Positioning methods and related equipment

Abstract

This application provides a positioning method and related equipment. The method includes: constructing a dynamic ranging estimation model based on the measured distance and estimated distance; constructing a motion model based on prior information based on the prior estimated movement distance and the system estimated movement distance; constructing a factor graph model based on the dynamic ranging estimation model and the motion model based on prior information; iteratively updating the factor graph model using an adaptive gradient descent algorithm to obtain a first positioning position; and taking the first positioning position as the final positioning position in response to the iterative update satisfying a first condition. This application's embodiments consider the reliability of ranging observations at different positions, converting ranging observations at different positions and relative prior motion knowledge into a probability domain, enabling joint optimization of multiple positions at different locations, thereby reducing the reliability of interference-affected ranging measurements. Furthermore, a gradient descent optimization method is proposed, effectively reducing the time complexity of solving the factor graph.

Description

Technical Field

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

[0002] In existing technologies, the accuracy of Fine Time Measurement (FTM) positioning significantly decreases in complex indoor environments. In an ideal line-of-sight (LoS) environment, FTM ranging can achieve an accuracy of 1.4 meters with a speed of 1 sigma. However, in real-world indoor environments, due to interference such as multipath effects, non-line-of-sight signal propagation, and electrical noise, ranging accuracy drops to 3.4 meters. Therefore, achieving accurate and universal FTM indoor positioning remains a challenge due to various interferences.

[0003] Furthermore, in existing technologies, factor graph algorithms result in high-dimensional cost functions when considering all variables when solving for factor graphs. Additionally, the non-convex cost surface further increases computational complexity. Summary of the Invention

[0004] In view of this, the purpose of this application is to propose a positioning method and related equipment.

[0005] To achieve the above objectives, this application provides a positioning method, comprising:

[0006] Based on the obtained measured distance and estimated distance, a dynamic distance estimation model is constructed.

[0007] Based on the acquired prior estimated movement distance and the system estimated movement distance, a motion model based on prior information is constructed.

[0008] Based on the dynamic ranging estimation model and the motion model based on prior information, a factor graph model is constructed.

[0009] The factor graph model is iteratively updated using an adaptive gradient descent algorithm to obtain a first location; in response to the iterative update satisfying a first condition, the first location is taken as the final location.

[0010] In one possible implementation, the construction of a dynamic ranging estimation model based on the acquired measured distance and estimated distance includes:

[0011] The measured distance is obtained based on the acquired true distance and error term;

[0012] The estimated distance of the ranging is obtained based on the acquired system estimated distance and estimated noise;

[0013] The residual error is calculated based on the measured distance and the estimated distance of the distance measurement.

[0014] Based on the residual error and the measured distance, the adaptive mean and standard deviation of the error residual relative to the measured distance are calculated;

[0015] The dynamic ranging estimation model is constructed based on the adaptive mean and standard deviation of the error residual relative to the ranging distance.

[0016] In one possible implementation, constructing a motion model based on prior information, based on the acquired prior estimated movement distance and the system estimated movement distance, includes:

[0017] Based on the actual movement distance and the estimated noise, the estimated movement distance of the system is obtained;

[0018] Based on the actual movement distance and prior noise, the prior estimated movement distance is obtained;

[0019] Based on the prior estimated movement distance and the system estimated movement distance, the residual noise is calculated;

[0020] Based on the residual noise, the motion model based on prior information is constructed.

[0021] In one possible implementation, the dynamic ranging estimation model is represented by the following equation:

[0022]

[0023] Where r i,j This indicates the distance from path point j to the i-th wireless access point AP. i The ranging value, f i,j (v j |r i,j ) indicates that, given the distance value r i,j In this case, the client is located at position v j The probability, v j =(x j ,y j ), σ f (r i,j ) represents the residual noise relative to the ranging value r i,j Standard deviation, μ f (r i,j ) represents the residual noise relative to the ranging value r i,j The adaptive mean, X i and Y i Represents the i-th wireless access point AP i The coordinates.

