A positioning method, system and related devices

Abstract

Embodiments of the present application provide a positioning method, system and related device. The method effectively solves the core technical problem that the existing positioning method in the background art is based on a single satellite set for solving and cannot simultaneously consider positioning accuracy and robustness by adopting a core architecture of separately solving and result fusion of double satellite sets. The scheme can simultaneously combine the positioning performance advantages of the two satellite sets, avoid the inherent defects of single satellite set solving, and provide stable and reliable positioning results in different application scenarios. At the same time, the method can be independently executed on the user equipment side without relying on the assistance of external equipment, and has strong universality and wide scene applicability.

Description

Technical Field

[0001] This application relates to the field of positioning technology, and in particular to a positioning method, system and related equipment. Background Technology

[0002] With the rapid development of 6G Integrated Sensing, Communication and Computing (ISAC) technology and the large-scale deployment of industries such as intelligent connected vehicles and low-power Internet of Things, high-precision and high-reliability positioning services have become one of the core capabilities of next-generation mobile communication systems. Tightly coupled positioning technology combining Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS), by integrating GNSS absolute position information with INS continuous calculation capabilities, effectively compensates for the inherent shortcomings of a single navigation system and is the mainstream technical solution for achieving high-precision positioning in complex environments.

[0003] Existing tightly coupled GNSS / INS positioning systems rely on observation and processing based on all or some satellites, making it impossible to simultaneously ensure positioning accuracy and robustness, and thus failing to meet the requirements for positioning accuracy. Summary of the Invention

[0004] This application provides a positioning method, system, and related equipment, with the aim of improving the quality of services running on user devices, thereby enhancing the user experience provided by the services.

[0005] To achieve the above objectives, this application provides the following technical solution: Firstly, this application provides a positioning method for user equipment. This method, by employing a core architecture of separate calculations using two satellite sets and result fusion, effectively solves the core technical problem in existing positioning methods that rely solely on a single satellite set for calculation, failing to simultaneously ensure positioning accuracy and robustness. This solution combines the positioning performance advantages of both satellite sets, avoiding the inherent defects of single-satellite calculations, and provides stable and reliable positioning results in various application scenarios. Furthermore, this method can be executed independently on the user equipment side without relying on external equipment, exhibiting strong versatility and wide applicability to various scenarios.

[0006] In one possible implementation, the UE uses the same shared prior state and covariance matrix as a unified initial benchmark for updating the two satellite sets. This ensures that the difference between the first and second posterior results only stems from the different satellite sets, eliminating the interference of inconsistent initial benchmarks on subsequent fusion processing and effectively improving the accuracy of dual-result fusion. At the same time, the design that the first and second satellite sets contain the same satellites ensures the consistency of the basic observations of the two solution links, avoids the fusion deviation caused by the complete separation of the two posterior results, further improves the reliability of the final positioning result, and better achieves the effect of taking into account different positioning performance requirements.

[0007] In one possible implementation, the UE generates a cost function based on the posterior results corresponding to each trajectory, and then dynamically calculates the fusion weight based on the cost function. This achieves adaptive adjustment of the fusion weight, avoiding the defect that fixed fusion weights cannot adapt to different application scenarios. This method can objectively quantify the actual performance of the solved trajectories corresponding to the two satellite sets, so that the trajectory with better solved quality can obtain a higher contribution ratio in the fusion process. It can give full play to the performance advantages of the corresponding satellite sets in different scenarios, effectively improve the accuracy of the fusion state, and further ensure that the final positioning result takes into account both accuracy and stability. This better solves the core technical problem that a single satellite set solution cannot take into account the multi-dimensional positioning performance.

[0008] In one possible implementation, the UE selects at least two core indicators from different dimensions to jointly construct the cost function for each trajectory. This avoids the one-sidedness of evaluating the trajectory solution quality with a single indicator and can more comprehensively and objectively quantify the actual performance of the two trajectories. The different indicators cover key quality dimensions of positioning solution such as the consistency of observation data, uncertainty of state estimation, and the degree of interference of trajectory with abnormal observations. The cost function generated in this way can more accurately reflect the true credibility of each trajectory, thereby ensuring that the fusion weights of subsequent calculations are more reasonable, effectively improving the accuracy of the fusion state, and further enhancing the accuracy and stability of the final positioning result.

[0009] In one possible implementation, during the fusion of the first and second posterior results, the UE first determines the weighted sum of the first posterior covariance matrix in the first posterior result and the second posterior covariance matrix in the second posterior result based on the first and second fusion weights. Then, based on the first and second fusion weights, it determines a conservative compensation term for the state difference between the first and second posterior states. Finally, it determines the fusion covariance based on the aforementioned weighted sum and the conservative compensation term for state differences. Calculating the fusion covariance by combining the weighted sum of covariances with the conservative compensation term for state differences not only fully preserves the uncertainty quantification information of each of the two posterior results through the weighted sum, but also reasonably corrects the fusion covariance through the conservative compensation term for state differences. This avoids the problem of overly optimistic estimation results caused by simply using a weighted sum to calculate the covariance. This method can more realistically and accurately reflect the actual uncertainty level of the fused positioning result, improve the reliability and conservatism of the fusion covariance estimation, provide a quantitative basis for the credibility assessment of the positioning result, and further improve the overall reliability of the final positioning result.

[0010] In one possible implementation, the UE (User Equipment) determines the shared prior state and the covariance matrix at the k-th epoch based on inertial measurement data, where k is a positive integer. Generating a unified shared prior state and covariance matrix using inertial measurement data provides a completely consistent initial benchmark for the subsequent calculation processes corresponding to the two satellite sets, avoiding deviations in the calculation results caused by inconsistent initial benchmarks, and improving the comparability of the two posterior results and the rationality of the fusion processing. Simultaneously, relying on the continuous and stable characteristics of inertial measurement data, a reliable initial state basis can be provided for positioning calculation, effectively improving the stability of the overall calculation process and further ensuring the accuracy and reliability of the final positioning result.

[0011] In one possible implementation, during the process of determining the shared prior state and covariance matrix of the k-th epoch based on inertial measurement data, the shared prior state of the k-th epoch is first calculated based on the inertial measurement data from the (k-1)-th epoch to the k-th epoch and the positioning result of the (k-1)-th epoch, using the state propagation model of the inertial navigation system (INS). Then, based on the statistical characteristics of INS measurement noise, the shared prior covariance of the k-th epoch is calculated using the covariance propagation formula. The UE, based on the final positioning result of the previous epoch and the inertial measurement data between adjacent epochs, recursively generates a unified shared prior state using the INS state propagation model. This improves the state continuity and accuracy of the prior state in the positioning solution process and provides a completely consistent initial benchmark for the subsequent solution processes corresponding to the two satellite sets, reducing the interference of initial state differences on the two posterior results. Meanwhile, based on the statistical characteristics of INS measurement noise, the shared prior covariance is calculated through the covariance propagation formula, which can accurately quantify the uncertainty level of the prior state, provide a reliable initial covariance benchmark for subsequent solution updates, improve the stability and accuracy of the overall positioning solution process, and further improve the reliability of the final positioning result.

[0012] In one possible implementation, the UE determines a first satellite set and a second satellite set based on a candidate observation set. The first satellite set contains all satellites in the candidate observation set, and the second satellite set contains satellites selected from the first satellite set whose observation quality meets preset conditions. The second satellite set is a proper subset of the first satellite set. By designing two types of satellite sets with different characteristics, architectural decoupling of positioning performance is achieved at the observation input level: the first satellite set fully retains all satellites in the candidate observation set, maximizing the number of satellites participating in the solution and maintaining strong satellite geometric constraints, providing a foundation for high-precision positioning; the second satellite set retains only satellites whose observation quality meets preset conditions, effectively eliminating interference from potentially abnormal observations, providing a foundation for highly robust positioning; simultaneously, the design of the second satellite set as a proper subset of the first satellite set ensures the basic observation homogeneity of the two satellite sets, providing a reasonable input basis for subsequent dual-result fusion processing, effectively supporting the core requirement of the positioning method to simultaneously achieve high accuracy and high robustness.

[0013] In one possible implementation, the satellite scores of all satellites in the second satellite set are greater than the satellite selection score threshold. These satellite scores are determined based on at least one of the following: observation innovation statistics, elevation angle, carrier-to-noise ratio, and historical reputation value. By using the satellite selection score threshold to screen satellites in the second satellite set, the UE can ensure that all satellites included in the second satellite set meet the preset observation quality standards, thus improving the overall reliability of the observation data from the second satellite set. Simultaneously, generating satellite scores based on at least one core observation quality indicator among observation innovation statistics, elevation angle, carrier-to-noise ratio, and historical reputation value allows for the objective and accurate quantification of the observation quality of individual satellites. This enhances the rationality and accuracy of the second satellite set selection, eliminates potentially low-quality or abnormal observations, and further strengthens the stability and robustness of the corresponding solution results from the second satellite set, providing reliable support for the high-quality positioning results output through dual-result fusion.

[0014] In one possible implementation, the UE constructs a satellite score for each visible satellite based on observation data from all visible satellites, and then generates a candidate observation set based on the visibility scores of each satellite. By constructing a satellite score based on observation data from all visible satellites, a unified quantitative evaluation basis can be provided for the observation quality of each visible satellite, achieving standardized judgment of satellite quality. Generating a candidate observation set based on the satellite score can complete preliminary satellite screening in the observation preprocessing stage, eliminating satellites that do not meet the quality standards in advance, effectively improving the overall observation quality of the candidate observation set, reducing invalid calculations in subsequent solution stages, and providing a reliable basic input for the construction of the first and second satellite sets, thus providing a prerequisite guarantee for the accuracy and solution efficiency of the final positioning results.

[0015] In one possible implementation, during the UE's update of the first satellite set based on the shared prior state and covariance matrix, sequential updates are performed on each satellite in the first satellite set. The update process uses the shared prior state and covariance matrix as initial values, first calculating the Kalman gain of the i-th satellite in the first satellite set, where i is an integer greater than or equal to 1. Then, the covariance matrix and state corresponding to the i-th satellite are updated based on the calculated Kalman gain. This satellite-by-satellite sequential update approach solves and updates the first satellite set, using a unified shared prior state and covariance matrix as the initial benchmark for all satellite updates. This avoids solution deviations caused by differences in initial benchmarks and ensures the consistency of the solution process for all satellites within the first satellite set. By calculating the Kalman gain for each satellite and updating the state and covariance matrix accordingly, the observation information of each satellite can be absorbed sequentially, achieving gradual optimization of the state estimation and effectively improving the accuracy of the corresponding posterior results for the first satellite set. Simultaneously, the sequential update solution method has clear computational logic and controllable complexity, facilitating implementation and deployment on the user equipment side, and providing a reliable first posterior result foundation for subsequent dual-result fusion.

[0016] In one possible implementation, after determining the positioning result at the current epoch, the user equipment further determines the observation consistency assessment result of each candidate satellite in the candidate observation set based on the obtained positioning result at the current epoch. Using the final positioning result determined at the current epoch as a benchmark, the observation consistency of each candidate satellite is evaluated in reverse. Compared to the pre-evaluation method based on prior states, this evaluation method uses the optimal estimate result at the current epoch as the reference benchmark, which can more realistically and accurately reflect the degree of matching between the observation data of each candidate satellite and the optimal positioning result, and accurately identify satellites with observational biases. Simultaneously, this observation consistency assessment result can provide a reliable basis for the quantitative evaluation of satellite observation quality, provide accurate basic data for the quality optimization of the positioning process, and further improve the overall reliability and rationality of the positioning method.

