A low-latency positioning method based on bluetooth channel sounding and related devices

Abstract

This application provides a low-latency positioning method and related equipment based on Bluetooth channel probing, belonging to the field of communication technology. The method includes: acquiring the identification information and corresponding ranging data of each scanned Bluetooth anchor point; obtaining the weight of each Bluetooth anchor point based on target position estimation information and ranging data; selecting several Bluetooth anchor points to form an optimal anchor point subset based on a preset selection factor and the weights of the Bluetooth anchor points; establishing Bluetooth channel probing connections with each Bluetooth anchor point in the optimal anchor point subset using the identification information to obtain the ranging value at the current moment; predicting the particle state at the current moment based on the target state of the particles at the previous moment using a particle filter algorithm, updating the particle weights using the ranging value, and performing resampling and weighted averaging to obtain the current position information of the target device. This application can achieve continuous estimation of the target position, thereby improving the real-time performance and robustness of the system while ensuring positioning accuracy.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a low-latency positioning method and related equipment based on Bluetooth channel detection. Background Technology

[0002] In related technologies, positioning methods often fail to meet the requirements of positioning accuracy, robustness, and latency simultaneously, and usually require a trade-off between accuracy and latency.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this application is to propose a low-latency positioning method and related equipment based on Bluetooth channel detection, which can achieve continuous estimation of the target position, thereby improving the real-time performance and robustness of the system while ensuring positioning accuracy.

[0005] To achieve the above objectives, one aspect of this application proposes a low-latency positioning method based on Bluetooth channel detection, the method comprising the following steps: Obtain the identification information and corresponding ranging data of each scanned Bluetooth anchor point; Based on the target location estimation information and the ranging data, multiple anchor point combinations are constructed from the Bluetooth anchor points. The Fisher information matrix and random Cramerlow lower bound of each anchor point combination are calculated to obtain the weight of each Bluetooth anchor point. Based on the preset selection factor and the weight of the Bluetooth anchor point, a subset of Bluetooth anchor points is selected to form an optimal anchor point subset. The Bluetooth channel detection connection is established with each of the Bluetooth anchor points in the optimal anchor point subset using the identification information to obtain the ranging value at the current time. Based on the particle filtering algorithm, the particle state at the current moment is predicted according to the target state of the particle at the previous moment. The particle weight is updated using the ranging value, and resampling and weighted averaging are performed to obtain the current position information of the target device.

[0006] In some embodiments, acquiring the identification information of each scanned Bluetooth anchor point and the corresponding ranging data includes: The target device's Bluetooth module performs an environmental signal scan, recording the identification information of each Bluetooth anchor point during the scan and simultaneously acquiring the ranging data of each Bluetooth anchor point. The ranging data includes phase-based ranging data and round-trip time ranging data. The identification information is the physical address or preset number of the Bluetooth anchor point, used to uniquely identify the Bluetooth anchor point. The phase-based ranging data and the round-trip time ranging data are used to assist in determining the initial signal quality of the Bluetooth anchor point.

[0007] In some embodiments, the step of constructing multiple anchor point combinations from the Bluetooth anchor points based on the target location estimation information and the ranging data, calculating the Fisher information matrix and random Cramer-Rao lower bound for each anchor point combination, and obtaining the weight of each Bluetooth anchor point includes: Obtain target position estimation information; the acquisition of target position estimation information includes: if it is the first estimation, then the preset initial position is used as the target position estimation information; if it is not the first estimation, then the target position estimation information is the position estimation result obtained by weighted averaging of particle states through particle filtering algorithm at the previous moment; the preset initial position is the last position recorded before the target device was last powered off, or it is the initial coordinates input by the user; Based on the target location estimation information and the ranging data, all the scanned Bluetooth anchor points are combined into multiple anchor point combinations in a group of three Bluetooth anchor points each. For each of the anchor point combinations, a corresponding Fisher information matrix is ​​constructed, and the random Cramerlow lower bound of the anchor point combination is calculated based on the Fisher information matrix. Based on the random Cramerlow lower bound of each of the anchor point combinations, the contribution of each Bluetooth anchor point in all participating anchor point combinations is calculated, and the contribution is used as the weight of the Bluetooth anchor point.

[0008] In some embodiments, calculating the contribution of each Bluetooth anchor point in all participating anchor point combinations based on the random Cramerlow lower bound of each anchor point combination, and using the contribution as the weight of the Bluetooth anchor point, includes: The random Cramer-Rao lower bound values ​​of all anchor point combinations are normalized to obtain the normalized contribution value of each anchor point combination; the normalization of the random Cramer-Rao lower bound values ​​of all anchor point combinations includes: subtracting the random Cramer-Rao lower bound value of the current anchor point combination from the maximum random Cramer-Rao lower bound value among all anchor point combinations, and then dividing by the difference between the maximum random Cramer-Rao lower bound value and the minimum random Cramer-Rao lower bound value among all anchor point combinations; For each Bluetooth anchor point, the normalized contribution values ​​corresponding to all anchor point combinations in which the Bluetooth anchor point participates are summed, and then divided by the total number of anchor point combinations in which the Bluetooth anchor point participates to obtain the weight of the Bluetooth anchor point.

[0009] In some embodiments, the step of selecting a subset of Bluetooth anchors to form an optimal anchor set based on a preset selection factor and the weight of the Bluetooth anchors includes: The weights of the Bluetooth anchor points are sorted from largest to smallest to obtain the sorting result; Obtain a preset selection factor; the selection factor is a value between zero and one, representing the proportion of the number of Bluetooth anchors to be filtered to the total number of Bluetooth anchors; The number of Bluetooth anchors to be filtered is calculated based on the selection factor to obtain the target number of filters; the calculation of the number of Bluetooth anchors to be filtered based on the selection factor includes: multiplying the selection factor by the total number of Bluetooth anchors, and rounding the calculation result up or down. Based on the sorting results, the weight value corresponding to the Bluetooth anchor point ranked in the target filtering quantity position is selected as the weight threshold. The Bluetooth anchors whose weights are greater than or equal to the weight threshold are selected to form the optimal anchor subset.

