UWB multi-base station communication speed measurement method

Abstract

The invention provides a UWB multi-base-station communication speed measurement method. The method comprises the following steps: deploying a plurality of UWB base stations along an indoor track test line; installing a vehicle-mounted computer and a UWB tag, and integrating an IMU module; performing clock synchronization on all the UWB base stations and the vehicle-mounted computer, and performing bidirectional TOF distance measurement; the UWB signal and the IMU data are preprocessed; selecting at least three base stations with strongest signals, and performing fusion positioning based on a TDOAAOA algorithm; and calculating an instantaneous speed based on a continuous positioning result, and performing dynamic error compensation. The dynamic speed measurement precision bottleneck of the long marshalling train in the closed environment is broken through, and core data support is provided for intelligent driving.

Description

Technical Field

[0001] The present invention relates to the technical field of railway locomotive speed measurement, and in particular to a UWB multi-base station communication speed measurement method. Background Art

[0002] With the development of intelligent rail transit, dynamic speed measurement technology on test and debugging lines has become a key component in ensuring safe train operation. In GPS-denied environments such as indoor test lines and tunnels, high-precision, real-time speed measurement systems are essential for controlling train starting, stopping, braking, and curve negotiation. Traditional speed measurement solutions face challenges such as signal attenuation, multipath interference, and dynamic response delays in complex indoor environments, making them unable to meet the centimeter-level speed measurement accuracy and millisecond-level real-time performance requirements of modern rail transit testing scenarios.

[0003] In the existing technology, for example, patent CN114449442A provides a smart rail vehicle in-garage positioning system and method based on UWB technology, which deploys multiple base stations in closed scenes such as garages, and realizes two-dimensional positioning of vehicles through TOF ranging and central solution. This solution has a certain accuracy in low-speed static scenes, but it is difficult to achieve accurate positioning during high-speed speed measurement. Patent CN111970631A provides an underground train positioning and speed measurement system and method that combines ultra-wideband and inertial navigation. However, the multipath interference caused by the reflection of the metal track causes large fluctuations in UWB ranging, and because the LMS step size factor is fixed, it cannot adapt to high-dynamic scenes such as emergency braking of the train. Actual measurements show that convergence delays will occur when the acceleration suddenly changes, and UWB tags and INS devices are only deployed at the front of the train. When the train turns, there is a speed direction deviation between the front and rear of the train, and the motion state of the rear of the train is not considered. Summary of the Invention

[0004] The present invention is made in view of the above-mentioned problems, and its purpose is to provide a UWB multi-base station communication speed measurement method, reduce positioning error through TDOA-AOA joint positioning, use a third-order Lanczos differentiator to accurately calculate the instantaneous speed, suppress the posture error of long-formation trains through head-tail speed fusion verification, and reduce the metal reflection error of UWB signals through multipath suppression through the SAGE algorithm, breaking through the bottleneck of dynamic speed measurement accuracy of long-formation trains in closed environments, and providing core data support for intelligent driving.

[0005] Specifically, a first aspect of the present invention provides a UWB multi-base station communication speed measurement method, comprising the following steps: Step 1: Deploy several UWB base stations equipped with directional antennas at preset intervals along the indoor track test line; Step 2: Install onboard computers and UWB tags at the front and rear of the train, and integrate IMU modules; Step 3: Synchronize the clocks of all UWB base stations and on-board computers, and perform two-way TOF ranging between the UWB tag and the base station; Step 4: Preprocess UWB signals and IMU data; Step 5: Select at least three base stations with the strongest signals and perform fusion positioning based on the TDOA_AOA algorithm; Step 6: Based on the continuous positioning results, a third-order Lanczos differentiator is used to calculate the instantaneous velocity and perform dynamic error compensation.

[0006] Furthermore, the clock synchronization includes performing microsecond-level clock synchronization on all UWB base stations and on-board computers through the PTP protocol.

