit X is the corrected satellite position. beopadcast The satellite position is calculated from the broadcast ephemeris, and δX is the orbital correction information;

[0033] The formula for calculating δX is:

[0034]

[0035]

[0036] e along =e ross ×e radial

[0037] δr=[e radial e aliong e cross ]·δO

[0038] In the formula, These are the satellite position and velocity vectors for broadcast ephemeris, e radial e cross e along These correspond to the unit vectors in the radial, tangential, and normal directions, respectively.

[0039] The predicted clock error correction is then incorporated into the satellite orbit information using the following formula:

[0040]

[0041] In the formula, t broadcast The satellite clock bias calculated from the broadcast ephemeris, t satllite C represents the corrected satellite clock error correction, where C is the speed of light and C0 is the clock error correction information.

[0042] Furthermore, other common errors in S2 include one or more of the following: spherical delay error, Earth rotation error, and antenna phase winding error.

[0043] Furthermore, the method also includes the following steps:

[0044] The calculated vehicle location information is matched with the electronic map module to identify the vehicle's position on the electronic map, and the vehicle's position and movement trajectory on the electronic map are displayed in real time through the display terminal.

[0045] Furthermore, the method also includes the following steps:

[0046] The regions on the electronic map module are standardized and divided according to the complexity of the geographical environment, and the regions with complex geographical environments are highlighted on the display terminal.

[0047] By annotating and dividing the electronic map with geographical information, the geographical information of the car's location is identified. When the car enters a geographically complex area, the voice reminder module will remind the driver that they are about to enter a geographically complex area.

[0048] This disclosure also provides a real-time vehicle positioning system based on PPP-B2b, including: a receiving module, a display terminal, and an electronic map module, wherein:

[0049] The receiving module is used to receive satellite orbit and correction information of PPP-B2b signals and to monitor for interruptions. When an interruption occurs, it forecasts short-term orbit and clock correction information, and combines the forecasted satellite orbit and clock correction with broadcast ephemeris information to synthesize the precise position and clock error of the satellite in real time. Combined with real-time GNSS observation data, it calculates the vehicle position information in real time based on real-time dynamic single-point positioning.

[0050] The display terminal and electronic map module are used to match the calculated vehicle location information with the electronic map module, identify the vehicle's position on the electronic map, and display the vehicle's position and movement trajectory on the electronic map in real time through the display terminal.

[0051] This disclosure utilizes widely used precise point positioning technology, based on satellite orbit and clock correction information obtained from PPP-B2b signals, and considers the impact of signal obstruction and delay on positioning results to predict short-term orbit and clock correction information in real time; it also monitors the jumps in correction values ​​in real time to determine whether the prediction of correction values ​​needs to be corrected; and uses real-time GNSS data stream and PPP-B2b real-time clock difference and ephemeris correction information to obtain real-time vehicle location information.

[0052] Compared with the prior art, the beneficial effects of this disclosure are: (1) It makes full use of the satellite orbit and correction information of the PPP-B2b signal broadcast by the Beidou GEO satellite to obtain high-precision vehicle location information; (2) It effectively reduces the influence of factors such as network environment and short-term satellite blockage; (3) It takes into account the influence of other common errors; (4) It can solve vehicle location information in real time; (5) It can realize the visualization and voice reminder functions of real-time vehicle location information; (6) The algorithm has good versatility and practicality. Attached Figure Description

[0053] The above and other objects, features and advantages of this disclosure will become more apparent from the more detailed description of exemplary embodiments of this disclosure taken in conjunction with the accompanying drawings, in which the same reference numerals generally represent the same components.

[0054] Figure 1 This diagram illustrates a structural embodiment of the real-time vehicle positioning system according to the present disclosure.

[0055] Figure 2 This is a flowchart of the vehicle position calculation based on PPP-B2b signals. Detailed Implementation

[0056] Preferred embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure will be thorough and complete, and will fully convey the scope of the present disclosure to those skilled in the art.

[0057] This disclosure provides a real-time vehicle positioning system based on PPP-B2b and its implementation method.

[0058] The ground control center continuously monitors the BDS-3GEO satellite and all visible GNSS satellites, generating pseudorange and carrier observation information, and collecting meteorological data for preprocessing. The preprocessed raw data is then verified and evaluated, and the observation data, navigation messages, and satellite orbit and clock corrections are calculated. According to the protocol, corrections and other relevant parameter enhancement information are generated and transmitted to the GEO satellite, which then broadcasts the data via PPP-B2b signals.