[0010] In some embodiments, establishing a Bluetooth channel probing connection with each of the Bluetooth anchor points in the optimal anchor point subset using the identification information to obtain the ranging value at the current moment includes: Based on the identification information of each Bluetooth anchor point in the optimal anchor point subset, the target device initiates a Bluetooth channel probe connection request with each Bluetooth anchor point sequentially or in parallel. After successfully establishing a Bluetooth channel detection connection with each of the Bluetooth anchors, the target device performs channel detection communication with each of the Bluetooth anchors to obtain the ranging value at the current moment through signal interaction. The ranging value at the current moment includes phase-based ranging data and round-trip time ranging data. The phase-based ranging data is calculated by measuring the phase difference of the signal transmitted between the target device and the Bluetooth anchor. The round-trip time ranging data is calculated by measuring the round-trip time of the signal from transmission to reception.

[0011] In some embodiments, the step of predicting the particle state at the current moment based on the target state of the particle at the previous moment using the particle filtering algorithm, updating the particle weights using the ranging value, and performing resampling and weighted averaging to obtain the current position information of the target device includes: Based on the particle filtering algorithm, particles are set as hypothetical samples of the target device's location, each particle represents a candidate location of the target device, and each particle is encoded; the particle state includes the particle's x-coordinate, y-coordinate, and particle weight; the x-coordinate and y-coordinate are used to describe the target location hypothesized by the particle; the particle weight is used to describe the confidence level of the particle hypothesis, and the sum of the particle weights of all particles is equal to one; Obtain the target state of the particle in the previous moment; The motion model predicts the new position coordinates of each particle; the training process of the motion model uses the position coordinates of the particles at the previous moment, the moving speed of the target device, the control input, and process noise; the process noise is used to describe the randomness of the target device's movement. Each of the Bluetooth anchor points in the optimal anchor point subset is taken as the optimal anchor point, and the ranging value of the optimal anchor point is the actual ranging value. Based on the predicted new position coordinates of each particle and the actual distance measurement value, calculate the predicted distance measurement value from each particle to each of the optimal anchor points; For each particle, calculate the difference between the predicted distance value and the actual distance value from the particle to each of the optimal anchor points, and update the particle weight based on the difference. The updated particle weights of all the particles are normalized so that the sum of the particle weights of all the particles equals one, resulting in a normalized particle set. A resampling step is performed on the particle set, and a new particle set is formed by selecting particles whose particle weights are higher than a preset threshold. The positions of the particles in the new particle set are weighted and averaged to obtain the current position information of the target device; the weighted average of the positions of the particles in the new particle set includes: multiplying the x-coordinate of each particle by its normalized particle weight and summing the results to obtain the estimated x-coordinate of the target device, and multiplying the y-coordinate of each particle by its normalized particle weight and summing the results to obtain the estimated y-coordinate of the target device.

[0012] In some embodiments, obtaining the target state at the previous moment includes: If the target state of the previous moment does not exist, then multiple particles are initialized, wherein the horizontal and vertical coordinates of each particle are randomly distributed within a preset range of the target device, and the initial particle weights of each particle are set to be equal. If a target state exists from the previous time step, the particle set from the previous time step is inherited, and the x-coordinate, y-coordinate, and particle weight of each particle are inherited from the final resampled values ​​of the previous time step.

[0013] In some embodiments, the updated particle weight is equal to the value of an exponential function with a negative half of the result to be calculated as the exponent: the result to be calculated is the transpose of the ranging error vector multiplied by the inverse of the measurement noise covariance matrix and then multiplied by the ranging error vector; the ranging error vector is the vector of actual ranging values ​​minus the vector of predicted ranging values; the measurement noise covariance matrix is ​​used to describe the variance of the ranging errors of each Bluetooth anchor point and the correlation between the ranging errors of different Bluetooth anchor points.

[0014] To achieve the above objectives, another aspect of this application proposes a low-latency positioning device based on Bluetooth channel detection, the device comprising: The first module is used to obtain the identification information of each scanned Bluetooth anchor point and the corresponding ranging data; The second module is used to construct multiple anchor point combinations based on the target location estimation information and the ranging data, calculate the Fisher information matrix and random Cramerlow lower bound for each anchor point combination, and obtain the weight of each Bluetooth anchor point. The third module is used to select a number of Bluetooth anchor points to form an optimal anchor point subset based on a preset selection factor and the weight of the Bluetooth anchor points. The fourth module is used to establish a Bluetooth channel detection connection with each of the Bluetooth anchor points in the optimal anchor point subset using the identification information, and to obtain the ranging value at the current time. The fifth module is used to predict the particle state at the current moment based on the target state of the particle at the previous moment using a particle filtering algorithm, update the particle weights using the ranging value, and perform resampling and weighted averaging to obtain the current position information of the target device.

[0015] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0016] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.

[0017] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0018] The embodiments of this application include at least the following beneficial effects: This application provides a low-latency positioning method, apparatus, electronic device, storage medium, and program product based on Bluetooth channel probing. The method includes: acquiring the identification information and corresponding ranging data of each scanned Bluetooth anchor point; obtaining the weight of each Bluetooth anchor point based on target position estimation information and ranging data; selecting several Bluetooth anchor points to form an optimal anchor point subset based on a preset selection factor and the weight of the Bluetooth anchor points; establishing a Bluetooth channel probing connection with each Bluetooth anchor point in the optimal anchor point subset using the identification information to obtain the ranging value at the current moment; predicting the particle state at the current moment based on the target state of the particle at the previous moment using a particle filtering algorithm, updating the particle weight using the ranging value, and performing resampling and weighted averaging to obtain the current position information of the target device. This application can achieve continuous estimation of the target position, thereby improving the real-time performance and robustness of the system while ensuring positioning accuracy. Attached Figure Description

[0019] Figure 1 This is a flowchart of a low-latency positioning method based on Bluetooth channel detection provided in an embodiment of this application; Figure 2 This is a scene layout diagram provided in the embodiments of this application; Figure 3 This is a flowchart illustrating the anchor weight calculation and anchor selection module provided in an embodiment of this application. Figure 4 This is a flowchart of the positioning system provided in the embodiments of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0022] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0023] 1) Fisher Information Matrix (FIM): A mathematical tool used to measure the amount of information contained in a random variable about an unknown parameter. In the field of positioning, it is used to quantify the amount of information about a target's location that measurement data can provide.