[0007] A PTP master clock is deployed at the master base station, and synchronization messages are broadcast over Ethernet. The onboard computer and the slave base station serve as PTP slave clocks. Hardware records the timestamps of message transmission and reception. Clock offset is compensated by calculating the two-way transmission delay to eliminate clock drift errors between base stations and ensure TDOA ranging accuracy. Through hardware timestamps and the master-slave clock mechanism, synchronization accuracy can be controlled to the sub-microsecond level. In high-speed train scenarios, such as at a speed of 200 km / h, a 1 microsecond time error will result in a 5.5 cm distance error. Calculation of clock deviation in two-way transmission: ; in: is the clock bias; The time when the master clock message is sent; The time of receiving the slave clock message; The time when the slave clock message is sent; The time when the master clock message is received.

[0008] Furthermore, the step four includes using the SAGE algorithm to process the UWB channel impulse response and applying a fourth-order Butterworth low-pass filter to the IMU data.

[0009] The SAGE algorithm is used to process the UWB channel impulse response. It is necessary to first collect the UWB channel impulse response CIR, iteratively separate the multipath components, and then determine the direct wave. If the delay difference is greater than 10ns, the direct wave is retained, otherwise the reflected wave is discarded. In the strong reflection environment of metal tracks, it can suppress the ranging fluctuations caused by multipath interference.

[0010] A fourth-order Butterworth low-pass filter is applied to the IMU data. The cutoff frequency threshold of the fourth-order Butterworth low-pass filter is set to 10Hz~30Hz. Since the main vibration frequency of the subway track is below 30Hz, the cutoff frequency can be flexibly adjusted according to actual conditions to effectively filter out the track vibration noise.

[0011] Furthermore, the three base stations with the strongest signals are selected to have RSSI>-80dBm and SNR>15dB.

[0012] Select base stations with RSSI>-80dBm to exclude weak signal base stations (tunnel obstruction scenarios); select base stations with SNR>15dB to avoid high noise base stations (high voltage electromagnetic interference areas). Selection of base station data threshold: The measured data of rail trains show that when RSSI<-80dBm, the ranging error will increase sharply from 10cm to more than 50cm; when SNR<15dBm, the bit error rate will exceed , which will have a great impact on the accuracy of ranging.

[0013] Furthermore, the step five comprises the following steps: Step 5.1: Obtain the TOF ranging value and AOA angle measurement value of each selected base station, and obtain the position coordinates of the corresponding base station; Obtain the TOF ranging values ​​of the three best base stations after screening based on RSSI and SNR, simultaneously obtain the AOA angle measurement values ​​of these base stations (provided by directional antennas), and retrieve the precise coordinate positions of the corresponding base stations from the system database (pre-calibrated when the base stations are deployed).

[0014] Step 5.2: Use the base station with the strongest signal as the reference point to construct a TDOA hyperbola to obtain the preliminary positioning area of ​​the target tag; Taking the base station with the strongest signal as the reference point, and using the distance difference between other base stations and the reference base station (i.e., TDOA measurement results), a cluster of hyperbolas is drawn on a two-dimensional plane: each hyperbola represents a set of points that meet a specific distance difference condition. The intersection area of ​​multiple hyperbolas is the area where the target tag may exist. The intersection area of ​​multiple hyperbolas is used as the preliminary positioning area of ​​the target tag.

[0015] Step 5.3: Based on the preset direction vector of the indoor track test line, AOA angle constraint is performed to compress the preliminary positioning area into a linear candidate area along the track; Based on the preset direction vector of the track test line (e.g., 30° due north), the AOA measurement value of each base station is converted into a directional ray; The physical constraints imposed on the track are as follows: limiting the solution space to within ±2° of the track direction; truncating invalid solutions that exceed the track (such as lateral deviation); The output is compressed from an elliptical region to a linear proposal along the track.

[0016] Step 5.4: Based on the comprehensive matching degree, find the optimal estimated position within the linear candidate area; Dense sampling points (e.g., 10 points per meter) are set within the candidate track linear area to discretize the continuous track into a high-density grid, providing a basis for refined scoring. A matching score is then calculated for each sampling point: ; ; ; in: is the distance matching degree; is the actual distance measurement value; is the geometric distance from the point to the base station; is the maximum tolerable deviation of the distance; is the angle matching degree; is the AOA measurement value; is the direction angle of the point relative to the base station; is the maximum tolerable deviation of the angle; For the comprehensive rating.