[0024] In one possible implementation, the motion model based on prior information is represented by the following equation:

[0025] g j (v j ,v j+1 |Δt j ) = g j (x j ,y j ,x j+1 ,y j+1 |Δt j )

[0026]

[0027] Among them, g j (v j ,v j+1 |Δt j ) represents the motion factor, v j =(x j ,y j ), v j+1 =(x j+1 ,y j+1 ), σ g (Δt j ) represents the standard deviation function, ||v j+1 -v j ||2 represents the estimated travel distance of the system, μ V ·Δt j This represents the prior estimated distance of movement.

[0028] In one possible implementation, the objective function of the factor graph model is expressed by the following equation:

[0029]

[0030] Among them, f i,j (v j |r i,j ) indicates that, given the distance value r i,j In this case, the client is located at position v j The probability, g j (v j ,v j+1 |Δt j ) represents the motion factor.

[0031] In one possible implementation, the first condition is that the average positional variance of the factor graph model is less than or equal to a preset threshold.

[0032] Based on the same inventive concept, embodiments of this application also provide a positioning device, including:

[0033] The first construction module is configured to build a dynamic distance estimation model based on the acquired measured distance and estimated distance.

[0034] The second building module is configured to build a motion model based on prior information, based on the acquired prior estimated movement distance and the system estimated movement distance.

[0035] The third construction module is configured to construct a factor graph model based on the dynamic ranging estimation model and the motion model based on prior information.

[0036] The gradient descent module is configured to iteratively update the factor graph model using an adaptive gradient descent algorithm to obtain a first location; in response to the iterative update satisfying a first condition, the first location is used as the final location.

[0037] Based on the same inventive concept, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the positioning method as described in any of the above.

[0038] Based on the same inventive concept, embodiments of this application also provide a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute any of the positioning methods described above.

[0039] As can be seen from the above, the positioning method and related equipment provided in this application construct a dynamic ranging estimation model based on the measured distance and estimated distance of the acquired ranging; construct a motion model based on prior information based on the acquired prior estimated movement distance and the system estimated movement distance; construct a factor graph model based on the dynamic ranging estimation model and the motion model based on prior information; iteratively update the factor graph model using an adaptive gradient descent algorithm to obtain a first positioning position; and, in response to the iterative update satisfying a first condition, use the first positioning position as the final positioning position. In this embodiment, the weight of the ranging measurement corresponding to each wireless access point (AP) is first estimated from the FTM ranging matrix using statistical methods. Then, a probability model is established for the distance between two positions of two consecutive ranging measurements, while simultaneously considering the prior motion statistics and time intervals of the target during movement, thereby constructing a motion model based on prior information. Finally, the ranging and motion models constructed in the aforementioned steps are used to jointly construct a factor graph model. In this model, a joint cost function is developed, and the results of user position estimation are enhanced through maximum probability rule optimization. The aforementioned model effectively improves indoor positioning accuracy and maintains high-precision indoor positioning under varying ranging periods, poses, users, movement speeds, and environments. Furthermore, to minimize the time complexity associated with the factor graph framework, an acceleration algorithm is employed to effectively identify locations with the highest probability, significantly reducing computational overhead and thus improving the speed of indoor positioning. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a schematic flowchart of the positioning method according to an embodiment of this application;

[0042] Figure 2 This is a schematic diagram illustrating the statistical data analysis of different ranging measurements in an embodiment of this application;

[0043] Figure 3 This is a schematic diagram of the factor graph model structure in an embodiment of this application;

[0044] Figure 4 This is a schematic diagram of a positioning device according to an embodiment of this application;

[0045] Figure 5 This is a schematic diagram of the electronic device structure according to an embodiment of this application. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0047] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0048] As described in the background section, in existing technologies, the accuracy of precise time measurement (FTM) positioning is significantly reduced in complex indoor environments. In an ideal line-of-sight environment, FTM ranging can achieve an accuracy of 1.4 meters with a speed of 1 sigma. However, in real-world indoor environments, due to interference such as multipath effects, non-line-of-sight signal propagation, and electrical noise, the ranging accuracy drops to 3.4 meters. Therefore, achieving accurate and universal FTM indoor positioning remains a challenge due to various interferences.