[0017] In one possible implementation, during the process of determining the observation consistency assessment results for each satellite in the candidate observation set, for each candidate satellite in the candidate observation set, the posterior residual of the candidate satellite is first determined using the positioning result of the current epoch as a reference state. The posterior residual is the difference between the measured observation vector of the candidate satellite and the predicted observation vector predicted based on the reference state. Then, the historical reputation value of the candidate satellite is recursively updated based on the posterior residual. By calculating the posterior residual using the final positioning result of the current epoch as a reference state, the UE can more realistically and accurately reflect the degree of matching between the observation data of a single satellite and the optimal positioning result, and accurately quantify the actual error level of single-satellite observations. Furthermore, by recursively updating the historical reputation value of the satellite based on the posterior residual, the observation consistency assessment result of a single epoch can be transformed into a long-term quality record of the satellite, effectively avoiding the interference of random errors in a single epoch, realizing continuous tracking and quantitative assessment of satellite observation quality, providing a reliable satellite quality basis for subsequent optimization of the positioning process, and further improving the rationality and reliability of the positioning method.

[0018] In one possible implementation, the UE adjusts the selection rules for the second satellite set and / or the measurement weights of the first satellite set in the next epoch based on the updated historical reputation values ​​of each candidate satellite. By applying the satellite historical reputation values ​​obtained in the current epoch to the positioning solution parameters in the next epoch, closed-loop adaptive optimization of the positioning process is achieved. By adjusting the selection rules for the second satellite set, the long-term observation quality of the satellites can be dynamically adapted, improving the overall observation reliability of the second satellite set and further enhancing the robustness of the corresponding solution results. By adjusting the measurement weights of the first satellite set, the contribution of satellites of different qualities in the solution process can be dynamically allocated, reducing the negative impact of low-quality satellites on the high-precision solution branch.

[0019] In one possible implementation, when the UE adjusts the selection rules for the second satellite set in the next epoch, candidate satellites with satellite scores higher than a preset selection threshold are identified as satellites in the second satellite set for the next epoch. These satellite scores are determined based on updated historical reputation values. When adjusting the measurement weights of the first satellite set in the next epoch, the measurement noise covariance of the candidate satellite in the first satellite set is adjusted according to the updated historical reputation values. On the one hand, generating satellite scores based on updated historical reputation values ​​and then combining them with the preset selection threshold to select the second satellite set incorporates the long-term observation quality of satellites into the selection criteria, improving the rationality and accuracy of the second satellite set selection, ensuring the overall observation reliability of the second satellite set, and enhancing the robustness of the corresponding solution results. On the other hand, dynamically adjusting the measurement noise covariance of satellites in the first satellite set through updated historical reputation values ​​dynamically allocates the contribution of satellites in the solution process according to their long-term observation quality, reducing the negative impact of low-quality satellites on the solution results of the first satellite set, improving the accuracy of the corresponding solution results of the first satellite set, and further supporting the core requirement of the final positioning result to simultaneously achieve high accuracy and high robustness.

[0020] In one possible implementation, the UE sends a first signaling message to the network device and receives a second signaling message from the network device. The second signaling message carries an auxiliary satellite selection configuration, which is generated by the network device based on the received first signaling message and combined with global auxiliary data from the network side. The UE adjusts the candidate observation set, second satellite set, or measurement weights for the next epoch according to the auxiliary satellite selection configuration. This terminal-network collaborative positioning optimization is achieved through the interaction between the terminal and the network device. Leveraging the information advantage of global auxiliary data from the network side, it overcomes the limitations of the UE relying solely on local observation data for satellite selection and weight adjustment. The network device, combining the information reported by the UE with global auxiliary data to generate the auxiliary satellite selection configuration, provides the UE with more comprehensive and accurate guidance for satellite selection and weight adjustment. By adjusting the candidate observation set, second satellite set, or measurement weights for the next epoch, the rationality of the positioning solution input is further improved, enhancing the adaptability of the positioning method in complex scenarios and improving the accuracy and stability of the final positioning result.

[0021] In one possible implementation, the auxiliary satellite selection configuration includes one or more of the following: recommended satellite set, priority robust subset, satellite confidence information, satellite selection threshold, and feedback mode indication. By clearly defining multiple optional contents for the auxiliary satellite selection configuration, covering various optimization dimensions of end-to-end network collaborative positioning such as specific satellite recommendations, robust subset priority settings, satellite quality quantification reference, satellite selection rule parameter adjustments, and end-to-end interaction mode settings, the network side can flexibly select and issue one or more configuration contents based on global auxiliary data and actual scenario requirements. This ensures the flexibility and scenario adaptability of the auxiliary satellite selection configuration, provides UE with multi-dimensional positioning optimization guidance, enriches the optimization methods of end-to-end network collaborative positioning, improves the practicality of the end-to-end network collaborative positioning mechanism, and further supports the improvement of terminal positioning accuracy and stability.

[0022] Secondly, this application also provides a positioning method applied to a network device, wherein the network device sends indication information to a user device, the indication information being information for indicating a first satellite set and a second satellite set, so that the user device determines a first posterior result based on the first satellite set and a second posterior result based on the second satellite set, and fuses the first posterior result and the second posterior result to determine a positioning result.

[0023] Thirdly, this application provides a UE, which includes a transceiver and a processor; wherein the transceiver is used to perform the receiving operation and the transmitting operation in the method described in the first aspect or any embodiment of the first aspect; and the processor is used to perform other operations in the method described in the first aspect or any embodiment of the first aspect besides the receiving operation and the transmitting operation.

[0024] Fourthly, this application provides a network element, which includes a transceiver and a processor; wherein the transceiver is used to perform the receiving operation and the transmitting operation in the method described in the second aspect or any embodiment of the second aspect; and the processor is used to perform other operations in the method described in the second aspect or any embodiment of the second aspect besides the receiving operation and the transmitting operation.

[0025] Fifthly, this application provides a communication system including a UE and a network element, wherein the UE is used to execute the method described in the first aspect or any embodiment of the first aspect; and the network element is used to execute the method described in the second aspect or any embodiment of the second aspect.

[0026] Sixthly, this application provides a computer storage medium for storing a computer program, which, when executed, implements any of the positioning methods provided in the first to second aspects of this application.

[0027] In a seventh aspect, this application provides a computer program product including instructions that, when run on at least one computing device, cause the at least one computing device to implement any of the positioning methods provided in the first to second aspects of this application. Attached Figure Description

[0028] Figure 1 This application provides a schematic diagram of the architecture of a communication system. Figure 2 A flowchart illustrating a positioning method provided in an embodiment of this application; Figure 3 A flowchart illustrating another positioning method provided in an embodiment of this application; Figure 4 A flowchart illustrating another positioning method provided in an embodiment of this application; Figure 5 A flowchart illustrating the interaction between a UE and a network device is provided in this application embodiment. Figure 6 This application provides a schematic diagram of the structure of a network device according to an embodiment of the present application. Figure 7 This is a schematic diagram of the structure of a UE provided in an embodiment of this application. Detailed Implementation

[0029] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. The terminology used in the following embodiments is for the purpose of describing specific embodiments only and is not intended to be a limitation of this application. As used in the specification of this application, the singular expressions "a," "an," "the," "the," "the," and "this" are intended to also include expressions such as "one or more," unless the context clearly indicates otherwise. It should also be understood that in the embodiments of this application, "one or more" refers to one, two, or more; "and / or" describes the relationship between related objects, indicating that three relationships can exist; for example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship.

[0030] References to "one embodiment" or "some embodiments" as used in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless otherwise specifically emphasized.

[0031] The "multiple" mentioned in the embodiments of this application refers to two or more. It should be noted that in the description of the embodiments of this application, terms such as "first" and "second" are used only for the purpose of distinguishing descriptions and should not be construed as indicating or implying relative importance, nor should they be construed as indicating or implying order.

[0032] As mentioned earlier, 6G ISAC technology is rapidly penetrating the industrial sector. Core scenarios such as intelligent connected vehicles, autonomous driving, and the Industrial Internet of Things (IIoT) place dual demands on positioning services, requiring both high precision and robustness. This necessitates not only centimeter- to decimeter-level positioning accuracy in open environments but also continuous and stable positioning results under complex dynamic conditions such as tall building obstructions, multipath interference, and intermittent satellite signals. GNSS / INS tightly coupled positioning technology, as the most mature combined positioning solution currently available, has become the standard technology for these scenarios.

[0033] Existing GNSS / INS tightly coupled positioning systems all rely on a single extended Kalman filter (EKF) link for state estimation. Currently, they primarily employ either full satellite observation or partial satellite observation methods.

[0034] However, both of the above observation methods have certain drawbacks. While using full satellite observations can maintain high positioning accuracy in open environments, abnormal satellite observations in complex environments can directly contaminate the filtering results, leading to positioning errors. Using anomaly removal methods, while improving anti-interference capabilities, weakens the strength of satellite geometric constraints, causing a decrease in positioning accuracy. Existing tightly coupled GNSS / INS positioning systems cannot simultaneously meet the requirements of high accuracy and high robustness, making them unsuitable for positioning scenarios in complex and dynamic environments.

[0035] To address this, this application provides a positioning method. In this method, the UE first completes unified state propagation via INS to obtain the shared prior state and its covariance between the two trajectories at the current epoch. Then, a candidate observation set is constructed based on the received satellite observation data, and a full satellite set including all available satellites and a robust subset including only high-confidence satellites are generated from the candidate observation set. Next, using the same shared prior state and its corresponding covariance matrix as initial values, EKF tightly coupled updates are performed in parallel based on the full satellite set and the robust subset, respectively, to obtain two independent posterior state estimates. Finally, the two posterior state estimates are fused to output the final positioning result for the current epoch.

[0036] This application embodiment generates a shared initial state reference for two trajectories using INS, performs filtering updates based on all available satellites and high-reliability satellites respectively, and fuses the two independent state estimation results to obtain the final positioning output. Among them, the update process based on all available satellites can maintain strong satellite geometric constraints and ensure the accuracy of the positioning results, while the update process based on high-reliability satellites can avoid interference from abnormal observations and ensure the stability of the positioning results. The fused positioning result can combine the advantages of both and provide accurate and stable positioning services in different environments, meeting the positioning needs in complex dynamic scenarios.

[0037] The technical solutions provided in this application can be used in any communication system, such as 3GPP communication systems, including radio frequency identification (RFID) systems, long-term evolution (LTE) systems, fifth-generation (5G) mobile communication systems, new radio (NR) communication systems, vehicle-to-everything (V2X) systems, LTE and 5G hybrid networking systems, non-terrestrial network (NTN) systems, device-to-device (D2D) communication systems, machine-to-machine (M2M) communication systems, Internet of Things (IoT) systems, ambient IoT (AIoT) systems, universal mobile telecommunications systems (UMTS) systems, code division multiple access (CDMA) systems, and other next-generation communication systems, such as 6G and other future communication systems. They can also be non-3GPP communication systems, such as wireless local area networks (WLANs). (network, WLAN, etc.) are not limited.

[0038] The communication system provided in this application embodiment is used to achieve tightly coupled GNSS / INS positioning based on dual-track EKF fusion. An example of the communication system is as follows... Figure 1 As shown, the communication system includes network equipment and at least one user equipment (UE), such as UE1 and UE2.

[0039] Network equipment refers to devices located on the network side with wireless transceiver capabilities, responsible for providing mobile communication access services to user equipment and providing positioning assistance information in the end-to-end network collaborative enhancement mode. Network equipment includes, but is not limited to, base stations (gNodeBs or gNBs) or Transmission Reception Points (TRPs) in New Radio (NR), specifically macro base stations, micro base stations, pico base stations, small cells, relay stations, or balloon stations; it can also be radio controllers, centralized units (CUs), and / or distributed units (DUs) in Cloud Radio Access Network (CRAN) scenarios. Network equipment may include one or more co-located or non-co-located TRPs, which can communicate directly with user equipment or through relay stations. In this solution, the core function of the network equipment is to receive the positioning fusion results and satellite selection information reported by the user equipment, combine them with global auxiliary data from the network side to generate auxiliary satellite selection configurations, and distribute them to the user equipment to achieve closed-loop satellite selection enhancement through end-to-end network collaboration.