[0024] 2) Stochastic Cramerlow Lower Bound (S-CRLB): Used to describe the minimum theoretical error that an unbiased estimator can achieve in the presence of random noise and prior parameter distribution.

[0025] 3) Particle Filter (PF) Algorithm: A mathematical method for estimating the state of a target (such as position or velocity) by approximating the probability distribution of the target state using a large number of weighted random samples (called particles).

[0026] 4) Measurement noise covariance matrix: Used to describe the noise characteristics between different measurements. The elements on the diagonal of the matrix represent the variance of each measurement (i.e., the noise level), and the elements off-diagonal represent the correlation between different measurements.

[0027] 5) CS (Channel Sounding): This is a technique that measures the characteristics of a wireless channel by sending and receiving signals, and can be used to estimate the distance between the transmitter and receiver.

[0028] 6) PBR (Phase Based Ranging): A ranging technique that estimates distance by measuring the phase difference of signals transmitted between the transmitter and receiver.

[0029] 7) RTT (Round-Trip Time) is a ranging technique that estimates distance by measuring the round-trip time of a signal from the transmitter to the receiver and back.

[0030] In related technologies, positioning methods often fail to meet the requirements of positioning accuracy, robustness, and latency simultaneously, and usually require a trade-off between accuracy and latency.

[0031] In view of this, this application provides a low-latency positioning method and related equipment based on Bluetooth channel probing. This scheme performs indoor positioning based on anchor-weighted particle filtering (PF). Anchor weights are calculated using the Stochastic Cramér-Rao Lower Bound (S-CRLB), and anchors are selected based on these weights to identify high-quality anchors. This method effectively filters low-contribution anchors, reduces redundant observations, and shortens the time required to establish a CS link between the anchor and the target, thereby significantly reducing positioning latency. Furthermore, a particle filtering-based positioning framework is introduced to effectively handle nonlinear and non-Gaussian noise conditions, improving the robustness and accuracy of state estimation. This method integrates weighted anchor selection with particle filtering, achieving synergistic optimization of anchor selection and the PF algorithm. While maintaining positioning accuracy, it significantly reduces positioning latency, improves system real-time performance and scalability, and ultimately achieves efficient and stable indoor wireless positioning performance.

[0032] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0033] Figure 1 This is an optional flowchart of a low-latency positioning method based on Bluetooth channel detection provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S105.

[0034] Step S101: Obtain the identification information of each scanned Bluetooth anchor point and the corresponding ranging data; Step S102: Based on the target location estimation information and ranging data, construct multiple anchor point combinations from the Bluetooth anchor points, calculate the Fisher information matrix and random Cramerlow lower bound for each anchor point combination, and obtain the weight of each Bluetooth anchor point. Step S103: Select several Bluetooth anchors to form an optimal anchor subset based on preset selection factors and Bluetooth anchor weights. Step S104: Establish Bluetooth channel detection connection with each Bluetooth anchor point in the optimal anchor point subset using the identification information to obtain the ranging value at the current moment. Step S105: Based on the particle filtering algorithm, predict the particle state at the current moment according to the target state of the particle at the previous moment, update the particle weight using the ranging value, and perform resampling and weighted averaging to obtain the current position information of the target device.

[0035] Steps S101 to S105, as illustrated in the embodiments of this application, achieve low-latency, high-precision indoor positioning. First, steps S101 and S102 acquire anchor point information and calculate the weights of each anchor point based on a random Cramer-Rao lower bound, achieving precise quantification and evaluation of anchor point quality. Step S103 selects high-quality anchor points to form an optimal anchor point subset based on preset selection factors, effectively filtering low-contribution anchor points and reducing redundant observations and the number of channel probe connection establishments, thereby reducing system positioning latency and improving real-time performance. Step S104 establishes Bluetooth channel probe connections only with the selected high-quality anchor points to obtain reliable current-time ranging values, avoiding contamination of positioning results by low-quality ranging data. Finally, step S105 uses a particle filtering algorithm, combined with high-quality ranging values, to predict particle states, update weights, resample, and perform weighted averaging, effectively addressing the common nonlinear and non-Gaussian noise problems in indoor environments, and improving the robustness and accuracy of state estimation. The above steps deeply integrate the anchor point selection mechanism with the particle filter algorithm to achieve collaborative processing of observation source optimization and state estimation. This allows the scheme to reduce latency while maintaining high-precision positioning, and improves positioning accuracy, robustness and real-time performance.

[0036] In some embodiments, step S101 may include, but is not limited to, step S111: Step S111: Perform an environmental signal scan using the target device's Bluetooth module. During the scan, record the identification information of each Bluetooth anchor point and simultaneously acquire the ranging data of each Bluetooth anchor point. The ranging data includes phase-based ranging data and round-trip time ranging data. The identification information is the physical address or preset number of the Bluetooth anchor point, used to uniquely identify the Bluetooth anchor point. The phase-based ranging data and round-trip time ranging data are used to assist in judging the initial signal quality of the Bluetooth anchor point.

[0037] In step S111 of some embodiments, initial ranging data is collected by synchronously acquiring phase-based ranging data and round-trip time ranging data, providing a basis for subsequent anchor point quality assessment. For example, a shopping mall deploys six Bluetooth anchor points indoors, numbered A1, A2, A3, A4, A5, and A6. When a user enters the mall with a smartphone supporting the Bluetooth 6.0 protocol, the phone's Bluetooth module performs an environmental scan, discovers and records the physical addresses of these six anchor points, and simultaneously acquires the PBR ranging data (such as phase difference) and RTT ranging data (such as signal round-trip time) corresponding to each anchor point, as well as the timestamp of the scan completion.

