A method for error analysis of round trip time based 5g positioning

Abstract

This invention relates to an error analysis method for 5G positioning based on round-trip time (RTT), comprising: introducing non-ideal factors from the actual physical environment into the geometric distance observation equation; obtaining the Rx-Tx time difference between the Transmission Receiver Point (TRP) and the User Equipment (UE) during the positioning process; calculating the Time of Arrival (TOA) based on the Rx-Tx time difference; analyzing the RTT positioning error based on the TOA; establishing a time error model for the timing error in the error analysis; and obtaining the timing error distribution after elimination by the Timing Error Group (TEG). This invention improves the accuracy and reliability of 5G positioning technology by reducing time synchronization errors, decreasing dependence on time synchronization, enhancing system stability, and providing positioning solutions suitable for various scenarios.

Description

Technical Field

[0001] This invention relates to the field of high-precision positioning technology in 5G communication technology, and in particular to an error analysis method for 5G positioning based on round-trip time. Background Technology

[0002] 5G communication technology, with its high bandwidth, low latency, and wide coverage, has shown great potential in the field of high-precision positioning. During the development of 5G positioning standards, 3GPP (3rd Generation Partnership Project) proposed various positioning technologies and performance requirements, including positioning methods based on RTT (Round-Trip Time) and TDOA (Time Difference of Arrival). These technologies aim to meet the high-precision positioning needs in different scenarios, such as intelligent transportation, industrial automation, and the Internet of Things (IoT).

[0003] Although the RTT positioning method theoretically has the advantages of high accuracy and real-time performance, it still faces many challenges in practical applications: Clock synchronization error: RTT measurement relies on time synchronization between the base station and the user equipment (UE). However, hardware latency and clock drift can cause measurement errors, affecting positioning accuracy.

[0004] Multipath and NLOS (Non-Line-of-Sight) Errors: In complex environments, signals may reach the receiver via multiple paths, leading to multipath errors. Furthermore, NLOS propagation further increases positioning errors.

[0005] Impact of Signal-to-Noise Ratio (SNR): Low SNR environments reduce the accuracy of RTT measurements, which in turn affects positioning accuracy.

[0006] To address the above problems, existing research has proposed some improved methods: Error correction algorithms: By introducing error correction algorithms, such as Kalman filters and extended Kalman filters (EKF), the effects of clock errors and multipath errors can be reduced.

[0007] Multi-system integration: Integrating 5G with other positioning systems such as GNSS (Global Navigation Satellite System) to improve the robustness and adaptability of the positioning system.

[0008] Hardware optimization: Reduce hardware latency and clock drift by improving the hardware design of base stations and UEs.

[0009] Although the above-mentioned improvement methods have improved the accuracy of RTT positioning to some extent, they still have the following shortcomings: Inadequate error modeling: Existing research does not provide accurate error modeling for RTT positioning, making it difficult to fully reflect the error distribution in the actual environment.

[0010] Poor adaptability to complex environments: In highly dynamic environments and complex terrains (such as urban canyons), the performance of RTT positioning remains unstable.

[0011] Cost and efficiency issues: Some improvement methods require additional hardware support or complex algorithm implementation, which increases system cost and computational complexity.

[0012] In summary, while existing 5G RTT positioning technology has made some progress in high-precision positioning, it still faces challenges such as clock synchronization errors, multipath errors, and adaptability to complex environments. These issues limit the widespread adoption of RTT positioning technology in practical applications, thus requiring further research and improvement. Summary of the Invention

[0013] The purpose of this invention is to provide an error analysis method for 5G positioning based on round-trip time. Through theoretical analysis and simulation verification, this invention delves into the error characteristics of 5G RTT positioning and establishes a corresponding error model to improve positioning accuracy and system stability in 5G networks.

[0014] To achieve the above objectives, the present invention provides the following solution: An error analysis method for 5G positioning based on round-trip time, comprising: By introducing non-ideal factors from the actual physical environment into the geometric distance observation equation, the Rx-Tx time difference obtained by the transmission and reception point (TRP) and the user equipment (UE) during the positioning process is obtained. Calculate the arrival time TOA based on the Rx-Tx time difference; The RTT positioning error is analyzed based on the Time of Arrival (TOA). A time error model is established for timing errors in error analysis, and the distribution of timing errors after elimination by the timing error group TEG is obtained.