[0049] Furthermore, in existing technologies, factor graph algorithms result in high-dimensional cost functions when considering all variables when solving for factor graphs. Additionally, the non-convex cost surface further increases computational complexity.

[0050] In summary, this application proposes a positioning method that constructs a dynamic ranging estimation model based on the acquired measured distance and estimated distance; constructs a motion model based on prior information based on the acquired prior estimated movement distance and system estimated movement distance; constructs a factor graph model based on the dynamic ranging estimation model and the motion model based on prior information; iteratively updates the factor graph model using an adaptive gradient descent algorithm to obtain a first positioning position; and, in response to the iterative update satisfying a first condition, uses the first positioning position as the final positioning position. This application first estimates the weight of each ranging measurement corresponding to each Wireless Access Point (AP) from the FTM ranging matrix using statistical methods. Then, it establishes a probabilistic model for the distance between two positions of two consecutive ranging measurements, simultaneously considering the prior motion statistics and time intervals of the target during movement, thereby constructing a motion model based on prior information. Finally, it uses the ranging and motion models constructed in the aforementioned steps to jointly construct a factor graph model. In this model, a joint cost function is developed, and the results of user location estimation are enhanced through maximum probability rule optimization. The aforementioned model effectively improves indoor positioning accuracy and maintains high-precision indoor positioning under varying ranging periods, poses, users, movement speeds, and environments. Furthermore, to minimize the time complexity associated with the factor graph framework, an acceleration algorithm is employed to effectively identify locations with the highest probability, significantly reducing computational overhead and thus improving the speed of indoor positioning.

[0051] The technical solutions of the embodiments of this application will be described in detail below through specific examples.

[0052] refer to Figure 1 The positioning method of this application embodiment includes the following steps:

[0053] Step S101: Based on the acquired measured distance and estimated distance, construct a dynamic ranging estimation model;

[0054] Step S102: Based on the acquired prior estimated movement distance and the system estimated movement distance, construct a motion model based on prior information;

[0055] Step S103: Construct a factor graph model based on the dynamic ranging estimation model and the motion model based on prior information;

[0056] Step S104: Iteratively update the factor graph model using the adaptive gradient descent algorithm to obtain the first location; in response to the iterative update satisfying the first condition, use the first location as the final location.

[0057] The technical solution of this application, in general, first acquires the FTM ranging matrix and time data. APs with FTM enabled are deployed at known locations. A user's handheld mobile device is positioned to walk within the test area, and ranging measurements are periodically collected from these APs. The waypoints of the FTM performed by the positioning device during RTT are marked as v1, v2, ..., v M M represents the number of path points traversed. From path point j to AP... i The ranging value is r i,j (1≤i≤N, 1≤j≤M). The FTM ranging measurements obtained at different waypoints can be represented as a ranging matrix R, expressed by the following formula:

[0058]

[0059] It should be noted that some measurements may fail due to interference; these cases are represented as zero in the matrix. Additionally, the time interval Δt between the collected path points is also considered. k (1≤k≤M-1), using client-based mobility information to estimate potential distances.

[0060] After obtaining the above data, a factor graph is constructed and a fast solution is obtained based on adaptive gradient.

[0061] Regarding step S101, in some embodiments, constructing a dynamic ranging estimation model based on the acquired measured distance and estimated distance includes: obtaining the measured distance based on the acquired true distance and error term; obtaining the estimated distance based on the acquired system estimated distance and estimated noise; calculating the residual error based on the measured distance and estimated distance; calculating the adaptive mean and standard deviation of the error residual relative to the measured distance based on the residual error and the measured distance; and constructing the dynamic ranging estimation model based on the adaptive mean and standard deviation of the error residual relative to the measured distance.

[0062] Specifically, a major challenge in FTM ranging is addressing the confidence level of fluctuations associated with different ranging distances. FTM ranging noise is represented by the following formula:

[0063]

[0064] The distance r measured in the distance measurement i,j It is the actual distance. And error term δ f1 (r i,j The sum of ) and the error term δ. f1 (r i,j ) is an inherent stochastic process in ranging measurements. N is the number of FTM AP sites deployed in the environment.