[0017] The point with the highest comprehensive score is selected as the optimal estimated position.

[0018] Step 5.5: Project the obtained optimal estimated position coordinates vertically onto the track centerline for correction to obtain the fused positioning coordinates.

[0019] Project the coordinates obtained in step 5.4 vertically onto the centerline of the track, and calculate the offset distance between the projection point and the original point: if the offset is >0.1m, trigger a positioning abnormality alarm; if there is no abnormality, output the obtained fused positioning coordinates on the track.

[0020] Furthermore, the step six includes: Step 6.1: Obtain at least three consecutive positioning coordinates of the vehicle and process them through a third-order Lanczos differentiator to obtain the initial value of the instantaneous velocity at the current moment; Obtain the positioning coordinates of the target vehicle at three consecutive time points (t-2, t-1, t), process these three position points through a third-order Lanczos differentiator, and output the instantaneous initial velocity value at the current moment (the initial velocity value at this moment contains high-frequency noise).

[0021] Step 6.2: Input the instantaneous initial velocity value, IMU acceleration data, and historical velocity sequence into the adaptive Kalman filter, dynamically adjust the filter parameters, and obtain a smoothed velocity estimate; The instantaneous initial velocity value, IMU acceleration data, and historical velocity sequence are input into the adaptive Kalman filter. The filter parameters are dynamically adjusted according to the current acceleration amplitude. The high acceleration state (acceleration and deceleration process) increases the process noise tolerance, and the stable state (uniform speed process) increases the observation data weight, and outputs a smoothed velocity estimate.

[0022] Step 6.3: Perform physical constraint verification on the smoothed velocity estimate. If the verification passes, perform head and tail data verification. Otherwise, trigger the three-level exception handling mechanism. Step 6.4: Output the final velocity value and record the physical constraint verification of this velocity solution.

[0023] Furthermore, the dynamic adjustment of the filter parameters includes: when the absolute value of the acceleration exceeds a preset range, increasing the process noise tolerance; when the absolute value of the acceleration is within the preset range, that is, in a stable state, increasing the weight of the observation data.

[0024] Furthermore, the physical constraint verification of the smoothed velocity estimate includes: checking whether the angular deviation between the velocity direction and the track tangent and the acceleration change rate exceed set values.

[0025] Check the angular deviation between the velocity direction and the track tangent. If the angular deviation is >2°, it is determined to be an abnormal value. Detect the acceleration change rate. If the acceleration change rate is >0.3g / s, it is determined to be jitter interference.

[0026] Furthermore, the head-to-tail data verification includes: synchronously obtaining the speed solution results of the front and rear of the vehicle, performing a two-way data consistency check, and performing weighted fusion output if the difference between the head-to-tail speed data is less than a threshold; otherwise, starting data comparison diagnosis and outputting high-confidence data.

[0027] The difference between the head and tail speed data is less than the threshold, that is: When driving in a straight line, the speed difference between the front and rear ends must be less than or equal to 0.1m / s; When driving on a curve, the speed difference between the front and rear ends must be less than or equal to 0.2m / s; In case of emergency braking, the speed difference between the front and rear ends must be less than or equal to 0.3m / s; If they meet the requirements, weighted fusion output is performed, and different weights are set for different driving routes: Straight-line driving: the front weight is 0.8, and the rear weight is 0.2; Curving: the weight of the front of the vehicle is 0.75, and the weight of the rear of the vehicle is 0.25; Tunnel driving: the weight of the front of the vehicle is 0.7, and the weight of the rear of the vehicle is 0.3; Otherwise, start data comparison diagnosis and output high-confidence data: To compare IMU confidence, the formula is as follows: ; in: is the confidence level; is the minimum value function; is the acceleration and position differential deviation of the IMU; is the angular velocity of the IMU and the trajectory curvature deviation; If the confidence of the front data is greater than the rear data + 0.2, the front data is output; otherwise, the rear data is output.

