A UWB positioning method, storage medium, device and computer program product suitable for LOS and NLOS mixed environment

Abstract

The present invention discloses a UWB positioning method, storage medium, device and computer program product suitable for a mixed environment of LOS and NLOS, comprising: arranging UWB base stations in a positioning environment, using the UWB base stations to measure the distance of UWB tags in the LOS environment, finding three UWB base stations with the largest sum of distances, and determining the LOS environment threshold through the Cayley-Menger determinant; at the current moment, reusing the UWB base stations to measure the distance of UWB tags, finding three UWB base stations that minimize the value of the Cayley-Menger determinant, and forming a positioning system with the UWB tags; according to the LOS environment threshold, identifying that the positioning system is in the LOS or NLOS environment, and determining the distance correction values ​​of the three UWB base stations in the positioning system; inputting the distance correction values ​​into an established new three-sided positioning solution model, obtaining the coordinates of the UWB tags, and improving the positioning accuracy.

Description

Technical Field

[0001] The present invention relates to the field of positioning technology, and more specifically, to a UWB positioning method, storage medium, device and computer program product suitable for a mixed LOS and NLOS environment. Background Art

[0002] Traditional indoor positioning technologies mainly include infrared, ultrasonic, Bluetooth, Wi-Fi, etc. However, these technologies are easily affected by the complexity of the indoor environment and other surrounding interference factors, resulting in positioning errors, and the positioning accuracy is difficult to meet the applicable requirements. Ultra-Wide Band (UWB) positioning technology stands out among many positioning technologies with its advantages such as strong anti-interference, large channel capacity, and centimeter-level error. It is widely used in indoor positioning fields such as tunnel operations, smart parking, smart homes, etc.

[0003] According to the complexity of the indoor environment, it can be divided into non-line of sight (NLOS) environment and line of sight (LOS) environment. The NLOS environment is complex and changeable, with narrow space and many obstacles, which can easily cause multipath propagation, shadow effect, time domain selective fading and frequency domain selective fading of UWB signals, which ultimately leads to large errors in the ranging value of the UWB base station. The LOS environment is relatively simple and has little impact on the propagation of UWB signals, resulting in smaller errors in the ranging value of the UWB base station. Therefore, effectively identifying the LOS / NLOS environment and taking different methods to suppress the ranging error of the UWB base station is one of the keys to improving the positioning accuracy of UWB technology.

[0004] Existing LOS / NLOS environment identification and ranging error suppression technologies are mainly divided into two categories: one is to regard the ranging errors in both environments as ranging errors in the NLOS environment, and use a filtering method to suppress them. This ignores the diversity of ranging environments, has low accuracy and robustness, and is difficult to ensure the effect of ranging error suppression; the other is to measure and analyze the characteristics of UWB signals through additional equipment to determine whether they are affected by the NLOS environment, and use different filtering methods to suppress them, but the introduction of additional equipment will result in higher economic costs.

[0005] In addition, after obtaining the ranging value of the UWB base station, it is necessary to establish a positioning solution model to solve the ranging value to achieve positioning. The existing positioning solution models are either based on the traditional three-sided positioning method, or the fusion of multi-positioning methods, or the use of machine learning technology. The latter two have a large computational complexity and are not easy to implement in practical applications. The former has a low positioning accuracy and is difficult to meet the applicable requirements. Summary of the invention

[0006] In view of the problems existing in the prior art, the present invention provides a UWB positioning method, storage medium, device and computer program product suitable for a mixed LOS and NLOS environment, which can realize the identification of LOS environment and NLOS environment, suppress ranging errors and improve positioning accuracy with lower computational complexity.

[0007] To achieve the above technical objectives, the present invention adopts the following technical solution: a UWB positioning method suitable for a mixed LOS and NLOS environment, specifically comprising the following steps:

[0008] Step 1: Arrange three or more UWB base stations at the same height as the UWB tags in the positioning environment;

[0009] Step 2: In the LOS environment, use the UWB base station to measure the distance of the UWB tag, find out the three UWB base stations with the largest sum of distance measurements, and use the Cayley-Menger determinant of the three UWB base stations with the largest sum of distance measurements to determine the LOS environment threshold;

[0010] Step 3: At the current moment, the UWB base station is used again to measure the distance of the UWB tag, and the three UWB base stations that minimize the value of the Cayley-Menger determinant are found to form a positioning system with the UWB tag;

[0011] Step 4: According to the LOS environment threshold, identify whether the positioning system is in a LOS environment or a NLOS environment;

[0012] Step 5: According to the environment in which the positioning system is located, different methods are used to suppress the ranging errors of the three UWB base stations in the positioning system, and the ranging correction values ​​of the three UWB base stations in the positioning system are obtained;

[0013] Step 6: Establish a new three-sided positioning solution model, input the ranging correction values ​​of the three UWB base stations in the positioning system into the established new three-sided positioning solution model, and obtain the coordinates of the UWB tag.

[0014] Furthermore, step 2 includes the following sub-steps:

[0015] Step 2.1, use each UWB base station in the positioning environment to perform multiple ranging of the UWB tag, and calculate the ranging mean and standard deviation of each UWB base station;

[0016] Step 2.2, sum up the ranging averages of any three UWB base stations, and select the three UWB base stations corresponding to the maximum summed values;

[0017] Step 2.3, taking the triangle formed by the three UWB base stations corresponding to the maximum accumulated value as the bottom surface, taking the sum of the ranging mean value and three times the standard deviation corresponding to the three UWB base stations as the side edge length, to form a tetrahedron;

[0018] Step 2.4: construct a Cayley-Menger determinant according to the distances between the three UWB base stations corresponding to the maximum accumulated value, the ranging mean and the standard deviation, calculate the volume of the tetrahedron by the Cayley-Menger determinant, and calculate the height on the bottom surface of the tetrahedron by the volume of the tetrahedron and the area of ​​the bottom surface as the LOS environment threshold.

[0019] Furthermore, the process of obtaining the volume of the tetrahedron by the Cayley-Menger determinant in step 2.4 is specifically as follows:

[0020]

[0021] Among them, V lyz represents the volume of a tetrahedron; s ly represents the distance between UWB base station l and UWB base station y corresponding to the maximum accumulated value, s yz Indicates the distance between UWB base station y and UWB base station z corresponding to the maximum accumulated value, s lz represents the distance between UWB base station l and UWB base station z corresponding to the maximum accumulated value; μ l represents the mean distance measurement value of UWB base station l, σ l represents the ranging standard deviation of UWB base station l; μ y represents the mean distance measurement value of UWB base station y, σ y represents the ranging standard deviation of UWB base station y; μ z represents the mean distance measurement value of UWB base station z, σ z Represents the ranging standard deviation of UWB base station z.

