Bluetooth positioning method and system based on multi-beacon RSSI (Received Signal Strength Indicator) weighted optimization

Abstract

The invention relates to the technical field of Bluetooth positioning, and discloses a Bluetooth positioning method and system based on multi-beacon RSSI weighted optimization, and the method comprises the steps: sequentially switching and scanning a broadcast signal of a target Bluetooth tag through a plurality of Bluetooth beacons disposed at a fixed position, so as to obtain a plurality of groups of RSSI measurement values from different directions; performing filtering processing on the multiple groups of RSSI measurement values to suppress signal fluctuation; calculating the weight of each beacon based on the filtered RSSI value, and constructing a weighted least square positioning model; and solving the coordinate position of the target Bluetooth tag by using a weighted least square positioning model. According to the invention, through adoption of a multi-beacon rapid switching acquisition and RSSI weighted optimization mechanism, signal quality differences of different beacons can be effectively distinguished, and dynamic weight descending processing is carried out on measurement values which are seriously interfered by a multipath effect and shielding, so that the positioning precision and stability in a complex indoor environment are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of Bluetooth positioning technology, and particularly relates to a Bluetooth positioning method and system based on multi-beacon RSSI weighted optimization. BACKGROUND

[0002] Bluetooth technology is widely used in near-field positioning due to its low power consumption and low cost, and is particularly suitable for scenarios such as access control triggering and area sensing. Among them, the RSSI-based ranging method has become the preferred solution for most Bluetooth positioning systems due to its simplicity.

[0003] However, in a complex indoor environment, Bluetooth signals are easily affected by multipath effects and shielding interference, resulting in severe fluctuations in RSSI values, which makes the error of traditional multi-beacon positioning algorithms significantly in a non-uniform noise environment. Related methods fail to effectively distinguish the signal quality differences of each beacon, and high-precision alternative solutions are costly and difficult to achieve an effective balance between precision and cost in consumer scenarios. Therefore, how to effectively compensate and optimize unstable RSSI signals on the basis of low-cost Bluetooth hardware, and thus obtain stable and accurate positioning results in complex environments, has become a problem that needs to be solved. SUMMARY

[0004] Therefore, the present disclosure provides a Bluetooth positioning method and system based on multi-beacon RSSI weighted optimization to at least solve the problem of how to effectively compensate and optimize unstable RSSI signals on the basis of low-cost Bluetooth hardware, and thus obtain stable and accurate positioning results in complex environments.

[0005] The present disclosure provides a Bluetooth positioning method based on multi-beacon RSSI weighted optimization, the method comprising: switching and scanning the broadcast signal of a target Bluetooth tag in turn by a plurality of Bluetooth beacons deployed at fixed positions to obtain a plurality of groups of RSSI measurement values from different directions; filtering the plurality of groups of RSSI measurement values to suppress signal fluctuations; calculating the weight of each beacon based on the filtered RSSI values and constructing a weighted least squares positioning model; and solving the coordinate position of the target Bluetooth tag using the weighted least squares positioning model.

[0006] This disclosure also provides a Bluetooth positioning system, comprising: an algorithm unit, multiple Bluetooth beacons, a high-speed switch control unit, a power control unit, and a target Bluetooth tag, wherein: the multiple Bluetooth beacons are deployed at fixed known locations for receiving Bluetooth signals; the high-speed switch control unit is connected to the multiple Bluetooth beacons for controlling the switching and scanning between the Bluetooth beacons; the power control unit provides a stable operating voltage during the switching and scanning process; the algorithm unit is configured to perform the following steps: sequentially switching the Bluetooth beacons by controlling the high-speed switch control unit to scan the broadcast signal of the target Bluetooth tag and obtain multiple sets of RSSI measurement values ​​from different directions; filtering the multiple sets of RSSI measurement values ​​to suppress signal fluctuations; calculating the weight of each beacon based on the filtered RSSI values ​​to construct a weighted least squares positioning model; and using the weighted least squares positioning model to solve for the coordinate position of the target Bluetooth tag.

[0007] This disclosure also provides an electronic device, including: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of any of the above-described Bluetooth positioning methods based on multi-beacon RSSI weighted optimization.

[0008] This disclosure also provides a computer-readable storage medium storing a computer program, wherein when executed by a processor, the computer program implements the steps of any of the above-described Bluetooth positioning methods based on multi-beacon RSSI weighted optimization.

[0009] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of any of the above-described Bluetooth positioning methods based on multi-beacon RSSI weighted optimization.