[0015] Optionally, the Rx-Tx time difference obtained by the transmission receiving point (TRP) includes: ; in, This represents the time difference Rx-Tx obtained by the transmit / receive point TRP. and These represent the transmission delay incurred by the TRP when transmitting the PRS signal through the radio frequency link, and the transmission delay incurred when receiving the SRS signal, respectively. , This indicates the time-of-flight of the signal between the TRP's antenna and the UE's antenna, and the time-of-flight of the signal between the UE's antenna and the TRP's antenna. and This represents the reception and transmission delay from the antenna to the baseband when the UE receives the PRS signal from the TRP and transmits the SRS signal through the radio frequency link. This indicates the measurement error of TRP. This indicates other errors present in the channel. This indicates the internal processing delay that occurs between the received signal and the transmitted signal in the UE.

[0016] Optionally, the Rx-Tx time difference obtained by the user equipment (UE) includes: ; in, This represents the Rx-Tx time difference obtained by the user equipment (UE). This indicates the measurement error of the UE.

[0017] Optionally, calculating the Time of Arrival (TOA) includes: ; in, This indicates measurement error.

[0018] Optionally, establishing a time error model for timing errors in error analysis includes: Based on the disturbances of temperature, signal frequency, and amplitude on the propagation delay of radio frequency components, the delay effect of each radio frequency component on the signal is obtained as independent and identically distributed. According to the central limit theorem, the sum of n random variables approximately follows a normal distribution. By pre-setting the mean of the standard deviation, the timing error distribution is obtained, and a time error model is established.

[0019] Optionally, obtaining the timing error satisfying distribution after elimination by the timing error group TEG includes: setting the TEG timing adjustment to the mean of several timing error samples under the timing error distribution, and obtaining the timing error satisfying distribution after elimination by the timing error group TEG.

[0020] The beneficial effects of this invention are: 1. Improved positioning accuracy: (1) High-precision positioning: The Multi-RTT method locates users by measuring the signal transmission time between multiple base stations and the mobile terminal. Compared with the traditional DL-TDOA method, the Multi-RTT method adopted in this invention utilizes a two-way ranging mechanism to effectively offset the clock deviation between the transmitting and receiving parties, fundamentally avoiding the systematic errors introduced by the asynchronous clocks between base stations in the traditional method. Through this improvement, the main error sources in the positioning process are significantly reduced, resulting in a substantial reduction in the root mean square error of positioning. Its root mean square error is only 5% of that of DL-TDOA, thus ensuring extremely high accuracy without adding additional timing error compensation, and enabling high-precision positioning services.

[0021] (2) Error modeling and simulation optimization: Various errors, such as timing errors, in 5G positioning are analyzed and modeled. The error model is added to the positioning method simulation model. Through simulation testing and analysis, the impact of different errors is compared, providing a theoretical basis and data support for optimizing the positioning algorithm, which helps to further improve positioning accuracy.

[0022] 2. Enhanced error analysis and processing: (1) Comprehensive analysis of error factors: In-depth analysis of various error factors including timing error, measurement error, multipath error, NLOS error, Gaussian white noise, etc. of base station and UE, clarifying the specific impact of various errors on positioning accuracy, and providing a basis for targeted error handling measures.

[0023] (2) Timing error modeling and correction: Based on the central limit theorem, the timing error is modeled and it is concluded that the timing error approximately follows a normal distribution. The timing error group (TEG) is introduced to eliminate the influence of the timing error of UE / gNB transmission and reception, which effectively improves the reliability of the positioning results.

[0024] 3. Signal-to-noise ratio impact assessment: Quantifying the impact of signal-to-noise ratio (SNR) on positioning accuracy: By changing the SNR and conducting simulation analysis, the changes in positioning accuracy under different SNR conditions were clarified. Without adding timing errors, increasing the SNR can significantly improve positioning accuracy. For example, increasing the SNR from 15dB to 35dB can improve the average positioning accuracy by 46.5%, providing a reference for adjusting system parameters based on SNR in practical applications.

[0025] The weakening effect of timing error on signal-to-noise ratio (SNR): As timing error increases, the improvement effect of SNR on positioning accuracy gradually weakens. When the standard deviation of timing error is large, the effect of improving SNR is very weak. This finding helps to reasonably balance factors such as SNR and timing error in practical applications and formulate more effective optimization strategies.

