A high-precision positioning method and system based on the collaborative operation of smart badges and RTK positioning base stations

Abstract

This invention discloses a high-precision positioning method and system based on the collaborative operation of a smart badge and an RTK positioning base station, relating to the field of RTK positioning technology. It includes the following steps: S1: Constructing a quantum key distribution network using the set RTK base stations, establishing and optimizing the spatiotemporal correlation matrix between the RTK base stations; S2: Obtaining the encrypted differential data stream of the RTK base stations using the smart badge, and simultaneously obtaining the predicted spatiotemporal drift error of the RTK base stations based on the spatiotemporal correlation matrix and an LSTM network, and determining the optimal base station through a DQN network; S3: Determining the Mahalanobis distance using the original pseudorange observations and original carrier phase observations, and filtering the base station data to obtain the position correction amount of the base station. This not only adaptively switches between quantum channel and UWB positioning modes based on signal strength, but also achieves seamless indoor and outdoor positioning with centimeter-level accuracy, solving the problems of accuracy drops caused by base station switching and multi-source data fusion deviations.

Description

Technical Field

[0001] This invention relates to the field of RTK positioning technology, specifically to a high-precision positioning method and system based on the collaborative operation of a smart badge and an RTK positioning base station. Background Technology

[0002] With the rapid development of the Internet of Things (IoT), artificial intelligence, and communication technologies, the demand for precise positioning of personnel and assets is increasing, especially in fields such as industrial safety, smart parks, emergency rescue, mining operations, and medical monitoring. High-precision, real-time, and seamless indoor / outdoor positioning systems have become a key technological support. While traditional positioning technologies such as GPS, Wi-Fi, Bluetooth, and UWB can meet the positioning needs of different scenarios to some extent, they still have many limitations.

[0003] Real-Time Kinematic (RTK) technology, as a high-precision differential positioning method, is gradually becoming an important solution for solving outdoor high-precision positioning problems. This technology achieves centimeter-level or even sub-meter-level dynamic positioning accuracy through carrier phase differential calculation between a base station and a rover, and possesses strong anti-interference capabilities and a high data update frequency. However, RTK cannot work independently in indoor environments due to severe GNSS signal attenuation, and must be combined with other auxiliary positioning methods to achieve integrated indoor and outdoor positioning.

[0004] Chinese invention patent CN119689533A discloses a seamless positioning method based on single-base station UWB / GNSS-RTK suitable for large warehouse environments. This method combines single-base station UWB and GNSS-RTK data to establish a fusion positioning model and employs a weighted extended Kalman filter (WEKF) to achieve high-precision positioning and seamless handover both inside and outside the warehouse. Optimizing the fusion algorithm parameters and handover strategy to suit the characteristics of the warehouse environment: inside the warehouse, single-base station UWB is fused with residual GNSS-RTK signals to achieve wide coverage and easy deployment of high-precision positioning; outside the warehouse, GNSS-RTK technology provides centimeter-level accuracy. Real-time monitoring and correction of positioning errors ensure system stability and reliability, ultimately outputting high-precision positioning results. This method effectively integrates positioning information inside and outside the warehouse, reduces the complexity of multi-base station deployment, and features high precision, low cost, and high stability. It can be widely applied in scenarios such as material management, vehicle scheduling, and automated equipment navigation.

[0005] However, during the collaborative positioning process between smart badges and RTK base stations, differences in the spatiotemporal references of different RTK base stations trigger base station handover as the target moves, causing positioning accuracy to plummet from centimeter-level to decimeter-level. Simultaneously, within the coverage edge of the base stations, the combined effects of multipath propagation and signal attenuation lead to RTK fixed degradation, further impacting positioning reliability. Furthermore, systematic deviations in spatiotemporal alignment, confidence assessment, and optimal fusion strategies among the multi-source differential data streams from different base stations further limit the continuity and stability of positioning accuracy in complex dynamic environments. Summary of the Invention

[0006] The purpose of this invention is to provide a high-precision positioning method and system based on the collaborative operation of a smart badge and an RTK positioning base station, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a high-precision positioning method based on the collaborative operation of a smart badge and an RTK positioning base station, comprising:

[0008] S1: Constructing a quantum key distribution network: Construct a quantum key distribution network through the set RTK base stations, and establish and optimize the spatiotemporal correlation matrix between the RTK base stations through the quantum key distribution network;

[0009] S2: Optimal base station handover: The encrypted differential data stream of the RTK base station is obtained through the smart badge. At the same time, the predicted spatiotemporal drift error of the RTK base station is obtained according to the spatiotemporal correlation matrix and LSTM network. The optimal base station is determined through the DQN network.

[0010] S3: Determine the positioning result: Determine the Mahalanobis distance using the original pseudorange observations and the original carrier phase observations, and filter the base station data based on the Mahalanobis distance. Simultaneously, obtain the position correction amount for the smart badge based on the filtered base station data, including:

[0011] S3.1: Obtaining the Mahalanobis distance: The Mahalanobis distance is determined using the original pseudorange observations and the original carrier phase observations, specifically as follows:

[0012]

[0013] in: The Mahalanobis distance, This is the transpose of the residual vector. For the residual vector, It is the inverse of the covariance matrix;

[0014] S3.2: Determine the processing status: Based on the Mahalanobis distance and the preset distance threshold, and through the RTK base station or UWB anchor point, determine the location of the base station / anchor point;

[0015] S3.3: Determine the fused coordinates: Determine the optimal fused coordinates using the UWB anchor points and GNSS satellites;

[0016] S3.4: Determine the coordinate correction amount: Correct the smart badge coordinates using the weighted least squares fusion algorithm and the Mahalanobis distance to obtain the corrected smart badge coordinates.

