Train fusion positioning method and system based on 5G communication

Abstract

The invention discloses a train fusion positioning method and system based on 5G communication, and the method comprises the steps: obtaining the 5G wireless communication measurement information of a train, including RTT, RSSI, AoA, TDoA and other parameters, and calculating a continuous positioning result of the train based on the measurement information; the system adopts a 5G A network and B network dual-network redundancy design, and a single-network positioning mode, a dual-network fusion positioning mode or a dead reckoning positioning mode is dynamically selected according to signal quality; when the 5G signal quality meets a threshold value, positioning is carried out based on 5G measurement information, and speed sensor data is used as auxiliary verification; and when the signal quality is insufficient, switching to a dead reckoning mode based on the speed sensor. According to the invention, the confidence of a positioning result can be evaluated in real time, and positioning integrity monitoring and grading speed control are implemented; positioning calculations may be performed at an edge computing node (MEC) or an in-vehicle computing unit. The reliability and precision of the positioning system are improved, and the positioning precision can reach the sub-meter level.

Description

Technical Field

[0001] This invention relates to the field of rail transit technology, specifically to a train fusion positioning method and system based on 5G communication. Background Technology

[0002] In rail transit systems, precise train positioning is a key technology for ensuring operational safety and improving efficiency. Traditional train positioning systems primarily rely on ground equipment such as physical transponders (balises) and track circuits. With the development of 5G communication technology, its high bandwidth, low latency, and high reliability provide a new technological path for rail transit positioning. 5G networks can provide various positioning measurement parameters, including signal round-trip time, angle of arrival, and signal strength, creating conditions for achieving high-precision positioning. Summary of the Invention

[0003] This application provides a train fusion positioning method and system based on 5G communication to achieve high-precision positioning of trains using 5G communication networks, ensuring the continuity and reliability of positioning services.

[0004] According to the first aspect, one embodiment provides a train fusion positioning method based on 5G communication, the method comprising:

[0005] The train establishes 5G communication with 5G base stations deployed along the train track while it is in motion;

[0006] Based on the dual-network redundant communication mode, 5G communication measurement information is collected and the train continuous positioning results are calculated.

[0007] The system assesses the confidence level of the positioning results in real time and implements graded speed control based on the confidence level. When the confidence level is lower than the safety threshold, an alarm is sent to the train control system.

[0008] Furthermore, based on the dual-network redundant communication mode, 5G communication measurement information is collected and the train continuous positioning results are calculated, specifically including:

[0009] The 5G communication measurement information includes one or more of the following: Round-trip time (RTT), Received signal strength (RSSI), Angle of arrival (AoA), or Time difference of arrival (TDoA).

[0010] Furthermore, based on the dual-network redundant communication mode, 5G communication measurement information is collected and the train continuous positioning results are calculated, specifically including:

[0011] The signal quality of networks A and B is evaluated, and the signal quality evaluation indicators include signal-to-noise ratio, round-trip delay variance, reference signal received power, multipath delay spread, and geometric accuracy factor.

[0012] The positioning mode is selected based on the signal quality assessment results. The positioning modes include single-network positioning, dual-network fusion positioning, and satellite inference mode.

[0013] Further, the signal quality of networks A and B is evaluated, specifically including:

[0014]

[0015] in, Network signal quality assessment value; SNR: Signal-to-noise ratio; RTT_var: Round-trip delay variance; RSRP: Reference signal received power; The weight of the i-th indicator satisfies .

[0016] Furthermore, positioning mode selection is based on signal quality assessment results, specifically including:

[0017] The single-network positioning mode is as follows: when the signal quality of at least one network in network A or network B exceeds the high quality threshold, the 5G communication measurement information collected by a single network is used to perform positioning calculation using trilateration or angle positioning algorithms.

[0018] The dual-network fusion positioning mode is as follows: when the signal quality of both network A and network B exceeds the basic threshold but does not exceed the high quality threshold, the positioning results obtained based on the measurement information of network A and network B are weighted and fused, and the weight is proportional to the signal quality of each network.