[0065] In various ranging measurements conducted in a typical office environment, we can observe that the error can be divided into two distinct regions: high accuracy and gradually decreasing accuracy. The high-accuracy region corresponds to short-distance measurements where there are virtually no obstacles between the client and the access point (AP). Furthermore, as the ranging distance increases, factors such as non-line-of-sight (NLoS), multipath effects, or moving obstacles begin to affect ranging accuracy, leading to a decrease in precision.

[0066] The relationship between the measured distance and the estimated distance in distance measurement can be described as follows: Given an estimated position v j =(x j ,y j The estimated distance of the system is expressed by the following formula:

[0067]

[0068] Among them, v j =(x j ,y j ), X i and Y i Represents the i-th wireless access point AP i coordinates δ represents the true distance. f2 (x j ,y j ) indicates estimated noise.

[0069] Furthermore, by subtracting the measured distance from the estimated distance from the system's estimated distance, the residual error between the system's estimation and the FTM ranging measurement can be determined, expressed by the following formula:

[0070]

[0071] Where, δ f (x j ,y j ,r i,j ) represents the residual error, δ f1 (r i,j ) represents the error term, δ f2 (x j ,y j ) represents the estimated noise, r i,j This indicates the distance from path point j to the i-th wireless access point AP. i The ranging value, v j =(x j ,y j ), X i and Y i Represents the i-th wireless access point AP i The coordinates.

[0072] Furthermore, a ranging factor model enhanced by a dynamic Gaussian stochastic process is used to improve the factor graph optimization algorithm.

[0073] Smaller ranging measurements exhibit smaller errors and a more balanced distribution. However, as the number of measurements increases, the average accuracy decreases, and the distribution shows a more pronounced long-tail effect due to increased multipath and NLoS effects. Therefore, embodiments of this application propose an adaptive Gaussian model to approximate different ranging measurements. This involves reducing the model reliability for larger measurements, not only because of their lower accuracy but also because of the amplified model fitting inaccuracies caused by the long-tail effect.

[0074] In some embodiments, the dynamic ranging estimation model is represented by the following formula:

[0075]

[0076] Where r i,j This indicates the distance from path point j to the i-th wireless access point AP. i The ranging value, f i,j (v j |r i,j ) indicates that, given the distance value r i,j In this case, the client is located at position v j The probability, v j =(x j ,y j ), σ f (r i,j ) represents the residual noise relative to the ranging value r i,j Standard deviation, μ f (r i,j ) represents the residual noise relative to the ranging value r i,j The adaptive mean, X i and Y i Represents the i-th wireless access point AP i The coordinates.

[0077] Furthermore, assuming the final position estimate is very close to the actual position, i.e., δ at this point... f2 Approaching zero, therefore δ can be used in this application. f1 To approximate δ f .

[0078] refer to Figure 2 This is a schematic diagram illustrating the statistical data analysis of different ranging measurements in an embodiment of this application.

[0079] Figure 2The figure displays statistical data on the mean and standard deviation. The points in the figure represent the mean and standard deviation of the residuals obtained from approximate distance measurements. Figure 2 In (a), the line is fitted with a quadratic curve to the residual mean data, reflecting the amplification of the interference exponent due to the increase in intermediate obstacles. Therefore, this application can use a quadratic function to measure μ f (r i,j Modeling, expressed by the following formula:

[0080] μ f (r i,j )=a*r i,j 2 +b*r i,j +c

[0081] Where a, b, c represent curve parameters, μ f (r i,j ) represents the residual noise relative to the ranging value r i,j The adaptive mean, r i,j This indicates the distance from path point j to the i-th wireless access point AP. i The distance measurement value.

[0082] Figure 2 (b) explains that the standard deviation of error can be divided into two regions: reliable and unreliable.

[0083] Furthermore, based on the principles of robust statistics, this application can incorporate the Huber function into the confidence adjustment of distance measurement, expressed by the following formula:

[0084]

[0085] Where T represents the threshold hyperparameter, which is approximately 1.4 times the standard deviation of all errors, and σ f (r i,j ) represents the residual noise relative to the ranging value r i,j standard deviation, r i,j This indicates the distance from path point j to the i-th wireless access point AP. i The distance measurement value.