[0017] Furthermore, the spatiotemporal correlation matrix between the RTK base stations is established and optimized, including:

[0018] S1.1: Setting the initial spatiotemporal correlation matrix: Within a preset target area, at least three RTK base stations are set up, and an initial spatiotemporal correlation matrix is ​​set according to the spatial location of the RTK base stations, specifically as follows:

[0019]

[0020] in: The initial spatiotemporal correlation matrix, Let x be the X-axis deviation between the i-th base station and the j-th base station in three-dimensional space. Let represent the Y-axis deviation between the i-th base station and the j-th base station in three-dimensional space. Let be the Z-axis deviation between the i-th base station and the j-th base station in three-dimensional space. Let be the clock difference between the i-th base station and the j-th base station;

[0021] S1.2: Optimize the initial spatiotemporal correlation matrix: Generate candidate spatiotemporal correlation matrices using the differential evolution algorithm and the initial spatiotemporal correlation matrix, and obtain the residual between the initial spatiotemporal correlation matrix and each candidate spatiotemporal correlation matrix. At the same time, determine the minimum residual. The candidate spatiotemporal correlation matrix corresponding to the minimum residual is the final optimized spatiotemporal correlation matrix.

[0022] Furthermore, the optimal base station is determined, including:

[0023] S2.1: Differential data stream encryption: In the RTK base station, the differential data stream is encrypted using a lattice cipher. At the same time, a lattice cipher decryption algorithm is set at the smart badge terminal. The location of the smart badge is determined through the RTK base station and the smart badge.

[0024] S2.2: Determine the optimal base station: Obtain the predicted spatiotemporal drift error of the RTK base station through the LSTM network, and determine the location of the optimal RTK base station based on the predicted spatiotemporal drift error and the DQN network.

[0025] Furthermore, the location of the smart badge is determined, including:

[0026] S2.1.1: Signal Division: The carrier-to-noise ratio (CNR) of each GNSS satellite is obtained through a phase-locked loop (PLL) and a delay-locked loop (DLL). Based on the CNR and a preset CNR threshold, the signal strength of the GNSS satellite is determined. Specifically:

[0027] When the carrier-to-noise ratio is greater than a preset carrier-to-noise ratio threshold, the satellite signal of the GNSS satellite corresponding to the carrier-to-noise ratio is strong; otherwise, the satellite signal of the GNSS satellite corresponding to the carrier-to-noise ratio is weak.

[0028] S2.1.2: Location Determination of the Smart Badge Position: When the GNSS satellite signal is strong, the pseudorange observation value, carrier phase observation value, pseudorange correction value, and carrier phase correction value of the GNSS satellite are obtained through the quantum channel in the quantum key distribution network to determine the position of the smart badge, specifically:

[0029]

[0030] in: Let be the corrected pseudorange value of the k-th GNSS satellite. The location coordinates of the smart badge. Let K be the position coordinates of the k-th GNSS satellite. The speed of light in a vacuum. For receiver clock bias, This refers to residual error;

[0031] S2.1.3: Tight Coupling to Determine the Location of the Smart Badge: When the satellite signal of the GNSS satellite is weak, the quantum channel is switched to a UWB anchor point, and the location of the smart badge is determined through the UWB anchor point, specifically as follows:

[0032]

[0033] in: This represents the total number of UWB anchor points. For the index of the UWB anchor point, The weight of the ranging value of the q-th UWB anchor point is... The location coordinates of the smart badge. Let q be the position coordinates of the q-th UWB anchor point. Let be the distance between the smart badge and the q-th UWB anchor point.

[0034] Furthermore, the location of the optimal RTK base station is determined, including:

[0035] S2.2.1: Obtaining Compensated Observations: Using the IMU (Inertial Measurement Unit) and GNSS / RTK module, obtain the RTK base station signal strength, historical positioning error, and environmental interference index. Then, using the LSTM network model, determine the compensated pseudorange and compensated carrier phase. Specifically:

[0036]

[0037] in: The pseudorange observations after compensation. The compensated carrier phase observation value, These are the original pseudorange observations from the receiver. The speed of light in a vacuum. For the receiver clock bias predicted by LSTM, For the eastward position drift predicted by LSTM, This refers to the azimuth angle of a GNSS satellite. For the northward position drift predicted by LSTM, To receive the original carrier phase observations from the receiver, For GNSS satellite carrier frequencies;

[0038] S2.2.2: Determine the reward function: Based on the changes in accuracy, communication latency, and number of handovers, determine the reward function, specifically as follows:

[0039]

[0040] in: For instant reward value, The weighting coefficient for improving positioning accuracy. To improve positioning accuracy, A normalized reference value for improved accuracy. The weighting coefficient is reduced due to delay. To reduce communication latency, To delay the reduction of the normalized baseline value, To switch the weighting coefficient of the penalty, This represents the cumulative number of switching actions.

[0041] S2.2.3: Q-value update: Construct a state matrix using the carrier-to-noise ratio, compensated pseudorange, compensated carrier phase, positioning error, and historical handover success rate of the RTK base station. Use the state matrix as input to the DQN network model and output the Q-value of the RTK base station. At the same time, determine the maximum Q-value based on the Q-values ​​of all RTK base stations. The RTK base station corresponding to the maximum Q-value is the optimal RTK base station.

[0042] Furthermore, the weight coefficients in the reward function are adjusted based on the running time of the DQN network model and the environmental change cycle, specifically as follows:

[0043]

[0044] in: These are the adjusted weighting coefficients. Based on the weighting coefficient, This is the current system uptime. The period for weight changes.

[0045] Furthermore, the Mahalanobis distance is compared with a preset distance threshold, and the base station / anchor point location is determined based on the comparison result, specifically:

[0046] When the Mahalanobis distance is greater than a preset distance threshold, the final positioning location is determined using the RTK base station and UWB anchor point; otherwise, the coordinate correction amount of the base station is determined using the RTK base station.