[0019] When the signal quality of both networks does not exceed the basic threshold, the dead reckoning mode based on the velocity sensor is switched to. The displacement increment is calculated by integrating the pulse count of the velocity sensor and combined with the position of the previous moment to estimate the current position.

[0020] Furthermore, based on the dual-network redundant communication mode, 5G communication measurement information is collected and the train continuous positioning results are calculated, specifically including:

[0021] Using speed sensor data as auxiliary verification, the displacement increment obtained by integrating the pulse count based on the speed sensor is compared with the 5G positioning result. If the deviation exceeds the threshold, an abnormal alarm is triggered.

[0022] Furthermore, the confidence level of the positioning results is assessed in real time, specifically including:

[0023]

[0024] in, To determine location reliability, For signal quality factor, Geometric precision factor, For the multi-source consistency factor, α, β, and γ are weighting coefficients, satisfying... .

[0025] Furthermore, graded speed control is implemented based on confidence levels, specifically including:

[0026] If the confidence level is ≥0.9, it will run at normal speed;

[0027] If 0.7 ≤ confidence level < 0.9, then the speed limit is 80% of the rated speed;

[0028] If 0.5 ≤ confidence level < 0.7, then the speed limit is 20 km / h;

[0029] If the confidence level is less than 0.5, then emergency braking is initiated.

[0030] Furthermore, the train's onboard terminal includes: a 5G communication module, equipped with a dual-mode terminal supporting both A-network and B-network; a speed sensor interface for collecting wheel axle pulse signals; and a positioning calculation module.

[0031] According to a second aspect, one embodiment provides a train fusion positioning system based on 5G communication, the system comprising:

[0032] The 5G communication module is used to establish 5G communication between the train and 5G base stations deployed along the train track during train operation;

[0033] The positioning module is used to collect 5G communication measurement information and calculate the continuous positioning results of the train based on the dual-network redundant communication mode.

[0034] The positioning result evaluation module is used to evaluate the confidence level of the positioning result in real time and implement graded speed control according to the confidence level. When the confidence level is lower than the safety threshold, an alarm is sent to the train control system.

[0035] This application provides a train fusion positioning method and system based on 5G communication, which has the following beneficial effects:

[0036] 1) High positioning accuracy: In the midpoint area between two 5G base stations with antenna arrays, the positioning accuracy can reach 0.5-0.8 meters.

[0037] 2) High system reliability: It adopts a dual-network redundancy design of 5G A network and B network, which can seamlessly switch when a single network fails, ensuring the continuity of positioning services.

[0038] 3) Good positioning continuity: When the 5G signal quality is insufficient, it automatically switches to dead reckoning mode based on speed sensor to ensure uninterrupted positioning.

[0039] 4) Superior real-time performance: Low latency and high reliability of location services are guaranteed through transmission via a dedicated 5G network slice URLLC channel.

[0040] 5) Strong security: Implement positioning integrity monitoring and graded speed control based on confidence level. Attached Figure Description

[0041] Figure 1 A flowchart illustrating a train fusion positioning method based on 5G communication, provided as an embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of the overall implementation architecture of a train fusion positioning method based on 5G communication provided in one embodiment of the present invention;

[0043] Figure 3 A schematic diagram of the 5G dual-network redundant positioning principle in a train fusion positioning method based on 5G communication provided in one embodiment of the present invention;

[0044] Figure 4 The diagram shows the main functional modules of a train fusion positioning method based on 5G communication, as provided in one embodiment of the present invention.

[0045] Figure 5 This is a flowchart of a positioning information fusion algorithm in a train fusion positioning method based on 5G communication, provided as an embodiment of the present invention. Detailed Implementation

[0046] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings. Similar elements in different embodiments are referred to by associated similar element reference numerals. In the following embodiments, many details are described to facilitate a better understanding of this application. However, those skilled in the art will readily recognize that some features may be omitted in different situations, or may be replaced by other elements, materials, or methods. In some cases, certain operations related to this application are not shown or described in the specification. This is to avoid obscuring the core parts of this application with excessive description. For those skilled in the art, detailed description of these related operations is not necessary; they can fully understand the related operations based on the description in the specification and general technical knowledge in the art.