[0010] In the Bluetooth positioning method and system based on multi-beacon RSSI weighted optimization according to the above embodiments of this disclosure, by employing a multi-beacon fast switching acquisition and RSSI weighted optimization mechanism, it is possible to effectively distinguish the signal quality differences of different beacons, and dynamically reduce the weight of measurement values ​​severely affected by multipath effects and obstruction interference. This significantly improves the positioning accuracy and stability in complex indoor environments, overcoming the shortcomings of traditional multi-beacon positioning algorithms in non-uniform noise environments where errors accumulate. Centimeter-level positioning accuracy can be achieved by combining high-speed switch control and weighted least squares algorithm on a single Bluetooth module, avoiding the high costs associated with increasing base station density. This makes it particularly suitable for cost-sensitive consumer applications such as smart door locks and area sensing.

[0011] Furthermore, by employing moving average filtering and a weighting strategy based on RSSI stability, the system does not simply process all signals equally, but rather dynamically senses and adaptively optimizes signal quality. This weighted method based on signal reliability assessment is more robust in complex environments, achieving environmentally adaptive positioning. Attached Figure Description

[0012] To more clearly illustrate the embodiments of this disclosure, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1a This is an exemplary schematic diagram of the architecture of a Bluetooth positioning system used in a Bluetooth positioning method based on multi-beacon RSSI weighted optimization provided in an embodiment of this disclosure; Figure 1b A flowchart illustrating a Bluetooth positioning method based on multi-beacon RSSI weighted optimization provided in this embodiment of the disclosure; Figure 2 A schematic diagram of Bluetooth beacon location distribution for a Bluetooth positioning method based on multi-beacon RSSI weighted optimization provided in this embodiment of the disclosure; Figure 3 This is a schematic diagram of the structure of a Bluetooth positioning system provided in an embodiment of the present disclosure; Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. Detailed Implementation

[0014] Bluetooth positioning technology, with its advantages of low power consumption, low module cost, and widespread integration into smartphones, has become the mainstream choice for near-field sensing and area positioning. Ranging methods based on received signal strength indication are widely used in consumer-grade scenarios such as access control systems, people proximity detection, and item tracking due to their simplicity and broad compatibility.

[0015] Among related technologies, the following positioning systems and methods based on Bluetooth RSSI exist: 1. Multi-beacon system based on trilateration: This system deploys multiple Bluetooth beacons, measures the RSSI values ​​between each beacon and the tag to be located, converts these values ​​into distance estimates, and then uses geometric relationships to solve for the tag's location. This method is simple to implement and is a fundamental solution for Bluetooth positioning.

[0016] 2. Fingerprint-based Matching and Positioning System: This system pre-collects RSSI signal features from a large number of locations within the positioning area to construct a location-signal strength fingerprint database. During the online positioning phase, the real-time collected RSSI sequences are matched with the database, and the current location is estimated using nearest neighbor or probabilistic algorithms. This method can mitigate the impact of signal fluctuations to some extent.

[0017] 3. Fusion positioning system based on inertial navigation assistance: This system combines Bluetooth RSSI ranging with inertial measurement unit data, and uses the motion trajectory provided by the IMU to smooth and correct the RSSI positioning results to improve the stability of continuous positioning.

[0018] However, when faced with real-world deployment scenarios, especially in complex indoor environments, the aforementioned technologies often have the following limitations: 1. Trilateral positioning-based multi-beacon systems are extremely sensitive to RSSI measurement errors. In environments with multipath effects, personnel movement, and wall obstructions, drastic fluctuations in RSSI values ​​can lead to inaccurate distance estimation, resulting in significant deviations in the geometric positioning solution. Traditional least squares methods treat all beacons as equally reliable, failing to distinguish between signal quality differences across different links, thus amplifying the error.

[0019] 2. While fingerprint-based matching and positioning systems can learn environmental features to some extent, the initial data collection workload is enormous, and deployment and maintenance costs are high. Any change in the environmental layout (such as furniture movement or changes in personnel density) may render the original fingerprint database invalid, requiring re-collection, thus limiting the system's robustness and practicality.

[0020] 3. While inertial measurement unit (IMU)-assisted fusion positioning systems can improve trajectory smoothness, the IMU sensor itself has accumulated errors, and the system cannot fundamentally correct the initial positioning deviation of Bluetooth RSSI. Furthermore, the system complexity and power consumption increase, making it unsuitable for long-term operation on low-cost, low-power terminal devices.

[0021] Therefore, existing Bluetooth RSSI positioning solutions struggle to balance accuracy, environmental adaptability, and cost. There is an urgent need for a solution that can effectively distinguish and suppress unreliable signals based on conventional hardware through intelligent signal processing and optimization algorithms, thereby achieving stable and accurate positioning to meet the dual requirements of high reliability and low cost for applications such as smart door locks and precise area sensing.