[0026] 4. Distance Impact Assessment: The impact of distance variation on positioning accuracy: The study investigated the impact of the average distance from the base station to the user on positioning accuracy. It was found that without adding timing error, an increase in distance leads to an increase in the root mean square error of ranging and positioning, and a significant decrease in average positioning accuracy. However, with the addition of timing error, the impact of distance variation on positioning accuracy is relatively small, while the impact of timing error is more significant. This provides a basis for selecting appropriate positioning methods and parameters in different distance scenarios.

[0027] 5. Practical application value: High-precision positioning service support: It provides an effective technical means for high-precision positioning applications in 5G networks, such as intelligent transportation, logistics tracking, and indoor navigation, which can meet the application scenarios with high positioning accuracy requirements and improve user experience and service quality.

[0028] Enhanced adaptability to complex environments: By analyzing and modeling various error factors and considering complex situations in the actual environment in the simulation, the positioning method has better adaptability and robustness when facing complex environments such as non-line-of-sight propagation and multipath interference, and can perform user positioning more accurately.

[0029] System performance optimization guidance: It provides theoretical guidance and data support for the performance optimization of 5G positioning systems, helping network operators and equipment manufacturers to better understand and evaluate the performance of different positioning methods, thereby making more reasonable decisions in system planning, parameter configuration and optimization adjustment, and improving the performance and efficiency of the entire positioning system. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a schematic diagram illustrating the RTT positioning principle of an embodiment of the present invention; Figure 2 This is a schematic diagram of Multi-RTT positioning according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the timing error of Multi-RTT according to an embodiment of the present invention; Figure 4 The following are the delay characteristic curves of the filter in satellite positioning according to an embodiment of the present invention, wherein (a) is the amplitude frequency response curve and (b) is the group delay curve; Figure 5 This is a schematic diagram of positioning without adding errors in Multi-RTT according to an embodiment of the present invention; Figure 6 The following is a comparison chart of positioning accuracy without external error injection in an embodiment of the present invention, wherein (a) is a comparison of ranging RMSE of DL-TDOA and Multi-RTT methods, and (b) is a comparison of positioning RMSE of DL-TDOA and Multi-RTT methods. Figure 7This is a comparison chart of the ranging RMSE of the DL-TDOA and Multi-RTT methods in this invention, with added timing errors of 0.5%, 1%, and 5% of the mean timing error standard deviation. Figure 8 This is a comparison chart of the localization RMSE of the DL-TDOA and Multi-RTT methods in this invention, with added timing errors of 0.5%, 1%, and 5% of the mean timing error standard deviation. Figure 9 This is a flowchart of an error analysis method for 5G positioning based on round-trip time, according to an embodiment of the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0034] In 5G positioning technology based on Time Difference of Arrival (TDOA), timing synchronization errors can affect positioning accuracy. In contrast, high-precision 5G positioning methods based on Round-Trip Time (RTT) synchronization can effectively mitigate these errors. This embodiment analyzes and models timing errors in 5G positioning and evaluates their impact on accuracy using simulations of Multi-RTT (Multiple Round-Trip Time) and TDOA (Time Difference of Arrival) methods. Various scenarios are studied to assess the impact of timing errors, signal-to-noise ratio (SNR), and the distance between the base station (BS) and user equipment (UE) on positioning accuracy. Theoretical analysis and experimental results show that the accuracy of the Multi-RTT method is significantly higher than that of DL-TDOA (Downlink Time Difference of Arrival).

[0035] like Figure 9 As shown, this embodiment provides an error analysis method for 5G positioning based on round-trip time, including: By introducing non-ideal factors from the actual physical environment into the geometric distance observation equation, the Rx-Tx time difference obtained by the transmission and reception point (TRP) and the user equipment (UE) during the positioning process is obtained. Calculate the arrival time TOA based on the Rx-Tx time difference; The RTT positioning error is analyzed based on the Time of Arrival (TOA). A time error model is established for timing errors in error analysis, and the distribution of timing errors after elimination by the timing error group TEG is obtained.