[0047] Furthermore, the optimal coordinates after fusion are determined, including:

[0048] S3.3.1: Determine the fusion weighting coefficients: Based on the number of UWB anchor points and the signal-to-noise ratio, and the number of GNSS satellites and the carrier-to-noise ratio, determine the fusion weighting coefficients, specifically as follows:

[0049]

[0050] in: To integrate the weighting coefficients, For the effective number of UWB anchor points, The signal-to-noise ratio of the UWB signal. For the effective number of GNSS satellites, For GNSS carrier-to-noise ratio;

[0051] S3.3.2: Determine the fused coordinates: Based on the fusion weight coefficients, the positioning coordinates of the UWB anchor points, and the residual positioning coordinates of the RTK base stations, determine the optimal fused coordinates, specifically as follows:

[0052]

[0053] in: The optimal coordinates after fusion. To integrate the weighting coefficients, The coordinates for the UWB anchor point. These are the residual positioning coordinates for the RTK base station.

[0054] Furthermore, the corrected smart badge coordinates are obtained, including:

[0055] S3.4.1: Data Filtering: The data weight of each RTK base station is obtained using the Mahalanobis distance. Simultaneously, the data weight is compared with a preset weight threshold, and the RTK base station data is filtered based on the comparison result. Specifically:

[0056] When the data weight is greater than a preset weight threshold, both the RTK base station data and the data weight are deleted; otherwise, both the RTK base station data and the data weight are retained.

[0057] S3.4.2: Constructing the Positioning Solution Model: Using the positions of GNSS satellites and RTK base stations, a geometric distance matrix is ​​constructed. Simultaneously, based on the retained data weights, a weighted diagonal matrix is ​​constructed. Based on the geometric distance matrix and the weighted diagonal matrix, the coordinate correction amount is determined, specifically as follows:

[0058]

[0059] in: This is the coordinate correction amount. It is the transpose of the geometric distance matrix. This is a weighted diagonal matrix. The geometric distance matrix, To observe the residual vector;

[0060] S3.4.3: Coordinate Correction: Based on the aforementioned coordinate correction amount, determine the three-dimensional coordinate correction amount, and using the current estimated coordinates, determine the corrected three-dimensional coordinates, specifically as follows:

[0061]

[0062] in: For the updated 3D coordinates, This is a 3D coordinate correction value. These are the current estimated coordinates.

[0063] A high-precision positioning system based on the collaborative operation of a smart badge and an RTK positioning base station uses a high-precision positioning method based on the collaborative operation of a smart badge and an RTK positioning base station as described in any of the above-mentioned methods.

[0064] Compared with the prior art, the beneficial effects of the present invention are:

[0065] Firstly, this invention uses an LSTM network to predict spatiotemporal drift errors and a DQN network to dynamically select the optimal base station, thereby enabling the base station to smoothly transition the handover process and ensuring that the positioning accuracy remains at the centimeter level, avoiding positioning jumps caused by handover.

[0066] Secondly, this invention filters out abnormal data using Mahalanobis distance and reduces the fusion error between multi-source data using a weighted least squares fusion algorithm, thereby improving the final positioning accuracy and enabling stable calculation even in areas at the edge of base station coverage.

[0067] Thirdly, this invention uses an adaptive switching mechanism to transmit RTK differential data via a quantum channel when the GNSS signal is strong, and automatically switches to UWB anchor point positioning when the GNSS signal is weak, thereby achieving seamless connection between indoor and outdoor signal positioning and meeting the continuous positioning requirements of complex scenarios.

[0068] Fourthly, this invention ensures secure communication between base stations through a quantum key distribution network, and encrypts differential data streams using lattice cryptography, thereby enabling data transmission to resist quantum computing attacks, preventing malicious tampering, and ensuring the reliability of the positioning system. Attached Figure Description

[0069] Figure 1 This is a flowchart illustrating the high-precision positioning method of the present invention;

[0070] Figure 2 This is a graph showing the optimization performance analysis of the spatiotemporal correlation matrix in this invention;

[0071] Figure 3 This is an anomaly detection diagram of Mahalanobis distance in this invention;

[0072] Figure 4 This is a robust fusion weight distribution diagram of Mahalanobis distance in this invention;

[0073] Figure 5 This is a comparison chart of the multi-source data fusion results in this invention;

[0074] Figure 6 This is a bar chart comparing the errors in this invention. Detailed Implementation

[0075] 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.

[0076] In the collaborative positioning process between smart badges and RTK base stations, differences in the spatiotemporal references between different RTK base stations trigger base station handover during target movement, causing positioning accuracy to plummet from centimeter-level to decimeter-level. Simultaneously, within the coverage edge area of ​​the base station, the combined effects of multipath propagation and signal attenuation lead to RTK fixed de-degradation, further impacting positioning reliability. Furthermore, systematic deviations in spatiotemporal alignment, confidence assessment, and optimal fusion strategies among the multi-source differential data streams from different base stations further limit the continuity and stability of positioning accuracy in complex dynamic environments. The technical solution of this application optimizes the spatiotemporal correlation matrix between RTK base stations through a constructed quantum key distribution network, predicts base station spatiotemporal drift errors using an LSTM network, and dynamically determines the optimal base station using a DQN network. Meanwhile, the base station data is filtered by Mahalanobis distance, and the corrected coordinates are obtained by weighted least squares algorithm based on UWB anchor points and GNSS satellite signals. This not only enables adaptive switching between quantum channel and UWB positioning mode according to signal strength, but also achieves seamless indoor and outdoor positioning with centimeter-level accuracy, solving the problems of sudden drop in accuracy caused by base station switching and deviation in multi-source data fusion.

[0077] Example 1

[0078] refer to Figures 1-6 This embodiment provides a high-precision positioning method based on the collaborative operation of a smart badge and an RTK positioning base station. The high-precision positioning method includes the following steps:

[0079] Step S1: Construct a quantum key distribution network. This involves building a quantum key distribution network by configuring RTK base stations with atomic clocks, and simultaneously establishing and optimizing the spatiotemporal correlation matrix between the RTK base stations through this network. Details are as follows:

[0080] Step S1.1: Set the initial spatiotemporal correlation matrix. This involves setting up at least three RTK base stations within a preset target area, forming a polygonal region distribution. Simultaneously, two RTK base stations at different locations are connected via quantum channels to construct a quantum key distribution network.