[0038] In some embodiments, step S102 may include, but is not limited to, steps S201 to S204: Step S201: Obtain target position estimation information; obtaining target position estimation information includes: if it is the first estimation, then the preset initial position is used as the target position estimation information; if it is not the first estimation, then the target position estimation information is the position estimation result obtained by weighting the particle state through the particle filtering algorithm at the previous moment; the preset initial position is the last position recorded before the target device was last powered off, or the initial coordinates input by the user. Step S202: Based on the target location estimation information and ranging data, construct multiple anchor point combinations by grouping all scanned Bluetooth anchor points into groups of three. Step S203: For each anchor combination, construct the corresponding Fisher information matrix and calculate the random Cramerlow lower bound of the anchor combination based on the Fisher information matrix. Step S204: Calculate the contribution of each Bluetooth anchor point in all participating anchor point combinations based on the random Cramerlow lower bound of each anchor point combination, and use the contribution as the weight of the Bluetooth anchor point.

[0039] In some embodiments, step S204 may include, but is not limited to, steps S241 to S242: Step S241: Normalize the random Cramer-Rao lower bound values ​​of all anchor point combinations to obtain the normalized contribution value of each anchor point combination; normalizing the random Cramer-Rao lower bound values ​​of all anchor point combinations includes: subtracting the random Cramer-Rao lower bound value of the current anchor point combination from the maximum random Cramer-Rao lower bound value among all anchor point combinations, and then dividing by the difference between the maximum random Cramer-Rao lower bound value and the minimum random Cramer-Rao lower bound value among all anchor point combinations; Step S242: For each Bluetooth anchor point, sum the normalized contribution values ​​corresponding to all anchor point combinations in which the Bluetooth anchor point participates, and then divide by the total number of anchor point combinations in which the Bluetooth anchor point participates to obtain the weight of the Bluetooth anchor point.

[0040] In steps S201 to S204 of some embodiments, the relative contribution level of each anchor point to the positioning accuracy is accurately quantified by normalizing the random Cramer-Rao lower bound and calculating the average contribution of each anchor point. For example, 6 anchor points are grouped into groups of three, constructing a total of 20 anchor point combinations (such as {A1,A2,A3}, {A1,A2,A4}, etc.). The system obtains the target position estimate output by the particle filter at the previous moment (assumed to be coordinates (5,8)), combines it with the ranging data of each anchor point, constructs a Fisher information matrix for each combination, and calculates the random Cramer-Rao lower bound for each combination. Assuming the lower bound of combination {A1,A2,A3} is 0.05 (small theoretical error), and the lower bound of combination {A4,A5,A6} is 0.25 (large theoretical error). After normalization, the weight of each anchor point is calculated. The results showed that A1, A2, and A3 had higher weights (e.g., 0.9), while A4, A5, and A6 had lower weights (e.g., 0.3).

[0041] In some embodiments, step S103 may include, but is not limited to, steps S301 to S305: Step S301: Sort the weights of the Bluetooth anchor points from largest to smallest to obtain the sorting result; Step S302: Obtain a preset selection factor; the selection factor is a value between zero and one, representing the proportion of the number of Bluetooth anchors to be filtered to the total number of Bluetooth anchors. Step S303: Calculate the number of Bluetooth anchors to be filtered based on the selection factor to obtain the target number of filters; the calculation of the number of Bluetooth anchors to be filtered based on the selection factor includes: multiplying the selection factor by the total number of Bluetooth anchors, and rounding the calculation result up or down. Step S304: Based on the sorting results, select the weight value corresponding to the Bluetooth anchor point ranked in the target filtering quantity position as the weight threshold. Step S305: Filter out all Bluetooth anchor points whose weights are greater than or equal to the weight threshold to form the optimal anchor point subset.

[0042] In steps S301 to S305 of some embodiments, anchor points with weights greater than or equal to a threshold are selected to effectively filter out low-contribution anchor points and retain high-quality anchor points. For example, the weight ranking of the 6 anchor points is: A1 (0.95), A2 (0.92), A3 (0.88), A4 (0.35), A5 (0.30), and A6 (0.25). The preset selection factor is 0.5, which means that 50% of the anchor points are selected. The target number of anchor points to be selected is 6 × 0.5 = 3. The third-ranked anchor point is A3, and its weight of 0.88 is used as the weight threshold. Anchor points with weights greater than or equal to 0.88, namely A1, A2, and A3, are selected to form the optimal anchor point subset. A4, A5, and A6 are removed because their weights are too low and they will not participate in subsequent positioning.

[0043] In some embodiments, step S104 may include, but is not limited to, steps S401 to S402: Step S401: Based on the identification information of each Bluetooth anchor point in the optimal anchor point subset, the target device initiates a Bluetooth channel probe connection request with each Bluetooth anchor point sequentially or in parallel. Step S402: After successfully establishing a Bluetooth channel detection connection with each Bluetooth anchor point, the target device performs channel detection communication with each Bluetooth anchor point to obtain the ranging value at the current moment through signal interaction. The ranging value at the current moment includes phase-based ranging data and round-trip time ranging data. The phase-based ranging data is calculated by measuring the phase difference of the signal transmitted between the target device and the Bluetooth anchor point. The round-trip time ranging data is calculated by measuring the round-trip time of the signal from transmission to reception.

[0044] In steps S401 to S402 of some embodiments, the current ranging value is obtained by performing channel sounding communication, thus acquiring reliable ranging data based on phase and round-trip time. For example, the system uses the physical addresses of A1, A2, and A3, and the smartphone sequentially establishes Bluetooth channel sounding connections with these three anchor points. When connecting to A1, the current PBR ranging value (e.g., phase difference corresponding to a distance of 5.1 meters) and RTT ranging value (e.g., round-trip time corresponding to a distance of 5.0 meters) are obtained through signal interaction; the ranging values ​​with A2 (distance of 3.2 meters) and A3 (distance of 4.8 meters) are obtained in the same way. Since A4, A5, and A6 are filtered out, the system does not need to establish connections with them, saving the time and power consumption of establishing three connections.