[0022] To address the aforementioned issues, various embodiments of this disclosure provide a Bluetooth positioning method based on multi-beacon RSSI weighted optimization. The method includes: sequentially switching and scanning the broadcast signal of a target Bluetooth tag using multiple Bluetooth beacons deployed at fixed locations to obtain multiple sets of RSSI measurements from different directions; filtering the multiple sets of RSSI measurements to suppress signal fluctuations; calculating the weight of each beacon based on the filtered RSSI values ​​and constructing a weighted least squares positioning model; and using the weighted least squares positioning model to solve for the coordinate position of the target Bluetooth tag.

[0023] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this disclosure.

[0024] It should be noted that, in the description of this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. The terms "first," "second," etc., in this disclosure are used to distinguish similar objects and are not used to describe a particular order or sequence.

[0025] To enable those skilled in the art to better understand the present disclosure, the present disclosure will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0026] Please refer to Figure 1a , Figure 1a This is an exemplary schematic diagram of the architecture of a Bluetooth positioning system used in a Bluetooth positioning method based on multi-beacon RSSI weighted optimization provided in an embodiment of this disclosure. Figure 1a As shown, the Bluetooth positioning system 100 includes: an algorithm unit 101, multiple Bluetooth beacons 102, a high-speed switch control unit 103, a power control unit 104, and a target Bluetooth tag 105.

[0027] Here, the Bluetooth positioning system 100 can be a dedicated system that uses devices such as smart door locks as carriers to achieve high-precision positioning in complex indoor environments.

[0028] Among them, multiple Bluetooth beacons 102 serve as signal acquisition units of the system, and are fixedly deployed at known coordinate locations to receive Bluetooth signals transmitted by the target Bluetooth tag 105.

[0029] The high-speed switch control unit 103, as the signal switching control core of the system, is connected to multiple Bluetooth beacons 102 and is configured to perform rapid cyclic switching between multiple Bluetooth beacons to ensure that the signal acquisition of all beacons is completed within the continuous broadcast cycle of the target Bluetooth tag 105.

[0030] The power control unit 104, as the core of the system's power management, is configured to provide a stable operating voltage during high-speed switching, ensuring the reliability of the system's power supply when frequently starting and stopping Bluetooth scanning.

[0031] The algorithm unit 101, as the core of the system's data processing and positioning calculation, is configured to be integrated into the device's main control chip or a separate processing module, and is configured to perform at least one of the following operations: RSSI data filtering, beacon weight calculation, weighted least squares positioning model construction, and target coordinate calculation.

[0032] The target Bluetooth tag 105, as the object to be located, can be a mobile device with Bluetooth broadcasting function, such as a smartphone or smart key, which periodically transmits Bluetooth broadcasting signals.

[0033] Further reference Figure 1b , Figure 1b The flowchart illustrates a Bluetooth positioning method based on multi-beacon RSSI weighted optimization provided in this embodiment of the disclosure. The method may include the following steps: Step S101: By deploying multiple Bluetooth beacons at fixed locations, the broadcast signals of the target Bluetooth tag are switched and scanned sequentially to obtain multiple sets of RSSI measurement values ​​from different directions.

[0034] In this embodiment, the algorithm unit 101 controls the high-speed switch control unit 103 to cyclically switch between multiple Bluetooth beacons 102 deployed at fixed, known locations. Each Bluetooth beacon 102, during its activated period, scans and receives broadcast signals emitted by the target Bluetooth tag 105. In this way, the system acquires multiple sets of RSSI measurements from beacons in different spatial directions within multiple consecutive broadcast cycles of the target tag.

[0035] The Bluetooth beacon 102 serves as the system's signal acquisition unit and is fixedly deployed at a known coordinate location. The number of Bluetooth beacons 102 can be specifically set based on actual conditions. For example, assuming the number of Bluetooth beacons 102 is 4, the 4 Bluetooth beacons 102 can be configured to be distributed in the front, back, left, and right directions of the smart door, forming omnidirectional signal coverage.

[0036] As an example, Figure 2 A schematic diagram of Bluetooth beacon location distribution for a Bluetooth positioning method based on multi-beacon RSSI weighted optimization provided in this disclosure embodiment is shown below.Figure 2 As shown, antennas 1 to 4 (i.e., four Bluetooth beacons 102) are deployed in the front, back, left and right directions of the smart door, respectively, to form all-round signal coverage.