[0086] In the above formula, ranging measurements less than T are assigned a constant standard deviation of 1 to represent their reliability. For measurements exceeding T, their standard deviation is increased using an exponential function with exponent q to reduce their weight, thereby mitigating the impact of low-reliability data on ranging measurements.

[0087] Furthermore, since the model proposed in this application is differentiable, it can effectively achieve faster convergence of the factor graph optimization process.

[0088] Regarding step S102, in some embodiments, constructing a motion model based on prior information based on the acquired prior estimated movement distance and the system estimated movement distance includes: obtaining the system estimated movement distance based on the acquired actual movement distance and estimated noise; obtaining the prior estimated movement distance based on the acquired actual movement distance and prior noise; calculating residual noise based on the prior estimated movement distance and the system estimated movement distance; and constructing the motion model based on prior information based on the residual noise.

[0089] Specifically, to address the issue of intermittent inaccurate measurements potentially leading to local positioning errors, embodiments of this application utilize prior information about the target's motion velocity to establish connections between adjacent ranging measurements. This transforms the local positioning challenge into local optimization of the FTM ranging chain, connected via the motion velocity prior. By employing this approach, this application improves reliability by considering a broader context and a larger number of ranging measurements, rather than relying solely on isolated and single measurements.

[0090] The modeling process for the prior motion velocity is as follows: Assume the client moves at a time interval of Δt. j Between two FTM measurements at the waypoint Move to The pedestrian's moving speed can be approximated by a Gaussian model N(μ). V ,σ V ), where μ V σ represents the mean. V This represents the standard deviation. It takes into account short time intervals Δt. j The client's movement trajectory can be approximated as a straight line. The system estimates the movement distance μ. V ·Δt j It can simulate the actual distance traveled. Adding additional estimated noise δ g1 (Δt j ), can be expressed by the following formula:

[0091]

[0092] Where, μ V ·Δt j This represents the estimated distance traveled by the system. δ represents the actual distance traveled. g1 (Δt j ) indicates estimated noise.

[0093] Additionally, the prior estimated distance of movement ||v j+1 -v j ‖2 represents the true distance and the prior noise term δ g2 (v j,v j+1 The sum of ) is expressed by the following formula:

[0094]

[0095] in, δ represents the actual distance traveled. g2 (v j ,v j+1 ) represents prior noise, ||v j+1 -v j ‖2 represents the prior estimated distance of movement.

[0096] Based on the above equations, the residual noise is obtained by subtracting the prior estimated distance from the system-estimated distance, and is expressed by the following formula:

[0097] δ g (v j ,v j+1 ,Δt j )=δ g2 (v j ,v j+1 )-δ g1 (Δt j )

[0098] =‖v j+1 -v j ||2-μ V ·Δt j (1≤j≤M)

[0099] Where, δ g (v j ,v j+1 ,Δt j ) represents residual noise, δ g1 (Δt j ) represents the estimated noise, δ g2 (v j ,v j+1 ) represents prior noise, ||v j+1 -v j ||2 represents the prior estimated distance of movement, σ V ·Δt j This represents the estimated travel distance by the system.

[0100] Residual noise δ g (v j ,v j+1 ,Δt j This can be represented as a strongly steady-state Gaussian process, given by δ g (v j ,v j+1 ,Δt j )~N(0,σ g(Δt j Modeling.

[0101] The standard deviation function σ is expressed by the following formula. g (Δt j ):

[0102] σ g (Δt j )=σ V ·Δt j (1≤j≤M)

[0103] Where, σ g (Δt j ) represents the standard deviation function, σ V Represents the client's motion parameters, Δt j Indicates a time interval.

[0104] In some embodiments, the motion model based on prior information is represented by the following formula:

[0105]

[0106] Among them, g j (v j ,v j+1 |Δt j ) represents the motion factor, v j =(x j ,y j ), v j+1 =(x j+1 ,y j+1 ), σ g (Δv j ) represents the standard deviation function, ||v j+1 -v j ||2 represents the estimated travel distance of the system, μ V ·Δt j This represents the prior estimated distance of movement.