[0059] The receiving module of the vehicle terminal receives and decodes the PPP-B2b signal, adopts a real-time dynamic single-point positioning mode, uses common mapping function models and error correction models to perform error correction, and uses Kalman filtering to calculate the vehicle position information in real time.

[0060] An exemplary embodiment of the real-time vehicle location calculation method is shown in the attached diagram. Figure 2 As shown.

[0061] Step 1: The receiver module of the vehicle terminal performs interruption monitoring on the received satellite orbit and clock correction data to determine the validity and continuity of the data.

[0062] The satellite orbit broadcast interval is 48 seconds, with a validity of 96 seconds; the satellite clock error broadcast interval is 6 seconds, with a validity of 12 seconds. The received clock error correction and orbit correction messages are decoded, and flag bits are added. If the interval between adjacent epochs of orbit corrections exceeds 96 seconds, or the interval between adjacent epochs of clock error corrections exceeds 12 seconds, the correction information is considered interrupted, and the flag bit is set to 1; otherwise, the flag bit is set to 0. The formula is as follows:

[0063]

[0064] in, This refers to the epoch times of the nth and (n-1th)th orbit and clock correction information for the i-th satellite, in seconds, flag. break This is an interrupt identifier.

[0065] When satellite orbit and clock error correction information is interrupted, the correction information of 100 consecutive epochs before the interruption is selected as the dataset, and the correction information in the dataset is monitored for jumps and flag bits are added to ensure the reliability of the prediction results.

[0066] The jump detection method involves taking the correction information of satellites with the same satellite number in the dataset, subtracting the values ​​between adjacent epochs, and obtaining a single-difference sequence for each satellite's correction information. This single-difference sequence is divided into two groups based on the GPS and BDS systems. The mean single-difference value of all satellite correction information under the same system at the same epoch is calculated. When the mean orbital single-difference value exceeds 0.3m or the mean clock difference single-difference value exceeds 0.03m, a jump in the correction information is considered to have occurred, and the flag is set to 1; otherwise, the flag is set to 0. The formula is as follows:

[0067]

[0068]

[0069]

[0070] in, These represent the single difference values ​​of the orbit and clock error corrections for the i-th satellite between epoch times t and t-1, in meters (m), where M is the total number of satellites in the single system at that epoch. (flag) jump This is an interrupt identifier.

[0071] When the correction information in the dataset changes, the data after the epoch of the change is extracted as a new dataset.

[0072] The total number of data corrections is collected. Based on the different total numbers of data, a polynomial fitting model of different exponents is adaptively selected to predict the correction information for each satellite. When the correction information for a satellite is received again, no further correction information prediction is made; if the correction information is continuously interrupted for more than 30 minutes, the satellite is considered to be invisible in the current area at the current time, and no further correction information prediction is made.

[0073] The predicted satellite orbit correction is added to the satellite orbit information using the following formula:

[0074] X orbit =X broadcast -δX (5)

[0075] In the formula, X orbit X is the corrected satellite position. broadcast The satellite position is calculated from the broadcast ephemeris, and δX is the orbital correction information.

[0076] The formula for calculating δX is:

[0077]

[0078]

[0079] e along =ecross ×e radial (8)

[0080] δr=[e radial e along e cross ]·δO (9)

[0081] In the formula, These are the satellite position and velocity vectors for broadcast ephemeris, e radial e cross e along These are the unit vectors corresponding to the radial, tangential, and normal directions, respectively.

[0082] The predicted clock error correction is then incorporated into the satellite orbit information using the following formula:

[0083]

[0084] In the formula, t broadcast The satellite clock bias calculated from the broadcast ephemeris, t satellite C represents the corrected satellite clock error correction, where C is the speed of light and C0 is the clock error correction information.

[0085] Step 2: Combining the GNSS observation information and broadcast ephemeris information received from the real-time data stream, based on the real-time dynamic single-point positioning mode, the GPT2 tropospheric delay estimation model and the dual-frequency de-ionization combination mode are used. At the same time, errors such as ocean tidal load and antenna phase winding are corrected, and the vehicle position information is calculated in real time using the least squares algorithm. The complete model and processing strategy example is shown in Appendix Table 1.