[0047] Furthermore, the features, operations, or characteristics described in the specification can be combined in any suitable manner to form various embodiments. At the same time, the steps or actions in the method description can be rearranged or adjusted in a manner obvious to those skilled in the art. Therefore, the various orders in the specification and drawings are only for the clear description of a particular embodiment and do not imply a necessary order, unless otherwise stated that a particular order must be followed.

[0048] The first embodiment of this invention provides a train fusion positioning method based on 5G communication. The following is in conjunction with... Figure 1 Please provide a detailed explanation.

[0049] like Figure 1 As shown, in step S100, the train establishes 5G communication with 5G base stations deployed along the train track during its operation.

[0050] The above steps specifically include:

[0051] Reference Figure 2 The train positioning system of the present invention comprises three main parts: on-board unit, ground network and control center.

[0052] The onboard unit includes: a 5G communication module, equipped with a dual-mode terminal supporting both A-network and B-network; a speed sensor interface for collecting wheel axle pulse signals; a positioning computing unit, which can be an independent processor or integrated with other onboard systems; and an interface with the train control system (ATO / ATP).

[0053] The terrestrial network includes: 5G base stations deployed along the railway, preferably macro base stations with antenna arrays, spaced 500-1000 meters apart; edge computing nodes (MECs) deployed near the base stations or in computer rooms along the railway line; and dedicated 5G network slices providing URLLC (ultra-reliable low-latency communication) services.

[0054] The main functional modules of the system are as follows Figure 4 As shown, the composition and interface relationships of each functional module are illustrated.

[0055] like Figure 1 As shown, in step S200, based on the dual-network redundant communication mode, 5G communication measurement information is collected and the train continuous positioning result is calculated.

[0056] The above steps specifically include:

[0057] S210 collects 5G communication measurement information.

[0058] In this embodiment, the 5G base station periodically transmits Position Reference Signals (PRS), and the train's onboard terminal receives these signals and extracts the following measurement parameters:

[0059] 1) Round-trip time (RTT) measurement: The vehicle-mounted terminal sends an uplink reference signal to the base station, the base station returns an acknowledgment, and the total round-trip time is measured. After accounting for the latency, the propagation distance can be calculated. Under line-of-sight conditions, the RTT measurement accuracy can reach 3-5 nanoseconds, corresponding to a distance accuracy of approximately 1-1.5 meters.

[0060] 2) Received Signal Strength Indicator (RSSI) Measurement: This measures the power level of the received signal. In typical railway environments such as open areas and elevated lines, RSSI is mainly affected by distance attenuation and large-scale fading, exhibiting regular variations. By establishing a mapping model between RSSI and distance, it can be used as an auxiliary positioning parameter. In special sections such as tunnels, reasonable base station deployment ensures continuous signal coverage. Especially in areas where the coverage of two adjacent base stations overlaps, comprehensively utilizing the RSSI measurements from both base stations can improve positioning accuracy.

[0061] 3) Angle of Arrival (AoA) Measurement: The angle of arrival of the signal is calculated using the antenna array of the base station through phase difference. The angle resolution can reach 1-2 degrees.

[0062] 4) Time Difference of Arrival (TDoA) Measurement: Measure the time difference of arrival of signals from different base stations to form hyperbolic positioning.

[0063] S220 evaluates the signal quality of networks A and B. The signal quality evaluation metrics include signal-to-noise ratio, round-trip time variance, and reference signal received power.

[0064] S230, Based on the signal quality assessment results, the positioning mode is selected, including single-network positioning, dual-network fusion positioning, and satellite prediction mode.

[0065] The single-network positioning mode is as follows: when the signal quality of at least one of the networks, A or B, exceeds the high-quality threshold, the 5G communication measurement information collected by a single network is used to perform positioning calculations using trilateration or angle positioning algorithms.