[0081] Furthermore, based on the spatial location of each base station in the quantum key distribution network, an initial spatiotemporal correlation matrix is ​​set as follows:

[0082]

[0083] in: The initial spatiotemporal correlation matrix, Let x be the X-axis deviation between the i-th base station and the j-th base station in three-dimensional space. Let represent the Y-axis deviation between the i-th base station and the j-th base station in three-dimensional space. Let be the Z-axis deviation between the i-th base station and the j-th base station in three-dimensional space. Let be the clock difference between the i-th base station and the j-th base station.

[0084] In the specific implementation process, three RTK base stations are set up within the target area, forming an equilateral triangle region. Specifically, the coordinates of the three RTK base stations are (0,0,10m), (100m,0,10m), and (50m,86.6m,10m), respectively. The corresponding bidirectional ranging distances are 100.002m and 100.001m, and the corresponding clock differences are 6.67ns and 3.33ns, respectively. Therefore, the corresponding initial spatiotemporal correlation matrix is: .

[0085] Step S1.2: Optimize the initial spatiotemporal correlation matrix. This involves optimizing the initial spatiotemporal correlation matrix obtained in step S1.1 using a differential evolution algorithm to obtain an optimized spatiotemporal correlation matrix, specifically:

[0086]

[0087] in: To optimize the spatiotemporal correlation matrix, The optimized X-axis deviation between the i-th base station and the j-th base station in three-dimensional space. The optimized Y-axis deviation between the i-th base station and the j-th base station in three-dimensional space. The optimized Z-axis deviation between the i-th base station and the j-th base station in three-dimensional space. The optimized clock difference between the i-th base station and the j-th base station.

[0088] Furthermore, using the initial spatiotemporal correlation matrix obtained in step S1.1 as a benchmark, multiple candidate spatiotemporal correlation matrices are randomly generated, and the residuals between the initial spatiotemporal correlation matrix and each candidate spatiotemporal correlation matrix are obtained, specifically as follows:

[0089]

[0090] in: Let be the difference between the measured distance and the predicted distance between the i-th base station and the j-th base station. Let x be the X-axis deviation between the i-th base station and the j-th base station in three-dimensional space. Let represent the Y-axis deviation between the i-th base station and the j-th base station in three-dimensional space. Let be the Z-axis deviation between the i-th base station and the j-th base station in three-dimensional space. Let be the clock difference between the i-th base station and the j-th base station. Let be the measured distance between the i-th base station and the j-th base station. , For the base station index, The speed of light in a vacuum.

[0091] Specifically, based on the initial spatiotemporal correlation, the residuals corresponding to the matrix and the residuals corresponding to each candidate spatiotemporal correlation matrix are compared, and the minimum residual is determined. The spatiotemporal correlation matrix corresponding to the minimum residual is the final optimized spatiotemporal correlation matrix.

[0092] refer to Figure 2 , Figure 2 This is a performance analysis diagram of the spatiotemporal correlation matrix in this embodiment, derived from... Figure 2 It can be seen that after optimization by the differential evolution algorithm, the residual of the spatiotemporal correlation matrix decreased from the initial 0.15m to 0.012m, and the convergence speed was improved by 2.5 times. At the same time, the optimized residual curve is always lower than the initial residual curve and has less fluctuation. In other words, the optimized spatiotemporal correlation matrix can more stably represent the spatiotemporal relationship between base stations.

[0093] Step S2: Optimal base station switching. This involves obtaining the encrypted differential data stream of the RTK base station in the quantum key distribution network from Step S1 using the smart badge. Simultaneously, based on the optimized initial spatiotemporal correlation matrix and LSTM network obtained in Step S1.2, the predicted spatiotemporal drift error of the RTK base station is acquired, and the optimal base station is determined using the DQN network. Details are as follows:

[0094] Step S2.1: Differential Data Stream Encryption. This involves encrypting the differential data stream using a lattice cipher at the RTK base station to obtain the encrypted differential data stream. Simultaneously, a lattice cipher decryption algorithm is set at the smart badge terminal. This algorithm is used to decrypt the encrypted differential data stream sent from the RTK base station, thereby enabling encrypted information transmission between the RTK base station and the smart badge.

[0095] Step S2.2: Determine the optimal base station. This involves obtaining the spatiotemporal drift error between RTK base stations using the LSTM network model, and then determining the optimal base station from all RTK base stations based on the spatiotemporal drift error and the DQN network model. Specifically:

[0096] Step S2.2.1: Obtain compensated observations. This involves setting up an IMU (Inertial Measurement Unit) and a GNSS / RTK module in the smart badge. In other words, the RTK base station signal strength, historical positioning error, and environmental interference index are collected through the IMU and GNSS / RTK modules.

[0097] Specifically, the obtained RTK base station signal strength, historical positioning error, and environmental interference index are used as inputs to the LSTM network model, and the outputs are the corresponding compensated pseudorange and compensated carrier phase, as follows:

[0098]

[0099] in: The pseudorange observations after compensation. The compensated carrier phase observation value, These are the original pseudorange observations from the receiver. The speed of light in a vacuum. For the receiver clock bias predicted by LSTM, For the eastward position drift predicted by LSTM, This refers to the azimuth angle of a GNSS satellite. For the northward position drift predicted by LSTM, To receive the original carrier phase observations from the receiver, This refers to the carrier frequency of GNSS satellites.

[0100] During the implementation process, the following results were obtained using the LSTM network model: the LSTM-predicted receiver clock error is 0.00002s, the LSTM-predicted eastward position drift is 0.03m, and the LSTM-predicted northward position drift is -0.02m. Simultaneously, the GNSS satellite azimuth angle is 30°, and the original pseudorange observation value of the receiver is 20000km. Therefore, the corresponding compensated pseudorange observation value is approximately 20006km, and the corresponding compensated carrier phase observation value is approximately 136740 cycles.