[0086] Table 1 Real-time Dynamic PPP Processing Strategy in Vehicle Positioning

[0087] Error term Strategy Processing system GPS, BDS Observational data Real-time data stream reception Satellite ephemeris, orbit and clock corrections B2b real-time calibration information Cutoff elevation angle 10° Tidal model correction FES2004 Tidal Model Ionospheric delay Dual-frequency deionization combination Tropospheric delay GPT2 Mapping function GMF Receiver phase center igs14.atx Satellite Phase Center igs14.atx Antenna phase wrapping Model calibration Earth's rotation Model calibration

[0088] In the real-time dynamic PPP of the vehicle positioning in Table 1, the basic observation equations using pseudorange and phase are as follows:

[0089]

[0090]

[0091] In the formula, s, r, and i represent the satellite, receiver, and frequency number, respectively. The values ​​represent phase and pseudorange observations in meters (m), ρ represents the geometric distance between the satellite and the receiver, c represents the speed of light in a vacuum, and dt represents the distance between the satellite and the receiver. r dt s This represents receiver clock bias and satellite clock bias, where I represents ionospheric error. The tropospheric error is represented by λ, the wavelength factor by λ, and the integer ambiguity by N. This indicates the phase deviation between the receiver and the satellite. Indicates the pseudorange deviation between the receiver and the satellite. This represents pseudorange and phase noise.

[0092] As a preferred embodiment, the exemplary embodiment further includes the following steps: matching the vehicle location information calculated by the receiver with the electronic map module, identifying the vehicle's location on the electronic map, and displaying the vehicle's location and movement trajectory on the electronic map in real time on the display.

[0093] Furthermore, it may also include: standardizing and dividing the areas on the electronic map module according to the complexity of the geographical environment, highlighting the areas with complex geographical environments on the display terminal; identifying the geographical information of the car's location by marking and dividing the geographical information on the electronic map; and when the car enters a geographically complex area, the voice reminder module will remind the driver of the steps to enter the geographically complex area.

[0094] This disclosure also provides a real-time vehicle positioning system based on PPP-B2b, and an exemplary embodiment structural diagram is attached. Figure 1 As shown, it includes: a receiver module, a display screen, and an electronic map module, wherein:

[0095] The receiver receives satellite orbit and correction information from PPP-B2b signals and performs interruption monitoring. When an interruption occurs, it forecasts short-term orbit and clock error correction information and combines the forecasted satellite orbit and clock error correction with broadcast ephemeris information to synthesize the precise position and clock error of the satellite in real time. Combined with real-time GNSS observation data, the vehicle position information is calculated in real time based on real-time dynamic single-point positioning.

[0096] The display screen and electronic map module are used to match the calculated vehicle location information with the electronic map module, identify the vehicle's position on the electronic map, and display the vehicle's position and movement trajectory on the electronic map in real time through the display screen.

[0097] As a preferred embodiment, this embodiment also standardizes and divides the areas on the electronic map module according to the complexity of the geographical environment, and highlights the areas with complex geographical environments on the display screen. Additionally, it includes a voice prompt module that identifies the vehicle's location by annotating the electronic map and dividing the geographical information. When the vehicle enters a geographically complex area, the voice prompt module provides a reminder to the driver, informing them that they are about to enter such an area.

[0098] This embodiment also includes: a power supply module, which is connected to the receiver module, the display screen, the electronic map module, and the voice prompt module; the display screen is connected to the receiver module, the electronic map module, and the voice prompt module.

[0099] The above technical solutions are merely exemplary embodiments of the present invention. For those skilled in the art, based on the application methods and principles disclosed in the present invention, it is easy to make various types of improvements or modifications, and not limited to the methods described in the specific embodiments of the present invention. Therefore, the methods described above are merely preferred and not restrictive.