[0036] Furthermore, the Rx-Tx time difference obtained by the transmission receiving point (TRP) includes: ; in, This represents the time difference Rx-Tx obtained by the transmit / receive point TRP. and These represent the transmission delay incurred by the TRP when transmitting the PRS signal through the radio frequency link, and the transmission delay incurred when receiving the SRS signal, respectively. , This indicates the time-of-flight of the signal between the TRP's antenna and the UE's antenna, and the time-of-flight of the signal between the UE's antenna and the TRP's antenna. and This represents the reception and transmission delay from the antenna to the baseband when the UE receives the PRS signal from the TRP and transmits the SRS signal through the radio frequency link. This indicates the measurement error of TRP. This indicates other errors present in the channel. This indicates the internal processing delay that occurs between the received signal and the transmitted signal in the UE.

[0037] Furthermore, the Rx-Tx time difference obtained by the user equipment (UE) includes: ; in, This represents the Rx-Tx time difference obtained by the user equipment (UE). This indicates the measurement error of the UE.

[0038] Furthermore, calculating the Time of Arrival (TOA) includes: ; in, This indicates measurement error.

[0039] Furthermore, establishing a time error model for timing errors in error analysis includes: Based on the disturbances of temperature, signal frequency, and amplitude on the propagation delay of radio frequency components, the delay effect of each radio frequency component on the signal is obtained as independent and identically distributed. According to the central limit theorem, the sum of n random variables approximately follows a normal distribution. By pre-setting the mean of the standard deviation, the timing error distribution is obtained, and a time error model is established.

[0040] Specifically, error analysis involves a detailed analysis of various errors in RTT positioning, including base station time error, UE time error, measurement error, multipath error, NLOS error, and Gaussian white noise. These errors affect positioning accuracy and need to be considered in the model.

[0041] Timing error modeling: Based on the 3GPP protocol, a timing error model is established, approximating the timing error as a Gaussian distribution. Specific steps include: Analyze the propagation delay of each part in the RF circuit to determine its constancy and group delay characteristics.

[0042] Considering the effects of factors such as temperature, signal frequency, and amplitude on propagation delay, it is assumed that the delay introduced by a single RF component is independent and identically distributed.

[0043] According to the central limit theorem, the delays of multiple RF components can be approximated as a normal distribution.

[0044] The mean and standard deviation of the timing error were determined using laboratory data, and a timing error model was introduced into the simulation.

[0045] Furthermore, obtaining the timing error satisfying distribution after elimination by the timing error group TEG includes: setting the TEG timing adjustment to the mean of several timing error samples under the timing error distribution, and obtaining the timing error satisfying distribution after elimination by the timing error group TEG.

[0046] The method of this embodiment will be further described below: 1. RTT positioning principle and model construction: The localization model constructed in this step is based on the geometric distance observation equation. Specifically, the model is expressed as: ,in For user equipment and the The distance between base stations At the speed of light, The round-trip time was measured. This was achieved by constructing a system containing unknown user coordinates. The nonlinear equations are used to solve for the user's location using the least squares method. The specific steps are as follows: Measuring round-trip time: RTT technology is used to measure the round-trip time of a signal between the Transmitting and Receiving Point (TRP) and the User Equipment (UE), and the transmission time is converted into distance. Specifically, the distance between the UE and the TRP is determined by measuring the time difference between the Positioning Reference Signal (PRS) received by the UE and the Sounding Reference Signal (SRS-Pos) transmitted by the UE, and the time difference between the SRS-Pos received by the TRP and the Downlink PRS (DL-PRS) transmitted by the TRP.

[0047] Determining User Location: By measuring the distances between multiple base stations (BSs) and the mobile terminal, the user's coordinates are calculated using the least squares (LS) method, thus determining the user's location. Specific steps include: Measure the positions of N TRPs and their distances from the UE.

[0048] Establish a system of equations to represent the distance relationship between each TRP and the UE.

[0049] The system of equations is converted into matrix form, and the coordinates of the UE are solved using the LS method.

[0050] Specifically, RTT technology is used to measure the round-trip time of a signal and convert it into distance to determine the user's location. By using multiple RTT measurements and combining them with the least squares (LS) method to solve for the user's coordinates, time synchronization issues between base stations or between a base station and user equipment are avoided.