[0028] Furthermore, the three-level exception handling mechanism includes: When the instantaneous jump of the data exceeds the set threshold, the IMU dominant mode is activated and the first-level compensation is performed, that is, velocity integral compensation is performed based on the three-axis acceleration of the IMU; When the velocity / acceleration mutation is greater than 3σ (σ is the standard deviation of the data in the previous 10 seconds), first-level compensation is performed. The three-axis acceleration of the IMU is taken, the three-axis acceleration is projected along the track, and velocity integral compensation is performed: ; in: is the speed at time t; is the speed at time t-1; is the projected acceleration of IMU; is the time difference between time t and time t-1.

[0029] When the first-level compensation fails continuously, the second-level compensation is performed, that is, extrapolation compensation is performed by judging the current movement mode based on the historical data sequence; When the first-level compensation exceeds the tolerance for two consecutive frames and the compensation is not enough to make the data jump back to the set threshold instantaneously, or the IMU hardware fails and speed compensation cannot be performed, the second-level compensation is performed. According to the acceleration of motion modes such as uniform speed, uniform acceleration, and uniform deceleration in the historical data sequence, and then according to the current motion mode matching historical data, the current speed is extrapolated and compensated.

[0030] When the secondary compensation fails or the data exceeds the safety boundary, a fault alarm will be issued.

[0031] When the deviation between the extrapolated speed and the line speed limit is greater than 15% or the angle between the acceleration direction and the track is greater than 5° (derailment risk), a fault alarm will be issued. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present drawings or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present drawings. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0033] Figure 1 It is a flow chart of the steps of the present invention.

[0034] The purpose, features and advantages of this drawing will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention. Based on the embodiments provided by the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work are within the scope of protection of the present invention.

[0036] Obviously, the drawings described below are merely examples or embodiments of the present invention. Those skilled in the art can apply the present invention to other similar scenarios based on these drawings without inventive effort. Furthermore, it is understood that while the effort involved in such a development process may be complex and lengthy, for those skilled in the art related to the disclosure of the present invention, any design, manufacturing, or production changes based on the technical content disclosed in the present invention are merely conventional technical means and should not be construed as an inadequacy of the disclosure of the present invention.

[0037] Unless otherwise specified, all embodiments and optional embodiments of the present invention can be combined with each other to form new technical solutions.

[0038] Unless otherwise specified, all technical features and optional technical features of the present invention can be combined with each other to form a new technical solution.

[0039] Unless otherwise specified, all steps of the present invention may be performed sequentially or randomly, preferably sequentially. For example, the method includes steps (a) and (b), which means that the method may include steps (a) and (b) performed sequentially, or may include steps (b) and (a) performed sequentially. For example, the method may further include step (c), which means that step (c) may be added to the method in any order, for example, the method may include steps (a), (b) and (c), or may include steps (a), (c) and (b), or may include steps (c), (a) and (b), etc.

[0040] Unless otherwise specified, the terms "include" and "comprising" used in the present invention may be open-ended or closed-ended. For example, "include" and "comprising" may mean that other components not listed may also be included or that only the listed components are included.

[0041] Unless otherwise specified, the term "or" is inclusive in this disclosure. For example, the phrase "A or B" means "A, B, or both A and B." More specifically, the condition "A or B" is satisfied if any of the following conditions are met: A is true (or exists) and B is false (or does not exist); A is false (or does not exist) and B is true (or exists); or both A and B are true (or exist).

[0042] In order to better understand the solutions of the embodiments of the present invention, some relevant terms and concepts that may be involved in the embodiments of the present invention are first introduced below.

[0043] (1) UWB (Ultra-Wideband) is a carrier-free communication technology that uses non-sinusoidal narrow pulses in the nanosecond to picosecond range to transmit data. It has the characteristics of high-precision positioning, strong anti-interference and strong multipath resolution capabilities.

[0044] (2) IMU (Inertial Measurement Unit), a sensor consisting of a three-axis accelerometer and a gyroscope, is used to measure the angular velocity and linear acceleration of an object to achieve motion trajectory estimation.