[0040] A User Equipment (UE) is a terminal device with GNSS reception and inertial measurement capabilities. A UE can be a mobile phone, tablet, computer with wireless transceiver capabilities, virtual reality (VR) user equipment, augmented reality (AR) user equipment, a wireless terminal in industrial control, a vehicle-mounted user equipment, a wireless terminal in self-driving vehicles, a wireless terminal in remote medical care, a wireless terminal in a smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home, a wearable user equipment, etc. A UE may also be referred to as user equipment, access user equipment, vehicle-mounted terminal, industrial control terminal, UE unit, UE station, mobile station, mobile station, remote station, remote user equipment, mobile device, UE user equipment, user equipment, wireless communication equipment, UE agent, or UE device, etc. A terminal can also be a fixed terminal or a mobile terminal.

[0041] UE1 and UE2 are implemented in the same way. Both can independently complete dual-track tightly coupled positioning on the pure terminal side, and can also interact with network devices to achieve end-to-end network collaborative enhanced positioning. The UE can communicate with multiple network devices of different technologies. For example, it can simultaneously establish dual connections with network devices that support Long Term Evolution (LTE) networks and network devices that support 5G networks to obtain more stable communication links and more comprehensive auxiliary information.

[0042] In the embodiments provided in this application, UE1 can be in various forms, such as a mobile phone, tablet computer, computer with wireless transceiver capabilities, virtual reality (VR) user equipment, augmented reality (AR) user equipment, wireless terminal in industrial control, vehicle-mounted user equipment, wireless terminal in self-driving, wireless terminal in remote medical care, wireless terminal in smart grid, wireless terminal in transportation safety, wireless terminal in smart city, wireless terminal in smart home, wearable user equipment, etc. UE can sometimes also be referred to as user equipment, access user equipment, vehicle-mounted terminal, industrial control terminal, UE unit, UE station, mobile station, mobile station, remote station, remote user equipment, mobile device, UE user equipment, user equipment, wireless communication equipment, UE agent, or UE device, etc. The terminal can also be a fixed terminal or a mobile terminal. The implementation of UE2 is similar to that of UE1, and will not be described in detail here.

[0043] The communication system in this application supports both UE-independent operation mode and end-to-end network collaborative enhancement mode. In UE-independent operation mode, the UE does not need to interact with network devices for positioning; it can complete the entire positioning calculation process solely using its own GNSS receiver and INS module. In end-to-end network collaborative enhancement mode, the UE and network devices interact through a dedicated signaling mechanism added in this scheme. The UE reports the fused positioning results and satellite selection summary to the network device, and the network device sends auxiliary satellite selection configuration to the UE, jointly achieving better positioning performance. The communication system can include any number of UEs and network devices. Multiple UEs can simultaneously report positioning information to the same network device, and the network device can generate a unified auxiliary configuration based on global observation data from multiple users, providing enhanced positioning services to all UEs within the area.

[0044] The above description uses a communication system including network equipment and a UE as an example. In other possible implementations, the network equipment in the communication system can be replaced with other types of network elements, and this is not limited. For ease of understanding, the following description will still use a UE and a network equipment as examples.

[0045] The positioning method provided in the embodiments of this application will now be described in conjunction with the accompanying drawings.

[0046] This embodiment provides a dual-track GNSS / INS tightly coupled positioning method based on shared INS priors, which addresses the core technical problem in existing technologies that cannot simultaneously achieve both positioning accuracy and robustness when using a single filtered trajectory for positioning. The core inventive concept is: within the same epoch, based on the shared prior state obtained through unified INS propagation, a "precision-first" full-set filtered trajectory and a "robustness-first" robust subset filtered trajectory are constructed simultaneously. After parallel tightly coupled updates, the results are fused to obtain the final positioning result, thus breaking the inherent limitations of a single trajectory in terms of architecture.

[0047] like Figure 2 As shown, Figure 2 This is a flowchart illustrating a positioning method provided in an embodiment of this application. The method includes: S201: The UE generates the shared prior state and the covariance matrix of the kth epoch.

[0048] In this embodiment, the k-th epoch can refer to the current epoch, which is the time interval between two consecutive positioning calculations in the satellite navigation system, for example, 1 second. All parameters generated in step S201 correspond to the time when the positioning result needs to be calculated. Shared prior state refers to the state estimate obtained by INS calculation before correction using GNSS observation data. The covariance matrix is ​​a matrix used to quantify the uncertainty of the corresponding prior state estimate. The larger the matrix element value, the greater the estimation error of the corresponding state and the lower the reliability.

[0049] The UE can use the fused and corrected positioning result from the previous epoch as the initial reference for the current state estimation. Based on the inertial measurement data from the gyroscope and accelerometer in the INS between two adjacent epochs, and combined with the inertial navigation mechanics arrangement rules, the UE calculates the system state of the current epoch, i.e., the shared prior state of the k-th epoch. Simultaneously, based on the statistical characteristics of INS measurement noise, the UE calculates the covariance matrix of the k-th epoch using the covariance propagation formula. The generated shared prior state and the covariance matrix of the k-th epoch will serve as the common initial input for multiple trajectories. The fused and corrected positioning result from the previous epoch can be stored locally by the UE or obtained by the UE from the network device.

[0050] Inertial measurement data consists of measurements from the gyroscope and accelerometer in the INS, which can be generated in real time by the Inertial Measurement Unit (IMU) integrated within the UE. The gyroscope measures the UE's angular velocity, and the accelerometer measures the UE's specific force. The IMU's acquisition frequency is typically higher than the positioning calculation frequency. Step S201 can use all IMU sampled data from the previous epoch calculation time to the current epoch calculation time.

[0051] The UE completes a state propagation through INS to provide a completely consistent initial benchmark for multiple subsequent filtered trajectories. This ensures that all subsequent differences between multiple trajectories only come from different satellite observation sets used, avoiding distortion of the fusion results due to inconsistent initial benchmarks.

[0052] To further clarify the specific generation method of the shared prior state and the covariance matrix of the k-th epoch, this application embodiment also provides an implementation method of unified state propagation based on the error state model, as follows: This application adopts the error state model commonly used in tightly coupled GNSS / INS systems, and defines the system error state vector as follows: ; in, Let be the system error state vector of the k-th epoch. Let $\frac{k}{k}$ be the position error at epoch k. The velocity error in the k-th epoch is... Let be the attitude error at the k-th epoch. The gyroscope has zero bias in the k-th epoch. The accelerometer bias is zero at the k-th epoch. Let be the receiver clock bias at epoch k. The receiver clock drift at epoch k is indicated by the superscript. This is a transpose.

[0053] The system propagation model is as follows: ; ; in, For the predicted state in the k-th epoch, This represents the update state of the (k-1)th epoch (which is also the fusion state of the (k-1)th epoch). Let be the state transition matrix for the (k-1)th epoch. For the noise of the (k-1)th epoch, This is the predicted covariance matrix for the k-th epoch (which is also the covariance matrix for the k-th epoch). Let be the updated covariance matrix for the (k-1)th epoch. Let $\mathbf{k-1}$ be the process noise covariance, where $\mathbf{k-}$ represents the predicted value and $\mathbf{k-}$ represents the updated value. Let k be the shared prior state of the k-th epoch. k is a positive integer.

[0054] In this embodiment of the application, multiple subsequent trajectories or multiple satellite sets share the same prior state and covariance matrix. , ).

[0055] Step S201 described above introduced how the UE determines the shared prior state and covariance matrix. In another possible implementation, the network device can determine the shared prior state and covariance matrix and inform the UE. The UE can then perform subsequent steps based on the shared prior state and covariance matrix determined by the network device. The implementation method for the network device to determine the shared prior state and covariance matrix can be found in step S201 and will not be repeated here.

[0056] S202: The UE generates a candidate observation set based on the observation data of all visible satellites.

[0057] The UE filters satellites based on observation data from all visible satellites to identify those that are initially usable, and then includes all initially usable satellites in the candidate observation set. In other words, the candidate observation set refers to the collection of all satellites that, after a basic availability check, meet the fundamental conditions for participating in positioning calculations.

[0058] Visible satellites are those whose transmitted signals, at the current moment, can be received by the UE's GNSS antenna and whose signal strength meets the basic acquisition requirements. Observational data for visible satellites includes pseudorange and Doppler observations. Pseudorange observations are distance values ​​obtained by multiplying the propagation time of the satellite signal from transmission to reception by the speed of light, as measured by the GNSS receiver; these are used to determine the geometric distance between the satellite and the receiver. Doppler observations refer to the frequency offset of the satellite signal caused by the relative motion between the satellite and the GNSS receiver, which can be converted into their relative velocity.

[0059] The UE can acquire observation data from all visible satellites through its local GNSS receiver. The UE's integrated antenna receives satellite signals from different satellite navigation systems. After signal processing including signal acquisition, carrier tracking, code tracking, and demodulation, basic observation data such as pseudorange, Doppler, and ephemeris for each visible satellite are extracted. In scenarios such as initial UE startup, cold start, or poor signal conditions, the UE can also receive auxiliary information such as ephemeris and satellite visibility forecasts from network devices to quickly acquire observation data from all visible satellites.

[0060] In one possible implementation, the UE can perform a basic availability check on the observation data of each satellite. This check includes, but is not limited to, elevation angle threshold checks, carrier-to-noise ratio (C / N0) threshold checks, and the removal of observations showing significant lock-out. The elevation angle is the angle between the line connecting the satellite and the receiver and the horizontal plane. A lower elevation angle results in a longer path for the satellite signal through the atmosphere, making it more susceptible to atmospheric refraction and multipath interference. The carrier-to-noise ratio is the ratio of the satellite signal carrier power to the noise power, reflecting the strength and quality of the satellite signal. The UE can directly remove satellites that fail the basic availability check, and the remaining satellites form the candidate observation set for the current epoch.

[0061] In another possible implementation, embodiments of this application also provide a method for generating candidate observation sets based on innovation statistics and multi-dimensional scoring. Specifically, the UE constructs satellite scores for each visible satellite based on observation data from all visible satellites, and generates candidate observation sets based on the visibility scores of each satellite.

[0062] In the k-th epoch, the pseudorange and Doppler observations for the i-th satellite are expressed as follows: ; in, For the pseudorange observation of the i-th satellite at the k-th epoch, For the Doppler observations of the i-th satellite at the k-th epoch, Let be the measured pseudorange of the i-th satellite at the k-th epoch. Let be the measured pseudorange rate of the i-th satellite in the k-th epoch. i is a positive integer.

[0063] UE shares prior states based on the k-th epoch. The corresponding observed prediction values ​​can be constructed: ; in, Let be the pseudorange prediction value of the i-th satellite in the k-th epoch. The Doppler prediction value of the i-th satellite in the k-th epoch; Let i be the pseudorange observation function for the i-th satellite. Let be the Doppler observation function for the i-th satellite.

[0064] Furthermore, the single-star observation vector and the single-star predicted observation vector can be expressed as: ; in, Let be the single-satellite observation vector of the i-th satellite at the k-th epoch. Let be the single-satellite prediction observation vector of the i-th satellite at the k-th epoch, calculated from the prior state.

[0065] Furthermore, single-star innovation can be expressed as: ; in, Let be the innovation vector of the i-th satellite in the k-th epoch.

[0066] Furthermore, the linearized single-star observation equation can be expressed as: ; in, Let be the single-satellite observation matrix of the i-th satellite in the k-th epoch, which is the first-order linearized Jacobian of the observation function with respect to the state; Let be the state error vector of the k-th epoch; To measure the noise, it follows a mean of 0 and a covariance matrix of... Gaussian distribution; Let be the single-satellite observation noise covariance matrix of the i-th satellite at the k-th epoch.

[0067] Furthermore, the Normalized Innovation Squared (NIS) is calculated for each candidate satellite: ; in, Let the normalized squared innovation of the i-th satellite in the k-th epoch be... Let be the innovation covariance matrix of the i-th satellite in the k-th epoch. Let be the inverse of the innovation covariance matrix of the i-th satellite at the k-th epoch; the superscript T denotes transpose.