[0037] During deployment, each beacon has a certain signal coverage range (e.g., Figure 2 As shown in the diagram (r1, r2, r3, r4), a reasonable layout is used to ensure that the target Bluetooth tag 105 can be stably detected by at least multiple Bluetooth beacons 102 within the positioning area.

[0038] Here, r1 can represent the signal coverage radius of antenna 1, r2 can represent the signal coverage radius of antenna 2, r3 can represent the signal coverage radius of antenna 3, and r4 can represent the signal coverage radius of antenna 4.

[0039] Step S102: Filter the multiple sets of RSSI measurements to suppress signal fluctuations.

[0040] In this embodiment, the algorithm unit 101 performs signal preprocessing on the acquired multiple sets of RSSI measurement values ​​and suppresses random fluctuation components in the measurement values ​​through digital filtering technology.

[0041] Here, filtering refers to smoothing the original RSSI measurement sequence using a specific algorithm, aiming to retain the signal trend characteristics while filtering out abnormal fluctuations caused by environmental multipath effects, electromagnetic interference, or momentary obstruction.

[0042] Specifically, the core function of filtering is to improve the stability and reliability of RSSI measurements, providing a high-quality data foundation for subsequent weight calculation and positioning solutions, thereby reducing positioning errors caused by signal fluctuations at the source.

[0043] The filtering process in this step can effectively improve the signal-to-noise ratio of RSSI measurements and enhance the robustness of the positioning system in complex indoor environments.

[0044] Step S103: Based on the filtered RSSI values, calculate the weights of each beacon and construct a weighted least squares localization model.

[0045] In this embodiment, the algorithm unit 101 assigns corresponding weight coefficients to each beacon based on the quality characteristics of the filtered RSSI value, and establishes a weighted least squares positioning model on this basis.

[0046] Here, the weighting coefficients are assigned based on the evaluation of the signal quality of each beacon. The evaluation criteria include, but are not limited to, the stability, strength, or a combination thereof of the RSSI value, with the core principle being to assign higher weights to beacons with better signal quality.

[0047] Preferably, the process of constructing a weighted least squares positioning model may include: establishing a set of positioning equations based on the known location coordinates of the beacon, and introducing the aforementioned weight coefficients to weight the set of equations, thereby incorporating signal quality differences into the mathematical model.

[0048] Here, the model can optimize the positioning solution process, allowing measurement data from highly reliable beacons to have a greater impact on the final positioning results.

[0049] Step S104: Use the weighted least squares positioning model to solve for the coordinate position of the target Bluetooth tag.

[0050] In this embodiment, the algorithm unit 101 performs mathematical solutions on the constructed weighted least squares positioning model and outputs the estimated coordinate position of the target Bluetooth tag 105.

[0051] Here, the solution process uses numerical calculation methods to handle the positioning equations containing weighted coefficients. The core objective is to find an optimal solution within the contradictory equations containing measurement errors, minimizing the sum of squared weighted residuals. This optimal solution represents the coordinates of the target Bluetooth tag in two-dimensional or three-dimensional space.

[0052] This step is the final output of the positioning process. It integrates the multi-source measurement data, filtering results, and beacon weight evaluations acquired in the preceding steps into a precise location estimate through an optimized mathematical framework. By applying the weighted least squares method, the system can effectively suppress interference from low-quality measurement data in complex signal environments, significantly improving the accuracy and reliability of the final positioning result.

[0053] In the Bluetooth positioning method and system based on multi-beacon RSSI weighted optimization according to the above embodiments of this disclosure, by employing a multi-beacon fast switching acquisition and RSSI weighted optimization mechanism, the signal quality differences of different beacons can be effectively distinguished. Furthermore, measurements severely affected by multipath effects and obstruction interference are dynamically weighted, significantly improving positioning accuracy and stability in complex indoor environments. This overcomes the error accumulation defects of traditional multi-beacon positioning algorithms in non-uniform noise environments. Centimeter-level positioning accuracy can be achieved by combining high-speed switch control and weighted least squares algorithm on a single Bluetooth module, avoiding the high costs associated with increasing base station density. This is particularly suitable for cost-sensitive consumer applications such as smart door locks and area sensing. Through moving average filtering and RSSI stability-based weight allocation strategy, the system does not simply average all signals but dynamically senses and adaptively optimizes signal quality. This weighted method based on signal reliability assessment is more robust in complex environments, achieving environmentally adaptive positioning.