[0022] Furthermore, the calculation process of the area of ​​the bottom surface of the tetrahedron in step 2.4 is:

[0023]

[0024] Among them, S lyz represents the area of ​​the base of the tetrahedron.

[0025] Furthermore, step 4 includes the following sub-steps:

[0026] Step 4.1, using the triangle formed by the three UWB base stations in the positioning system as the bottom surface, and using the ranging values ​​of the three UWB base stations in the positioning system at the current moment as the side edge lengths to form a new tetrahedron;

[0027] Step 4.2, constructing the Cayley-Menger determinant of the positioning system according to the distances between the three UWB base stations in the positioning system and the ranging value at the current moment, calculating the volume of the new tetrahedron by the Cayley-Menger determinant of the positioning system, and calculating the height on the bottom surface of the new tetrahedron by the volume and bottom surface area of ​​the new tetrahedron;

[0028] Step 4.3: If the height on the bottom surface of the new tetrahedron is less than the LOS environment threshold, the positioning system is in a LOS environment; otherwise, the positioning system is in a NLOS environment.

[0029] Furthermore, in step 4.2, the process of obtaining the volume of the new tetrahedron by using the Cayley-Menger determinant of the positioning system is specifically as follows:

[0030]

[0031] Among them, V uvw represents the volume of the new tetrahedron, u t 、v t and w t They represent the distance values ​​of the three UWB base stations in the positioning system at the current moment, s uv 、s vw and uw Respectively represent the distances between the three UWB base stations in the positioning system.

[0032] Furthermore, the process of obtaining the bottom area of ​​the new tetrahedron in step 4.2 is:

[0033]

[0034] Among them, S uvw represents the base area of ​​the new tetrahedron.

[0035] Furthermore, step 5 includes the following sub-steps:

[0036] Step 5.1, if the positioning system is in a LOS environment, a polynomial curve fitting method integrating cyclic iteration and Bayesian maximum a posteriori probability is used to suppress the ranging errors of the three UWB base stations in the positioning system, and the ranging correction values ​​of the three UWB base stations in the positioning system are obtained;

[0037] Step 5.2: If the positioning system is in a NLOS environment, the GMM-EM-IMM method is used to suppress the ranging errors of the three UWB base stations in the positioning system, and the ranging correction values ​​of the three base stations in the positioning system are obtained.

[0038] Furthermore, step 5.1 includes the following sub-steps:

[0039] Step 5.1.1, in the LOS environment, place the UWB tag at several positions in the positioning environment, use any UWB base station to measure the distance of the UWB tag at each position, and obtain the ranging set of the UWB base station; at the same time, obtain the real distance from the UWB base station to the UWB tag at each position, and obtain the real data set of the UWB base station;

[0040] Step 5.1.2, according to the ranging value of each UWB base station in the positioning system, establish a polynomial curve fitting function to solve the ranging correction value of the corresponding UWB base station;

[0041] The polynomial curve fitting function is:

[0042]

[0043] Among them, x t,LOS is the ranging value of a UWB base station in the positioning system at the current time t under the LOS environment, W is the polynomial coefficient vector, W = (w0, w1, ..., w M ) T ; M is the polynomial order, M=f+2, f is the number of iterations of the polynomial curve fitting function;

[0044] Step 5.1.3, in the single iterative solution process of the ranging correction value corresponding to the UWB base station, according to the ranging set of the UWB base station and the real data set in step 5.1.1, the Bayesian maximum a posteriori probability polynomial curve fitting method is used to determine the accuracy of the optimal solution of the polynomial coefficient vector and the ranging correction value corresponding to the UWB base station;

[0045] Step 5.1.4: If the variance of the Gaussian distribution obeyed by the ranging correction value corresponding to the UWB base station does not converge, adjust the polynomial order according to the number of iterations, and repeat step 5.1.3 until the variance of the Gaussian distribution obeyed by the ranging correction value corresponding to the UWB base station converges, and obtain the ranging correction value corresponding to the UWB base station in the positioning system.

[0046] Furthermore, the calculation process of the ranging correction value corresponding to the UWB base station in the positioning system in step 5.1.4 is specifically as follows:

[0047]

[0048] in, is the ranging correction value of the corresponding UWB base station in the positioning system at the current time t in the LOS environment, β MAP is the accuracy of the optimal solution of the polynomial coefficient vector, φ(x t,LOS ) is the first parameter to be solved for the ranging correction value, φ(x n,LOS ) is the second parameter for solving the ranging correction value, x n,LOS is the distance measurement value of any UWB base station to the UWB tag at the nth position; N is the number of positions where the UWB tag is placed in the positioning environment, S is the third parameter for solving the distance correction value, α is the precision parameter of the prior probability distribution of the polynomial coefficient vector, and I is the M+1 order identity matrix; is the actual distance from any UWB base station to the UWB tag at the nth position.

[0049] Furthermore, step 5.2 includes the following sub-steps:

[0050] Step 5.2.1: In the NLOS environment, the probability distribution of the ranging value of each UWB base station in the positioning system at the current time t is represented by the Gaussian mixture model GMM:

[0051]

[0052] Among them, K is the number of single Gaussian models in the Gaussian mixture model GMM, k is the index of K, and α k is the weight coefficient of the kth single Gaussian model, is the probability distribution of the kth single Gaussian model, η k , are the mean and variance of the kth single Gaussian model, x t,NLOS is the distance measurement value of any base station in the positioning system at the current time t in the NLOS environment, p(x t,NLOS |θ) represents the value of x under a given parameter vector θ t,NLOS The probability distribution of

[0053] Step 5.2.2: Use the expected maximum EM algorithm to solve the parameter vectors θ and x of the Gaussian mixture model GMM t,NLOS The probability distribution of

[0054] Step 5.2.3: x established based on Gaussian mixture model GMM t,NLOS The probability distribution of x is determined by the interactive multi-model IMM algorithm, and the interactive multi-model IMM algorithm is used to reconstruct x t,NLOS , obtain the ranging correction value of the corresponding UWB base station in the positioning system at time t.