[0101] Step S2.2.2: Determine the reward function. This involves obtaining the corresponding reward function based on the changes in accuracy, communication latency, and number of handovers under different scenarios. Specifically:

[0102]

[0103] in: For instant reward value, The weighting coefficient for improving positioning accuracy. To improve positioning accuracy, A normalized reference value for improved accuracy. The weighting coefficient is reduced due to delay. To reduce communication latency, To delay the reduction of the normalized baseline value, To switch the weighting coefficient of the penalty, This represents the cumulative number of times the action was switched.

[0104] In this embodiment, the weight coefficients can be adjusted according to the running time of the DQN network model and the environmental change cycle, specifically as follows:

[0105]

[0106] in: These are the adjusted weighting coefficients. Based on the weighting coefficient, This is the current system uptime. The period for weight changes.

[0107] In the specific implementation process, day and night operations are carried out in the open-pit mine. The basic weight coefficient is set to 0.6, the weight change cycle is set to 12 hours, and the current system running time is 3:00 AM. Therefore, the corresponding adjusted weight coefficient is 0.72.

[0108] Step S2.2.3: Q-value update. This involves constructing a state matrix based on the current RTK base station's carrier-to-noise ratio (CNR), the CNR of neighboring RTK base stations, the compensated observations obtained in step S2.2.1, the positioning error, and the historical handover success rate. This constructed state matrix is ​​then used as input to the DQN network model, and the corresponding Q-value is output, specifically:

[0109]

[0110] in: For the updated Q value, The initial Q value, For learning rate, For instant reward value, As a discount factor, This represents the maximum Q value for the next state.

[0111] In the specific implementation process, the initial Q value is 0.8, the learning rate is set to 0.05, the immediate reward value is 1.2, the discount factor is set to 0.9, and the maximum Q value of the next state is 1.5, so the corresponding updated Q value is 0.8875.

[0112] Furthermore, based on the obtained Q value, the Q value corresponding to each RTK base station is determined. At the same time, the Q values ​​corresponding to each RTK base station are compared to obtain the maximum Q value. The RTK base station corresponding to the maximum Q value is the optimal RTK base station.

[0113] Step S3: Determine the positioning result. This involves obtaining the corresponding Mahalanobis distance using the receiver's raw pseudorange and carrier phase observations, and then filtering the base station data using this distance. Simultaneously, based on the filtered base station data, the position correction amount for each base station is obtained, and the position correction amount for the smart badge is determined. Details are as follows:

[0114] Step S3.1: Obtain the Mahalanobis distance. This involves obtaining the corresponding residual vector based on the receiver's original pseudorange observations and predictions, and the receiver's original carrier phase observations and predictions. Specifically:

[0115]

[0116] in: For the residual vector, These are the original pseudorange observations from the receiver. To receive the original carrier phase observations from the receiver, The pseudorange is predicted based on the current location. This is the carrier phase predicted based on the current location.

[0117] Furthermore, based on the pseudorange measurement standard deviation, the carrier phase measurement standard deviation, and the pseudorange-carrier phase covariance, the corresponding covariance matrix is ​​obtained, specifically:

[0118]

[0119] in: Let covariance matrix be the variance matrix. The standard deviation of pseudorange measurement. Standard deviation of carrier phase measurement. This represents the covariance between the pseudorange and the carrier phase.

[0120] In this embodiment, the corresponding Mahalanobis distance is determined based on the obtained residual vector and covariance matrix, specifically as follows:

[0121]

[0122] in: The Mahalanobis distance, This is the transpose of the residual vector. For the residual vector, is the inverse of the covariance matrix.

[0123] In the specific implementation process, the difference between the receiver's original pseudorange observation and the predicted value is 0.05m, and the difference between the receiver's original carrier phase observation and the predicted value is 0.006 cycles. The corresponding residual vector and covariance matrix are as follows: and That is to say, the corresponding Mahalanobis distance is 0.78.

[0124] Step S3.2: Determine the processing status. This involves comparing the Mahalanobis distance obtained in step S3.1 with a preset distance threshold, and based on the comparison result, determining the corresponding base station / anchor point location using either an RTK base station or a UWB anchor point. Specifically:

[0125] If the obtained Mahalanobis distance is greater than the preset distance threshold, then step S3.3 is executed to determine the final positioning location using the RTK base station and UWB anchor point. Conversely, if the obtained Mahalanobis distance is not greater than the preset distance threshold, then step S3.4 is executed to determine the coordinate correction amount of the base station using the RTK base station.

[0126] Step S3.3: Determine the fused coordinates. This involves determining the optimal fused coordinates using the set UWB anchor points and GNSS satellites. Specifically:

[0127] Step S3.3.1: Determine the fusion weight coefficients. Specifically, based on the number and signal-to-noise ratio of UWB anchor points, and the number and carrier-to-noise ratio of GNSS satellites, determine the corresponding fusion weight coefficients.

[0128]

[0129] in: To integrate the weighting coefficients, For the effective number of UWB anchor points, The signal-to-noise ratio of the UWB signal. For the effective number of GNSS satellites, This refers to the GNSS carrier-to-noise ratio.

[0130] In the specific implementation process, there are 4 UWB anchor points and the UWB signal-to-noise ratio is 38dB. There are 2 effective GNSS satellites and the GNSS carrier-to-noise ratio is 32dB-Hz. The corresponding fusion weighting coefficient is 0.7.

[0131] Step S3.3.2: Determine the fused coordinates. Specifically, based on the fusion weight coefficients determined in step S3.3.1, the positioning coordinates of the UWB anchor point, and the residual positioning coordinates of the RTK base station, determine the optimal fused coordinates.

[0132]

[0133] in: The optimal coordinates after fusion. To integrate the weighting coefficients, The coordinates for the UWB anchor point. These are the residual positioning coordinates for the RTK base station.

[0134] During the specific implementation process, the positioning coordinates of the UWB anchor point are: m, the residual positioning coordinates of the RTK base station are m, then the corresponding optimal coordinates after fusion are m.