[0045] In some embodiments, step S105 may include, but is not limited to, steps S501 to S509: Step S501: Based on the particle filtering algorithm, set the particles as hypothetical samples of the target device's location. Each particle represents a candidate location of the target device, and each particle is encoded. The particle state includes the particle's horizontal coordinate, vertical coordinate, and particle weight. The horizontal and vertical coordinates are used to describe the target location hypothesized by the particle. The particle weight is used to describe the credibility of the particle's hypothesis. The sum of the particle weights of all particles is equal to one. Step S502: Obtain the target state of the particle in the previous moment; Step S503: Predict the new position coordinates of each particle based on the motion model; the training process of the motion model uses the position coordinates of the particles at the previous moment, the moving speed of the target device, the control input, and the process noise; the process noise is used to describe the randomness of the target device's movement process. Step S504: Select each Bluetooth anchor point in the optimal anchor point subset as the optimal anchor point, and the ranging value of the optimal anchor point is the actual ranging value. Step S505: Calculate the predicted distance value from each particle to each optimal anchor point based on the predicted new position coordinates and actual distance value of each particle. Step S506: For each particle, calculate the difference between the predicted distance value and the actual distance value from the particle to each optimal anchor point, and update the particle weight based on the difference. Step S507: Normalize the updated particle weights of all particles so that the sum of the particle weights of all particles equals one, and obtain the normalized particle set. Step S508: Perform a resampling step on the particle set by selecting particles with weights higher than a preset threshold to form a new particle set; Step S509: Perform a weighted average of the positions of the particles in the new particle set to obtain the current position information of the target device; the weighted average of the positions of the particles in the new particle set includes: multiplying the abscissa of each particle by its normalized particle weight and summing the results to obtain the estimated abscissa of the target device, and multiplying the ordinate of each particle by its normalized particle weight and summing the results to obtain the estimated ordinate of the target device.

[0046] In some embodiments, step S502 may include, but is not limited to, steps S521 to S522: Step S521: If the target state of the previous moment does not exist, then initialize multiple particles, wherein the horizontal and vertical coordinates of each particle are randomly distributed within a preset range of the target device, and the initial particle weights of each particle are set to be equal. Step S522: If the target state of the previous time step exists, then the particle set of the previous time step is inherited, and the x-coordinate, y-coordinate and particle weight of each particle are inherited from the final resampled value of the previous time step.

[0047] In some embodiments, the updated particle weights in step S506 are equal to the value of an exponential function with a negative half of the result to be calculated as the exponent: the result to be calculated is the transpose of the ranging error vector multiplied by the inverse of the measurement noise covariance matrix and then multiplied by the ranging error vector; the ranging error vector is the vector of the actual ranging value minus the vector of the predicted ranging value; the measurement noise covariance matrix is ​​used to describe the variance of the ranging error of each Bluetooth anchor point and the correlation between the ranging errors of different Bluetooth anchor points.

[0048] In steps S501 to S509 of some embodiments, multiple weighted particles are set as position assumptions using a particle filtering algorithm to adapt to nonlinear and non-Gaussian noise environments. For example, the system initializes 1000 particles, each containing coordinates and weights. The target position estimate at the previous moment is (5, 8). Based on the motion model (assuming the target moves eastward at a speed of 1 m / s), the particle position distribution at the current moment is predicted. The distance measurements obtained in step S104 are obtained: approximately 5.05 meters to A1, approximately 3.2 meters to A2, and approximately 4.8 meters to A3. For each particle, its predicted distances to A1, A2, and A3 are calculated and compared with the actual distance measurements; particles with smaller differences have higher weights. After weight normalization, resampling is performed, eliminating particles with low weights and replicating particles with high weights. Finally, a weighted average of all particle positions is calculated to obtain the target position estimate at the current moment as (6.05, 7.95). The result is more accurate than the positioning method that uses all 6 anchor points (including low-quality A4, A5, and A6), and it requires less computation and has lower latency.

[0049] As an optional implementation, a user carries a smartphone supporting Bluetooth 6.0 and moves within an indoor environment. Multiple Bluetooth sensor anchors with known locations are deployed in this environment. The system first generates a combination scheme of all available anchors. For each anchor combination, the relationship between S-CRLB and positioning error is established by deriving the Fisher Information Matrix (FIM), theoretically quantifying the impact of environmental noise and hardware defects on positioning accuracy. Based on the S-CRLB contribution of each anchor combination, the weight of the anchor is further derived. This weight directly reflects the relative contribution level of a single anchor to the final positioning accuracy. Finally, anchors with higher weights are selected based on a scaling factor to form a high-quality subset, which is then applied to the subsequent ranging and positioning process. This effectively reduces the number of times the CS channel needs to be established for ranging and lowers the system's positioning latency.

[0050] Using an optimized subset of anchor points, the system then enters the particle filtering stage. In the motion prediction phase, the system generates the predicted state of each particle based on a preset motion model. Subsequently, the smartphone establishes a channel connection with the Bluetooth anchor points and completes communication interaction. After acquiring signal data, it updates the particle states using CS-based distance measurement results and calculates particle weights through state likelihood assessment. After weight normalization, the system uses it for target state estimation and performs resampling to mitigate particle degradation. As the target moves, the above process is repeated cyclically, ultimately achieving continuous estimation of the target position.

[0051] This application discloses a hybrid localization method integrating S-CRLB and particle filtering to achieve synergistic optimization between the anchor point selection mechanism and the PF algorithm. This method effectively reduces localization latency while maintaining high-precision positioning.

[0052] The solutions of this application embodiment will be described in detail and explained below with reference to specific application examples: The target device first acquires its own operational status data in real time, including the current timestamp and communication status information. Simultaneously, the target device performs an environmental signal scan via its Bluetooth module to identify surrounding Bluetooth anchor signals. During the scan, the system records the identification information of each anchor and simultaneously acquires its corresponding PBR, RTT, and scan completion time. This data serves as the foundational input for subsequent anchor quality assessment and positioning estimation.