[0055] Further, step 5.2.3 includes the following sub-steps: step 5.2.3.1, x established according to Gaussian mixture model GMM t,NLOS The probability distribution of the UWB base station and the UWB tag in the positioning system is decomposed into Y sub-models that obey the Gaussian distribution. The state equation and measurement equation are established for each sub-model:

[0056] Xt,y =φX t-1,y +Γa t

[0057]

[0058] Among them, X t,y is the state of the y-th sub-model at the current time t, x t,y is the distance measurement value of the y-th sub-model at the current time t, v t,y is the speed of the y-th sub-model at the current time t, y is the index of Y; φ is the state transfer matrix, a t is the state noise of the y-th sub-model at the current time t, Γ is the noise driving matrix, η t,k is the mean of the kth single Gaussian model at the current time t obtained by the expected maximum EM algorithm, is the observation matrix, σ t,k The standard deviation of the k-th single Gaussian model at the current time t obtained by the expected maximum EM algorithm;

[0059] Step 5.2.3.2: Perform Kalman filtering on each sub-model:

[0060]

[0061] in, It represents the result of Kalman filtering of the y-th sub-model at time t-1. Indicates based on The predicted state of the y-th sub-model at the current time t, P t-1,y Represents the state covariance matrix of the Kalman filter of the y-th sub-model at time t-1, According to P t-1,y The predicted state covariance matrix of the y-th sub-model at the current time t, Q t Represents the state noise covariance matrix of each sub-model from time t-1 to the current time t;

[0062] Step 5.2.3.3: According to the predicted state covariance matrix of the y-th sub-model at the current time t Calculate the Kalman filter gain K t,y , and combined with the predicted state of the y-th sub-model at the current time t Update the Kalman filter result of the yth sub-model at the current time t

[0063]

[0064] Among them, I 2×2 represents the 2×2 unit matrix, P t,yRepresents the state covariance matrix of the Kalman filter of the y-th sub-model at the current time t;

[0065] Step 5.2.3.4: According to the predicted state covariance matrix of the y-th sub-model at the current time t and the predicted state of the y-th sub-model at the current time t Get the maximum likelihood function Λ that best matches the y-th sub-model t,y :

[0066]

[0067] Among them, S t,y is the first parameter to be solved for the maximum likelihood function, d t,y The second parameter to be solved for the maximum likelihood function;

[0068] Step 5.2.3.5: According to the maximum likelihood function Λ that best matches the y-th sub-model t,y Update the weight of the y-th sub-model:

[0069]

[0070] Among them, α t,k is the weight of the kth single Gaussian model solved by the expected maximum EM algorithm at the current time t;

[0071] Step 5.2.3.6, fusing the updated Kalman filter results of each sub-model and the updated weights to obtain the optimal state of the ranging model at the current time t, wherein the optimal state is composed of the ranging correction value and the speed correction value of the UWB base station in the ranging model at the current time t;

[0072] The calculation process of the optimal state of the ranging model at the current time t is:

[0073]

[0074] Furthermore, step 6 includes the following sub-steps:

[0075] Step 6.1, establish a plane rectangular coordinate system of the positioning environment, determine the position coordinates of the three UWB base stations u, v and w in the positioning system, use the position coordinates of the three UWB base stations in the positioning system as the center of the circle and the corresponding ranging correction value as the radius to make an initial circle, and obtain the coordinates of the intersection points where the three circles intersect each other;

[0076] Step 6.2, select the intersection coordinates that are closer to the center of the third circle from the intersection coordinates of the three circles as the vertex coordinates of the initial error triangle, construct the initial error triangle, and use the centroid method to obtain the centroid coordinates of the initial error triangle;

[0077] Step 6.3: In the first iteration, the vertex coordinates of the initial error triangle and the sum of the distances from the centroid coordinates of the initial error triangle to the three UWB base stations in the positioning system are calculated, which is recorded as and D 1 , respectively calculated With D 1 The absolute value of the difference, retaining the vertex coordinates of the error triangle with the smallest absolute value;

[0078] Step 6.4, draw a circle with the coordinates of the vertex of the retained error triangle as the center and the distance from the vertex of the retained error triangle to the coordinates of the centroid of the initial error triangle as the radius, update the corresponding circle, and obtain the coordinates of the intersection points where the three circles intersect each other after the update;

[0079] Step 6.5, select the intersection coordinates that are closer to the center of the third circle from the updated intersection coordinates of the three circles, update the vertex coordinates of the error triangle, construct a new error triangle, and use the centroid method to obtain the centroid coordinates of the new error triangle;

[0080] Step 6.6, determine whether the centroid coordinates of the new error triangle converge. If so, use the centroid coordinates of the new error triangle as the UWB tag coordinates; otherwise, enter the next iteration process, calculate the sum of the distances from the vertex coordinates of the new error triangle and the centroid coordinates of the new error triangle to the three UWB base stations in the positioning system, and record them as and D d , respectively calculated With D d The absolute value of the difference, retaining the vertex coordinates of the error triangle with the smallest absolute value;

[0081] Step 6.7, draw a circle with the retained new error triangle vertex coordinates as the center and the distance from the retained new error triangle vertex coordinates to the new error triangle centroid coordinates as the radius, update the corresponding circle, and obtain the coordinates of the intersection points where the three circles intersect after the update, and repeat steps 6.5-6.6.

[0082] Furthermore, in step 6.6 D d The specific calculation process is:

[0083]

[0084] in, is the vertex coordinate of the error triangle of the circle related to UWB base stations u and v in the positioning system under the d-th iteration, is the vertex coordinate of the error triangle of the circle related to UWB base stations u and w in the positioning system under the d-th iteration, is the vertex coordinate of the error triangle of the circle related to UWB base stations v and w in the positioning system under the d-th iteration, is the centroid coordinate of the error triangle at the dth iteration, (u x ,u y ) is the coordinate of the UWB base station u in the positioning system, (v x ,v y ) is the coordinate of the UWB base station v in the positioning system, (w x ,w y ) is the coordinate of the UWB base station w in the positioning system.

[0085] Furthermore, the present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the UWB positioning method applicable to a mixed environment of LOS and NLOS.

[0086] Furthermore, the present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the UWB positioning method applicable to a mixed environment of LOS and NLOS is implemented.

[0087] Furthermore, the present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the UWB positioning method applicable to a mixed environment of LOS and NLOS is implemented.