[0051] Multi-RTT positioning technology uses an RTT-based time synchronization method to measure the signal transmission time between the Transmitter and Receiver Point (TRP) and the UE, and converts the transmission time into distance. By measuring the distances between multiple BSs and the mobile terminal, the user's location can be determined. The Multi-RTT positioning method employs two types of measurements: UE Rx-Tx time difference, which is the time difference between the arrival of the Positioning Reference Signal (PRS) transmitted by the UE and the transmission of the Detection Reference Signal (SRS-Pos) used for positioning from the UE to the TRP; and BS Rx-Tx time difference, which is the transmission time of the SRS-Pos measured by the TRP from the UE to the downlink PRS (DL-PRS) from the TRP. These principles are as follows: Figure 1 As shown.

[0052] Among them, UE Rx-Tx time difference Represented as: (1); gNB Rx-Tx time difference (base station reception and transmission time difference) Represented as: (2); c represents the speed of light, t UE,Tx The time when the user equipment (UE) sends the SRS signal, t UE,Rx t is the time when the user equipment (UE) receives the PRS signal. TRP,Rx t is the time when the Transmitter Receiver Point (TRP) receives the SRS. TPR,Tx The time when the Transmission Receiver Point (TRP) sends the PRS.

[0053] The distance r between the UE and the TRP is expressed as: (3); When the location information of N TRPs and the distance between the TRPs and the UE are obtained At that time, the UE's position should be located in a circle centered on these N TRPs. The intersection of radii. The basic principle of Multi-RTT in a two-dimensional plane is as follows: Figure 2 As shown.

[0054] The final coordinates of the UE are solved using the least squares method. Taking a two-dimensional plane as an example, let the position coordinates of the TRP be respectively... The location coordinates of the user equipment are The distance between each TRP and the UE is The formula can be obtained as follows: (4); Solve for the position coordinates of the UE in each formula: (5); Solve the matrix: (6); The solution can be obtained using the least squares method: (7); (8); Where X is a vector containing UE location information, and Y is the vector based on the ranging result. A is a constant vector calculated from the known TRP coordinates, and A is a matrix composed of all known TRP coordinates.

[0055] Since the timestamp coefficients of the UE and TRP are one positive and one negative when solving the RTT time, time synchronization between base stations and between base stations and users is not required, effectively solving the problem of difficult clock synchronization.

[0056] 2. Error Analysis and Modeling: This paper analyzes various errors in RTT positioning, including timing errors, measurement errors, multipath errors, and NLOS errors. Multi-RTT positioning methods rely on hardware timestamps to obtain accurate propagation times. 3GPP specifies that the timestamp reference point should be located at the antenna connector. However, in practice, the earliest node triggering the timestamp is at the baseband processor, causing propagation delay. 3GPP defines this propagation delay as a timing error. 3GPP Release 17 standardizes a scheme based on measurement reporting enhancements to eliminate the impact of user and base station transceiver timing errors, defining a Timing Error Group (TEG) to eliminate the impact of UE / gNB transceiver timing errors.

[0057] 2.1 Error Analysis: Based on the geometric distance observation equation constructed in step 1, this step further introduces non-ideal factors from the actual physical environment. Specifically, the geometric distance observation equation is expressed as: ,in For user equipment and the The distance between base stations At the speed of light, The round-trip time is the measured value. A practical observation model incorporating error terms is established by superimposing hardware delay and environmental noise terms into the ideal observation equation. Figure 3 The detailed process of the Multi-RTT positioning method in information transmission is shown, and the Rx-Tx time difference obtained by TRP during the positioning process can be obtained as follows: (9); The Rx-Tx time difference obtained by the UE is as follows: (10); in: and These represent the transmission delay incurred by the TRP when transmitting the PRS signal through the RF link, and the transmission delay incurred when receiving the SRS signal, respectively. This delay occurs between the baseband and the antenna. and This represents the time of flight of the signal as it travels between the TRP's antenna and the UE's antenna, which is the time of arrival that is expected to be obtained precisely during the positioning process. and This indicates the reception and transmission delay from the antenna to the baseband when the UE receives the PRS signal from the TRP and transmits the SRS signal through the radio frequency link. and These represent the measurement errors of UE and TRP, respectively. This indicates other errors present in the channel, including multipath error, NLOS error, and Gaussian white noise. After the TRP and UE each measure the Rx-Tx time difference, the TRP and UE report the data to the Location Management Function (LMF). The LMF calculates the TOA as follows: (11); The TOA calculated by LMF based on the reported time difference is actually the observed TOA. By comparing this observed TOA with the geometrically theoretical TOA calculated based on the known coordinates of the base station and the estimated position of the UE, the residual between the two reflects the various errors present in the system.