[0068] Furthermore, the innovation covariance matrix of the i-th satellite at the k-th epoch... The calculation formula is: ; in, Let i be the single-satellite innovation covariance matrix of the i-th satellite at the k-th epoch; Let be the single-satellite observation matrix of the i-th satellite at the k-th epoch; Let be the covariance matrix of the prior states in the k-th epoch; Let be the single-satellite observation noise covariance matrix of the i-th satellite at the k-th epoch.

[0069] UE constructs a satellite score by combining NIS, elevation angle, carrier-to-noise ratio, and historical reputation: ; in, Give the satellite score for the i-th satellite in the k-th epoch; For scoring functions; Let be the satellite elevation angle of the i-th satellite in the k-th epoch; Let be the carrier-to-noise ratio of the i-th satellite in the k-th epoch. Let be the historical reputation value of the i-th satellite in the k-th epoch.

[0070] The carrier-to-noise ratio (CNR) can be estimated by the UE's GNSS receiver during signal acquisition and tracking based on measurements such as signal correlation peaks and noise power. It is typically output directly as the raw GNSS observation or observation quality indicator. The satellite elevation angle is not obtained directly from the signal strength of a single satellite, but rather by calculating the satellite position based on the satellite ephemeris, the UE's approximate position, or the prior GNSS / INS position, and further calculating the satellite's elevation angle relative to the UE's local horizontal coordinate system.

[0071] The candidate observation set after the basic availability check at epoch k can be defined as: ; in: For the kth epoch The candidate observation set; i is the satellite number; k is the epoch number.

[0072] In one possible implementation, basic gating includes, but is not limited to, satellite elevation angle threshold, carrier-to-noise ratio threshold, and rejection of obvious loss of lock or serious anomaly observations.

[0073] In one possible implementation, the construction of the candidate observation set can also be modified by combining network-side auxiliary configuration provided by the network device. Network-side auxiliary configuration can compensate for the limitations of local observation by the terminal. The UE can only evaluate the availability of a single satellite based on the signal quality it receives, while the network device can aggregate global auxiliary data such as observation data from multiple UEs in the same area, satellite ephemeris predictions, ionospheric activity monitoring data, and terrain obstruction information to generate more comprehensive and accurate satellite availability assessment results, providing global guidance for the construction of the candidate observation set.

[0074] Network-side auxiliary configurations include one or more of the following: candidate observation set configuration, priority satellite subset configuration, satellite confidence configuration, and set update trigger configuration. The candidate observation set configuration is a recommended satellite list generated by the network based on satellite visibility forecasts for the current area. The UE can directly use this list as the selection range for candidate satellites, eliminating the need to search all on-orbit satellites and significantly reducing the UE's signal acquisition time and computational load. The priority satellite subset configuration is a subset of satellites with stable signal quality and no obvious anomalies in the current area, selected by the network based on observation data reported by multiple UEs. The UE can prioritize including these satellites in the candidate observation set, improving the speed and reliability of initial positioning calculations. The satellite confidence configuration is a quantified confidence value assigned to each satellite by the network, reflecting the overall observation quality of that satellite in the current area. The UE can combine this confidence value with locally observed parameters such as elevation angle and carrier-to-noise ratio to more accurately assess the availability of individual satellites. The set update trigger configuration is a trigger command sent by the network side to the UE. When the satellite constellation changes, there is large-scale signal interference in the area, or the terrain obstruction conditions change, the network side can use this command to trigger the UE to re-execute the candidate observation set screening process to ensure the timeliness of the candidate set.

[0075] After receiving auxiliary configuration from the network side, the UE does not directly replace the local basic availability check. Instead, it jointly processes the network-side configuration with local observation data. For example, for satellites in the candidate observation set recommended by the network side, the UE still needs to perform local elevation angle and carrier-to-noise ratio threshold checks, and include satellites that pass both the network-side recommendation and the local checks in the final candidate observation set. For satellites marked as high confidence by the network side, the UE can appropriately relax the local basic thresholds to avoid mistakenly removing high-quality satellites. For satellites marked as low confidence by the network side, the UE can appropriately raise the local basic thresholds to remove potentially abnormal satellites in advance.

[0076] This candidate observation set construction method, which combines network-side assistance with local observation, retains the real-time and targeted nature of UE local observations while making full use of the global information advantages of the network side. It can improve the quality of the candidate observation set in complex environments and reduce the probability of abnormal satellites entering the subsequent filtering and solving process.

[0077] Step S202 described above introduced how the UE determines the candidate observation set. In another possible implementation, the network device can also determine the candidate observation set and inform the UE, allowing the UE to perform subsequent steps based on the candidate observation set determined by the network device. The implementation method for the network device to determine the candidate observation set can be found in the description of step S202, and will not be repeated here.

[0078] S203: The UE constructs a first satellite set and a second satellite set based on the candidate observation set.

[0079] The first satellite set is the whole satellite set, the second satellite set is a robust subset, and the robust subset is a proper subset of the whole satellite set.

[0080] The UE can construct two satellite sets with different characteristics based on the candidate observation set, providing input for subsequent dual-track parallel filtering updates. The first satellite set, also called the full satellite set, refers to the complete set including all satellites in the candidate observation set. The second satellite set, also called the robust subset, refers to the set of satellites with higher observation quality selected from the full satellite set. All elements of the robust subset belong to the full satellite set, and the number of elements in the robust subset is less than or equal to the number of elements in the full satellite set.

[0081] In one possible implementation, the UE can use a candidate observation set as the first satellite set without performing any additional satellite removal operations. Furthermore, based on the fundamental quality indicators of each satellite, the UE selects satellites from the first satellite set that meet preset conditions to form a second satellite set. These fundamental quality indicators may include parameters reflecting the reliability of satellite observation data, such as satellite elevation angle, carrier-to-noise ratio, and observation consistency. The UE can set one or more screening thresholds as preset conditions, including satellites whose fundamental quality indicators meet the threshold requirements in the second satellite set, while excluding satellites that do not meet the requirements. After construction, the second satellite set is strictly a proper subset of the first satellite set; that is, all satellites in the second satellite set are included in the first satellite set.

[0082] This application embodiment achieves architectural separation between positioning accuracy and robustness by constructing two satellite sets with different characteristics. The satellite set maximizes the number of satellites participating in the solution, maintaining strong satellite geometric constraints and providing a foundation for subsequent high-precision positioning solutions; the robust subset eliminates potentially anomalous satellites with poor observation quality, effectively reducing the interference of anomalous observations on positioning solutions and providing a foundation for subsequent highly robust positioning solutions.

[0083] To further clarify the specific construction rules of the first and second satellite sets, this application also provides a method for constructing a dual satellite set based on a candidate observation set.

[0084] Specifically, the first trajectory corresponds to the first satellite set, i.e., the entire satellite set, which can be represented as: ; in, This is the full star set used by the first trajectory at the k-th epoch. Let be the candidate observation set for the k-th epoch. The full set directly and completely inherits all satellites from the candidate observation set without performing satellite removal operations, ensuring that the number of satellites participating in the solution is maximized and maintaining the optimal satellite geometric constraint strength.

[0085] The second trajectory corresponds to the second satellite set, i.e., the robust subset, which can be represented as: ; in, For the robust subset used by the second trajectory at epoch k, This is the star selection scoring threshold for the second trajectory. The star selection scoring threshold can be determined based on the actual situation.

[0086] The robust subset retains only high-reliability satellites whose overall score in the full satellite set is higher than the satellite selection score threshold. The first satellite set and the second satellite set satisfy the subset relationship: ; in," "" indicates a subset relationship, meaning that all elements of a robust subset belong to the whole set.

[0087] It should be noted that the embodiments in this application only illustrate the construction of the first and second satellite sets. In practical applications, three or more satellite observation sets with different screening conditions and characteristics can be constructed based on the candidate observation sets according to different positioning performance requirements, so as to achieve more accurate positioning.

[0088] S204: The UE updates the first satellite set and the second satellite set based on the shared prior state of the k-th epoch and the covariance matrix of the k-th epoch, respectively, to obtain the first posterior result and the second posterior result.

[0089] UE shares prior states based on the k-th epoch. and the covariance matrix of the k-th epoch Tightly coupled Kalman filtering updates are performed on the first and second satellite sets respectively, yielding first and second posterior results. Tightly coupled Kalman filtering updates refer to a filtering update method that fuses the INS state estimate with GNSS pseudorange and Doppler observations, unlike loosely coupled updates which first calculate the GNSS position separately and then fuse it with the INS. The first posterior result is based on the update of the first satellite set, and the second posterior result is based on the update of the second satellite set. Both filtering branches share the same prior state. Covariance As an initial input, ensure that all subsequent differences arise only from the different sets of satellite observations used.

[0090] The two filtering branches can execute their update processes in parallel and independently, without interfering with each other. For each branch, the UE extracts the pseudorange and Doppler observations for each satellite based on the satellite set corresponding to that filtering branch. Then, the UE calculates the residual between the predicted and actual observations and calculates the Kalman gain by combining the statistical characteristics of the observation noise. Finally, the UE uses the Kalman gain to correct the shared prior state, obtains the posterior state of that filtering branch, and updates the corresponding covariance matrix.

[0091] This application's embodiments achieve parallel optimization of positioning accuracy and robustness through tightly coupled updates using dual filtering branches. The update branch based on the full satellite set maximizes the strength of satellite geometric constraints, enabling high positioning accuracy in open environments; the update branch based on the robust subset eliminates interference from potential anomalous observations, maintaining the stability of positioning results in complex environments. The two filtering branches share the same initial benchmark, improving the comparability of the two posterior results.

[0092] To further clarify the specific implementation of the dual-branch tightly coupled Kalman filter update, this application also provides a refined implementation of the dual-trajectory tightly coupled filter based on sequential updates.

[0093] In one possible implementation, during the update process of the first satellite set corresponding to the first trajectory, the initial value adopts the shared prior state of the k-th epoch. and the covariance matrix of the k-th epoch The first trajectory can be represented as: ; in: This represents the initial state of the first trajectory before the sequential update begins at the k-th epoch. Let be the initial covariance matrix of the first trajectory before the sequential update begins at the k-th epoch.

[0094] For the i-th satellite in the first satellite set corresponding to the first trajectory ( Sequential updates consist of three steps: First, calculate the Kalman gain: ; in, Let K be the Kalman gain of the first trajectory for the i-th satellite. Kalman gain is a coefficient used to measure the reliability of the observation. The larger the gain, the higher the weight of the observation in the state correction. The covariance matrix of the first trajectory before processing the i-th satellite; Let be the single-satellite observation matrix of the i-th satellite at the k-th epoch; Let be the measurement noise covariance of the i-th satellite in the first trajectory at the k-th epoch. Slight weighting can be applied based on satellite ratings: ; in, The measurement weighting coefficients for the first trajectory are... The larger the value, the lower the level of confidence in the satellite's observations.

[0095] Then, perform a status update: ; in, For the first trajectory in absorbing the first The state after the satellite For the first trajectory to absorb the first The state in front of the satellite, For the first trajectory pair Satellite innovation.

[0096] Finally, the covariance matrix is ​​updated: ; in, For the first trajectory in absorbing the first The covariance matrix after satellite, It is an identity matrix.

[0097] After the update is completed, the posterior result of the first trajectory is obtained: ; in, This represents the first posterior state of the first trajectory. Let be the first posterior covariance matrix of the first trajectory.

[0098] In another possible implementation, during the update process of the second satellite set corresponding to the second trajectory, the initial value is uniformly adopted as the shared prior state of the k-th epoch. and the covariance matrix of the k-th epoch The first trajectory can be represented as: ; in: This represents the initial state of the second trajectory before the sequential update begins at the k-th epoch. Let be the initial covariance matrix of the second trajectory before the sequential update begins at the k-th epoch.

[0099] The second trajectory uses the exact same filtering architecture as the first trajectory, and for the l-th satellite in the second satellite set corresponding to the second trajectory ( Sequential updates consist of three steps: First, calculate the Kalman gain: ; in, Let K be the Kalman gain of the second trajectory for the j-th satellite. Kalman gain is a coefficient used to measure the reliability of the observation. The larger the gain, the higher the weight of the observation in the state correction. The covariance matrix of the second trajectory before processing the j-th satellite; Let be the single-satellite observation matrix of the j-th satellite at the k-th epoch; Let be the measurement noise covariance of the j-th satellite in the second trajectory at the k-th epoch. Slight weighting can be applied based on satellite ratings: ; in, The measurement weighting coefficients for the second trajectory are... The larger the value, the lower the level of confidence in the satellite's observations.