[0088] Compared with the prior art, the present invention has the following beneficial effects:

[0089] (1) The present invention uses the Cayley-Menger determinant and geometric method to identify whether the positioning system is in a LOS environment or a NLOS environment. It is not necessary to use additional equipment to measure and analyze the characteristics of the UWB signal to determine whether it is affected by the NLOS environment, thereby saving economic and computing costs.

[0090] (2) The present invention adopts a polynomial curve fitting method that integrates cyclic iteration and Bayesian maximum a posteriori probability to suppress the ranging error of each UWB base station in the positioning system under the LOS environment. Compared with the traditional polynomial curve fitting method, there is no need to manually pre-set the polynomial order. Instead, the polynomial order is determined according to the variance of the curve fitting by using cyclic iteration and Bayesian maximum a posteriori probability method, thereby ensuring the accuracy of the curve fitting and the suppression effect of the ranging error.

[0091] (3) The present invention adopts the GMM-EM-IMM method to suppress the ranging errors of each UWB base station in the positioning system under the NLOS environment, and samples the Gaussian mixture model GMM to establish the distribution obeyed by the UWB base station ranging value. Compared with machine learning algorithms, it does not require a large number of training samples and has better feasibility and practical engineering application value; the expected maximum EM algorithm is used to solve the parameters of the GMM Gaussian mixture model; according to the distribution obeyed by the UWB base station ranging value established by the Gaussian mixture model GMM, the input of the interactive multi-model IMM algorithm is determined, and the interactive multi-model IMM algorithm is used to suppress the ranging errors of each UWB base station in the positioning system. Compared with traditional filtering and its derivative algorithms, the computational complexity is saved;

[0092] (4) The present invention establishes a new three-sided positioning solution model, and adopts a cyclic iteration method to update and reduce the error triangle generated by the traditional three-sided positioning method until the centroid of the error triangle converges to a certain value, thereby avoiding the disadvantage that the traditional three-sided positioning method can only obtain an estimated value, and improving the positioning accuracy with lower computational complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] Figure 1 It is a flow chart of the UWB positioning method applicable to a mixed environment of LOS and NLOS of the present invention;

[0094] Figure 2 A flowchart for determining the ranging correction value of the UWB base station in the positioning system under LOS environment by integrating the polynomial curve fitting method of cyclic iteration and Bayesian maximum a posteriori probability;

[0095] Figure 3 Flow chart for determining the ranging correction value of UWB base station in the positioning system under NLOS environment by GMM-EM-IMM method;

[0096] Figure 4 It is a schematic diagram of the novel three-sided positioning solution model in the present invention. DETAILED DESCRIPTION

[0097] The technical solution of the present invention is further explained below in conjunction with the accompanying drawings.

[0098] like Figure 1 The flowchart of the UWB positioning method of the present invention is applicable to a mixed environment of LOS and NLOS. The UWB positioning method specifically comprises the following steps:

[0099] Step 1: Arrange L UWB base stations at the same height as the UWB tags in the positioning environment, L ≥ 3;

[0100] Step 2: In the LOS environment, use the UWB base station to measure the distance of the UWB tag, find the three UWB base stations with the largest sum of distances, and use the Cayley-Menger determinant of the three UWB base stations with the largest sum of distances to determine the LOS environment threshold. With the triangle formed by the three UWB base stations as the bottom surface, and the distance value corresponding to the UWB base station as the side edge length, a tetrahedron can be constructed. When the area of ​​the triangle formed by the three UWB base stations is constant, the greater the deviation between the distance measurement value and the actual distance, the greater the height and volume of the tetrahedron, the closer to the NLOS environment, the greater the deviation between the distance measurement value and the actual distance, and the farther the distance between the UWB base station and the UWB tag, the greater the deviation between the distance measurement value and the actual distance. Therefore, in the LOS environment, the volume of the tetrahedron constructed by the three UWB base stations with the largest sum of distances can be calculated by the Cayley-Menger determinant, and then the LOS environment threshold can be determined. This method determines the LOS environmental threshold only through geometric principles and the Cayley-Menger determinant, without the need for additional equipment to measure and analyze the characteristics of the UWB signal, thus saving economic and computational costs.

[0101] Specifically, the following sub-steps are included: Step 2.1: Place the UWB tag at a position in the positioning environment and keep it stationary. Each UWB base station in the positioning environment performs multiple ranging on the stationary UWB tag. The ranging result of any UWB base station b for the i-th time is b i , calculate the mean distance μ of any UWB base station b b and standard deviation σ b :

[0102]

[0103] Where, I is the number of ranging times, I≥100;

[0104] Step 2.2, sum up the ranging mean values ​​of any three UWB base stations, and select the three UWB base stations corresponding to the maximum sum value, which are l, y and z respectively;

[0105] Step 2.3, taking the triangle formed by the three UWB base stations corresponding to the maximum accumulated value as the bottom surface, taking the sum of the ranging mean value and three times the standard deviation corresponding to the three UWB base stations as the side edge length, to form a tetrahedron;

[0106] Step 2.4: Construct the Cayley-Menger determinant based on the distances between the three UWB base stations corresponding to the maximum accumulated value, the distance measurement mean and the standard deviation. Use the Cayley-Menger determinant to calculate the volume of the tetrahedron, and use the volume of the tetrahedron and the area of ​​the bottom surface to calculate the height on the bottom surface of the tetrahedron as the LOS environment threshold λ. Los :

[0107]

[0108] Among them, V lyz is the volume of the tetrahedron. The process of obtaining the volume of the tetrahedron by the Cayley-Menger determinant is as follows:

[0109]

[0110] s ly represents the distance between UWB base station l and UWB base station y corresponding to the maximum accumulated value, s yz Indicates the distance between UWB base station y and UWB base station z corresponding to the maximum accumulated value, s lz represents the distance between UWB base station l and UWB base station z corresponding to the maximum accumulated value; μ l represents the mean distance measurement value of UWB base station l, σ l represents the ranging standard deviation of UWB base station l; μ y represents the mean distance measurement value of UWB base station y, σ y represents the ranging standard deviation of UWB base station y; μ z represents the mean distance measurement value of UWB base station z, σ z represents the ranging standard deviation of UWB base station z;

[0111] S lyz is the area of ​​the tetrahedron base:

[0112]

[0113] Step 3: At the current moment, use the UWB base station to measure the distance of the UWB tag again, find the three UWB base stations u, v and w that minimize the value of the Cayley-Menger determinant, and form a positioning system with the UWB tag. The smaller the value of the Cayley-Menger determinant, the closer the three UWB base stations are to the UWB tag, and the smaller the ranging error contained in the ranging values ​​of the three UWB base stations. Further, it can be shown that the accuracy of the positioning system is higher.