[0053] This application provides embodiments that Figure 2 This is a scene layout diagram, where the target device is represented by a yellow square, high-quality anchor points are marked in blue and establish a CS connection with the target device, and low-quality anchor points are marked in red and do not participate in any action. The target device moves along a red diamond-shaped trajectory, and the lines marked with arrows in the diagram represent the target's movement trajectory and direction.

[0054] In this embodiment, the system pre-sets N in the indoor space. A There are several Bluetooth anchor points. Due to differences in hardware performance, spatial location, environmental obstruction, and multipath effects, the ranging and positioning quality of different anchor points varies. This invention improves overall positioning performance by using an anchor point quality assessment and dynamic selection mechanism to screen high-quality anchor points that contribute significantly to positioning accuracy. Figure 2 Typical scenario layouts of the system are given. Figure 2In the diagram, high-quality anchors are marked in blue, indicating high ranging reliability and establishing a Bluetooth channel detection connection with the target device. Low-quality anchors are marked in red, indicating larger ranging errors and not participating in subsequent positioning processing. The target device is represented by a yellow box, moving along a red diamond trajectory in space, with arrows indicating its direction of movement. The CS ranging link between high-quality anchors and the target device is represented by a gray dashed line.

[0055] This application provides embodiments that Figure 3 This invention describes the overall process of anchor point quality assessment and selection mechanism. Based on the system's input environmental conditions and anchor point characteristics, this module progressively completes information content assessment and the selection of high-quality anchor points.

[0056] When the system is running, the Bluetooth quality is first assessed, such as... Figure 3 As shown. The system obtains N from the environment. A The Bluetooth anchor point information is used to construct N positioning combinations, with each group consisting of three anchor points. C 1 , C 2 , …, C n Subsequently, the system combines the target device's estimated position information from the previous moment to construct a corresponding Fisher Information Matrix (FIM) for each anchor point combination, quantifying the contribution of that combination to the positioning accuracy at the current moment. Based on this FIM, the corresponding S-CRLB value is calculated. S 1 , S 2 , …, S n} represents the theoretical lower bound of the position estimation for this combination. The system analyzes the impact of each anchor point combination on S-CRLB, first using the formula... Perform weight normalization, and then use the formula The corresponding anchor point weights are calculated to measure the effectiveness of the anchor point in the current spatial geometry and signal environment. This represents the normalized contribution value of the i-th anchor point combination. This represents the maximum random Craméror lower bound among all anchor point combinations. This represents the minimum random Cramerlow lower bound among all anchor point combinations. Let represent the random Craméror lower bound of the i-th anchor point combination. This represents the weight of the j-th Bluetooth anchor point. This indicates that the j-th anchor point belongs to the i-th anchor point combination. This represents the total number of anchor point combinations in which the j-th anchor point participates.

[0057] After the anchor point weights are generated, the system sorts the anchor points according to a preset selection factor K, where K is a fixed parameter, and then selects a set of high-quality anchor points N. S=K*N A The set of anchor points used for the final ranging calculation is selected. Low-quality anchor points are discarded due to their low contribution, and their data is no longer used as the basis for updating particle filter weights. Through this selection strategy, the selected set of anchor points will serve as the coordinate set for further use in subsequent positioning calculations. The system can adaptively adjust the set of anchor points used in different location areas to ensure that the ranging data always maintains high reliability.

[0058] After obtaining high-quality anchor point data, the system uses a particle filter algorithm to estimate the position of the target device, such as... Figure 4 As shown, the particle filter localization method of the present invention includes the following main steps: First, the system checks whether there are high-quality anchor points, and then obtains new measurement values ​​by establishing a Bluetooth channel with the target. After receiving valid measurement data and confirming that the anchor point quality meets the requirements, the system proceeds to the next step. Subsequently, the particle filter generates M particles based on the estimation results from the previous time step and encodes the particles. ,in and The coordinates of the particle ( Represents the x-coordinate of the m-th particle. (represents the ordinate of the m-th particle). This represents the weight of the m-th particle, which is then determined based on the motion model. Perform location prediction, where and These are the state transition matrix and the control input matrix, respectively. This represents process noise. After particle state prediction is completed, the system calculates the predicted distance value based on the spatial location of the high-quality anchor point. Compare it with the actual CS ranging results Matching is performed to construct the likelihood value corresponding to each particle. The particle's weight is updated based on this likelihood function, calculated using the following formula: The weights are then normalized. The use of high-quality anchor points effectively improves the accuracy of weight updates and avoids the filtering process being biased by low-quality ranging data. The system then uses the formula... A resampling step is performed. Resampling optimizes the particle set by selecting particles with higher weights, preventing particle degeneration. Finally, the system uses the formula... The current position estimate of the target device is calculated by weighted averaging of the positions of all particles. This represents the state vector of the m-th particle at time t; This represents the state vector of the m-th particle at time t-1; This represents the state transition matrix, which describes the position change of a particle from the previous time step to the current time step, mapping the state of the previous time step to the predicted state of the current time step. This represents the control input matrix, used to map external control inputs (such as the movement distance of the target device) to changes in the particle state; This represents the control input vector, which is the control input at time t, and typically contains information such as the distance or speed at which the target device has moved. This represents process noise, used to describe the randomness and uncertainty in the movement of the target equipment, reflecting the deviation between the actual motion and the ideal motion model. Represents the vector of actual distance measurements; Represents the vector of predicted distance values; Represents the ranging error vector; The measurement noise covariance matrix is ​​a matrix that describes the variance of the distance measurement error at each anchor point and the correlation between the distance measurement errors at different anchor points. It is a resampling function, which indicates that a resampling operation is performed, which resamples the particle set according to the particle weights; This represents the state vector of the m-th particle after resampling, and the particle states in the new particle set obtained after the resampling operation. These represent parameters related to resampling, which are auxiliary parameters used in the resampling process, such as random numbers or weight distribution information. This represents the estimated current location of the target device. Indicates the starting index for summation. Indicates the end index of the summation.