[0054] In one possible implementation of step S101 above, multiple Bluetooth beacons deployed at fixed locations are used to sequentially switch and scan the broadcast signal of the target Bluetooth tag, including: The high-speed switch control unit cycles through multiple Bluetooth beacons. During each Bluetooth broadcast cycle, a beacon is controlled to scan in order to capture the broadcast signal of the target Bluetooth tag and record the corresponding RSSI measurement value; The RSSI measurements of all beacons are collected over multiple consecutive broadcast cycles.

[0055] In this embodiment, the high-speed switch control unit 103 is configured to perform timing switching control between beacons.

[0056] The working process of the high-speed switch control unit 103 includes: establishing a cyclic scanning path among multiple Bluetooth beacons 102 according to a preset switching sequence; and activating only a single beacon in the receiving state in each independent time slice, while the other beacons are in the sleep or off state.

[0057] Here, the Bluetooth broadcast period refers to the time interval between two consecutive broadcast signals transmitted by the target Bluetooth tag 105. The system uses this clock reference as a synchronization signal for beacon switching, ensuring that each beacon can complete one effective signal acquisition within a complete broadcast period.

[0058] Specifically, the cyclic switching mechanism ensures that each beacon operates independently within its dedicated time window, avoiding channel conflicts and mutual interference that may occur when multiple receivers operate simultaneously. This time-division multiplexing acquisition method enables sequential acquisition of signals from multiple points and directions in space, even with limited hardware resources.

[0059] As an example, when the broadcast period of the target Bluetooth tag is set to 20ms, the high-speed switch control unit 103 will control the switching to a beacon within each 20ms time interval. Assuming the system has four beacons deployed, a complete round of data acquisition can be completed within 80ms, obtaining a set of RSSI measurements from four different spatial directions.

[0060] In the Bluetooth positioning method and system based on multi-beacon RSSI weighted optimization of the above embodiments of this disclosure, the cyclic switching and time-division acquisition mechanism ensures that each Bluetooth beacon can independently complete signal acquisition within a complete broadcast cycle, effectively avoiding signal conflicts when multiple receivers work in parallel, and significantly improving the integrity and consistency of multiple sets of RSSI measurements. The synchronous switching strategy based on the Bluetooth broadcast cycle ensures that each RSSI measurement is acquired within a defined time window, effectively reducing measurement errors caused by instantaneous interference or signal aliasing between beacons, providing a highly timely and reliable data foundation for subsequent positioning calculations. Without adding complex multi-channel hardware, the sequential acquisition of multiple signals is achieved through a high-speed switching control unit, ensuring multi-dimensional signal coverage while significantly simplifying the system structure and reducing costs, thus improving the economy and widespread adoption of high-precision Bluetooth positioning technology.

[0061] In one possible implementation of step S102 above, filtering of multiple sets of RSSI measurements includes: The continuously acquired RSSI measurements are filtered using a moving average filtering algorithm; the window length of the moving average filtering algorithm is set to 5.

[0062] In this embodiment, the algorithm unit 101 uses a moving average filtering algorithm to perform real-time smoothing on the continuously acquired RSSI measurement sequence. This algorithm maintains a fixed-length data window and calculates the arithmetic mean of multiple historical measurements within the window, thereby outputting a smoothed RSSI estimate.

[0063] Here, the window length parameter is set to 5, indicating that the algorithm uses the average of the five most recent consecutive RSSI measurements for calculation. This parameter configuration has been experimentally verified to achieve the best balance between signal smoothing effect and system response speed: a shorter window will reduce the filtering effect, while an excessively long window will introduce unnecessary processing delays.

[0064] Specifically, the mathematical expression for the moving average filtering algorithm can be represented as:

[0065] in, This represents the measurement value at the current moment. to This represents the historical measurements from the previous four moments.

[0066] In the Bluetooth positioning method and system based on multi-beacon RSSI weighted optimization according to the above embodiments of this disclosure, the use of a moving average filtering algorithm with a fixed window length effectively suppresses high-frequency noise components in RSSI measurements, significantly improving the stability and reliability of signal measurements. The specific configuration of setting the window length to 5 ensures sufficient noise suppression capability while avoiding excessive filtering delay, ensuring the system maintains excellent real-time performance even in complex electromagnetic environments. This lightweight filtering scheme has low processing resource requirements, making it particularly suitable for implementation in embedded Bluetooth positioning devices.

[0067] In one possible implementation of step S103 above, the weights of each beacon are calculated based on the filtered RSSI values, including: Calculate the corresponding distance estimate based on the RSSI value of each beacon; Based on the stability of the distance estimates, each beacon is assigned a corresponding weight factor; where the weight factor is proportional to the stability.

[0068] In this embodiment, the algorithm unit 101 first converts the filtered RSSI values ​​of each beacon into corresponding distance estimates. This conversion is based on the radio propagation model and is achieved through the mathematical relationship between RSSI and propagation distance.