[0114] Step 4: Identify whether the positioning system is in a LOS environment or a NLOS environment according to the LOS environment threshold; specifically, the following sub-steps are included:

[0115] Step 4.1, using the triangle formed by the three UWB base stations in the positioning system as the bottom surface, and using the ranging values ​​of the three UWB base stations in the positioning system at the current moment as the side edge lengths to form a new tetrahedron;

[0116] Step 4.2: Construct the Cayley-Menger determinant of the positioning system according to the distances between the three UWB base stations in the positioning system and the distance measurement value at the current moment, calculate the volume of the new tetrahedron through the Cayley-Menger determinant of the positioning system, and calculate the height λ on the bottom surface of the new tetrahedron through the volume and bottom surface area of ​​the new tetrahedron uvw :

[0117]

[0118] Among them, V uvw represents the volume of the new tetrahedron. The process of obtaining the volume of the new tetrahedron through the Cayley-Menger determinant of the positioning system is as follows:

[0119]

[0120] u t 、v t and w t They represent the distance values ​​of the three UWB base stations in the positioning system at the current moment, s uv 、s vw and uw Respectively represent the distances between the three UWB base stations in the positioning system;

[0121] S uvw is the base area of ​​the new tetrahedron:

[0122]

[0123] Step 4.3: If the height of the bottom surface of the new tetrahedron is less than the LOS environment threshold, the positioning system is in a LOS environment; otherwise, the positioning system is in a NLOS environment.

[0124] Step 5: According to the environment in which the positioning system is located, different methods are used to suppress the ranging errors of the three UWB base stations in the positioning system to obtain ranging correction values ​​of the three UWB base stations in the positioning system; specifically, the following sub-steps are included:

[0125] Step 5.1. If the positioning system is in a LOS environment, a polynomial curve fitting method that integrates loop iteration and Bayesian maximum a posteriori probability is used to suppress the ranging errors of the three UWB base stations in the positioning system, and obtain ranging correction values ​​for the three UWB base stations in the positioning system. In a LOS environment, the ranging deviations of UWB base stations usually have a certain regularity. Therefore, a polynomial fitting method can be used to construct a function model that satisfies the ranging value of the UWB base station and the true distance, and the ranging value of the UWB base station is corrected accordingly. Compared with the traditional polynomial fitting method, the polynomial curve fitting method that integrates loop iteration and Bayesian maximum a posteriori probability does not require manual selection of the order of the polynomial or determination of the complexity of the function through methods such as cross-validation, and has higher flexibility and accuracy. Figure 2 , the specific process is as follows:

[0126] Step 5.1.1: In the LOS environment, place the UWB tag at N locations in the positioning environment, N ≥ 20, and use any UWB base station to measure the distance of the UWB tag at each location to obtain the distance set X of the UWB base station. LOS =(x 1,LOS ,x 2,LOS ,...,x N,LOS ) T At the same time, the real distance from the UWB base station to the UWB tag at each location is obtained to obtain the real data set of the UWB base station

[0127] Step 5.1.2, according to the ranging value of each UWB base station in the positioning system, establish a polynomial curve fitting function to solve the ranging correction value of the corresponding UWB base station;

[0128] The polynomial curve fitting function in the present invention is:

[0129]

[0130] Among them, x t,LOS is the ranging value of a UWB base station in the positioning system at the current time t under the LOS environment, W is the polynomial coefficient vector, W = (w0, w1, ..., w M ) T ; M is the polynomial order, M = f + 2, f is the number of iterations of the polynomial curve fitting function, in the polynomial curve fitting function, x t,LOS is the independent variable, W and M are the parameters to be determined;

[0131] Step 5.1.3: In the single iterative solution process of the ranging correction value corresponding to the UWB base station, the order of the polynomial function is determined according to the number of iterations. According to the ranging set of the UWB base station and the real data set in step 5.1.1, the polynomial curve fitting method of Bayesian maximum a posteriori probability is used to determine the accuracy of the optimal solution of the polynomial coefficient vector and the Gaussian distribution obeyed by the ranging correction value of the corresponding UWB base station. The specific process is as follows:

[0132] (1) When W is fixed, for a given distance value x t,LOS , the actual distance corresponding to the distance measurement value The mean is F(x t,LOS |W), the variance is β -1 Gaussian distribution, that is:

[0133]

[0134] in, For a given x t,LOS Down, The conditional probability of W, also known as the likelihood function of W; β can control the accuracy of the distribution, also known as the accuracy parameter; express The mean is F(x t,LOS |W), the variance is β -1 Gaussian distribution of , where N( ) is the symbol of Gaussian distribution;

[0135] Similarly, for a given data set X LOS =(x 1,LOS ,x 2,LOS ,…,x N,LOS ) T , dataset The probability distribution of is as follows:

[0136]

[0137] in, For a given X LOS , The conditional probability of W is also expressed as the likelihood function of W, x n,LOS is the distance measurement value of any UWB base station to the UWB tag at the nth position;

[0138] (2) Assume that the prior distribution of W has a mean of 0 and a variance of α -1 The multidimensional Gaussian distribution of I, that is:

[0139]

[0140] Where p(W|α) represents the prior probability of W with α as parameter; α is the precision parameter of the prior probability distribution of the polynomial coefficient vector, α=10 -6 ; I is the M+1 order unit matrix;

[0141] (3) According to Bayesian theory, the posterior distribution of W is proportional to the product of its prior distribution and the likelihood function, that is:

[0142]

[0143] in, For a given X LOS , The posterior probability of W;

[0144] (4) By maximizing the posterior probability W The method to solve W is as follows:

[0145] Solution goal:

[0146]

[0147] Equivalent to:

[0148]

[0149] calculate

[0150]

[0151] Simplify the terms that are not related to W and simplify to:

[0152]

[0153] Solution result:

[0154]

[0155]

[0156] Among them, W WAP is the optimal solution of the polynomial function coefficient vector, β MAP is the accuracy of the optimal solution of the polynomial coefficient vector;

[0157] (5) Since it is known and the precision parameters α and β MAP , we can calculate the given x by the multiplication and addition formulas of probability theory t,LOS In case, The probability distribution of is calculated as follows:

[0158]

[0159] in, For a given X LOS , and x t,LOS In case, The probability distribution of Indicates that when W is fixed, for a given x t,LOS , The probability distribution of For a given X LOS , In this case, the posterior probability of W. The integral of the above formula is as follows:

[0160]

[0161] Among them, m(x t,LOS ) and s 2 (x t,LOS ) represent x t,LOS The polynomial curve fitting results and variance are calculated as follows:

[0162]

[0163]

[0164] Among them, φ(x t,LOS ) is the first parameter to be solved for the ranging correction value, φ(x n,LOS ) is the second parameter for solving the ranging correction value, S is the third parameter for solving the ranging correction value. is the actual distance from any UWB base station to the UWB tag at the nth position.