[0045] (3) Third-order Lanczos Differentiator: A numerical differentiation algorithm based on the Lanczos method, used to estimate the time derivative of the signal (such as acceleration and jerk) with high precision. In IMU data processing, it can improve the estimation accuracy of the motion state (velocity and acceleration). It is suitable for high-dynamic systems (such as drone navigation) that require low latency and high-frequency computing.

[0046] In this embodiment, Figure 1 As shown, a UWB multi-base station communication speed measurement method includes the following steps: Step 1: Deploy several UWB base stations equipped with directional antennas at preset intervals along the indoor track test line; Step 2: Install onboard computers and UWB tags at the front and rear of the train, and integrate IMU modules; Step 3: Synchronize the clocks of all UWB base stations and on-board computers, and perform two-way TOF ranging between the UWB tag and the base station; Step 4: Preprocess UWB signals and IMU data; Step 5: Select at least three base stations with the strongest signals and perform fusion positioning based on the TDOA_AOA algorithm; Step 6: Based on the continuous positioning results, a third-order Lanczos differentiator is used to calculate the instantaneous velocity and perform dynamic error compensation.

[0047] Furthermore, clock synchronization includes microsecond-level clock synchronization of all UWB base stations and on-board computers through the PTP protocol.

[0048] In this embodiment, 1588v2 clocks are used, and a PTP master clock is deployed at the master base station. Synchronization messages are broadcast via Ethernet. The onboard computer and the slave base station serve as PTP slave clocks. The hardware records the timestamps of message transmission and reception. The clock offset is compensated by calculating the two-way transmission delay to eliminate the clock drift error between base stations and ensure the TDOA ranging accuracy. Through the hardware timestamp and master-slave clock mechanism, the synchronization accuracy can be controlled to the sub-microsecond level. In high-speed train scenarios, such as at a speed of 200 km / h, a 1 microsecond time error will result in a 5.5 cm distance error. The calculation of clock deviation in two-way transmission is as follows: ; in: is the clock bias; The time when the master clock message is sent; The time of receiving the slave clock message; The time when the slave clock message is sent; The time when the master clock message is received.

[0049] Furthermore, step four includes using the SAGE algorithm to process the UWB channel impulse response and applying a fourth-order Butterworth low-pass filter to the IMU data.

[0050] The SAGE algorithm is used to process the UWB channel impulse response. It is necessary to first collect the UWB channel impulse response CIR, iteratively separate the multipath components, and then determine the direct wave. If the delay difference is greater than 10ns, the direct wave is retained, otherwise the reflected wave is discarded. In the strong reflection environment of metal tracks, it can suppress the ranging fluctuations caused by multipath interference.

[0051] A fourth-order Butterworth low-pass filter is applied to the IMU data. The cutoff frequency threshold of the fourth-order Butterworth low-pass filter is set to 10Hz~30Hz. Since the main vibration frequency of the subway track is below 30Hz, the cutoff frequency can be flexibly adjusted according to actual conditions to effectively filter out the track vibration noise.

[0052] Furthermore, the three base stations with the strongest signals are selected, and the base stations with RSSI>-80dBm and SNR>15dB need to be selected.

[0053] Select base stations with RSSI>-80dBm to exclude weak signal base stations (tunnel obstruction scenarios); select base stations with SNR>15dB to avoid high noise base stations (high voltage electromagnetic interference areas). Selection of base station data threshold: The measured data of rail trains show that when RSSI<-80dBm, the ranging error will increase sharply from 10cm to more than 50cm; when SNR<15dBm, the bit error rate will exceed , which will have a great impact on the accuracy of ranging.

[0054] Furthermore, step five includes the following steps: Step 5.1: Obtain the TOF ranging value and AOA angle measurement value of each selected base station, and obtain the position coordinates of the corresponding base station; Obtain the TOF ranging values ​​of the three best base stations after screening based on RSSI and SNR, simultaneously obtain the AOA angle measurement values ​​of these base stations (provided by directional antennas), and retrieve the precise coordinate positions of the corresponding base stations from the system database (pre-calibrated when the base stations are deployed).