[0066] The dual-network fusion positioning mode is as follows: when the signal quality of both network A and network B exceeds the basic threshold but does not exceed the high quality threshold, the positioning results obtained based on the measurement information of network A and network B are weighted and fused, and the weight is proportional to the signal quality of each network.

[0067] When the signal quality of both networks does not exceed the basic threshold, the dead reckoning mode based on the velocity sensor is switched to. The displacement increment is calculated by integrating the pulse count of the velocity sensor and combined with the position of the previous moment to estimate the current position.

[0068] 5G dual-network redundant positioning principle diagram as follows Figure 3 The diagram illustrates the handover mechanism between network A and network B.

[0069] In this embodiment, when the 5G signal quality meets the preset threshold, positioning is performed based on 5G measurement information. Speed ​​sensor data is used as an auxiliary verification. The displacement increment obtained by integrating the speed sensor is compared with the 5G positioning result. If the deviation exceeds the threshold, an abnormal alarm is triggered.

[0070] In this embodiment, when the 5G signal quality is lower than a preset threshold, dead reckoning is performed based on the accumulated mileage of the speed sensor. The displacement is obtained by multiplying the speed sensor pulse count by the wheel diameter coefficient, and then accumulated to the previous position to obtain the current position, which serves as the main input for positioning.

[0071] In this embodiment, signal quality assessment:

[0072] Define comprehensive quality indicators:

[0073] Based on the above formula, the signal quality Q_A of network A and the signal quality Q_B of network B are calculated in real time.

[0074] Quality grade classification: Excellent (Q>0.9), Good (0.7≤Q≤0.9), Average (0.5≤Q<0.7), Poor (Q<0.5).

[0075] Mode 1 (Single A network): Q_A ≥ 0.9 or (Q_A ≥ 0.7 and Q_B < 0.5);

[0076] Mode 2 (Single B network): Q_B≥0.9 or (Q_B≥0.7 and Q_A<0.5);

[0077] Mode 3 (Dual-network convergence): Q_A ≥ 0.5 and Q_B ≥ 0.5;

[0078] Mode 4 (Dead End Retrieval): Q_A < 0.5 and Q_B < 0.5.

[0079] in:

[0080] Network signal quality assessment value;

[0081] SNR: Signal-to-noise ratio, obtained from 5G physical layer measurement reports;

[0082] RTT_var: Round-trip time variance, calculated statistically using a sliding window.

[0083] RSRP: Reference Signal Received Power, a 5G RRC layer measurement;

[0084] Signal quality indicators for networks A and B;

[0085] The dynamic weight of the i-th indicator satisfies ;

[0086] The reciprocal of the variance; the smaller the variance, the larger this term, indicating a more stable measurement.

[0087] Seamless location mode switching mechanism:

[0088] 1) Predictive handover: Based on signal quality trend prediction, handover is initiated 2-3 seconds in advance.

[0089] 2) Smooth transition: A weighted average of the two positioning results is used during the handover period;

[0090] 3) Switching record: Record all switching events for system optimization.

[0091] Positioning calculations employ either weighted least squares or extended Kalman filtering. Taking positioning with two base stations as an example:

[0092] Let the coordinates of base station 1 be... The coordinates of base station 2 are The train's location is The distance was obtained based on RTT measurements. and The angle was obtained from AoA measurements. and Establish a system of equations:

[0093]

[0094]

[0095]

[0096]

[0097] The optimal position estimate is obtained by solving the overdetermined system of equations.

[0098] The location information fusion algorithm process is as follows: Figure 5 The diagram illustrates the process of processing multi-source information.

[0099] In this embodiment, the positioning calculation is performed on the edge computing node MEC or the vehicle computing unit, or in collaboration between the two.

[0100] like Figure 1 As shown, in step S300, the confidence level of the positioning result is evaluated in real time, and graded speed control is implemented according to the confidence level. When the confidence level is lower than the safety threshold, an alarm is sent to the train control system.