[0165] Step 5.1.4: If the variance of the Gaussian distribution obeyed by the ranging correction value corresponding to the UWB base station does not converge, add one iteration and adjust the polynomial order according to the number of iterations, and repeat step 5.1.3 until the variance of the Gaussian distribution obeyed by the ranging correction value corresponding to the UWB base station converges. The ranging correction value corresponding to the UWB base station in the positioning system is obtained as the mean of the Gaussian distribution.

[0166] Step 5.2: If the positioning system is in an NLOS environment, the GMM-EM-IMM method is used to suppress the ranging errors of the three UWB base stations in the positioning system, and the ranging correction values ​​of the three base stations in the positioning system are obtained. In the NLOS environment, the ranging values ​​of the UWB base stations are interfered by various factors such as time delay, multipath effect and shadow, and the distribution they obey is difficult to represent with a single model. Furthermore, it is also difficult to suppress the ranging errors of the UWB base stations by the existing single filtering method. Therefore, the Gaussian mixture model GMM is used to represent the distribution obeyed by the ranging values ​​of each UWB base station in the positioning system as the weighted accumulation of three single Gaussian models, and the expected maximum EM algorithm is used to solve the parameters of the Gaussian mixture model GMM. According to the distribution obeyed by the ranging values ​​of each UWB base station in the positioning system established by the Gaussian mixture model GMM, the input of the interactive multi-model IMM algorithm is determined, and the interactive multi-model IMM algorithm is used to suppress the ranging errors of the UWB base stations. Compared with machine learning algorithms, Gaussian mixture model GMM does not require a large number of training samples and has better feasibility and practical engineering application value. Compared with traditional filtering and its derivative algorithms, the interactive multi-model IMM algorithm takes into account both filtering accuracy and computational complexity. Figure 3 , the specific process is as follows:

[0167] Step 5.2.1: In the NLOS environment, the probability distribution of the ranging value of each UWB base station in the positioning system at the current time t is represented by the Gaussian mixture model GMM:

[0168]

[0169] Where K is the number of single Gaussian models in the Gaussian mixture model GMM. Considering that in the NLOS environment, time delay, multipath effect and shadow are the main causes of ranging errors, let K = 3; k is the index of K, α k is the weight coefficient of the kth single Gaussian model, is the probability distribution of the kth single Gaussian model, η k , are the mean and variance of the kth single Gaussian model, x t,NLOS is the distance measurement value of any base station in the positioning system at the current time t in the NLOS environment, p(x t,NLOS |θ) represents the value of x under a given parameter vector θ t,NLOS The probability distribution of

[0170] Step 5.2.2: Use the expected maximum EM algorithm to solve the parameter vectors θ and x of the Gaussian mixture model GMM t,NLOS The probability distribution of ; specifically,

[0171] Step 5.2.2.1. Obtain the ranging set X of any base station x in the positioning system in the NLOS environment during the period (t-Δt, t). NLOS =(x 1,NLOS ,x 2,NLOS ,…,x H,NLOS ), where Δt represents the time difference, which is a minimum time;

[0172] Step 5.2.2.2, define the discrete random variable z h Represents x h,NLOS The Gaussian distribution to which it belongs is h∈[1,H]. Let the kth Gaussian distribution be c k , then z h The possible values ​​of are C=(c1,c2,c3), z h Also called hidden variables. For the distance set X NLOS =(x 1,NLOS ,x 2,NLOS ,…,x H,NLOS ), corresponding to the hidden variable set Z = (z1, z2, …, z H );

[0173] Step 5.2.2.3: The process of solving θ using the EM algorithm is an iterative process. Each iteration consists of two steps, namely E-Step and M-Step. The purpose of E-Step is to find the expectation, and the purpose of M-Step is to find the maximum. The details are as follows:

[0174] Select the initial value of θ Start iteration;

[0175] E-Step:

[0176]

[0177] Among them, θ c is the estimated value of θ at the cth iteration; p(X NLOS ,Z|θ) means that under given θ, X NLOS The joint probability with Z; p(Z|X NLOS ,θ c ) indicates that given θ c Next, Z about X NLOS The conditional probability of Q(θ,θ c ) means log(p(X NLOS ,Z|θ)) about p(Z|X NLOS ,θ c ). Since Z is a discrete variable, the above formula can be simplified to

[0178]

[0179] Among them, p(xh,NLOS ,z h |θ) means that given θ, x h,NLOS and z h The joint probability of h |x h,NLOS ,θ c ) represents θ c Given that z h About x h,NLOS The conditional probability of . Furthermore, due to:

[0180]

[0181] Among them, p(x h,NLOS |θ) means that given θ, x h,NLOS The probability, p(z h |θ) means that under given θ, z h The probability of zh ; p(x h,NLOS |z h ,θ) means that under given θ, x h,NLOS About z h The conditional probability of z h Gaussian distribution corresponding to the value p(z h |x h,NLOS ,θ) means that under given θ, z h About x h,NLOS The conditional probability of Q(θ,θ c ) is further simplified to:

[0182]

[0183] in, and Respectively represent the cth iteration and α k An estimated value of

[0184] M-Step:

[0185]

[0186] The details are as follows:

[0187]

[0188]

[0189] Among them, p(z h =c k |x h,NLOS ,θ c ) represents θ cGiven that z h =c k About x h,NLOS The conditional probability of

[0190] Repeat E-Step and M-Step until θ converges. Note that θ converges to θ t This is the final solution of θ. Therefore, at the current time t, in the NLOS environment, x t,NLOS The probability distribution of is as follows:

[0191]