[0055] Step 5.2: Use the base station with the strongest signal as the reference point to construct a TDOA hyperbola to obtain the preliminary positioning area of ​​the target tag; Taking the base station with the strongest signal as the reference point, and using the distance difference between other base stations and the reference base station (i.e., TDOA measurement results), a cluster of hyperbolas is drawn on a two-dimensional plane: each hyperbola represents a set of points that meet a specific distance difference condition. The intersection area of ​​multiple hyperbolas is the area where the target tag may exist. The intersection area of ​​multiple hyperbolas is used as the preliminary positioning area of ​​the target tag.

[0056] Step 5.3: Based on the preset direction vector of the indoor track test line, AOA angle constraint is performed to compress the preliminary positioning area into a linear candidate area along the track; Based on the preset direction vector of the track test line (e.g., 30° due north), the AOA measurement value of each base station is converted into a directional ray; The physical constraints imposed on the track are as follows: limiting the solution space to within ±2° of the track direction; truncating invalid solutions that exceed the track (such as lateral deviation); The output is compressed from an elliptical region to a linear proposal along the track.

[0057] Step 5.4: Based on the comprehensive matching degree, find the optimal estimated position within the linear candidate area; Dense sampling points (e.g., 10 points per meter) are set within the candidate track linear area to discretize the continuous track into a high-density grid, providing a basis for refined scoring. A matching score is then calculated for each sampling point: ; ; ; in: is the distance matching degree; is the actual distance measurement value; is the geometric distance from the point to the base station; is the maximum tolerable deviation of the distance; is the angle matching degree; is the AOA measurement value; is the direction angle of the point relative to the base station; is the maximum tolerable deviation of the angle; For the comprehensive rating.

[0058] The point with the highest comprehensive score is selected as the optimal estimated position.

[0059] In this embodiment, the maximum tolerance deviation of the distance is 1.5 m, and the maximum tolerance deviation of the angle is 10°.

[0060] Step 5.5: Project the obtained optimal estimated position coordinates vertically onto the track centerline for correction to obtain the fused positioning coordinates.

[0061] Project the coordinates obtained in step 5.4 vertically onto the centerline of the track, and calculate the offset distance between the projection point and the original point: if the offset is >0.1m, trigger a positioning abnormality alarm; if there is no abnormality, output the obtained fused positioning coordinates on the track.

[0062] Furthermore, step six includes: Step 6.1: Obtain at least three consecutive positioning coordinates of the vehicle and process them through a third-order Lanczos differentiator to obtain the initial value of the instantaneous velocity at the current moment; Obtain the positioning coordinates of the target vehicle at three consecutive time points (t-2, t-1, t), process these three position points through a third-order Lanczos differentiator, and output the instantaneous initial velocity value at the current moment (the initial velocity value at this moment contains high-frequency noise).

[0063] Step 6.2: Input the instantaneous initial velocity value, IMU acceleration data, and historical velocity sequence into the adaptive Kalman filter, dynamically adjust the filter parameters, and obtain a smoothed velocity estimate; The instantaneous initial velocity value, IMU acceleration data, and historical velocity sequence are input into the adaptive Kalman filter. The filter parameters are dynamically adjusted according to the current acceleration amplitude. The high acceleration state (acceleration and deceleration process) increases the process noise tolerance, and the stable state (uniform speed process) increases the observation data weight, and outputs a smoothed velocity estimate.

[0064] Step 6.3: Perform physical constraint verification on the smoothed velocity estimate. If the verification passes, perform head and tail data verification. Otherwise, trigger the three-level exception handling mechanism. Step 6.4: Output the final velocity value and record the physical constraint verification of this velocity solution.

[0065] Furthermore, the filter parameters are dynamically adjusted, including: when the absolute value of acceleration exceeds a preset range, the process noise tolerance is increased; when the absolute value of acceleration is within the preset range, that is, in a stable state, the observation data weight is increased.