[0101] The above steps specifically include:

[0102] Real-time assessment of the confidence level of the positioning results, specifically including:

[0103]

[0104] in:

[0105] Location reliability (Confidence Index);

[0106] Signal quality factor;

[0107] Geometric precision factor;

[0108] Multi-source consistency factor;

[0109] α, β, and γ are weighting coefficients that satisfy... Typical values:

[0110] α = 0.4 signal quality weight;

[0111] β = 0.3 geometric configuration weight;

[0112] γ = 0.3 Consistency weight.

[0113] Implement graded speed control based on confidence levels, specifically including:

[0114] If the confidence level is ≥0.9, it will run at normal speed;

[0115] If 0.7 ≤ confidence level < 0.9, then the speed limit is 80% of the rated speed;

[0116] If 0.5 ≤ confidence level < 0.7, then the speed limit is 20 km / h;

[0117] If the confidence level is less than 0.5, then emergency braking is initiated.

[0118] Corresponding to the train fusion positioning method based on 5G communication disclosed above, this invention also discloses a train fusion positioning system based on 5G communication, which specifically includes:

[0119] The 5G communication module is used to establish 5G communication between the train and 5G base stations deployed along the train track during train operation;

[0120] The positioning module is used to collect 5G communication measurement information and calculate the continuous positioning results of the train based on the dual-network redundant communication mode.

[0121] The positioning result evaluation module is used to evaluate the confidence level of the positioning result in real time and implement graded speed control according to the confidence level. When the confidence level is lower than the safety threshold, an alarm is sent to the train control system.

[0122] It should be noted that for a detailed description of a train fusion positioning system based on 5G communication provided in the embodiments of the present invention, please refer to the relevant description of a train fusion positioning method based on 5G communication provided in the embodiments of this application, which will not be repeated here.

[0123] In addition, embodiments of the present invention also provide an electronic device, the device comprising: a processor and a memory; the memory being used to store one or more program instructions; the processor being used to execute one or more program instructions to perform the steps of a train fusion positioning method based on 5G communication as described in any of the preceding embodiments.

[0124] It should be noted that for a detailed description of an electronic device provided in the embodiments of the present invention, please refer to the relevant description of a train fusion positioning method based on 5G communication provided in the embodiments of this application, which will not be repeated here.

[0125] In addition, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of a train fusion positioning method based on 5G communication as described in any of the preceding embodiments.

[0126] It should be noted that for a detailed description of a computer-readable storage medium provided in the embodiments of the present invention, please refer to the relevant description of a train fusion positioning method based on 5G communication provided in the embodiments of this application, which will not be repeated here.

[0127] Those skilled in the art will understand that all or part of the functions of the various methods in the above embodiments can be implemented by hardware or by computer programs. When all or part of the functions in the above embodiments are implemented by computer programs, the program can be stored in a computer-readable storage medium, which may include: read-only memory, random access memory, disk, optical disk, hard disk, etc., and the program is executed by a computer to achieve the above functions. For example, the program can be stored in the memory of a device, and when the program in the memory is executed by the processor, all or part of the above functions can be achieved. In addition, when all or part of the functions in the above embodiments are implemented by computer programs, the program can also be stored in a server, another computer, disk, optical disk, flash drive, or external hard drive, etc., and can be downloaded or copied to the memory of a local device, or the system of the local device can be updated. When the program in the memory is executed by the processor, all or part of the functions in the above embodiments can be achieved.

[0128] The above examples illustrate the present invention only to aid in understanding it and are not intended to limit the scope of the invention. Those skilled in the art can make various simple deductions, modifications, or substitutions based on the principles of this invention.

Claims

1. A train fusion positioning method based on 5G communication, characterized in that, The method includes: The train establishes 5G communication with 5G base stations deployed along the train track while it is in motion; Based on the dual-network redundant communication mode, 5G communication measurement information is collected and the train continuous positioning results are calculated. The system assesses the confidence level of the positioning results in real time and implements graded speed control based on the confidence level. When the confidence level is lower than the safety threshold, an alarm is sent to the train control system.