[0192] Step 5.2.3: x established based on Gaussian mixture model GMM t,NLOS The probability distribution of x is determined by the interactive multi-model IMM algorithm, and the interactive multi-model IMM algorithm is used to reconstruct x t,NLOS , obtain the ranging correction value of the corresponding UWB base station in the positioning system at time t. Specifically,

[0193] Step 5.2.3.1: Gaussian mixture model GMM takes the distance value x of any UWB base station in the positioning system t,NLOS The probability distribution is expressed as the weighted accumulation of K single Gaussian models. The corresponding IMM algorithm decomposes the ranging model consisting of the corresponding UWB base station and UWB tag in the positioning system into Y sub-models that obey the Gaussian distribution, Y = K, and establishes the state equation and measurement equation for each sub-model:

[0194] X t,y =φX t-1,y +Γa t

[0195]

[0196] Among them, X t,y is the state of the y-th sub-model at the current time t, x t,y is the distance measurement value of the y-th sub-model at the current time t, v t,y is the velocity of the y-th sub-model at the current time t, y is the index of Y; φ is the state transfer matrix, a t is the state noise of the y-th sub-model at the current time t, Γ is the noise driving matrix, η t,k is the mean of the kth single Gaussian model at the current time t obtained by the expected maximum EM algorithm, is the observation matrix, σ t,kThe standard deviation of the k-th single Gaussian model at the current time t obtained by the expected maximum EM algorithm can represent the observation noise of the y-th sub-model at the current time t. The value of k is always equal to the value of y.

[0197] Step 5.2.3.2: Perform Kalman filtering on each sub-model:

[0198]

[0199] in, It represents the result of Kalman filtering of the y-th sub-model at time t-1. Indicates based on The predicted state of the y-th sub-model at the current time t, P t-1,y Represents the state covariance matrix of the Kalman filter of the y-th sub-model at time t-1, According to P t-1,y The predicted state covariance matrix of the y-th sub-model at the current time t, Q t represents the state noise covariance matrix of each sub-model from time t-1 to the current time t, represents Γa from time t-1 to the current time t t and (Γa t ) T The covariance of 2 (a t ) represents the variance of the acceleration of the ranging model from time t-1 to the current time t;

[0200] Step 5.2.3.3: According to the predicted state covariance matrix of the y-th sub-model at the current time t Calculate the Kalman filter gain K t,y , and combined with the predicted state of the y-th sub-model at the current time t Update the Kalman filter result of the yth sub-model at the current time t

[0201]

[0202] Among them, I 2×2 represents the 2×2 unit matrix, P t,y Represents the state covariance matrix of the Kalman filter of the y-th sub-model at the current time t;

[0203] Step 5.2.3.4: According to the predicted state covariance matrix of the y-th sub-model at the current time t and the predicted state of the y-th sub-model at the current time t Get the maximum likelihood function Λ that best matches the y-th sub-model t,y :

[0204]

[0205] Among them, S t,y is the first parameter to be solved for the maximum likelihood function, d t,y The second parameter to be solved for the maximum likelihood function;

[0206] Step 5.2.3.5: According to the maximum likelihood function Δ that best matches the y-th sub-model t,y Update the weight of the y-th sub-model:

[0207]

[0208] Among them, α t,k is the weight of the kth single Gaussian model solved by the expected maximum EM algorithm at the current time t;

[0209] Step 5.2.3.6, fuse the updated Kalman filter results of each sub-model and the updated weights to obtain the optimal state of the ranging model at the current time t. The optimal state consists of the optimal ranging value and the optimal speed correction value corresponding to the UWB base station in the ranging model at the current time t:

[0210]

[0211] in, Represents the optimal ranging value of the ranging model at the current time t; It represents the optimal speed of the ranging model at the current time t. Since the ranging model is composed of any UWB base station x and UWB tag in the positioning system, the ranging correction value corresponding to the UWB base station x in the positioning system at the current time t is

[0212] Step 6. Establish a new three-sided positioning solution model, input the ranging correction values ​​of the three UWB base stations in the positioning system into the established new three-sided positioning solution model, and obtain the coordinates of the UWB tag. Traditional three-sided positioning methods usually produce error triangles, and the coordinates of the UWB tag determined by the centroid of the error triangle are only an estimate; the new three-sided positioning solution model uses a cyclic iteration method to update and reduce the error triangle produced by the traditional three-sided positioning method until the centroid of the error triangle converges to a certain value, avoiding the disadvantage of the traditional three-sided positioning method that only an estimate can be obtained, and improving positioning accuracy with lower computational complexity. Figure 4 , specifically including the following sub-steps:

[0213] Step 6.1: Establish a plane rectangular coordinate system for the positioning environment and determine the position coordinates (u x ,u y )、(vx ,v y ) and (w x ,w y ), take the position coordinates of the three UWB base stations in the positioning system as the center of the circle, and the corresponding ranging correction value as the radius to make an initial circle and The three circles intersect each other, and there are six intersection points in total. The coordinates of the intersection points where the three circles intersect each other are calculated. The specific calculation process is:

[0214]

[0215] in, is the horizontal coordinate of the intersection point where the three circles intersect each other, and ξ is the vertical coordinate of the intersection point where the three circles intersect each other;

[0216] Step 6.2: Select the intersection coordinates that are closer to the center of the third circle from the intersection coordinates of the three circles. and As the vertex coordinates of the initial error triangle, construct the initial error triangle Δ 1 uvw, use the centroid method to obtain the centroid coordinates of the initial error triangle

[0217]

[0218] Step 6.3: During the first iteration, calculate the vertex coordinates of the initial error triangle and the centroid coordinates of the initial error triangle. The sum of the distances to the three UWB base stations in the positioning system is denoted as and D 1 , respectively calculate With D 1 The absolute value of the difference is retained, and the vertex coordinates of the error triangle with the smallest absolute value are retained; With D 1 Taking the absolute value of the difference as an example, the vertex coordinates of the retained error triangle are

[0219] Step 6.4: Use the retained error triangle vertex coordinates The center of the circle is the coordinates of the vertex of the retained error triangle To the initial error triangle centroid coordinates Draw a circle with the distance as the radius and update the corresponding circle And find the coordinates of the intersection points of the three circles after the update, that is, calculate The coordinates of the intersection points where the three circles intersect each other;