[0066] Furthermore, the physical constraint verification is performed on the smoothed velocity estimate, including checking whether the angular deviation between the velocity direction and the track tangent and the acceleration change rate exceed the set value.

[0067] Check the angular deviation between the velocity direction and the track tangent. If the angular deviation is >2°, it is determined to be an abnormal value. Detect the acceleration change rate. If the acceleration change rate is >0.3g / s, it is determined to be jitter interference.

[0068] Furthermore, the head-to-tail data verification includes: synchronously obtaining the speed solution results of the front and rear of the vehicle, performing a two-way data consistency check, and performing weighted fusion output if the difference between the head-to-tail speed data is less than a threshold; otherwise, starting data comparison diagnosis and outputting high-confidence data.

[0069] The difference between the head and tail speed data is less than the threshold, that is: When driving in a straight line, the speed difference between the front and rear ends must be less than or equal to 0.1m / s; When driving on a curve, the speed difference between the front and rear ends must be less than or equal to 0.2m / s; In case of emergency braking, the speed difference between the front and rear ends must be less than or equal to 0.3m / s; If they meet the requirements, weighted fusion output is performed, and different weights are set for different driving routes: Straight-line driving: the front weight is 0.8, and the rear weight is 0.2; Curving: the weight of the front of the vehicle is 0.75, and the weight of the rear of the vehicle is 0.25; Tunnel driving: the weight of the front of the vehicle is 0.7, and the weight of the rear of the vehicle is 0.3; Otherwise, start data comparison diagnosis and output high-confidence data: To compare IMU confidence, the formula is as follows: ; in: is the confidence level; is the minimum value function; is the acceleration and position differential deviation of the IMU; is the angular velocity of the IMU and the trajectory curvature deviation; If the confidence of the front data is greater than the rear data + 0.2, the front data is output; otherwise, the rear data is output.

[0070] Furthermore, the three-level exception handling mechanism includes: When the instantaneous jump of the data exceeds the set threshold, the IMU dominant mode is activated and the first-level compensation is performed, that is, velocity integral compensation is performed based on the three-axis acceleration of the IMU; When the velocity / acceleration mutation is greater than 3σ (σ is the standard deviation of the data in the previous 10 seconds), first-level compensation is performed. The three-axis acceleration of the IMU is taken, the three-axis acceleration is projected along the track, and velocity integral compensation is performed: ; in: is the speed at time t; is the speed at time t-1; is the projected acceleration of IMU; is the time difference between time t and time t-1.

[0071] When the first-level compensation fails continuously, the second-level compensation is performed, that is, extrapolation compensation is performed by judging the current movement mode based on the historical data sequence; When the first-level compensation exceeds the tolerance for two consecutive frames and the compensation is not enough to make the data jump back to the set threshold instantaneously, or the IMU hardware fails and speed compensation cannot be performed, the second-level compensation is performed. According to the acceleration of motion modes such as uniform speed, uniform acceleration, and uniform deceleration in the historical data sequence, and then according to the current motion mode matching historical data, the current speed is extrapolated and compensated.

[0072] When the secondary compensation fails or the data exceeds the safety boundary, a fault alarm will be issued.

[0073] When the deviation between the extrapolated speed and the line speed limit is greater than 15% or the angle between the acceleration direction and the track is greater than 5° (derailment risk), a fault alarm will be issued.

[0074] It should be noted that the present invention is not limited to the above-mentioned embodiments. The above-mentioned embodiments are merely examples, and any embodiments having substantially the same structure and effect as the technical concept within the scope of the technical solution of the present invention are all included in the technical scope of the present invention. In addition, without departing from the scope of the present invention, other embodiments that can be conceived by those skilled in the art and that combine some of the constituent elements in the embodiments are also included in the scope of the present invention.