2. The train fusion positioning method based on 5G communication as described in claim 1, characterized in that, Based on a dual-network redundant communication mode, 5G communication measurement information is collected and the train continuous positioning results are calculated, specifically including: The 5G communication measurement information includes one or more of the following: Round-trip time (RTT), Received signal strength (RSSI), Angle of arrival (AoA), or Time difference of arrival (TDoA).

3. The train fusion positioning method based on 5G communication as described in claim 1, characterized in that, Based on a dual-network redundant communication mode, 5G communication measurement information is collected and the train continuous positioning results are calculated, specifically including: The signal quality of networks A and B is evaluated, and the signal quality evaluation indicators include signal-to-noise ratio, round-trip delay variance, reference signal received power, multipath delay spread, and geometric accuracy factor. The positioning mode is selected based on the signal quality assessment results. The positioning modes include single-network positioning, dual-network fusion positioning, and satellite inference mode.

4. The train fusion positioning method based on 5G communication as described in claim 3, characterized in that, The evaluation of signal quality for networks A and B specifically includes: in, Network signal quality assessment value; SNR: Signal-to-noise ratio; Round-trip time delay variance; RSRP: Reference signal received power; The weight of the i-th indicator satisfies .

5. The train fusion positioning method based on 5G communication as described in claim 3, characterized in that, Positioning mode selection is based on signal quality assessment results, specifically including: The single-network positioning mode is as follows: when the signal quality of at least one network in network A or network B exceeds the high quality threshold, the 5G communication measurement information collected by a single network is used to perform positioning calculation using trilateration or angle positioning algorithms. The dual-network fusion positioning mode is as follows: when the signal quality of both network A and network B exceeds the basic threshold but does not exceed the high quality threshold, the positioning results obtained based on the measurement information of network A and network B are weighted and fused, and the weight is proportional to the signal quality of each network. When the signal quality of both networks does not exceed the basic threshold, the dead reckoning mode based on the velocity sensor is switched to. The displacement increment is calculated by integrating the pulse count of the velocity sensor and combined with the position of the previous moment to estimate the current position.

6. The train fusion positioning method based on 5G communication as described in claim 5, characterized in that, Based on a dual-network redundant communication mode, 5G communication measurement information is collected and the continuous positioning results of the train are calculated. Specifically, this also includes: Using speed sensor data as auxiliary verification, the displacement increment obtained by integrating the pulse count based on the speed sensor is compared with the 5G positioning result. If the deviation exceeds the threshold, an abnormal alarm is triggered.

7. The train fusion positioning method based on 5G communication as described in claim 1, characterized in that, Real-time assessment of the confidence level of the positioning results, specifically including: in, To determine location reliability, For signal quality factor, Geometric precision factor, For the multi-source consistency factor, α, β, and γ are weighting coefficients, satisfying... .

8. The train fusion positioning method based on 5G communication as described in claim 7, characterized in that, Implement graded speed control based on confidence levels, specifically including: If the confidence level is ≥0.9, it will run at normal speed; If 0.7 ≤ confidence level < 0.9, then the speed limit is 80% of the rated speed; If 0.5 ≤ confidence level < 0.7, then the speed limit is 20 km / h; If the confidence level is less than 0.5, then emergency braking is initiated.

9. The train fusion positioning method based on 5G communication as described in claim 1, characterized in that, The train's onboard terminal includes: a 5G communication module, equipped with a dual-mode terminal supporting both A and B networks; a speed sensor interface for collecting wheel axle pulse signals; and a positioning calculation module.

10. A train fusion positioning system based on 5G communication, characterized in that, The system includes: The 5G communication module is used to establish 5G communication between the train and 5G base stations deployed along the train track during train operation; The positioning module is used to collect 5G communication measurement information and calculate the continuous positioning results of the train based on the dual-network redundant communication mode. The positioning result evaluation module is used to evaluate the confidence level of the positioning result in real time and implement graded speed control according to the confidence level. When the confidence level is lower than the safety threshold, an alarm is sent to the train control system.

Images