[0059] This invention achieves robust positioning in high-noise environments by introducing a dynamic anchor point selection mechanism based on S-CRLB into the particle filter positioning framework. Compared with traditional methods that use all anchor points, this invention uses only high-quality anchor point data, which significantly reduces the impact of ranging errors on the system, as well as reducing computational load and positioning latency, making the positioning process more efficient and reliable. The method of this invention is applicable to arbitrary anchor point layouts, different Bluetooth channel configurations, and various mobile target devices, possessing good scalability and versatility.

[0060] Advantages of the embodiments of this application: 1. By accurately quantifying and evaluating the quality of anchor points, the system can prioritize the use of high-quality observation sources, thereby significantly improving the accuracy and stability of positioning results.

[0061] 2. Due to the adoption of a dynamic filtering mechanism based on anchor point weights and selection factors, the optimal subset of anchor points can be selected in real time, effectively reducing redundant ranging and channel establishment processes, significantly reducing cumulative system latency and improving overall real-time performance.

[0062] 3. By deeply integrating the weighted anchor selection strategy with the particle filter algorithm, the system can simultaneously meet the requirements of low latency and high-precision positioning by achieving coordinated processing of observation source optimization and state estimation.

[0063] 4. Since the present invention is superior to existing positioning methods based on Bluetooth channel detection in terms of positioning accuracy, robustness and latency, it can achieve more reliable and efficient indoor positioning performance in more complex environments.

[0064] This application also provides a low-latency positioning device based on Bluetooth channel detection, which can implement the above method. The device includes: The first module is used to obtain the identification information of each scanned Bluetooth anchor point and the corresponding ranging data; The second module is used to construct multiple anchor point combinations from Bluetooth anchor points based on target location estimation information and ranging data, calculate the Fisher information matrix and random Cramerlow lower bound for each anchor point combination, and obtain the weight of each Bluetooth anchor point. The third module is used to select several Bluetooth anchors to form an optimal subset of anchors based on preset selection factors and the weights of Bluetooth anchors. The fourth module is used to establish a Bluetooth channel detection connection with each Bluetooth anchor point in the optimal anchor point subset using the identification information, and to obtain the ranging value at the current moment. The fifth module is used to predict the particle state at the current moment based on the target state of the particle at the previous moment using the particle filtering algorithm, update the particle weights using the ranging value, and perform resampling and weighted averaging to obtain the current position information of the target device.

[0065] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0066] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0067] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0068] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0069] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0070] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0071] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0072] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0073] This application provides a low-latency positioning method, apparatus, electronic device, storage medium, and program product based on Bluetooth channel probing. It calculates the weight of each Bluetooth anchor point using a random Cramer-Rao lower bound and selects anchor points with higher weights to form an optimal subset of anchor points based on a preset selection factor. This reduces the number of anchor points requiring channel probing connections, thereby lowering positioning latency. Simultaneously, it uses a particle filter algorithm to predict, update, resample, and weighted average the ranging data from the selected anchor points, achieving continuous estimation of the target location. This improves the system's real-time performance and robustness while maintaining positioning accuracy.

[0074] The embodiments described in 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 by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0075] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0076] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0077] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0078] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0079] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, 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. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0080] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0081] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0082] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0083] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0084] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A low-latency positioning method based on Bluetooth channel detection, characterized in that, The method includes the following steps: Obtain the identification information and corresponding ranging data of each scanned Bluetooth anchor point; Based on the target location estimation information and the ranging data, multiple anchor point combinations are constructed from the Bluetooth anchor points. The Fisher information matrix and random Cramerlow lower bound of each anchor point combination are calculated to obtain the weight of each Bluetooth anchor point. Based on the preset selection factor and the weight of the Bluetooth anchor point, a subset of Bluetooth anchor points is selected to form an optimal anchor point subset. The Bluetooth channel detection connection is established with each of the Bluetooth anchor points in the optimal anchor point subset using the identification information to obtain the ranging value at the current time. Based on the particle filtering algorithm, the particle state at the current moment is predicted according to the target state of the particle at the previous moment. The particle weight is updated using the ranging value, and resampling and weighted averaging are performed to obtain the current position information of the target device.

2. The method according to claim 1, characterized in that, The acquisition of the identification information of each scanned Bluetooth anchor point and the corresponding ranging data includes: The target device's Bluetooth module performs an environmental signal scan, recording the identification information of each Bluetooth anchor point during the scan and simultaneously acquiring the ranging data of each Bluetooth anchor point. The ranging data includes phase-based ranging data and round-trip time ranging data. The identification information is the physical address or preset number of the Bluetooth anchor point, used to uniquely identify the Bluetooth anchor point. The phase-based ranging data and the round-trip time ranging data are used to assist in determining the initial signal quality of the Bluetooth anchor point.

3. The method according to claim 1, characterized in that, Based on the target location estimation information and the ranging data, multiple anchor point combinations are constructed from the Bluetooth anchor points. The Fisher information matrix and random Cramérault lower bound of each anchor point combination are calculated to obtain the weight of each Bluetooth anchor point, including: Obtain target position estimation information; the acquisition of target position estimation information includes: if it is the first estimation, then the preset initial position is used as the target position estimation information; if it is not the first estimation, then the target position estimation information is the position estimation result obtained by weighted averaging of particle states through particle filtering algorithm at the previous moment; the preset initial position is the last position recorded before the target device was last powered off, or it is the initial coordinates input by the user; Based on the target location estimation information and the ranging data, all the scanned Bluetooth anchor points are combined into multiple anchor point combinations in a group of three Bluetooth anchor points each. For each of the anchor point combinations, a corresponding Fisher information matrix is ​​constructed, and the random Cramerlow lower bound of the anchor point combination is calculated based on the Fisher information matrix. Based on the random Cramerlow lower bound of each of the anchor point combinations, the contribution of each Bluetooth anchor point in all participating anchor point combinations is calculated, and the contribution is used as the weight of the Bluetooth anchor point.