Claims

1. A UWB multi-base station communication speed measurement method, characterized in that: The following steps are involved: Step 1: Deploy several UWB base stations equipped with directional antennas at preset intervals along the indoor track test line; Step 2: Install onboard computers and UWB tags at the front and rear of the train, and integrate IMU modules; Step 3: Synchronize the clocks of all UWB base stations and on-board computers, and perform two-way TOF ranging between the UWB tag and the base station; Step 4: Preprocess UWB signals and IMU data; Step 5: Select at least three base stations with the strongest signals and perform fusion positioning based on the TDOA_AOA algorithm; Step 6: Based on the continuous positioning results, a third-order Lanczos differentiator is used to calculate the instantaneous velocity and perform dynamic error compensation.

2. A UWB multi-base station communication speed measurement method according to claim 1, characterized in that: The clock synchronization includes performing microsecond-level clock synchronization on all UWB base stations and on-board computers through the PTP protocol.

3. A UWB multi-base station communication speed measurement method according to claim 1, characterized in that: The fourth step includes using the SAGE algorithm to process the UWB channel impulse response and applying a fourth-order Butterworth low-pass filter to the IMU data.

4. A UWB multi-base station communication speed measurement method according to claim 1, characterized in that: The three base stations with the strongest signals are selected to have RSSI>-80dBm and SNR>15dB.

5. The UWB multi-base station communication speed measurement method according to claim 1, characterized in that: The step five comprises the following steps: Step 5.1: Obtain the TOF ranging value and AOA angle measurement value of each selected base station, and obtain the position coordinates of the corresponding base station; Step 5.2: Use the base station with the strongest signal as the reference point to construct a TDOA hyperbola to obtain the preliminary positioning area of ​​the target tag; Step 5.3: Based on the preset direction vector of the indoor track test line, AOA angle constraint is performed to compress the preliminary positioning area into a linear candidate area along the track; Step 5.4: Based on the comprehensive matching degree, find the optimal estimated position within the linear candidate area; Step 5.5: Project the obtained optimal estimated position coordinates vertically onto the track centerline for correction to obtain the fused positioning coordinates.

6. A UWB multi-base station communication speed measurement method according to claim 1, characterized in that: The step six comprises: Step 6.1: Obtain at least three consecutive positioning coordinates of the vehicle and process them through a third-order Lanczos differentiator to obtain the initial value of the instantaneous velocity at the current moment; Step 6.2: Input the instantaneous initial velocity value, IMU acceleration data, and historical velocity sequence into the adaptive Kalman filter, dynamically adjust the filter parameters, and obtain a smoothed velocity estimate; Step 6.3: Perform physical constraint verification on the smoothed velocity estimate. If the verification passes, perform head and tail data verification. Otherwise, trigger the three-level exception handling mechanism. Step 6.4: Output the final velocity value and record the physical constraint verification of this velocity solution.

7. A UWB multi-base station communication speed measurement method according to claim 6, characterized in that: The dynamic adjustment of the filter parameters includes: when the absolute value of the acceleration exceeds the preset range, increasing the process noise tolerance; when the absolute value of the acceleration is within the preset range, that is, in a stable state, increasing the observation data weight.

8. A UWB multi-base station communication speed measurement method according to claim 6, characterized in that: The physical constraint verification of the smoothed velocity estimate includes: checking whether the angular deviation between the velocity direction and the track tangent and the acceleration change rate exceed set values.

9. A UWB multi-base station communication speed measurement method according to claim 6, characterized in that: The head-to-tail data verification includes: synchronously obtaining the speed solution results of the front and rear of the vehicle, performing a two-way data consistency check, and performing weighted fusion output if the difference between the head-to-tail speed data is less than a threshold; otherwise, starting data comparison diagnosis and outputting high-confidence data.

10. A UWB multi-base station communication speed measurement method according to claim 6, characterized in that: The three-level exception handling mechanism includes: When the instantaneous jump of the data exceeds the set threshold, the IMU dominant mode is activated and the first-level compensation is performed, that is, velocity integral compensation is performed based on the three-axis acceleration of the IMU; When the first-level compensation fails continuously, the second-level compensation is performed, that is, extrapolation compensation is performed by judging the current movement mode based on the historical data sequence; When the secondary compensation fails or the data exceeds the safety boundary, a fault alarm will be issued.

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