[0069] After obtaining the distance estimate sequence, the system performs a quantitative assessment of the stability of the distance estimates for each beacon.

[0070] Here, the stability of distance estimates is defined as the degree of statistical dispersion of distance estimates for each beacon within a certain time window. Higher stability indicates that the measurement results of the beacon are less affected by environmental interference and have higher reliability.

[0071] Specifically, the allocation of weighting factors follows the principle that the beacon with higher stability of distance estimates has a larger weighting factor.

[0072] In one implementation, the stability of each beacon distance estimate can be quantified by calculating the variance or standard deviation, and the weighting factors can be designed as a function inversely proportional to the variance or standard deviation.

[0073] In the Bluetooth positioning method and system based on multi-beacon RSSI weighted optimization according to the above embodiments of this disclosure, a weight allocation mechanism based on the stability of distance estimates is used to achieve differentiated processing of measurements from beacons of different quality. This intelligent weight allocation strategy can automatically identify and suppress unreliable beacons that are severely affected by the environment, while enhancing the contribution of stable beacons to the positioning solution, enabling the system to have environmental adaptability and significantly improving positioning accuracy and robustness in complex indoor environments. This scheme achieves fully automatic weight optimization allocation without manual intervention or environmental pre-calibration.

[0074] In one possible implementation of step S103 above, the weighted least squares localization model is constructed, including: Based on the known coordinates of each beacon and the corresponding weighted distance estimates, a linearized distance equation is established; By selecting a beacon as a reference and performing differences with other beacons to eliminate quadratic terms, an overdetermined system of equations is formed. By introducing weighting factors into the overdetermined system of equations, a weighted least squares localization model is constructed.

[0075] In this embodiment, algorithm unit 101 first bases the algorithm on the known coordinates of each beacon. and the corresponding distance estimate Taking a Bluetooth beacon count of 4 as an example (i.e., n=4), the following standard distance equation is established: (where i = 1, 2, 3, 4) To simplify computational complexity, the system can select a beacon as the baseline (e.g., beacon 1) and subtract the distance equations of the other beacons (beacons 2, 3, 4) from the equation of the baseline beacon. After expansion and simplification, a system of linear equations in the following form is obtained:

[0076] Where i = 1, 2, 3, 4.

[0077] Thus, the four beacons generated three independent linear equations, forming an overdetermined system of equations.

[0078] Due to measurement errors, this system of equations typically has no exact solution. In this case, the system constructs a least-squares problem to solve for the optimal target coordinates. As shown in this invention, the system of equations is represented in matrix form. The solution is:

[0079] Where A is the coefficient matrix (3×2 in this example) and b is the constant vector.

[0080] The weighted least squares localization model constructed here is characterized by the fact that, in the above solution process, weight factors are used to construct a weighted objective function (such as...). , where W is a diagonal matrix composed of weighting factors), or it can be used directly to weight matrix A and vector b and then substituted into the above solution formula.

[0081] By introducing weights, the equations corresponding to beacons with high signal quality occupy a larger proportion in the solution, thereby achieving weighted optimization.

[0082] In the Bluetooth positioning method and system based on multi-beacon RSSI weighted optimization according to the above embodiments of this disclosure, the traditional positioning problem is transformed into a weighted optimization problem through linearization and the introduction of weighting factors. This method not only reduces computational complexity and improves positioning efficiency, but more importantly, through the adjustment of the weight matrix, beacons with good signal quality are given higher weights in the positioning solution, while the influence of beacons with severe interference is effectively suppressed. This solves the problem of decreased positioning accuracy of the traditional least squares method in non-uniform noise environments, and achieves accurate and stable positioning in complex indoor environments.

[0083] In one possible implementation of the above embodiments, the method further includes: After determining the coordinates of the target Bluetooth tag, the motion trajectory is corrected by combining the data from the inertial measurement unit.

[0084] In this embodiment, after obtaining the coordinate position of the target Bluetooth tag using the weighted least squares method, the algorithm unit 101 further integrates motion data from the IMU to optimize the positioning result. Specifically, the system obtains raw data such as triaxial acceleration and angular velocity provided by the IMU, and obtains the target's motion state information, including displacement, velocity, and direction changes, through attitude calculation.

[0085] Here, motion trajectory correction can be achieved using sensor fusion algorithms, such as fusing Bluetooth positioning results with IMU-calculated trajectories through Kalman filtering or complementary filtering. Bluetooth positioning provides an absolute position reference, suppressing the accumulated error of the IMU; while IMU data provides high-frequency motion state updates, compensating for the insufficient update frequency of Bluetooth positioning, and maintaining continuous trajectory tracking even when the Bluetooth signal is briefly interrupted.