[0135] Step S3.4: Determine the coordinate correction amount. This involves correcting the current smart badge coordinates using a weighted least squares fusion algorithm and the Mahalanobis distance obtained in step S3.1, thus obtaining the corrected smart badge coordinates. Details are as follows:

[0136] Step S3.4.1: Data Filtering. This involves obtaining the weight of each RTK base station data point based on the Mahalanobis distance obtained in Step S3.1. Specifically:

[0137]

[0138] in: Let i be the data weight corresponding to the i-th base station. It is a smoothing constant. This is the Mahalanobis distance.

[0139] Furthermore, based on the obtained data weights of each base station, the data weight of each base station is compared with a preset weight threshold, and the data of each base station is filtered according to the comparison results, specifically as follows:

[0140] If the obtained data weight of a base station is greater than a preset weight threshold, both the base station data and its data weight will be deleted. Conversely, if the obtained data weight of a base station is not greater than the preset weight threshold, both the base station data and its data weight will be retained.

[0141] refer to Figures 3-5 , Figure 3 This is an anomaly detection map of Mahalanobis distance in this embodiment. Figure 4 This is a robust fusion weight distribution diagram of Mahalanobis distance in this embodiment. Figure 5 This is a comparison chart of the multi-source data fusion results in this embodiment, provided by... Figures 3-5 It can be seen that: the Mahalanobis distance can be used to successfully identify and remove abnormal data points, and the data weight can be used to dynamically allocate the credibility of the data. The smaller the Mahalanobis distance, the greater the corresponding data weight. In other words, the problem of outlier interference caused by signal obstruction or multipath effect during multi-base station data fusion is solved.

[0142] Step S3.4.2: Construct the positioning solution model. This involves constructing a geometric distance matrix based on the positional relationship between GNSS satellites and RTK base stations. A weighted diagonal matrix is ​​then constructed based on the data weights retained in Step S3.4.1. Simultaneously, the corresponding coordinate corrections are determined using the constructed geometric distance matrix and weighted diagonal matrix, specifically:

[0143]

[0144] in: This is the coordinate correction amount. It is the transpose of the geometric distance matrix. This is a weighted diagonal matrix. The geometric distance matrix, To observe the residual vector.

[0145] Step S3.4.3: Coordinate Correction. Based on the coordinate correction amount obtained in step S3.4.2, determine the corresponding 3D coordinate correction amount. Simultaneously, based on the determined 3D coordinate correction amount and the currently estimated coordinate magnitude, determine the corresponding corrected 3D coordinates, specifically as follows:

[0146]

[0147] in: For the updated 3D coordinates, This is a 3D coordinate correction value. These are the current estimated coordinates.

[0148] During the specific implementation process, the coordinate correction amount is: The corresponding three-dimensional coordinate correction is Meanwhile, the current estimated coordinates are The corresponding updated 3D coordinates are .

[0149] refer to Figure 6 , Figure 6 This is a bar chart comparing the errors in this embodiment. Figure 6 It can be seen that the positioning error of this technical solution is significantly lower than that of traditional methods. Specifically, for open areas, the positioning error is reduced by 60%, for base station edges, the positioning error is reduced by 91.4%, and for indoor-outdoor handover, the positioning error is reduced by 92%.

[0150] This embodiment also provides a high-precision positioning system based on the collaborative operation of a smart badge and an RTK positioning base station. This high-precision positioning system uses the aforementioned high-precision positioning method based on the collaborative operation of a smart badge and an RTK positioning base station.

[0151] Example 2

[0152] This embodiment provides a high-precision positioning method based on the collaborative operation of a smart badge and an RTK positioning base station. The specific implementation method is the same as in Embodiment 1, except that in step S2.1, encrypted information transmission is performed between the RTK base station and the smart badge, and the location of the corresponding smart badge is determined based on the transmission information between the RTK base station and the smart badge. The invention will be illustrated below with specific examples of this embodiment.

[0153] In this embodiment, the location of the corresponding smart badge is determined based on the transmission information between the RTK base station and the smart badge, as follows:

[0154] Step S2.1.1: Signal Division. This involves obtaining the carrier-to-noise ratio (CNR) of each GNSS satellite through a phase-locked loop (PLL) and a delay-locked loop (DLL), and then determining the signal strength of the GNSS satellites based on the obtained CNR.

[0155] In this embodiment, the formula for obtaining the carrier-to-noise ratio of a GNSS satellite is as follows:

[0156]

[0157] in: Carrier-to-noise ratio, The received satellite signal power, This represents the noise power spectral density.

[0158] Furthermore, the acquired carrier-to-noise ratio (CNR) of the GNSS satellite is compared with a preset CNR threshold, and the strength of the satellite signal is determined based on the comparison result. Specifically:

[0159] When the obtained carrier-to-noise ratio (CNR) is greater than the preset CNR threshold, the satellite signal of the GNSS satellite corresponding to that CNR is strong. Conversely, when the obtained CNR is not greater than the preset CNR threshold, the satellite signal of the GNSS satellite corresponding to that CNR is weak.

[0160] During the implementation process, the received satellite signal power was 10. -16 W, noise power spectral density is 10 -20.4 W / Hz corresponds to a carrier-to-noise ratio of 44dB-Hz. Meanwhile, in this embodiment, the preset carrier-to-noise ratio threshold is set to 35dB-Hz (it is worth noting that since the normal carrier-to-noise ratio range of GNSS satellite signals is 35-55dB-Hz, the preset carrier-to-noise ratio threshold is set to 35dB-Hz in this embodiment), meaning that the GNSS satellite signal in this embodiment is strong.

[0161] Step S2.1.2: Locate and determine the position of the badge. That is, when the GNSS satellite signal strength is strong as determined in step S2.1.1, signal transmission can be directly performed through the quantum channel in the quantum key distribution network. Specifically, the pseudorange and carrier phase observations of the GNSS satellite are obtained through the GNSS receiver set in the smart badge. The pseudorange correction and carrier phase correction values ​​of the GNSS satellite are obtained through the GNSS receiver set in the RTK base station.