[0107] Essentially, the above formula describes the time interval Δt. j In the case of known client motion parameters μ V and σ V Estimated position v j and v j+1 The possibility of distance between them.

[0108] Based on the foregoing statements, the model in this application embodiment improves local localization by utilizing neighbor measurements. Unlike systems based on Pedestrian Dead Reckoning (PDR) that rely on gait patterns and specific postures to detect steps and determine user orientation, the technical solution in this application embodiment is completely independent of posture, making it more flexible and applicable to a wider range of situations.

[0109] Furthermore, in step S103, prior knowledge of pedestrian movement is integrated as relative information. This is achieved by establishing a chain connecting consecutive FTM ranging measurements of user locations and the user movements between them. Based on the relative information, the positioning accuracy can be improved by considering locations with sufficient observations. Similarly, the relative information connects consecutive poorly performing observation points, allowing for further optimization of their positions. In summary, the embodiments of this application utilize relative motion information to establish connections between adjacent FTM ranging measurements and comprehensively optimize the coordinates of each observation point. Therefore, the technical solution proposed in the embodiments of this application exhibits robustness under both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions.

[0110] In some embodiments, the objective function of the factor graph model is expressed by the following formula:

[0111]

[0112] Among them, f i,j (v j |r i,j ) indicates that, given the distance value r i,j In this case, the client is located at position v j The probability, g j (v j ,v j+1 |Δt j ) represents the motion factor.

[0113] Factor graphs are used to represent motion observation chains (MOCs) and are modeled using probabilities.

[0114] refer to Figure 3 This is a schematic diagram of the factor graph model structure in an embodiment of this application.

[0115] Figure 3 In this context, the user's location is represented by the variable v. j (1≤j≤M). Ranging and motion information are described using probability factors. Ranging factor f i,j (1≤i≤N, 1≤j≤M) represents the observed r i,jThe probability of motion factor g. k (1≤k≤M-1) represents the user starting from position v k Transition to v k+1 The probability of.

[0116] The above function utilizes f i,j (v j |r i,j Let (1≤i≤N, 1≤j≤M) represent the condition where, given the observed value r, ... i,j In this case, the client is located at position v j The probability of all ranging measurements collected at the current location point is expressed as: The joint probability of the current and previous client positions is expressed as: It is important to note that waypoints are independent of each other. To connect these locations, the model utilizes g... j (v j ,v j+1 |Δt j (1≤j≤M-1) represents the time interval Δt. j In this case, the client starts from position v j Move to position v j+1 The probability. The goal of the function is to find the optimal position V = {v1, v2, ..., v}. M This maximizes the joint probability of all ranging measurements and movement information.

[0117] Furthermore, in step S104, the factor graph model is iteratively updated using an adaptive gradient descent algorithm to obtain a first location; in response to the iterative update satisfying a first condition, the first location is taken as the final location.

[0118] The embodiments of this application estimate the optimal location by converting heterogeneous information into probabilities and maximizing the joint probability of these factors.

[0119] For the sake of simplicity, the embodiments of this application use Φ i,j (x j ,y j ) and Γ j (x j ,y j ,x j+1 ,y j+1 Let ) represent two sub-functions, expressed by the following formula:

[0120]

[0121]

[0122]

[0123] To reduce the location estimation time introduced by the factor graph framework, this application proposes a fast optimization algorithm based on MOC structure and adaptive gradient descent.

[0124] The gradient of the objective function is expressed by the following formula, which is derived from a combination of six partial derivatives: and Distance factors only affect the position estimate of the current waypoint. Therefore, we can compute each variable x1, y1, ..., x in parallel. M ,y M of and On the other hand, motion factors only affect adjacent waypoints. In other words, the first waypoint is only affected by the second waypoint. The influence of v1∈{x1,y1}, where the last point is only affected by the second to last point. The impact, of which v M ∈{x M ,y M Other waypoints are affected by the previous waypoint. and the next point The impact, of which v k ∈{x k ,y k}, 2≤k≤M-1.