[0058] Based on the above discussion, the errors existing in Multi-RTT positioning include: timing error of the base station, timing error of the UE, measurement error, and other errors including multipath error, NLOS error, Gaussian white noise, etc.

[0059] 2.2 Time Error Modeling: Based on the comprehensive analysis of Multi-RTT positioning errors in step 2.1, a detailed model of the key timing error is constructed. The constructed model is a Gaussian distribution model, which assumes that the superposition of delays introduced by multiple RF components conforms to the central limit theorem, and the timing error after TEG elimination satisfies the distribution. .

[0060] Multi-RTT positioning methods rely on hardware timestamps to obtain accurate propagation times. 3GPP specifies that the timestamp reference point should be located at the antenna connector. However, in practice, the earliest node to trigger the timestamp is at the baseband processor, causing propagation delay. 3GPP defines this propagation delay as timing error.

[0061] The propagation delay of each part in the radio frequency circuit is basically constant. Analogous to the positioning radio frequency link in a communication network, the group delay characteristic of the filter in a satellite positioning radio frequency link is approximately as follows: Figure 4 As shown in (a)-(b), its group delay characteristic is that the group delay at the center frequency point is almost constant. Considering that the carrier frequency is high in 5G positioning, the subcarrier spacing of PRS and SRS signals fluctuates very little compared to the carrier frequency, so the propagation delay of the filter can be approximated as a fixed parameter.

[0062] Considering that factors such as temperature, signal frequency, and amplitude will cause certain disturbances to the propagation delay of radio frequency components, we assume that the delay effect of each radio frequency component on the signal is independent and identically distributed, satisfying: (12); By the central limit theorem, for n independent and identically distributed random variables... When n is sufficiently large, the sum of n random variables approximately follows a normal distribution. The Central Limit Theorem states that n ≥ 30 is sufficiently large. According to... Figure 4 The RF link shown requires the signal to pass through 8-9 RF components per RF link. Therefore, in one RTT positioning process, the signal passes through at least 32 RF components, satisfying the central limit theorem condition. According to existing research, the average timing error generated by the SRS signal in Tx is 129.7 ns. SRS Rx, PRS Rx, and PRS Tx exhibit approximately the same time delay as SRS Tx. Here, the standard deviation is written as 1% of the mean. Therefore, the time error model established in the error analysis is: (13); 3GPP Release 17 standardized a scheme based on measurement reporting enhancement to eliminate the impact of user and base station transmission and reception timing errors, defining a Timing Error Group (TEG) to eliminate the impact of UE / gNB transmission and reception timing errors. The simplified setting of TEG timing adjustment is the mean of 10,000 timing error samples under a normal distribution, as shown in formula (13). The timing error after TEG elimination satisfies the following distribution: (14); Formula (14) is the timing error model after TEG elimination. The residual error in a single measurement after TEG elimination follows the distribution: (15); in, This represents the original timing error obtained from the i-th measurement.

[0063] In practical applications or simulations, specific error values ​​can be obtained by randomly sampling from this distribution to simulate the actual timing error after TEG elimination.

[0064] 3. Time-based simulation analysis of 5G positioning errors: To verify the accuracy of the above theoretical derivation, the timing error Gaussian model established by Equation 14 in step 2 was applied to a simulation environment. By injecting random error samples conforming to the distribution of this model into the ideal measurement data, a real 5G signal transmission environment was simulated, thereby evaluating the positioning performance under different error conditions.

[0065] 3.1 Error evaluation performance indicators: Root Mean Square Error (RMSE) measures the deviation between observed and true values, evaluating how well a model fits a given dataset. In positioning, RMSE characterizes the root mean square error between the user's actual and estimated location, expressed as: (16); in, Indicates the actual location of the UE. This indicates the estimated location of the UE.

[0066] The root mean square error of distance measurement is used to characterize the root mean square error between the measured distance and the actual distance in a distance measurement system. Its expression is: (17); in, This indicates the actual distance between the UE and the base station. This represents the estimated distance between the UE and the base station.