[0086] As an example, when the target Bluetooth tag is moving rapidly, relying solely on Bluetooth positioning may result in trajectory jumps or delays due to signal refresh rate limitations. In this case, the system uses IMU data to interpolate and smooth the motion path between two adjacent Bluetooth positioning points, generating a more continuous, natural motion trajectory that conforms to actual motion patterns.

[0087] In the Bluetooth positioning method and system based on multi-beacon RSSI weighted optimization according to the above embodiments of this disclosure, the introduction of inertial measurement unit data for motion trajectory correction effectively overcomes the problems of low update frequency and discontinuous trajectory that exist when relying solely on Bluetooth positioning. This multi-sensor fusion scheme retains the absolute accuracy advantage of Bluetooth positioning while leveraging the relative positioning accuracy of inertial navigation in the short term, significantly improving the positioning performance of the system in dynamic scenarios. This results in a smoother, more continuous output trajectory that conforms to actual kinematic laws, greatly enhancing the user experience.

[0088] In one embodiment, a Bluetooth positioning system 300 is provided, which corresponds one-to-one with the Bluetooth positioning method based on multi-beacon RSSI weighted optimization in the above embodiments. For example... Figure 3 As shown, the system includes an algorithm unit 301, multiple Bluetooth beacons 302, a high-speed switch control unit 303, a power control unit 304, and a target Bluetooth tag 305. The detailed descriptions of each functional module are as follows: Multiple Bluetooth beacons are deployed in fixed, known locations to receive Bluetooth signals; The high-speed switch control unit is connected to multiple Bluetooth beacons 302 and is used to control the switching scan between the Bluetooth beacons 302; The power control unit 304 is used to provide a stable operating voltage during scanning switching; Algorithm unit 301 is configured to perform the following steps: By controlling the high-speed switch control unit 303 to switch each Bluetooth beacon 302 in sequence, scanning the broadcast signal of the target Bluetooth tag 305, and obtaining multiple sets of RSSI measurement values ​​from different directions; Multiple sets of RSSI measurements are filtered to suppress signal fluctuations; the weights of each beacon are calculated based on the filtered RSSI values ​​to construct a weighted least squares positioning model; the coordinates of the target Bluetooth tag 305 are solved using the weighted least squares positioning model.

[0089] In one embodiment, the algorithm unit 301 is specifically used to cyclically switch between multiple Bluetooth beacons 302 via the high-speed switch control unit 303; During each Bluetooth broadcast cycle, a beacon is controlled to scan in order to capture the broadcast signal of the target Bluetooth tag 305 and record the corresponding RSSI measurement value; The RSSI measurements of all beacons are collected over multiple consecutive broadcast cycles.

[0090] In one embodiment, the algorithm unit 301 is specifically used to filter the continuously acquired RSSI measurement values ​​using a moving average filtering algorithm; wherein the window length of the moving average filtering algorithm is set to 5.

[0091] In one embodiment, the algorithm unit 301 is specifically used to calculate the corresponding distance estimate based on the RSSI value of each beacon; Based on the stability of the distance estimates, each beacon is assigned a corresponding weight factor; where the weight factor is proportional to the stability.

[0092] In one embodiment, the algorithm unit 301 is specifically used to establish a linearized distance equation based on the known coordinates of each beacon and the corresponding weighted distance estimate; By selecting a beacon as a reference and performing differences with other beacons to eliminate quadratic terms, an overdetermined system of equations is formed. By introducing weighting factors into the overdetermined system of equations, a weighted least squares localization model is constructed.

[0093] In one embodiment, the algorithm unit 301 is further configured to correct the motion trajectory of the coordinate position by combining the inertial measurement unit data after solving the coordinate position of the target Bluetooth tag 305.

[0094] It should be noted that the Bluetooth positioning system provided in the above embodiments, when implementing the corresponding Bluetooth positioning method based on multi-beacon RSSI weighted optimization, is only illustrated by the division of the above program modules. In practical applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the above system can be divided into different program modules to complete all or part of the processing described above. Furthermore, the system provided in the above embodiments and the corresponding... Figure 1b The embodiments of the methods shown belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0095] This disclosure also provides an electronic device having the above-described features. Figure 3 The Bluetooth positioning system shown.

[0096] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure.