[0100] Then, perform a status update: ; in, This represents the state of the second trajectory after absorbing the j-th satellite. For the second trajectory to absorb the first The state in front of the satellite, This is an innovation for the second trajectory on the j-th satellite.

[0101] Finally, the covariance matrix is ​​updated: ; in, Let be the covariance matrix of the second trajectory after absorbing the j-th satellite. It is an identity matrix.

[0102] After the update, the posterior result of the second trajectory is obtained: ; in, This represents the second posterior state of the second trajectory. Let be the second posterior covariance matrix of the second trajectory.

[0103] The two filtering branches are executed in parallel and independently, without affecting each other, ensuring that the two posterior estimates differ only due to the different satellite observation sets used.

[0104] S205: The UE fuses the first and second a posteriori results to determine the positioning result for the current epoch.

[0105] The UE fuses the first and second posterior results to generate the optimal positioning result for the current epoch. The first posterior result is obtained based on the full satellite set update and focuses on positioning accuracy; the second posterior result is obtained based on the robust subset update and focuses on positioning stability.

[0106] The UE can perform a weighted summation of the first and second posterior states to obtain the optimal state estimate after fusion. This optimal state estimate is the final positioning result for the current epoch. At the same time, a corresponding fusion covariance matrix is ​​generated to quantify the uncertainty of the final positioning result.

[0107] This application's embodiments achieve a balance between positioning accuracy and robustness by dynamically fusing two posterior states with different characteristics. In open environments, the posterior state of the full-star set branch has higher reliability, and the fusion result will primarily inherit its high-accuracy characteristics. In complex environments, the posterior state of the robust subset branch has higher reliability, and the fusion result will primarily inherit its high-stability characteristics. This effectively meets the positioning needs in complex dynamic scenarios.

[0108] To further improve the reliability of the dual-trajectory posterior state fusion results, this application embodiment also provides a multi-trajectory credibility fusion method based on the covariance matrix. Based on the above S201-S205, this application embodiment further describes the possible implementation methods of S205. When the dual-trajectory observation sets have an inclusion relationship, this method improves positioning accuracy and reduces the risks of information duplication and overly optimistic covariance matrix estimation caused by simple weighted fusion. Figure 3 As shown, the method includes: S301: The UE generates the shared prior state and the covariance matrix of the kth epoch.

[0109] S302: The UE generates a candidate observation set based on the observation data of all visible satellites.

[0110] S303: The UE constructs a first satellite set and a second satellite set based on the candidate observation set.

[0111] S304: The UE updates the first satellite set and the second satellite set based on the shared prior state of the k-th epoch and the covariance matrix of the k-th epoch, respectively, to obtain the first posterior result and the second posterior result.

[0112] In this embodiment, S301-S304 are the same as S201-S204 in the above embodiments in terms of technical content, implementation method and technical effect. In order to avoid unnecessary repetition, the above steps will not be described in detail here. The following only describes the dual trajectory fusion step in this embodiment in detail.

[0113] S305: The UE calculates the cost functions for the first trajectory and the second trajectory respectively.

[0114] UE uses a cost function to quantitatively evaluate the overall quality of the posterior results of the two filtering branches. The cost function is a quantitative indicator that comprehensively reflects the estimation error, uncertainty, and anomaly degree of the filtering branch. The smaller the cost function value, the higher the overall quality and the stronger the reliability of the posterior result of that branch. UE constructs independent cost functions for the first trajectory based on the full star set and the second trajectory based on the robust subset, respectively. Each cost function integrates multiple core dimensions that can reflect filtering performance, ensuring the comprehensiveness and accuracy of the evaluation results.

[0115] For each branch, the UE calculates the cost function values ​​for the first and second trajectories based on the average innovation statistic of all satellites participating in the update, the trace of the posterior covariance matrix, and the proportion of outlier observations in the branch relative to the total number of observations. The UE weights and sums these three dimensions using preset weighting coefficients. The average innovation statistic reflects the overall consistency between observed and predicted values, the trace of the posterior covariance matrix reflects the overall uncertainty level of the state estimation, and the proportion of outlier observations in the branch relative to the total number of observations reflects the degree to which the branch is affected by outliers.

[0116] Specifically, the cost function of the first trajectory can be defined as: ; in, Let be the cost function for the first trajectory. This represents the average innovation statistic for the branch corresponding to the first trajectory. For the trace operation of the posterior covariance matrix, Let be the posterior covariance matrix of the branch corresponding to the first trajectory. This represents the proportion of outlier observations in the branch corresponding to the first trajectory out of the total number of observations. , and The weight coefficient of the cost term for the branch corresponding to the first trajectory.

[0117] The cost function of the first trajectory can be defined as: ; in, Let be the cost function for the second trajectory. This represents the average innovation statistic for the branch corresponding to the second trajectory. For the trace operation of the posterior covariance matrix, Let be the posterior covariance matrix of the branch corresponding to the second trajectory. This represents the proportion of outlier observations in the branch corresponding to the second trajectory out of the total number of observations. , and The weight coefficient of the cost term for the branch corresponding to the second trajectory.

[0118] This application embodiment comprehensively evaluates the quality of dual trajectories through multi-dimensional indicators, avoiding the one-sidedness of single indicator evaluation, and accurately reflects the real performance differences between the two branches under different environments.

[0119] S306: The UE calculates the normalized confidence weights of the first and second trajectories based on the cost function.

[0120] The UE generates weights for state fusion based on the cost function values ​​of the first and second trajectories. The normalized confidence weights are dimensionless coefficients ranging from 0 to 1. The sum of the weights of the two trajectories is always equal to 1; a larger weight value indicates a higher proportion of the posterior result of the corresponding trajectory in the final fusion. By converting the abstract cost function values ​​into quantized weights that can be directly used for weighted fusion, the dynamic allocation of the dual-trajectory contributions is achieved.

[0121] The UE can use an exponential mapping method to convert cost function values ​​into initial confidence levels. This involves taking the negative exponent of the cost function values ​​for the first and second trajectories respectively to obtain initial confidence levels for each trajectory. Then, the two initial confidence levels are normalized: the initial confidence level of the first trajectory is divided by the sum of the two initial confidence levels to obtain the normalized confidence weight for the first trajectory. The normalized confidence weight for the second trajectory is then 1 minus the weight of the first trajectory. This exponential mapping method effectively amplifies the quality differences between different trajectories. When the cost function value of one trajectory is significantly higher than that of another, its weight decreases rapidly, thus preventing the lower-quality trajectory from having an excessive impact on the fusion result.

[0122] Specifically, the normalized confidence weights constructed based on the cost function can be expressed as: ; in, The normalized confidence weight of the first trajectory (also known as the first fusion weight) is used. The normalized confidence weight for the second trajectory (also known as the second fusion weight) is used. It is an exponential function.

[0123] S307: The UE calculates the fusion state and the fusion covariance with conservative compensation terms based on the credibility weight.

[0124] The UE generates the fusion state estimate and corresponding uncertainty quantification index for the current epoch based on normalized confidence weights. The fusion state is the optimal state estimate obtained by combining the posterior states of the two trajectories; the fusion covariance is a matrix used to quantify the uncertainty of the fusion state estimate; and the conservative compensation term is a correction term specifically designed for the inclusion relationship of the dual-trajectory observation sets, used to address the problem of overly optimistic covariance estimation caused by simple weighted fusion.

[0125] The UE multiplies the first and second posterior states by their respective confidence weights and then sums them to obtain the optimal fused state estimate. Next, the UE calculates the fusion covariance, which consists of two parts: the first part is a weighted sum of the two trajectory posterior covariance matrices, calculated by multiplying the first and second posterior covariance matrices by their respective confidence weights and then summing them; the second part is a conservative compensation term for state differences, which is the outer product of the difference vector between the two posterior states and its transpose, multiplied by the product of the two confidence weights. When the difference between the two posterior states is large, it indicates that at least one trajectory's state estimate has a significant error. In this case, the conservative compensation term significantly increases the fusion covariance, thus more accurately reflecting the uncertainty of the fusion result. When the difference between the two posterior states is small, the effect of the conservative compensation term can be ignored, and the fusion covariance is mainly determined by the weighted covariance.

[0126] Specifically, the fusion state can be represented as: ; in, For the estimation of the optimal state after fusion; This represents the posterior state of the first trajectory; This represents the posterior state of the second trajectory.

[0127] Covariance, in its conservative form, can be expressed as: ; in, The covariance after fusion Let be the posterior covariance matrix of the branch corresponding to the first trajectory. Let be the posterior covariance matrix of the branch corresponding to the second trajectory. A conservative compensation term is introduced to account for the state difference between the branch corresponding to the first trajectory and the molecule corresponding to the second trajectory.

[0128] This application's embodiments address the issues of information reuse and overly optimistic covariance estimation caused by simple weighted fusion when dual-trajectory observation sets have inclusion relationships by introducing a conservative compensation term.

[0129] S308: The UE outputs the positioning result and fusion covariance of the k-th epoch.

[0130] The UE converts the fused state and fused covariance into a positioning result for output. The positioning result is a state vector that includes complete navigation information, and the fused covariance is a quantitative indicator of uncertainty that has been conservatively compensated for.

[0131] The UE extracts core navigation parameters such as position, velocity, attitude, receiver clock bias, and receiver clock drift from the fused state vector and encapsulates them according to a unified navigation data format within the UE. Simultaneously, the UE processes the fused covariance, either directly outputting the complete covariance matrix or converting it into a corresponding positioning accuracy level or error range according to application requirements.

[0132] This application's embodiments employ a common error state estimation architecture for tightly coupled GNSS / INS positioning. The Kalman filtering process only estimates the error amount of each state, rather than directly estimating the absolute navigation state. The nominal navigation state of the UE at epoch k, i.e., the absolute navigation state vector obtained by independent INS propagation, can be defined as: ; in, This represents the absolute position coordinates of the UE. This represents the absolute speed of the UE. Indicates the UE's attitude. This indicates that the gyroscope has zero bias. This indicates that the accelerometer has zero bias. Indicates receiver clock bias. This indicates receiver clock drift, and the superscript T indicates vector transpose.

[0133] The fusion error state vector corresponding to the fusion state has the same dimension and components as the system error state vector. The fusion error state vector corresponding to the fusion state can be represented as: ; in, This is the position error correction amount obtained through fusion. The velocity error correction amount obtained by fusion, This is the attitude error correction amount obtained through fusion. This is the zero-bias correction amount for the gyroscope obtained through fusion. To obtain the accelerometer zero bias correction amount through fusion, This is the clock error correction amount obtained through fusion. This is the correction amount for the clock drift error obtained through fusion.

[0134] The UE uses the fusion error state vector as a correction factor to correct each component of the nominal prior state obtained from INS propagation, thereby obtaining the final optimal navigation state for the current epoch.

[0135] The absolute positioning position is calculated as follows: ; in, Let be the absolute location of the UE in the k-th epoch. The nominal absolute position of the k-th epoch obtained from INS propagation. This is the position error correction amount obtained from dual trajectory fusion.

[0136] Similarly, the correction methods for the other navigation state components are as follows: speed: ; attitude: , Represents the rotational superposition of attitude errors; Gyroscope zero bias: ; Accelerometer zero bias: ; Clockwork: ; Zhong Piao: .

[0137] The UE will correct the obtained absolute positioning position. As the core positioning result output for the current epoch, it can also output complete navigation status information such as velocity, attitude, and clock difference according to application requirements, as well as fused covariance. As a quantitative indicator of the uncertainty of the positioning result.

[0138] At the same time, the UE will fuse the position error correction amount in the error state vector. Absolute positioning Information such as the fusion covariance level is encapsulated into the fusionState field in the FusionReport signaling and reported to the network-side positioning entity, providing core input for end-to-end network collaborative satellite selection.