[0067] 3.2 Simulation Scenarios and Parameter Settings: The performance of Multi-RTT positioning results was compared under two conditions: no error and timing error. The positioning results were analyzed using ranging RMSE and positioning RMSE. Simulation parameters are shown in Table 1. Positioning parameters will not be repeated below unless otherwise specified.

[0068] Table 1 Without adding timing errors (Gaussian white noise exists in the channel), after 1000 repetitions and data statistics, the ranging RMSE of the Multi-RTT positioning method is 0.7181m, and the positioning RMSE is 0.6612m. Figure 5 This is a schematic diagram of one of the positioning operations, and the positioning estimation error for this operation is 0.3573m.

[0069] Based on the simulation in the first step, a timing error model was added. Under the same channel parameters as the simulation above, the standard deviation of the timing error was set to 0.5%, 1%, and 5% of the mean, respectively, to explore the impact of timing error on Multi-RTT. After 1000 repeated simulations and data statistics, the positioning accuracy of Multi-RTT under timing errors with different standard deviations is shown in Table 2.

[0070] Table 2 Table 2 shows that both the ranging RMSE and the positioning RMSE increase with the increase of the standard deviation. Based on the conclusion in section 2.2 that timing errors accumulate continuously during the Multi-RTT positioning process, the experimental results are in line with expectations.

[0071] Using DL-TDOA as a control group, the positioning accuracy of DL-TODA and Multi-RTT under various conditions without adding timing errors is compared in Table 3.

[0072] Table 3 Figure 6 A comparison chart showing the positioning accuracy without external error injection, where... Figure 6 (a) Comparison of ranging RMSE between DL-TDOA and Multi-RTT methods. Figure 6 (b) Comparison of positioning RMSE between DL-TDOA and Multi-RTT methods. Without adding timing errors, the positioning accuracy of Multi-RTT is significantly higher than that of DL-TDOA because the Multi-RTT positioning method is not affected by time synchronization errors.

[0073] 3.3. Timing Error Impact Assessment: Table 4 shows a comparison of positioning accuracy with and without timing errors. With timing errors added, Multi-RTT's positioning accuracy is significantly higher than DL-TDOA's. This is because DL-TDOA is still affected by time synchronization errors, while Multi-RTT is not affected by clock bias. When adding timing errors... When timing errors are present, their impact on the accuracy of both positioning methods is relatively small compared to when there are no timing errors. As the standard deviation of the timing error increases, both the ranging RMSE and positioning RMSE of the two positioning methods gradually increase, and they always maintain a large difference.

[0074] Table 4 Figure 7 A comparison of the ranging RMSE of DL-TDOA and Multi-RTT methods under timing errors with a standard deviation of 0.5%, 1%, and 5% of the mean timing error. Figure 8 Comparison of positioning RMSE between DL-TDOA and Multi-RTT methods under timing errors with standard deviations of 0.5%, 1%, and 5% of the mean timing error.

[0075] Parameter impact analysis: By changing the signal-to-noise ratio and the average distance from the base station to the user, the impact of different channel conditions on positioning accuracy is studied.

[0076] 3.4. Signal-to-noise ratio: Without changing other parameters, the signal-to-noise ratio was set to 15dB, 25dB, and 35dB respectively, and the simulation was repeated 1000 times and the data was statistically analyzed.

[0077] Table 5 shows the positioning accuracy under different signal-to-noise ratios and timing errors. In the case of timing errors, improving the signal-to-noise ratio (SNR) significantly improves positioning accuracy. With an SNR of 25 dB, the average positioning accuracy is improved by approximately 13.5% compared to an SNR of 15 dB, and with an SNR of 35 dB, the average positioning accuracy is improved by approximately 21.4% compared to an SNR of 15 dB.

[0078] Table 5 Different signal-to-noise ratios, The positioning accuracy under timing error is shown in Table 6. In the case of timing errors, increasing the signal-to-noise ratio can improve positioning accuracy, but the improvement is very slight. A comparison of timing errors shows that as the added timing error increases, the enhancement of the signal-to-noise ratio has almost no effect on improving positioning accuracy.

[0079] Table 6 3.5. Average distance from base station to user: Without changing other parameters, the average distance from the base station to the user was set to 50m, 100m, and 150m respectively, and the simulation was repeated 1000 times and the data was statistically analyzed.