[0162] Furthermore, based on the obtained pseudorange and carrier phase observations from the GNSS satellites, as well as the pseudorange and carrier phase correction values, the corresponding corrected pseudorange and carrier phase values ​​are determined, specifically as follows:

[0163]

[0164] in: The corrected pseudorange value. These are pseudorange observations. This is the pseudorange correction value. The corrected carrier phase value. For carrier phase observations, This is the carrier phase correction value.

[0165] In this embodiment, the location of the smart badge is determined based on the obtained corrected pseudorange value and the coordinates of each GNSS satellite, specifically as follows:

[0166]

[0167] in: Let be the corrected pseudorange value of the k-th GNSS satellite. The location coordinates of the smart badge. Let K be the position coordinates of the k-th GNSS satellite. The speed of light in a vacuum. For receiver clock bias, This represents the residual error.

[0168] Step S2.1.3: Tightly Coupled Determination of Badge Location. That is, when the GNSS satellite signal is weak as determined in step S2.1.1, the quantum channel in the quantum key distribution network is switched to UWB for signal transmission. Specifically, the location of the smart badge is determined through the set UWB anchor points, as follows:

[0169]

[0170] in: This represents the total number of UWB anchor points. For the index of the UWB anchor point, The weight of the ranging value of the q-th UWB anchor point is... The location coordinates of the smart badge. Let q be the position coordinates of the q-th UWB anchor point. Let be the distance between the smart badge and the q-th UWB anchor point.

[0171] During the implementation process, three UWB anchor points were set up, with the coordinates of the UWB anchor points being (0,0,3m), (10m,0,3m), and (5m,8.66m,3m), respectively. The distances between the UWB anchor points and the smart badges were 5.2m, 3.8m, and 4.1m, respectively, and the corresponding coordinates of the smart badges were (5.12m,4.95m,1.02m).

[0172] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended embodiments and their equivalents.

Claims

1. A high-precision positioning method based on the cooperative work of an intelligent chest card and an RTK positioning base station, characterized in that, The application relates to a method for determining the position of an intelligent chest card, and the method comprises the following steps: S1: constructing a quantum key distribution network: constructing a quantum key distribution network through a set RTK base station, and establishing and optimizing a space-time correlation matrix between the RTK base stations through the quantum key distribution network; S2: optimal base station switching: acquiring encrypted differential data streams of the RTK base station through an intelligent chest card, acquiring predicted space-time drift errors of the RTK base station according to the space-time correlation matrix and an LSTM network, and determining an optimal base station through a DQN network; S3: determining a positioning result: determining a Mahalanobis distance through original pseudo-range observation values and original carrier phase observation values, screening base station data according to the Mahalanobis distance, and acquiring a position correction amount of the intelligent chest card according to the screened base station data, and the method comprises the following steps: S3.1: acquiring a Mahalanobis distance: determining a Mahalanobis distance through original pseudo-range observation values and original carrier phase observation values, and the method comprises the following steps: ； wherein: is the Mahalanobis distance, is the transpose of the residual vector, is the residual vector, is the inverse of the covariance matrix; S3.2: determining a processing state: determining a base station / anchor point position through an RTK base station or a UWB anchor point according to the Mahalanobis distance and a preset distance threshold value; S3.3: determining a fused coordinate: determining an optimal fused coordinate through the UWB anchor point and a GNSS satellite; S3.4: determining a coordinate correction amount: correcting the intelligent chest card coordinate through a weighted least square fusion algorithm and the Mahalanobis distance, and acquiring a corrected intelligent chest card coordinate.

2. The high-precision positioning method based on the cooperation between the intelligent chest card and the RTK positioning base station according to claim 1, characterized in that, The application also relates to a method for establishing and optimizing a space-time correlation matrix between RTK base stations, and the method comprises the following steps: S1.1: setting an initial space-time correlation matrix: setting at least three RTK base stations in a preset target area range, and setting an initial space-time correlation matrix according to the spatial positions of the RTK base stations, and the method comprises the following steps: ； wherein: is an initial space-time correlation matrix, is an X-axis deviation of the i-th base station and the j-th base station in a three-dimensional space, is a Y-axis deviation of the i-th base station and the j-th base station in a three-dimensional space, is a Z-axis deviation of the i-th base station and the j-th base station in a three-dimensional space, is a clock difference between the i-th base station and the j-th base station; S1.2: optimizing the initial space-time correlation matrix: generating a candidate space-time correlation matrix through a differential evolution algorithm and the initial space-time correlation matrix, acquiring a residual error between the initial space-time correlation matrix and each candidate space-time correlation matrix, and determining a minimum residual error, wherein the candidate space-time correlation matrix corresponding to the minimum residual error is an optimal space-time correlation matrix. 3.The high-precision positioning method based on the cooperation between the smart chest card and the RTK positioning base station according to claim 1, characterized in that, The application also relates to a method for determining an optimal base station, and the method comprises the following steps: S2.1: differential data stream encryption: encrypting a differential data stream through a lattice cipher in the RTK base station, setting a lattice cipher decryption algorithm on the intelligent chest card side, and determining the position of the intelligent chest card through the RTK base station and the intelligent chest card; S2.2: determining an optimal base station: acquiring predicted space-time drift errors of the RTK base station through the LSTM network, and determining the position of an optimal RTK base station according to the predicted space-time drift errors and the DQN network.