[0125] The gradient descent process can be represented by the following formula:

[0126]

[0127] The calculation process of partial derivatives is expressed by the following formula:

[0128]

[0129]

[0130]

[0131]

[0132]

[0133]

[0134] Based on gradient evaluation, this application proposes to improve the speed of position estimation using a gradient descent optimization method with momentum, as expressed by the following formula:

[0135]

[0136] Among them, V p Let ΔV represent the position estimate in the p-th iteration. p Let η represent the descent gradient in the p-th iteration. p Let θ represent the learning rate in the p-th iteration, and let θ represent the decay rate of the learning rate, defined by the following formula:

[0137]

[0138] Here, ε is a constant value that is greater than 0 and much less than 1.

[0139] Specifically, the system updates the position estimate V in each iteration p. p To ensure stability and prevent the system from diverging near the nondifferentiable point, we use a function. The calculated cost gradient is limited to a maximum value of 1. This is achieved by using the sign function. Applied to and The Hadamard product preserves the gradient direction. Momentum strength is determined by the parameter γ. Momentum accumulation accelerates the algorithm's convergence, especially when traversing high-cost regions, and helps escape local minima, thus enhancing its ability to find the optimal solution. To guarantee system convergence, given a small positive number ε, in each iteration p, if the cost function J... p >J p-1 Then θ = 1 - ε, η p The learning rate decays exponentially with respect to θ. The cost function is quadratic, and the learning rate decays exponentially, ensuring system stability.

[0140] In this embodiment, whenever a new set of FTMs is scanned at a new waypoint, the system initializes the current position using previously known points. Subsequently, the system iterates through the proposed adaptive gradient descent algorithm to determine the optimal position estimate. In each iteration k, the partial derivatives of the ranging and motion factors are calculated using the above formula to compute the gradient of the objective function. Along this gradient, the waypoint estimate V is updated. p The system continues iterating until, relative to the last few iterations, the average location variance becomes less than a predefined threshold ξ. The locations obtained in that iteration are then output as the final estimate.

[0141] In some embodiments, the first condition is represented by the following formula:

[0142]

[0143] In some embodiments, the first condition is that the average positional variance of the factor graph model is less than or equal to a preset threshold.

[0144] The time complexity of this embodiment is O(M·N·I), where M represents the number of waypoints, N represents the number of nearby FTM APs, and I represents the number of iterations. It is worth noting that the values ​​of M and N are assumed to be static. Therefore, the computation time is linearly related to the problem size n, resulting in a time complexity of O(n). This computational complexity is acceptable for real-time localization scenarios.

[0145] As can be seen from the above embodiments, the positioning method described in this application constructs a dynamic ranging estimation model based on the acquired measured distance and estimated distance; constructs a motion model based on prior information based on the acquired prior estimated movement distance and system estimated movement distance; constructs a factor graph model based on the dynamic ranging estimation model and the motion model based on prior information; iteratively updates the factor graph model using an adaptive gradient descent algorithm to obtain a first positioning position; and, in response to the iterative update satisfying a first condition, uses the first positioning position as the final positioning position. This application embodiment first estimates the weight of the ranging measurement corresponding to each wireless access point (AP) from the FTM ranging matrix using statistical methods, then establishes a probability model for the distance between two positions of two consecutive ranging measurements, and simultaneously considers the prior motion statistics and time intervals of the target during movement, thereby constructing a motion model based on prior information. Then, using the ranging and motion models constructed in the aforementioned steps, a factor graph model is jointly constructed. In this model, a joint cost function is developed, and the results of user location estimation are enhanced through maximum probability rule optimization. The aforementioned model effectively improves indoor positioning accuracy and maintains high-precision indoor positioning under varying ranging periods, poses, users, movement speeds, and environments. Furthermore, to minimize the time complexity associated with the factor graph framework, an acceleration algorithm is employed to effectively identify locations with the highest probability, significantly reducing computational overhead and thus improving the speed of indoor positioning.

[0146] It should be noted that the method in this embodiment can be executed by a single device, such as a computer or server. The method can also be applied in a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method in this embodiment, and the multiple devices will interact with each other to complete the method described.

[0147] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0148] Based on the same inventive concept, corresponding to any of the above embodiments, this application also provides a positioning device.

[0149] refer to Figure 4 The positioning device includes:

[0150] The first construction module 41 is configured to construct a dynamic ranging estimation model based on the acquired measured distance and estimated distance of the ranging.