4. The method according to claim 3, characterized in that, The step of calculating the contribution of each Bluetooth anchor point in all participating anchor point combinations based on the random Cramerlow lower bound value of each anchor point combination, and using the contribution as the weight of the Bluetooth anchor point, includes: The random Cramer-Rao lower bound values ​​of all anchor point combinations are normalized to obtain the normalized contribution value of each anchor point combination; the normalization of the random Cramer-Rao lower bound values ​​of all anchor point combinations includes: subtracting the random Cramer-Rao lower bound value of the current anchor point combination from the maximum random Cramer-Rao lower bound value among all anchor point combinations, and then dividing by the difference between the maximum random Cramer-Rao lower bound value and the minimum random Cramer-Rao lower bound value among all anchor point combinations; For each Bluetooth anchor point, the normalized contribution values ​​corresponding to all anchor point combinations in which the Bluetooth anchor point participates are summed, and then divided by the total number of anchor point combinations in which the Bluetooth anchor point participates to obtain the weight of the Bluetooth anchor point.

5. The method according to claim 1, characterized in that, The step of selecting a subset of Bluetooth anchor points to form an optimal anchor point set based on a preset selection factor and the weight of the Bluetooth anchor points includes: The weights of the Bluetooth anchor points are sorted from largest to smallest to obtain the sorting result; Obtain a preset selection factor; the selection factor is a value between zero and one, representing the proportion of the number of Bluetooth anchors to be filtered to the total number of Bluetooth anchors; The number of Bluetooth anchors to be filtered is calculated based on the selection factor to obtain the target number of filters; the calculation of the number of Bluetooth anchors to be filtered based on the selection factor includes: multiplying the selection factor by the total number of Bluetooth anchors, and rounding the calculation result up or down. Based on the sorting results, the weight value corresponding to the Bluetooth anchor point ranked in the target filtering quantity position is selected as the weight threshold. The Bluetooth anchors whose weights are greater than or equal to the weight threshold are selected to form the optimal anchor subset.

6. The method according to claim 1, characterized in that, The step of establishing a Bluetooth channel detection connection with each of the Bluetooth anchor points in the optimal anchor point subset using the identification information to obtain the ranging value at the current moment includes: Based on the identification information of each Bluetooth anchor point in the optimal anchor point subset, the target device initiates a Bluetooth channel probe connection request with each Bluetooth anchor point sequentially or in parallel. After successfully establishing a Bluetooth channel detection connection with each of the Bluetooth anchors, the target device performs channel detection communication with each of the Bluetooth anchors to obtain the ranging value at the current moment through signal interaction. The ranging value at the current moment includes phase-based ranging data and round-trip time ranging data. The phase-based ranging data is calculated by measuring the phase difference of the signal transmitted between the target device and the Bluetooth anchor. The round-trip time ranging data is calculated by measuring the round-trip time of the signal from transmission to reception.

7. The method according to claim 1, characterized in that, The particle filtering algorithm predicts the particle state at the current moment based on the target state of the particles at the previous moment, updates the particle weights using the ranging values, and performs resampling and weighted averaging to obtain the current position information of the target device, including: Based on the particle filtering algorithm, particles are set as hypothetical samples of the target device's location, each particle represents a candidate location of the target device, and each particle is encoded; the particle state includes the particle's x-coordinate, y-coordinate, and particle weight; the x-coordinate and y-coordinate are used to describe the target location hypothesized by the particle; the particle weight is used to describe the confidence level of the particle hypothesis, and the sum of the particle weights of all particles is equal to one; Obtain the target state of the particle in the previous moment; The motion model predicts the new position coordinates of each particle; the training process of the motion model uses the position coordinates of the particles at the previous moment, the moving speed of the target device, the control input, and process noise; the process noise is used to describe the randomness of the target device's movement. Each of the Bluetooth anchor points in the optimal anchor point subset is taken as the optimal anchor point, and the ranging value of the optimal anchor point is the actual ranging value. Based on the predicted new position coordinates of each particle and the actual distance measurement value, calculate the predicted distance measurement value from each particle to each of the optimal anchor points; For each particle, calculate the difference between the predicted distance value and the actual distance value from the particle to each of the optimal anchor points, and update the particle weight based on the difference. The updated particle weights of all the particles are normalized so that the sum of the particle weights of all the particles equals one, resulting in a normalized particle set. A resampling step is performed on the particle set, and a new particle set is formed by selecting particles whose particle weights are higher than a preset threshold. The positions of the particles in the new particle set are weighted and averaged to obtain the current position information of the target device; the weighted average of the positions of the particles in the new particle set includes: multiplying the x-coordinate of each particle by its normalized particle weight and summing the results to obtain the estimated x-coordinate of the target device, and multiplying the y-coordinate of each particle by its normalized particle weight and summing the results to obtain the estimated y-coordinate of the target device.

8. The method according to claim 7, characterized in that, The step of obtaining the target state at the previous moment includes: If the target state of the previous moment does not exist, then multiple particles are initialized, wherein the horizontal and vertical coordinates of each particle are randomly distributed within a preset range of the target device, and the initial particle weights of each particle are set to be equal. If a target state exists from the previous time step, then the particle set from the previous time step is inherited, and the x-coordinate, y-coordinate, and particle weight of each particle are inherited from the final resampled values ​​of the previous time step.

9. The method according to claim 7, characterized in that, The updated particle weight is equal to the value of an exponential function with a negative half of the result to be calculated as the exponent: the result to be calculated is the transpose of the ranging error vector multiplied by the inverse of the measurement noise covariance matrix and then multiplied by the ranging error vector; the ranging error vector is the vector of the actual ranging value minus the vector of the predicted ranging value; the measurement noise covariance matrix is ​​used to describe the variance of the ranging error of each Bluetooth anchor point and the correlation between the ranging errors of different Bluetooth anchor points.

10. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 9.

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