[0097] The following is a detailed reference. Figure 4 The diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present disclosure. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 401, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 402 or a program loaded from memory 408 into random access memory (RAM) 403. RAM 403 also stores various programs and data required for the operation of the electronic device. The processor 401, ROM 402, and RAM 403 are interconnected via bus 404. Input / output (I / O) interface 405 is also connected to bus 404.

[0098] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0099] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 409, or installed from memory 408, or installed from ROM 402. When the computer program is executed by processor 401, it performs the functions defined in the network data stream hardware offloading method for heterogeneous descriptor unified processing of embodiments of this disclosure.

[0100] Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0101] This disclosure also provides a computer-readable storage medium in which the methods described in this disclosure can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the network data stream hardware offloading method for unified processing of heterogeneous descriptors shown in the above embodiments is implemented.

[0102] A portion of this disclosure can be applied to computer program products, such as computer program instructions, which, when executed by a computer, can invoke or provide methods and / or technical solutions according to this disclosure through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, and installation package files. Accordingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions; the computer compiling the instructions and then executing the corresponding compiled program; the computer reading and executing the instructions; or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0103] Although embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A Bluetooth positioning method based on multi-beacon RSSI weighted optimization, characterized in that, The method comprises: Switching and scanning the broadcast signal of the target Bluetooth tag in turn through a plurality of Bluetooth beacons deployed at fixed positions to obtain a plurality of groups of RSSI measurement values from different directions; Filtering the plurality of groups of RSSI measurement values to suppress signal fluctuations; Based on the filtered RSSI values, the weights of each beacon are calculated, and a weighted least squares positioning model is constructed; The coordinate position of the target Bluetooth tag is solved by using the weighted least squares positioning model.

2. The method of claim 1, wherein, The switching and scanning of the broadcast signal of the target Bluetooth tag in turn through a plurality of Bluetooth beacons deployed at fixed positions comprises: Cyclically switching between a plurality of Bluetooth beacons through a high-speed switch control unit; In each Bluetooth broadcast period, control one beacon to scan to capture the broadcast signal of the target Bluetooth tag and record the corresponding RSSI measurement value; In a plurality of consecutive broadcast periods, the RSSI measurement value collection of all beacons is completed.

3. The method of claim 1, wherein, The filtering of the plurality of groups of RSSI measurement values comprises: The sliding average filtering algorithm is used to filter the continuously collected RSSI measurement values; wherein the window length of the sliding average filtering algorithm is set to 5.

4. The method of claim 1, wherein, The calculation of the weight of each beacon based on the filtered RSSI value comprises: According to the RSSI value of each beacon, the corresponding distance estimation value is calculated; Based on the stability of the distance estimation value, a corresponding weight factor is assigned to each beacon; wherein the weight factor is proportional to the stability.

5. The method of claim 4, wherein, The construction of the weighted least squares positioning model comprises: Based on the known coordinates of each beacon and the corresponding weighted distance estimation value, a linearized distance equation is established; By selecting one beacon as a reference, difference is made with other beacons to eliminate quadratic terms to form an over-determined equation set; The weight factor is introduced into the over-determined equation set to construct a weighted least squares positioning model.

6. The method of claim 1, wherein, The method further comprises: After solving the coordinate position of the target Bluetooth tag, the coordinate position is corrected in motion trajectory combined with inertial measurement unit data.

7. A Bluetooth positioning system, characterized by The system comprises: an algorithm unit, a plurality of Bluetooth beacons, a high-speed switch control unit, a power supply control unit, and a target Bluetooth tag, wherein: The plurality of Bluetooth beacons are deployed at fixed known positions for receiving Bluetooth signals; The high-speed switch control unit is connected with the plurality of Bluetooth beacons for controlling the switching and scanning between the beacons; The power supply control unit is used to provide stable working voltage during the switching and scanning process; The algorithm unit is configured to perform the following steps: Switch each Bluetooth beacon in turn by controlling the high-speed switch control unit to scan the broadcast signal of the target Bluetooth tag, and obtain a plurality of groups of RSSI measurement values from different directions; Filter the plurality of groups of RSSI measurement values to suppress signal fluctuations; based on the filtered RSSI values, the weights of each beacon are calculated, and a weighted least squares positioning model is constructed; the coordinate position of the target Bluetooth tag is solved by using the weighted least squares positioning model.

8. An electronic device, comprising: Comprise: A memory for storing a computer program; A processor for implementing the steps of the method of claim 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that, A computer readable storage medium having stored therein a computer program, wherein the computer program, when executed by a processor, implements the steps of the method of claim 1 to 6.

10. A computer program product comprising a computer program, characterized in that, A computer program for implementing the steps of the method of claim 1 to 6 when executed by a processor.

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