[0139] To further improve the synergy between the satellite selection process and filter updates, and to achieve continuous adaptive optimization of the satellite selection strategy as the environment changes, this application also provides a method for satellite selection based on fusion result feedback. This application can add a single-satellite observation consistency assessment and satellite selection weighting step driven by the fusion result back to the above embodiments. For example... Figure 4 As shown, the method includes: S401: The UE generates the shared prior state and the covariance matrix of the kth epoch.

[0140] S402: The UE generates a candidate observation set based on the observation data of all visible satellites.

[0141] S403: The UE constructs a first satellite set and a second satellite set based on the candidate observation set.

[0142] S404: The UE updates the first satellite set and the second satellite set based on the shared prior state of the k-th epoch and the covariance matrix of the k-th epoch, respectively, to obtain the first posterior result and the second posterior result.

[0143] S405: The UE fuses the first and second a posteriori results to determine the positioning result for the current epoch.

[0144] In this application embodiment, S301-S405 are the same as S201-S205 in the above embodiment in terms of technical content, implementation method and technical effect. In order to avoid unnecessary repetition, the above steps will not be described in detail here. The following only describes the star selection based on the fusion result feedback provided in this application embodiment.

[0145] S406: The UE calculates the posterior residuals of all candidate satellites with reference to the fusion state.

[0146] The UE uses the fusion results of the current epoch to back-evaluate the observation data quality of each candidate satellite. Among them, the posterior residual refers to the difference between the satellite observation value and the predicted value calculated based on the optimal state after fusion. It is different from the prior residual used in the filtering update process (the prior residual is based on the prior state). The posterior residual can reflect the true error level of the observation data of a single satellite.

[0147] In one possible implementation, the UE obtains the fusion state of the current epoch, substitutes this fusion state into the pseudorange and Doppler observation functions of each candidate satellite, and calculates the corresponding posterior observation prediction value for each satellite. Then, the UE subtracts the posterior observation prediction value from the actual observation value of the candidate satellite to obtain the pseudorange posterior residual and the Doppler posterior residual, respectively. The UE can assign corresponding weights to the two residuals based on the noise characteristics of the pseudorange and Doppler observations and perform a weighted combination to obtain the comprehensive posterior residual of the candidate satellite, thereby quantifying the degree of consistency between the single-satellite observation data and the optimal fusion result.

[0148] In fusion state For reference, the posterior residuals of each candidate satellite were recalculated: ; in, Let be the posterior residual of the i-th satellite at the k-th epoch based on the fused solution. Let be the measured observation vector of the i-th satellite at the k-th epoch. It is the observation vector of the i-th satellite in the k-th epoch, which is obtained from the fusion state prediction.

[0149] Furthermore, a posterior normalized statistic based on the fused solution is defined: ; in, For the posterior normalized innovation square of the i-th satellite in the k-th epoch based on the fused solution, Let be the residual covariance matrix of the i-th satellite in the k-th epoch based on the fusion solution.

[0150] ; in, Let be the residual covariance matrix of the i-th satellite at the k-th epoch under the fusion solution condition. Let be the observation matrix of the i-th satellite in the k-th epoch. To integrate covariance, Let be the observation noise covariance of the i-th satellite in the k-th epoch.

[0151] S407: The UE updates the historical reputation value of each candidate satellite based on the posterior residuals of all candidate satellites.

[0152] The UE transforms the posterior residual assessment results of a single epoch into a long-term quality record for the satellite. The historical reputation value is a quantified long-term quality indicator used to reflect the consistency and reliability of satellite observations across multiple consecutive epochs, typically ranging from 0 to 1. A higher reputation value indicates better long-term observation quality for the satellite. This application's embodiment establishes a dynamic archive of satellite quality by continuously updating the historical reputation value, providing a long-term basis for subsequent satellite selection and weighting.

[0153] In one possible implementation, the UE can update historical reputation values ​​using a sliding window or an exponentially weighted moving average. The UE compares the overall posterior residual of each satellite with a preset residual threshold to obtain the instantaneous quality score for that satellite in the current epoch. Then, the UE performs a weighted fusion of this instantaneous quality score with the historical reputation value of the satellite from the previous epoch, with a lower weight for the instantaneous quality score and a higher weight for the historical reputation value to ensure the stability of the reputation value. Finally, the UE limits the updated reputation value to a preset upper and lower limit range to prevent extreme values. For abnormal satellites whose residuals significantly exceed the threshold, an additional penalty mechanism can be set to significantly reduce their historical reputation value.

[0154] Specifically, to reflect the long-term observation quality of satellites, a historical reputation value is defined for each satellite. Updated using a recursive method: ; in, Let be the historical reputation value of satellite i in epoch k. Let $i$ be the historical reputation value of satellite $i$ in the previous epoch of epoch $k-1$. Forgetting factor, satisfying , The instantaneous score of the i-th satellite in the k-th epoch.

[0155] in, It can be defined as: ; in, For instant scoring functions, To fuse the posterior consistency statistic of the i-th satellite in the k-th epoch; Let be the satellite elevation angle of the i-th satellite at the k-th epoch. Let be the carrier-to-noise ratio of the i-th satellite in the k-th epoch.

[0156] The overall score can be written as: ; in, The satellite score for satellite i after the update at epoch k. The normalization mapping function for each indicator; For scoring weights.

[0157] This application's embodiments compensate for the randomness of single-epoch quality assessment by establishing a dynamic update mechanism for historical reputation values, thus accurately reflecting the long-term observation stability of the satellite. Furthermore, it continuously tracks the slow changing trend of satellite quality and promptly identifies gradually deteriorating satellite signals.

[0158] S408: The UE adjusts the robust subset selection rules and full set measurement weights for the next epoch based on the updated historical reputation value.

[0159] The UE will update the obtained long-term satellite quality records and convert them into specific parameter adjustments for the next epoch positioning process. Specifically, the robust subset selection rule adjustment refers to dynamically modifying the satellite admission criteria for robust subsets based on historical reputation values; the full-set measurement weight adjustment refers to dynamically adjusting the contribution of each satellite observation in the full-set branch to the filtering update based on historical reputation values. This embodiment of the application implements reverse driving of the fusion results for the front-end satellite selection and filtering process, completing the entire closed-loop optimization process.

[0160] For the robust subset selection rules, the UE dynamically adjusts the corresponding satellite selection score threshold based on the historical reputation value of each satellite. For satellites with high historical reputation values, the selection threshold is appropriately lowered to make them easier to be included in the robust subset. For satellites with low historical reputation values, the selection threshold is appropriately raised to increase the difficulty for them to enter the robust subset, or even exclude them directly from the candidate range.

[0161] For the measurement weights of the entire satellite set, the UE dynamically adjusts the measurement noise covariance of each satellite in the entire satellite set branch based on its historical reputation value. Satellites with higher historical reputation values ​​have smaller measurement noise covariances and thus receive higher weights in the filter update. Satellites with lower historical reputation values ​​have larger measurement noise covariances and thus receive lower weights in the filter update.

[0162] Specifically, the second trajectory determines whether it is included in the high-reliability subset: ; in, For the robust subset of the second trajectory in the (k+1)th epoch, For the full star set of the first trajectory in the (k+1)th epoch, The satellite score for satellite i after the update at epoch k. The star selection threshold for the second trajectory.

[0163] The measurement weights are determined for the first trajectory: ; in, To measure the weighted noise covariance of the i-th satellite in the first trajectory at epoch k+1, Let be the weighting coefficient of the i-th satellite in the first trajectory of the (k+1)-th epoch. Let be the original measurement noise covariance of the i-th satellite at epoch k+1. Increases as satellite score decreases. The larger the value, the stronger the weighting of the satellite observations.

[0164] This application's embodiments apply long-term satellite quality records to the satellite selection and weighting process in the next epoch, enabling the satellite selection strategy and measurement weights to continuously and adaptively adjust with environmental changes and satellite quality fluctuations, thus avoiding the problem of excessive performance differences of fixed parameters under different environments.

[0165] S409: Enter the (k+1)th epoch and execute S401.

[0166] This application also provides an embodiment of enhanced satellite selection based on end-to-end network collaboration. This embodiment, based on the aforementioned terminal-side closed-loop satellite selection mechanism, adds an interaction process between the UE and network devices. By having the UE report fused positioning summary information and the network issue global auxiliary satellite selection configuration, joint optimization of positioning performance through end-to-end network collaboration is achieved. Figure 5 As shown, the method includes: S501: After the UE completes the current epoch positioning calculation, it sends a FusionReport signaling message to the network device.

[0167] FusionReport signaling is a standardized signaling used between the UE and the network side to transmit positioning process status information. It carries the execution status of the UE's local dual-track positioning and the single-satellite quality assessment results.

[0168] After completing the dual-track fusion positioning calculation for the current epoch, the UE selectively collects and reports one or more of the following auxiliary information according to the network configuration requirements: the candidate observation set for the current epoch, the full set of satellites used for the first track, and the robust subset used for the second track; the score, confidence level, priority, and historical reputation value corresponding to a single satellite or set of satellites; the posterior residual, consistency statistics, and anomaly markers obtained based on the fusion state; the quality of the first track, the quality of the second track, the quality of the fusion result, or the fusion covariance level; and time synchronization information such as time identifiers, measurement window identifiers, or epoch association identifiers. The UE can report complete information in a single epoch or report different types of information in batches across multiple epochs to balance signaling overhead and information integrity.

[0169] In one possible implementation, the FusionReport signaling can be specifically shown in Table 1: Table 1: FusionReport Signaling

[0170] In Table 1 above, fusionState consists of the fusion state. Encoded, it can include current position, velocity, clock bias, clock drift, or their summary information. fusionCovLevel is derived from the fused covariance. Quantized, it is used to represent the uncertainty or confidence level of the fusion result. trajectoryAQuality is determined by the branch quality of the first trajectory. Quantized to obtain; trajectoryBQuality is determined by the branch quality of the second trajectory. Quantized data is obtained; selectedSatelliteSet: coded from the first and second satellite sets. The initial source information of satelliteConfidenceSummary includes the normalized squared innovation, elevation angle, carrier-to-noise ratio, historical reputation value, baseline score, and anomaly markers for each satellite. The candidate source information of selectedSatelliteSet includes the formation of the candidate observation set for the current epoch.

[0171] To facilitate the terminal's reporting of the quality of each filtering branch to the network side, the branch quality index of the m-th trajectory in the k-th epoch is defined as: ; Alternatively, a normalized form can be used: ; in, Let be the cost function of the m-th trajectory at the k-th epoch. The smaller the cost function, the better the innovation consistency, the smaller the posterior covariance, and the lower the proportion of outlier observations in that branch, and therefore the higher the quality of the corresponding branch.

[0172] Furthermore, improve the quality of continuous branches. Mapped to signaling reporting fields: ; in, The range of values ​​is This is used to correspond to the trajectoryAQuality or trajectoryBQuality fields.

[0173] S502: The network device receives the FusionReport signaling and, in conjunction with the global auxiliary data on the network side, generates the auxiliary satellite selection configuration for the next epoch.

[0174] Network devices leverage the global information advantage of the network side combined with local information reported by the UE to generate auxiliary satellite selection configurations that are superior to the UE's local decisions. Among them, the global auxiliary data on the network side refers to information that the network side can obtain but the UE cannot obtain alone, including observation data from multiple UEs in the same geographical area, satellite orbit prediction data, ionospheric scintillation monitoring data, terrain occlusion database, satellite health status information, etc.

[0175] The network device receives and parses the FusionReport signaling sent by the UE, extracting various positioning status information and single-satellite quality assessment results reported by the UE. Then, the network device performs fusion analysis on this local information and global auxiliary data from the network side to identify regional satellite anomalies, multipath interference hotspots, and satellite signal obstruction patterns that are difficult for the UE to detect locally. Finally, based on the fusion analysis results, the network device generates configuration information to assist the UE in determining the selection of positioning satellites. This configuration information includes, for example, the recommended set of satellites for positioning, the recommended priority set of satellites to be included in the robust subset, the confidence level of a single satellite or satellite group, priority or weight parameters, satellite removal threshold, satellite selection threshold, deweighting threshold, or backoff mode indication.