[0080] Table 7 below shows the simulation results of positioning accuracy at different distances without adding timing errors: Table 7 Increasing the distance without adding timing error increases both the ranging RMSE and positioning RMSE, leading to a decrease in positioning accuracy. At a distance of 100m, the average positioning accuracy decreases by approximately 7.6% compared to 50m, and at a distance of 150m, the average positioning accuracy decreases by approximately 11.1% compared to 50m. With timing error added, the accuracy decreases at different distances... The simulation results of positioning accuracy under timing error are shown in Table 8 below: Table 8 exist In the presence of timing errors, increasing the distance has no significant impact on positioning accuracy. However, within a relatively small distance range, the impact of timing errors on positioning accuracy is more pronounced than the impact of distance. , The same applies.

[0081] This embodiment verifies the applicability of the Multi-RTT method in various scenarios by simulating positioning performance under different signal-to-noise ratios (SNR) and distances between base stations and user equipment, providing an effective solution for high-precision positioning in different environments. This includes: improving positioning accuracy: by utilizing round-trip time (RTT) for positioning, the impact of time synchronization errors on positioning accuracy is effectively reduced. Various errors in 5G positioning are analyzed, and a more accurate error model is proposed to improve positioning accuracy. Reducing dependence on time synchronization: traditional TDOA methods require precise time synchronization, while the Multi-RTT method does not require time synchronization between base stations or between base stations and user equipment, thus reducing the system's dependence on time synchronization and improving system stability and reliability. Enhancing the stability of the positioning system: through geometric optimization, multi-system fusion, and other methods, the adaptability and stability of the system in complex environments are improved. The system architecture innovations mentioned in the paper, such as the framework for avoiding timing loops and clock drift, also aim to improve system stability and reliability.

[0082] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. An error analysis method for 5G positioning based on round-trip time, characterized in that, include: By introducing non-ideal factors from the actual physical environment into the geometric distance observation equation, the Rx-Tx time difference obtained by the transmission and reception point (TRP) and the user equipment (UE) during the positioning process is obtained. Calculate the arrival time TOA based on the Rx-Tx time difference; The RTT positioning error is analyzed based on the Time of Arrival (TOA). A time error model is established for timing errors in error analysis, and the distribution of timing errors after elimination by the timing error group TEG is obtained.

2. The error analysis method for round trip time based 5G positioning of claim 1, wherein, The Rx-Tx time difference obtained by the Transmitter-Receiver Point (TRP) includes: ； in, This represents the time difference Rx-Tx obtained by the transmit / receive point TRP. and These represent the transmission delay incurred by the TRP when transmitting the PRS signal through the radio frequency link, and the transmission delay incurred when receiving the SRS signal, respectively. , This indicates the time-of-flight of the signal between the TRP's antenna and the UE's antenna, and the time-of-flight of the signal between the UE's antenna and the TRP's antenna. and This represents the reception and transmission delay from the antenna to the baseband when the UE receives the PRS signal from the TRP and transmits the SRS signal through the radio frequency link. This indicates the measurement error of TRP. This indicates other errors present in the channel. This indicates the internal processing delay that occurs between the received signal and the transmitted signal in the UE.

3. The error analysis method for round trip time based 5G positioning of claim 2, wherein, The Rx-Tx time difference obtained by the user equipment (UE) includes: ； wherein, denotes a Rx-Tx time difference obtained by a user equipment, UE, denotes a measurement error of the UE.

4. The error analysis method for round trip time based 5G positioning of claim 3, wherein, The calculation of Time of Arrival (TOA) includes: ； wherein represents the measurement error.

5. The error analysis method for 5G positioning based on round-trip time according to claim 1, characterized in that, The timing error model established for timing errors in error analysis includes: Based on the disturbances of temperature, signal frequency, and amplitude on the propagation delay of radio frequency components, the delay effect of each radio frequency component on the signal is obtained as independent and identically distributed. According to the central limit theorem, the sum of n random variables approximately follows a normal distribution. By pre-setting the mean of the standard deviation, the timing error distribution is obtained, and a time error model is established.

6. The error analysis method for round trip time based 5G positioning of claim 1, wherein, Obtaining the timing error distribution after elimination by the timing error group TEG includes: setting the TEG timing adjustment to the mean of several timing error samples under the timing error distribution, and obtaining the timing error distribution after elimination by the timing error group TEG.

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