[0151] The second building module 42 is configured to build a motion model based on prior information based on the acquired prior estimated movement distance and the system estimated movement distance.

[0152] The third construction module 43 is configured to construct a factor graph model based on the dynamic ranging estimation model and the motion model based on prior information.

[0153] The gradient descent module 44 is configured to iteratively update the factor graph model using an adaptive gradient descent algorithm to obtain a first positioning position; in response to the iterative update satisfying a first condition, the first positioning position is used as the final positioning position.

[0154] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing this application, the functions of each module can be implemented in one or more software and / or hardware.

[0155] The apparatus of the above embodiments is used to implement the corresponding positioning method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0156] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the positioning method described in any of the above embodiments.

[0157] Figure 5This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0158] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0159] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0160] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0161] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0162] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0163] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0164] The electronic devices described above are used to implement the corresponding positioning methods in any of the foregoing embodiments and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0165] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides a non-transitory computer-readable storage medium that stores computer instructions for causing the computer to perform the positioning method as described in any of the above embodiments.

[0166] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0167] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the positioning method as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0168] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0169] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0170] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0171] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

Claims

1. A positioning method, characterized by, include: Based on the obtained measured distance and estimated distance, a dynamic distance estimation model is constructed. Based on the acquired prior estimated movement distance and the system estimated movement distance, a motion model based on prior information is constructed. Based on the dynamic ranging estimation model and the motion model based on prior information, a factor graph model is constructed. The factor graph model is iteratively updated using an adaptive gradient descent algorithm to obtain a first location; in response to the iterative update satisfying a first condition, the first location is taken as the final location. The dynamic ranging estimation model is represented by the following formula: in Indicates from the path point To the Wireless access points The distance measurement value, Indicates a given distance measurement value In this case, the client is located in the location The probability, , Residual noise is represented relative to the ranging value. standard deviation Residual noise is represented relative to the ranging value. The adaptive mean, and Indicates the first Wireless access points The coordinates; The motion model based on prior information is represented by the following formula: in, Indicates the motion factor. , , Represents the standard deviation function. This represents the estimated distance traveled by the system. This represents the distance to move as estimated prior. The objective function of the factor graph model is expressed by the following formula: in, Indicates a given distance measurement value In this case, the client is located in the location The probability, Indicates the motion factor.

2. The method according to claim 1, characterized in that, The dynamic ranging estimation model is constructed based on the acquired measured distance and estimated distance, including: The measured distance is obtained based on the acquired true distance and error term; The estimated distance of the ranging is obtained based on the acquired system estimated distance and estimated noise; The residual error is calculated based on the measured distance and the estimated distance of the distance measurement. Based on the residual error and the measured distance, the adaptive mean and standard deviation of the residual error relative to the measured distance are calculated. The dynamic ranging estimation model is constructed based on the adaptive mean and standard deviation of the residual error relative to the ranging distance.

3. The method according to claim 1, characterized in that, The construction of a motion model based on prior information, based on the acquired prior estimated movement distance and the system estimated movement distance, includes: Based on the actual movement distance and the estimated noise, the estimated movement distance of the system is obtained; Based on the actual movement distance and prior noise, the prior estimated movement distance is obtained; Based on the prior estimated movement distance and the system estimated movement distance, the residual noise is calculated; Based on the residual noise, the motion model based on prior information is constructed.

4. The method according to claim 1, characterized in that, The first condition is that the average positional variance of the factor graph model is less than or equal to a preset threshold.

5. A positioning device employing the positioning method as described in any one of claims 1 to 4, characterized in that, include: The first construction module is configured to build a dynamic distance estimation model based on the acquired measured distance and estimated distance. The second building module is configured to build a motion model based on prior information, based on the acquired prior estimated movement distance and the system estimated movement distance. The third construction module is configured to construct a factor graph model based on the dynamic ranging estimation model and the motion model based on prior information. The gradient descent module is configured to iteratively update the factor graph model using an adaptive gradient descent algorithm to obtain a first location; in response to the iterative update satisfying a first condition, the first location is used as the final location.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1 to 4.

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