[0176] S503: The network device sends a SelectionFeedback signaling message to the UE.

[0177] SelectionFeedback signaling is a standardized signaling used between the network side and the UE to transmit auxiliary satellite selection configurations. It carries various satellite selection and parameter adjustment instructions generated by the network side.

[0178] The network device encapsulates the auxiliary satellite selection configuration information according to a preset signaling format to generate SelectionFeedback signaling. Then, the network device sends the SelectionFeedback signaling to the corresponding UE. The network device can select different signaling transmission methods and priorities based on the type and urgency of the configuration information. For urgent configurations that need to take effect immediately (such as satellite fault warnings), high-priority signaling transmission is used; for general auxiliary configurations, normal-priority signaling transmission is used.

[0179] S504: The UE receives the SelectionFeedback signaling, applies the auxiliary satellite selection configuration, and adjusts the candidate observation set, robust subset, or measurement weights for the next epoch.

[0180] The UE applies the auxiliary satellite selection configuration issued by the network side to the positioning process in the next epoch, thereby achieving positioning performance optimization through end-to-end network collaboration.

[0181] The UE receives and parses the SelectionFeedback signaling sent by the network device, extracting the auxiliary satellite selection configuration information. Then, based on the effective conditions and time range in the configuration information, the UE constrains or modifies the local dual-track filtering process at the corresponding epoch. This includes adjusting the selection range of the candidate observation set, modifying the construction rules and admission conditions of robust subsets, adjusting the measurement weights of each satellite in the full satellite set branch, or modifying the historical reputation value update strategy in the fusion feedback process. Furthermore, the UE can report the actual satellite set used at the current epoch, the confidence level of the fusion result, and the subset selection result to the network side. The network device adjusts the subsequent auxiliary configuration based on this reported result and then reissues it.

[0182] In one possible implementation, if the network device issued a recommended satellite set or a preferred robust subset in the previous round of SelectionFeedback, the UE can modify the current epoch satellite set construction process by incorporating network-aided information. Specifically: If the network device issues a recommendedSet, the UE can use the recommendedSet as a constraint or priority range for the current or next epoch candidate observation set.

[0183] If the network device issues a preferredRobustSet, the UE can use the preferredRobustSet as a preferred candidate range for the second robust subset of trajectories.

[0184] If the network device sends satelliteConfidenceInfo, the UE can compare the confidence level, priority or weight level of a single satellite or satellite group with the local score. Joint processing.

[0185] If the network device issues a selectionThreshold, the UE can use this threshold to correct its behavior. .

[0186] If the network device issues a feedbackMode, the UE will select either closed-loop mode or fallback mode according to its instructions.

[0187] Below, in conjunction with Figure 6 as well as Figure 7 This section further introduces the hardware implementation of network devices and UEs.

[0188] See Figure 6 The diagram shows a schematic of the hardware structure of a network device.

[0189] The network equipment in this application can be network-side equipment such as access network and core network equipment. Access network equipment is sometimes also called access point. Access network equipment has wireless transceiver capabilities and is used to communicate with terminals. Access network equipment includes, but is not limited to, base stations, evolved NodeBs (eNodeBs), transmission reception points (TRPs) in the above-mentioned communication systems, next-generation NodeBs (gNBs) in 5G mobile communication systems, aNBs in 6G mobile communication systems, access network equipment or modules of access network equipment in open RAN (ORAN) systems, satellites in NTN communication systems, base stations in future mobile communication systems, or access nodes in Wi-Fi systems. Access network equipment can also be modules or units capable of implementing some of the functions of a base station. Access network equipment can be macro base stations, micro base stations or indoor stations, relay nodes or donor nodes, or wireless controllers in cloud radio access network (CRAN) scenarios. Optionally, access network equipment can also be servers, wearable devices, or vehicle-mounted equipment, etc. For example, the access network equipment in vehicle-to-everything (V2X) technology can be a roadside unit (RSU). Multiple access network devices in a communication system can be base stations of the same type or different types. Base stations can communicate with terminals directly or via relay stations. Terminals can communicate with multiple base stations using different access technologies. This application does not limit the specific technology or device form used in the access network equipment. In this application, the access network equipment is referred to as a network device.

[0190] In this application, the means for implementing the functions of a network device can be a network device itself, or a means capable of supporting the network device in implementing those functions, such as a processor, circuit, chip, or chip system. This means can be installed in the network device or connected to the network device for use. In the technical solutions provided in this application, the example of a network device being used to implement the functions of a network device is used to describe the technical solutions provided in this application.

[0191] Figure 6 The network device shown includes at least one processor 111, at least one memory 112, at least one transceiver 113, at least one network interface 114, and one or more antennas 115. The processor 111, memory 112, transceiver 113, and network interface 114 are connected, for example, via a bus. In this embodiment, the connection may include various interfaces, transmission lines, or buses, etc., and this embodiment is not limited in this respect. The antenna 115 is connected to the transceiver 113. The network interface 114 is used to enable the network device to connect to other communication devices through a communication link. For example, the network interface 114 may include a network interface between the network device and network devices in the core network, such as an S1 interface; the network interface may also include a network interface between the network device and other network devices, such as an X2 or Xn interface.

[0192] in, Figure 6 The processor 111 shown can specifically perform the network device processing actions in the above method, the memory 112 can perform the storage actions in the above method, the transceiver 113 and the antenna 115 can perform the air interface transmission and reception actions in the above method, and the network interface 114 can perform the interaction actions with the network device or other network devices in the above method.

[0193] The processor in this application embodiment, such as processor 111, may include, but is not limited to, at least one of the following: a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a microcontroller unit (MCU), or an artificial intelligence processor, etc., which are various computing devices that run software. Each computing device may include one or more cores for executing software instructions to perform calculations or processing. The processor may be a single semiconductor chip or integrated with other circuits into a single semiconductor chip. For example, it may be integrated with other circuits (such as encoding / decoding circuits, hardware acceleration circuits, or various bus and interface circuits) to form a SoC (System-on-a-Chip), or it may be integrated as a built-in processor in an ASIC. The ASIC with the integrated processor may be packaged separately or packaged together with other circuits. In addition to including cores for executing software instructions to perform calculations or processing, the processor may further include necessary hardware accelerators, such as field-programmable gate arrays (FPGAs), PLDs (programmable logic devices), or logic circuits that implement dedicated logic operations.

[0194] The memory in the embodiments of this application may include at least one of the following types: read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions; random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions; or electrically erasable programmable-only memory (EEPROM). In some scenarios, the memory may also be a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto.

[0195] The memory 112 can exist independently and be connected to the processor 111. Optionally, the memory 112 can be integrated with the processor 111, for example, integrated into a single chip. The memory 112 can store program code that executes the technical solutions of the embodiments of this application, and its execution is controlled by the processor 111. The various types of computer program code being executed can also be considered as drivers for the processor 111. For example, the processor 111 executes the computer program code stored in the memory 112 to implement the technical solutions of the embodiments of this application.

[0196] Transceiver 113 can be used to support the reception or transmission of radio frequency (RF) signals between network devices and other devices. Transceiver 113 can be connected to antenna 115. Transceiver 113 includes a transmitter Tx and a receiver Rx. Specifically, one or more antennas 115 can receive RF signals. The receiver Rx of transceiver 113 is used to receive the RF signals from the antennas, convert the RF signals into digital baseband signals or digital intermediate frequency (IF) signals, and provide the digital baseband signals or IF signals to the processor 111 so that the processor 111 can perform further processing on the digital baseband signals or IF signals, such as demodulation and decoding. In addition, the transmitter Tx in transceiver 113 is also used to receive modulated digital baseband signals or IF signals from processor 111, convert the modulated digital baseband signals or IF signals into RF signals, and transmit the RF signals through one or more antennas 115. Specifically, the receiver Rx can selectively perform one or more stages of downmixing and analog-to-digital conversion on the radio frequency signal to obtain a digital baseband signal or a digital intermediate frequency (IF) signal. The order of the downmixing and IF conversion processes is adjustable. The transmitter Tx can selectively perform one or more stages of upmixing and digital-to-analog conversion on the modulated digital baseband signal or digital IF signal to obtain a radio frequency signal. The order of the upmixing and IF conversion processes is also adjustable. The digital baseband signal and the digital IF signal can be collectively referred to as digital signals.

[0197] Figure 7This application provides an example of the composition of a terminal device. The terminal device in this application can refer to user equipment (UE), station, access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent, or user device. The terminal device can also be a cellular phone, cordless phone, session initiation protocol (SIP) phone, wire local loop (WLL) station, personal digital assistant (PDA), handheld device with wireless communication capabilities, computing device or other processing device connected to a wireless modem, large screen, vehicle-mounted device, wearable device, terminal device in a 5G network, or terminal device in a future evolved public land mobile network (PLMN), etc. This application does not limit the scope of the application.

[0198] In this application, the apparatus for implementing the functions of the terminal device can be the terminal device itself, or any apparatus capable of supporting the terminal device in implementing those functions, such as a processor, circuit, or chip system. This apparatus can be installed in or connected to the terminal device. In the technical solutions provided in this application, the example of a terminal device being used to implement the functions of the terminal device is used to describe the technical solutions provided in this application.

[0199] The terminal device can be, for example, a mobile phone, a smart wearable device (such as a smartwatch), etc. Taking a mobile phone as an example, the UE can include a processor 310, an external memory interface 320, an internal memory 321, a display screen 330, a camera 340, an antenna 1, an antenna 2, a mobile communication module 350, and a wireless communication module 360, etc.

[0200] It is understood that the structure illustrated in this embodiment does not constitute a specific limitation on the terminal device. In other embodiments, the terminal device may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0201] Processor 310 may include one or more processing units, such as application processors (APs), modem processors, graphics processing units (GPUs), image signal processors (ISPs), controllers, video codecs, digital signal processors (DSPs), baseband processors, and / or neural network processing units (NPUs). These different processing units may be independent devices or integrated into one or more processors.

[0202] It is understood that the interface connection relationships between the modules illustrated in this embodiment are merely illustrative and do not constitute a limitation on the structure of the UE. In other embodiments of this application, the UE may also adopt different interface connection methods or a combination of multiple interface connection methods as described in the above embodiments.

[0203] The external memory interface 320 can be used to connect an external memory card, such as a Micro SD card, to expand the UE's storage capacity. The external memory card communicates with the processor 310 through the external memory interface 320 to perform data storage functions. For example, music, video, and other files can be saved on the external memory card.

[0204] Internal memory 321 can be used to store computer executable program code, including instructions. Processor 310 executes various functional applications and data processing of the UE by running the instructions stored in internal memory 321. Internal memory 321 may include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc. The data storage area may store data created during UE use (such as video stream data), etc. In addition, internal memory 321 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc. Processor 310 executes various functions and data processing of the UE by running instructions stored in internal memory 321 and / or instructions stored in memory disposed in the processor.

[0205] The UE's wireless communication function can be implemented through antenna 1, antenna 2, mobile communication module 350, wireless communication module 360, modem processor, and baseband processor.

[0206] Antenna 1 and antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in the UE can be used to cover one or more communication frequency bands. Different antennas can also be multiplexed to improve antenna utilization. For example, antenna 1 can be multiplexed as a diversity antenna for a wireless local area network. In some other embodiments, the antennas can be used in conjunction with tuning switches.

[0207] The mobile communication module 350 can provide solutions for wireless communication applications including 2G / 3G / 4G / 5G on the UE. The mobile communication module 350 may include at least one filter, switch, power amplifier, low-noise amplifier (LNA), etc. The mobile communication module 350 can receive electromagnetic waves via antenna 1, and perform filtering, amplification, and other processing on the received electromagnetic waves before transmitting them to a modem processor for demodulation. The mobile communication module 350 can also amplify the signal modulated by the modem processor and convert it into electromagnetic waves for radiation via antenna 1. In some embodiments, at least some functional modules of the mobile communication module 350 may be housed in the processor 310. In some embodiments, at least some functional modules of the mobile communication module 350 and at least some modules of the processor 310 may be housed in the same device.