Claims

1. A real-time vehicle positioning method based on PPP-B2b, comprising the following steps: S1, the receiver module of the vehicle terminal performs interruption monitoring on the received satellite orbit and clock correction information; If there is an interruption, then: The dataset is selected from data collected within a certain continuous period before the interruption. The correction data in the dataset is monitored for jumps. If a jump occurs, the data from the time of the jump to the time of the interruption is used as the new dataset. Based on the obtained dataset, a fitting function is selected to predict and repair the satellite orbit and clock error correction information during the interruption period; The predicted satellite orbit and clock error corrections are then incorporated into the satellite orbit and clock error information. S2 corrects for other common errors; S3 uses the Kalman filter algorithm to calculate the vehicle's position information in real time; In step S1, the method for detecting jumps includes the following steps: For satellites with the same satellite number in the dataset, the difference between adjacent epochs is calculated to obtain a single difference sequence of correction information for each satellite. The correction information single difference sequence was divided into two groups according to the GPS system and the BDS system; Calculate the mean of the single differences in correction information of all satellites under the same system at the same epoch time; When the average value of the single track difference exceeds the first threshold or the average value of the single clock difference exceeds the second threshold, the correction information is considered to have changed.

2. The method according to claim 1, characterized in that, The method for determining interruption in step S1 includes: The received clock correction and orbit correction messages are decoded. If the interval between adjacent epochs of orbit correction exceeds 96 seconds, or the interval between adjacent epochs of clock correction exceeds 12 seconds, the correction information is considered to be interrupted.

3. The method according to claim 1, characterized in that, In step S1, when the satellite orbit and clock error correction information is interrupted, the correction information of the 100 consecutive epochs before the interruption is selected as the dataset.

4. The method according to claim 1, characterized in that, The first threshold is 0.3m, and the second threshold is 0.03m.

5. The method according to claim 1, characterized in that, Step S1, based on the acquired dataset, involves selecting a fitting function to predict and repair the satellite orbit and clock error correction information during the interruption period. This step specifically includes: The total number of data corrections is collected and analyzed. Based on the different total numbers of data, a polynomial fitting model of different degrees is adaptively selected to predict the correction information for each satellite. When the satellite correction information is received again, the prediction of the correction information will no longer be made; If the correction data is interrupted for more than 30 minutes, the satellite is considered to be invisible in the current area and the prediction of correction data will no longer be made.

6. The method according to claim 1, characterized in that, Step S1 involves correcting the predicted satellite orbit and clock error corrections into the satellite orbit and clock error information, specifically including: The predicted satellite orbit correction is added to the satellite orbit information using the following formula: In the formula, The corrected satellite position, The satellite positions are calculated from the broadcast ephemeris. For track correction information; The calculation formula is: In the formula, , These are the satellite positions and velocity vectors for broadcast ephemeris satellites, respectively. , , These correspond to the unit vectors in the radial, tangential, and normal directions, respectively. The predicted clock error correction is then incorporated into the satellite orbit information using the following formula: In the formula, Satellite clock bias calculated from broadcast ephemeris, This is the corrected satellite clock bias correction. At the speed of light, Correct information for clock bias.

7. The method according to claim 1, characterized in that, Other common errors in S2 include one or more of the following: spherical delay error, Earth rotation error, and antenna phase winding error.

8. The method according to any one of claims 1-7, characterized in that, It also includes the following steps: The calculated vehicle location information is matched with the electronic map module to identify the vehicle's position on the electronic map, and the vehicle's position and movement trajectory on the electronic map are displayed in real time through the display terminal.

9. The method according to claim 8, characterized in that, It also includes the following steps: The regions on the electronic map module are standardized and divided according to the complexity of the geographical environment, and the regions with complex geographical environments are highlighted on the display terminal. By annotating and dividing the electronic map with geographical information, the geographical information of the car's location is identified. When the car enters a geographically complex area, the voice reminder module will remind the driver that they are about to enter a geographically complex area.

10. A real-time vehicle positioning system based on PPP-B2b, characterized in that, include: The receiver module, display terminal, and electronic map module include: The receiving module receives satellite orbit and correction information of PPP-B2b signals and performs interruption monitoring. When an interruption occurs, it predicts short-term orbit and clock error correction information, and combines the predicted satellite orbit and clock error correction with broadcast ephemeris information to synthesize the precise position and clock error of the satellite in real time. Combined with GNSS real-time observation data, it calculates the vehicle position information in real time based on real-time dynamic single-point positioning. The display terminal and electronic map module are used to match the calculated vehicle location information with the electronic map module, identify the vehicle's position on the electronic map, and display the vehicle's position and movement trajectory on the electronic map in real time through the display terminal.

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