4. The high-precision positioning method based on the cooperation between the intelligent chest card and the RTK positioning base station according to claim 3, characterized in that, The application also relates to a method for determining the position of an intelligent chest card, and the method comprises the following steps: S2.1.1: signal division: acquiring a carrier-to-noise ratio of each GNSS satellite through a phase-locked loop and a delay-locked loop, and determining the strength of a satellite signal of the GNSS satellite according to the carrier-to-noise ratio and a preset carrier-to-noise ratio threshold value, and the method comprises the following steps: when the carrier-to-noise ratio is greater than the preset carrier-to-noise ratio threshold value, the satellite signal of the GNSS satellite corresponding to the carrier-to-noise ratio is strong; otherwise, the satellite signal of the GNSS satellite corresponding to the carrier-to-noise ratio is weak. S2.1.2: Positioning determines the chest card position: when the satellite signal of the GNSS satellite is strong, the pseudo-range observation value and carrier phase observation value, pseudo-range correction value and carrier phase correction value of the GNSS satellite are obtained through the quantum channel in the quantum key distribution network, and the position of the intelligent chest card is determined, specifically: ； wherein: is the corrected pseudorange value for the kth GNSS satellite, is the position coordinates of the smart chest card, is the position coordinates of the kth GNSS satellite, is the speed of light in vacuum, is the receiver clock bias, is the residual error; S2.1.3: Tight coupling determines the chest card position: when the satellite signal of the GNSS satellite is weak, the quantum channel is switched to the UWB anchor point, and the position of the intelligent chest card is determined through the UWB anchor point, specifically: ； wherein: is the total number of UWB anchors, is the index of the UWB anchor, is the ranging value weight of the qth UWB anchor, is the position coordinate of the smart chest card, is the position coordinate of the qth UWB anchor, is the distance between the smart chest card and the qth UWB anchor.

5. The high-precision positioning method based on the cooperation between the intelligent chest card and the RTK positioning base station according to claim 3, characterized in that, Determine the position of the optimal RTK base station, including: S2.2.1: Obtain the compensation observation value: through the IMU inertial measurement unit and GNSS / RTK module, the RTK base station signal strength, historical positioning error and environmental interference index are obtained, and through the LSTM network model, the compensation pseudo-range and compensation carrier phase are determined, specifically: ； wherein: is the compensated pseudo-range observation, is the compensated carrier phase observation, is the raw pseudo-range observation of the receiver, is the speed of light in vacuum, is the LSTM-predicted receiver clock bias, is the LSTM-predicted eastward position drift, is the azimuth of the GNSS satellite, is the LSTM-predicted northward position drift, is the raw carrier phase observation of the receiver, is the carrier frequency of the GNSS satellite; S2.2.2: Determine the reward function: according to the accuracy change, communication delay change and switching frequency, the reward function is determined, specifically: ； wherein: is a weight coefficient of the instant reward value, is a weight coefficient of the positioning accuracy improvement, is a positioning accuracy improvement amount, is a normalization reference value of the accuracy improvement, is a weight coefficient of the delay reduction, is a communication delay reduction amount, is a normalization reference value of the delay reduction, is a weight coefficient of the switching penalty, is a cumulative number of switching actions; S2.2.3: Q value update: through the carrier-to-noise ratio of the RTK base station, the compensation pseudo-range, the compensation carrier phase, the positioning error and the historical switching success rate, a state matrix is constructed, and the state matrix is taken as the input of the DQN network model, the Q value of the RTK base station is output, and the maximum Q value is determined according to the Q value of all RTK base stations. The RTK base station corresponding to the maximum Q value is the optimal RTK base station. 6.The high-precision positioning method based on the cooperation between the smart chest card and the RTK positioning base station according to claim 5, wherein, Adjust the weight coefficient in the reward function according to the running time of the DQN network model and the environmental change period, specifically: ； wherein: is the adjusted weight coefficient, is the base weight coefficient, is the current system runtime, is the weight change period. 7.The high-precision positioning method based on the intelligent chest card and the RTK positioning base station cooperative work of claim 1, characterized in that, Compare the Mahalanobis distance with the preset distance threshold, and determine the base station / anchor point position according to the comparison result, specifically: When the Mahalanobis distance is greater than the preset distance threshold, the final positioning position is determined through the RTK base station and the UWB anchor point; otherwise, the coordinate correction amount of the base station is determined through the RTK base station. 8.The high-precision positioning method based on the cooperation between the smart chest card and the RTK positioning base station according to claim 1, characterized in that, Determine the optimal coordinate after fusion, including: S3.3.1: Determine the fusion weight coefficient: according to the number and signal-to-noise ratio of the UWB anchor point, and the number and carrier-to-noise ratio of the GNSS satellite, the fusion weight coefficient is determined, specifically: ； wherein: is a fusion weight coefficient, is a number of effective UWB anchors, is a UWB signal signal-to-noise ratio, is a number of effective GNSS satellites, is a GNSS carrier-to-noise ratio; S3.3.2: Determine the fusion coordinate: according to the fusion weight coefficient, the positioning coordinate of the UWB anchor point and the residual positioning coordinate of the RTK base station, the optimal coordinate after fusion is determined, specifically: ； wherein: is the optimal coordinate after fusion, is the fusion weight coefficient, is the positioning coordinate of the UWB anchor point, is the residual positioning coordinate of the RTK base station. 9.The high-precision positioning method based on the cooperation between the smart chest card and the RTK positioning base station according to claim 1, characterized in that, Get the corrected intelligent chest card coordinate, including: S3.4.1: Data screening: through the Mahalanobis distance, the data weight of each RTK base station is obtained, and the data weight is compared with the preset weight threshold, and according to the comparison result, the RTK base station data is screened, specifically: When the data weight is greater than the preset weight threshold, the RTK base station data and data weight are deleted; otherwise, the RTK base station data and data weight are retained; S3.4.2: Constructing positioning solution model: constructing geometric distance matrix through the positions of GNSS satellites and RTK base stations, constructing weight diagonal matrix according to the reserved data weight, and determining coordinate correction amount according to the geometric distance matrix and weight diagonal matrix, specifically as follows: ； wherein: is a coordinate correction, is the transpose of the geometric distance matrix, is a weight diagonal matrix, is the geometric distance matrix, is the observation residual vector; S3.4.3: Coordinate correction: determining three-dimensional coordinate correction amount according to the coordinate correction amount, and determining corrected three-dimensional coordinates through the current estimated coordinates, specifically as follows: ； wherein: is the updated three-dimensional coordinate, is the three-dimensional coordinate correction, is the current estimated coordinate.

10. A high-precision positioning system based on the cooperative work of an intelligent chest card and an RTK positioning base station, characterized in that, A high-precision positioning method based on intelligent chest card and RTK positioning base station cooperative work is used.

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