[0208] In some embodiments, the UE initiates or receives call requests via the mobile communication module 350 and the antenna 1.

[0209] Furthermore, an operating system runs on top of the aforementioned components. Examples include iOS, Android, and Windows operating systems. Applications can be installed and run on this operating system. Those skilled in the art will understand that, for the sake of convenience and brevity, explanations and beneficial effects of any of the UE components described above can be found in the corresponding method embodiments provided above, and will not be repeated here.

[0210] Access network equipment and / or terminal equipment can be fixed or mobile. They can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; on water; or in the air on aircraft, balloons, and satellites. This application does not limit the application scenarios of the access network equipment and terminal equipment. They can be deployed in the same or different scenarios; for example, both can be deployed on land simultaneously; or the access network equipment can be deployed on land while the terminal equipment is deployed on water, etc., and so on.

[0211] It is understood that the communication systems and scenarios applicable to this application described above are merely illustrative examples, and the communication systems applicable to this application are not limited thereto. This will be explained uniformly here and will not be repeated below.

[0212] In practical applications, multiple network devices can collaborate to assist terminals in achieving wireless access, with different network devices each implementing a portion of the base station's functions. For example, network devices can be central units (CUs), distributed units (DUs), CUs (control planes, CPs), CUs (user planes, UPs), or radio units (RUs), etc. CUs and DUs can be set up separately or included in the same network element, such as a baseband unit (BBU). RUs can be included in radio frequency equipment or radio frequency units, such as remote radio units (RRUs), active antenna units (AAUs), or remote radio heads (RRHs).

[0213] In different systems, CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meaning. For example, in an ORAN system, CU can also be called O-CU (Open CU), DU can also be called O-DU, CU-CP can also be called O-CU-CP, CU-UP can also be called O-CU-UP, and RU can also be called O-RU. Any of the units among CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software and hardware modules. CU (or CU-CP and CU-UP), DU, and RU can implement different protocol layer functions.

[0214] It should be noted that the system described in the embodiments of this application is for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and does not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0215] In the interaction process of this application embodiment, the message or signaling interaction involved can adopt the message or signaling in the NR standard, or it can be a newly introduced message or signaling. This application embodiment does not make specific limitations on this.

[0216] It is understood that in the embodiments of this application, each communication device may execute some or all of the steps in the embodiments of this application. These steps or operations are merely examples, and the embodiments of this application may also execute other operations or variations thereof. Furthermore, the steps may be executed in different orders as presented in the embodiments of this application, and it is not necessary to execute all the operations in the embodiments of this application.

[0217] It is understood that this application uses terminals and network devices as examples to illustrate the execution of the interaction, but this application does not limit the execution subject of the interaction. For example, the method executed by the terminal in this application can also be executed by a module applied to the terminal (e.g., a chip, chip system, or processor), or by a logical node, logical module, or software that can implement all or part of the terminal's functions; similarly, the method executed by the network device in this application can also be executed by a module applied to the network device (e.g., a chip, chip system, or processor), or by a logical node, logical module, or software that can implement all or part of the network device's functions.

[0218] Furthermore, embodiments of this application also provide a computer-readable storage medium storing instructions that, when executed on one or more computing devices, cause the one or more computing devices to perform the positioning method described in the above embodiments.

[0219] Furthermore, this application also provides a computer program product, which, when executed by one or more computing devices, allows the computing devices to perform any of the aforementioned location methods. The computer program product can be a software installation package; when any of the aforementioned location methods is required, the computer program product can be downloaded and executed on a computer.

[0220] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware, or it can be implemented by special-purpose hardware including application-specific integrated circuits, special-purpose CPUs, special-purpose memory, special-purpose components, etc. Generally, any function performed by a computer program can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be diverse, such as analog circuits, digital circuits, or special-purpose circuits. However, for this application, software program implementation is more often a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a readable storage medium, such as a computer floppy disk, USB flash drive, mobile hard disk, ROM, RAM, magnetic disk, or optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, training equipment, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0221] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.

[0222] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, training device, or data center to another website, computer, training device, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a training device or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0223] The system architecture and business scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

Claims

1. A positioning method, characterized in that, Applied to user equipment, including: The first satellite set is updated based on the shared prior state and covariance matrix to obtain the first posterior result; The second satellite set is updated based on the shared prior state and the covariance matrix to obtain the second posterior result, and the second satellite set is a proper subset of the first satellite set; The first posterior result and the second posterior result are fused to determine the positioning result; The fusion of the first posterior result and the second posterior result includes: Based on the first fusion weight and the second fusion weight, a weighted sum of the first posterior covariance matrix in the first posterior result and the second posterior covariance matrix in the second posterior result is determined; the first fusion weight is determined based on the first posterior result, and the second fusion weight is determined based on the second posterior result. Based on the first fusion weight and the second fusion weight, a conservative compensation term for the state difference between the first posterior state in the first posterior result and the second posterior state in the second posterior result is determined. The fusion covariance is determined based on the weighted sum and the state difference conservative compensation term.

2. The method according to claim 1, characterized in that, The process of fusing the first posterior result and the second posterior result to determine the positioning result for the current epoch includes: The cost function of the first trajectory is determined based on the first posterior result, and the first trajectory corresponds to the first satellite set; The cost function of the second trajectory is determined based on the second posterior result, and the second trajectory corresponds to the second satellite set; The first fusion weight is determined based on the cost function of the first trajectory, and the second fusion weight is determined based on the cost function of the second trajectory; Based on the first fusion weight and the second fusion weight, the first posterior state in the first posterior result and the second posterior state in the second posterior result are weighted to obtain the fusion state.

3. The method according to claim 2, characterized in that, The cost function of the first trajectory is determined based on at least two of the following: the average innovation statistic of the first trajectory, the trace of the posterior covariance matrix of the first trajectory, and the proportion of outliers in the first trajectory. The cost function of the second trajectory is determined based on at least two of the following: the average innovation statistic of the second trajectory, the trace of the posterior covariance matrix of the second trajectory, and the proportion of outliers in the second trajectory.

4. The method according to claim 1, characterized in that, The method further includes: Based on inertial measurement data, determine the shared prior state and covariance matrix of the k-th epoch, where k is a positive integer.

5. The method according to claim 4, characterized in that, The step of determining the shared prior state and the covariance matrix of the k-th epoch based on inertial measurement data includes: Based on the inertial measurement data from epoch (k-1) to epoch (k) and the positioning result of epoch (k-1), the shared prior state of epoch (k) is calculated based on the state propagation model of the inertial navigation system (INS). Based on the statistical characteristics of INS measurement noise, the covariance matrix of the k-th epoch is calculated using the covariance propagation formula.

6. The method according to claim 1, characterized in that, The method further includes: The first satellite set and the second satellite set are determined based on the candidate observation set. The first satellite set includes all satellites in the candidate observation set, and the second satellite set includes satellites selected from the first satellite set whose observation quality meets preset conditions.

7. The method according to claim 6, characterized in that, The satellite scores of all satellites in the second satellite set are greater than the satellite selection score threshold. The satellite scores are determined based on at least one of the following: observation innovation statistics, elevation angle, carrier-to-noise ratio, and historical reputation value.

8. The method according to claim 6, characterized in that, The method further includes: A satellite score for each visible satellite is constructed based on observation data from all visible satellites, and a candidate observation set is generated based on the scores of each visible satellite.

9. The method according to claim 1, characterized in that, The update of the first satellite set based on the shared prior state and covariance matrix includes: During the sequential update of each satellite in the first satellite set, the shared prior state and the covariance matrix are used as initial values ​​to calculate the Kalman gain of the i-th satellite in the first satellite set, where i is an integer greater than or equal to 1. The covariance matrix and state of the i-th satellite are updated based on the Kalman gain.

10. The method according to claim 2, characterized in that, After determining the positioning result for the current epoch, the method further includes: Based on the positioning results, the observation consistency assessment results of each candidate satellite in the candidate observation set are determined.

11. The method according to claim 10, characterized in that, The evaluation results of the observation consistency of each satellite in the candidate observation set include: For each candidate satellite in the candidate observation set, the posterior residual of the candidate satellite is determined with the positioning result as the reference state. The posterior residual is the difference between the measured observation vector of the candidate satellite and the predicted observation vector obtained based on the reference state. The historical reputation value of the candidate satellite is recursively updated based on the posterior residual.

12. The method according to claim 11, characterized in that, The method further includes: Based on the updated historical reputation values ​​of each candidate satellite, adjust the selection rules for the second satellite set and / or the measurement weights for the first satellite set in the next epoch.

13. The method according to claim 12, characterized in that, The adjustment of the filtering rules for the second satellite set in the next epoch includes: Candidate satellites whose satellite scores are higher than a preset satellite selection threshold in the next epoch are identified as satellites in the second satellite set of the next epoch, wherein the satellite scores are determined based on updated historical reputation values; The adjustment of the measurement weights of the first satellite set in the next epoch includes: The measurement noise covariance of the candidate satellites in the first satellite set of the next epoch is adjusted based on the updated historical reputation values ​​of the candidate satellites.

14. The method according to claim 1, characterized in that, The method further includes: Send the first signaling to the network device; The network device receives a second signaling message, which carries an auxiliary star selection configuration. The auxiliary star selection configuration is generated by the network device based on the received first signaling message and combined with global auxiliary data on the network side. Based on the aforementioned auxiliary satellite selection configuration, adjust the candidate observation set, second satellite set, or measurement weights for the next epoch.

15. The method according to claim 14, characterized in that, The auxiliary satellite selection configuration includes one or more of the following: recommended satellite set, priority robust subset, satellite confidence information, satellite selection threshold, and feedback mode indication.

16. A positioning method, characterized in that, Applied to network devices, including: The system sends indication information to the user equipment, the indication information being information for indicating a first satellite set and a second satellite set, so that the user equipment updates the first satellite set according to a shared prior state and a covariance matrix to obtain a first posterior result; updates the second satellite set according to the shared prior state and the covariance matrix to obtain a second posterior result; and fuses the first posterior result and the second posterior result to determine the positioning result; the second satellite set is a proper subset of the first satellite set. The fusion of the first posterior result and the second posterior result includes: Based on the first fusion weight and the second fusion weight, a weighted sum of the first posterior covariance matrix in the first posterior result and the second posterior covariance matrix in the second posterior result is determined; the first fusion weight is determined based on the first posterior result, and the second fusion weight is determined based on the second posterior result. Based on the first fusion weight and the second fusion weight, a conservative compensation term for the state difference between the first posterior state in the first posterior result and the second posterior state in the second posterior result is determined. The fusion covariance is determined based on the weighted sum and the state difference conservative compensation term.

17. The method according to claim 16, characterized in that, The method further includes: Receive the first signaling sent by the user equipment; A second signaling message is sent to the user equipment so that the user equipment adjusts the candidate observation set, the second satellite set, or the measurement weights for the next epoch according to the auxiliary satellite selection configuration carried by the second signaling message. The auxiliary satellite selection configuration is generated by the network device based on the received first signaling message and combined with global auxiliary data on the network side.

18. A user equipment, characterized in that, include: A transceiver is configured to perform the receiving and transmitting operations in the method according to any one of claims 1-15; A processor for performing operations other than the receiving operation and the sending operation in the method according to any one of claims 1-15.

19. A network device, characterized in that, include: A transceiver for performing the receiving and transmitting operations in the method of claim 16 or 17; A processor for performing operations other than the receiving operation and the sending operation in the method of claim 16 or 17.

20. A communication system, characterized in that, The device includes a user equipment (UE) and a network device, wherein the UE is used to perform the method according to any one of claims 1-15, and the network device is used to perform the method according to claim 16 or 17.

21. A computer storage medium for storing a computer program, which, when executed, implements the positioning method according to any one of claims 1 to 17.

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