[0220] Step 6.5, select the intersection coordinates that are closer to the center of the third circle from the updated intersection coordinates of the three circles, update the vertex coordinates of the error triangle, construct a new error triangle, and use the centroid method to obtain the centroid coordinates of the new error triangle;

[0221] Step 6.6, determine whether the centroid coordinates of the new error triangle converge. If so, use the centroid coordinates of the new error triangle as the UWB tag coordinates; otherwise, enter the next iteration process, calculate the sum of the distances from the vertex coordinates of the new error triangle and the centroid coordinates of the new error triangle to the three UWB base stations in the positioning system, and record them as and D d , respectively calculated With D d The absolute value of the difference, retaining the vertex coordinates of the error triangle with the smallest absolute value;

[0222] In the present invention D d The specific calculation process is:

[0223]

[0224] in, is the vertex coordinate of the error triangle of the circle related to UWB base stations u and v in the positioning system under the d-th iteration, is the vertex coordinate of the error triangle of the circle related to UWB base stations u and w in the positioning system under the d-th iteration, is the vertex coordinate of the error triangle of the circle related to UWB base stations v and w in the positioning system under the d-th iteration, is the centroid coordinate of the error triangle at the dth iteration, (u x ,u y ) is the coordinate of the UWB base station u in the positioning system, (v x ,v y ) is the coordinate of the UWB base station v in the positioning system, (w x ,w y ) is the coordinate of the UWB base station w in the positioning system.

[0225] Step 6.7, draw a circle with the retained new error triangle vertex coordinates as the center and the distance from the retained new error triangle vertex coordinates to the new error triangle centroid coordinates as the radius, update the corresponding circle, and obtain the coordinates of the intersection points where the three circles intersect after the update, and repeat steps 6.5-6.6.

[0226] The present invention is applicable to a UWB positioning method in a mixed environment of LOS and NLOS. The Cayley-Menger determinant is used to judge whether a positioning system is in a LOS environment or a NLOS environment. No additional equipment is needed to measure and analyze the characteristics of UWB signals, thereby saving economic costs. A polynomial curve fitting method integrating cyclic iteration and Bayesian maximum a posteriori probability is used to suppress the ranging error of each UWB base station in the positioning system in the LOS environment. No artificial presetting of the polynomial order is required, thereby ensuring the accuracy of curve fitting and the suppression effect of ranging error. A GMM-EM-IMM method is used to suppress the ranging error of each UWB base station in the positioning system in the NLOS environment. Compared with a machine learning algorithm, a large number of training samples are not required, thereby having better feasibility and practical engineering application value. A new three-sided positioning solution model is established, thereby avoiding the disadvantage that the traditional three-sided positioning method can only obtain one estimated value, and improving the positioning accuracy through smaller calculation complexity.

[0227] In a technical solution of the present invention, a computer-readable storage medium is further provided, storing a computer program, wherein the computer program enables a computer to execute the UWB positioning method applicable to a mixed environment of LOS and NLOS.

[0228] In a technical solution of the present invention, an electronic device is also provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the UWB positioning method applicable to a mixed environment of LOS and NLOS is implemented.

[0229] In a technical solution of the present invention, a computer program product is also provided, including a computer program, and when the computer program is executed by a processor, the UWB positioning method applicable to a mixed environment of LOS and NLOS is implemented.

[0230] In the embodiments disclosed in the present application, the computer storage medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. The computer storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. More specific examples of computer storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0231] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0232] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A UWB positioning method suitable for a mixed LOS and NLOS environment, characterized in that: The specific steps include: Step 1: Arrange three or more UWB base stations at the same height as the UWB tags in the positioning environment; Step 2: In the LOS environment, use the UWB base station to measure the distance of the UWB tag, find out the three UWB base stations with the largest sum of distance measurements, and use the Cayley-Menger determinant of the three UWB base stations with the largest sum of distance measurements to determine the LOS environment threshold; Step 3: At the current moment, the UWB base station is used again to measure the distance of the UWB tag, and the three UWB base stations that minimize the value of the Cayley-Menger determinant are found to form a positioning system with the UWB tag; Step 4: According to the LOS environment threshold, identify whether the positioning system is in a LOS environment or a NLOS environment; Step 5: According to the environment in which the positioning system is located, different methods are used to suppress the ranging errors of the three UWB base stations in the positioning system, and the ranging correction values ​​of the three UWB base stations in the positioning system are obtained; Step 6: Establish a new three-sided positioning solution model, input the ranging correction values ​​of the three UWB base stations in the positioning system into the established new three-sided positioning solution model, and obtain the coordinates of the UWB tag, including the following sub-steps: Step 6.1, establish a plane rectangular coordinate system of the positioning environment, determine the position coordinates of the three UWB base stations u, v and w in the positioning system, use the position coordinates of the three UWB base stations in the positioning system as the center of the circle and the corresponding ranging correction value as the radius to make an initial circle, and obtain the coordinates of the intersection points where the three circles intersect each other; Step 6.2, select the intersection coordinates that are closer to the center of the third circle from the intersection coordinates of the three circles as the vertex coordinates of the initial error triangle, construct the initial error triangle, and use the centroid method to obtain the centroid coordinates of the initial error triangle; Step 6.3: In the first iteration, the vertex coordinates of the initial error triangle and the sum of the distances from the centroid coordinates of the initial error triangle to the three UWB base stations in the positioning system are calculated, which is recorded as and D 1 , respectively calculated With D 1 The absolute value of the difference, retaining the vertex coordinates of the error triangle with the smallest absolute value; Step 6.4, draw a circle with the coordinates of the vertex of the retained error triangle as the center and the distance from the vertex of the retained error triangle to the coordinates of the centroid of the initial error triangle as the radius, update the corresponding circle, and obtain the coordinates of the intersection points where the three circles intersect each other after the update; Step 6.5, select the intersection coordinates that are closer to the center of the third circle from the updated intersection coordinates of the three circles, update the vertex coordinates of the error triangle, construct a new error triangle, and use the centroid method to obtain the centroid coordinates of the new error triangle; Step 6.6, determine whether the centroid coordinates of the new error triangle converge. If so, use the centroid coordinates of the new error triangle as the UWB tag coordinates; otherwise, enter the next iteration process, calculate the sum of the distances from the vertex coordinates of the new error triangle and the centroid coordinates of the new error triangle to the three UWB base stations in the positioning system, and record them as and D d , respectively calculate With D d The absolute value of the difference, retaining the vertex coordinates of the error triangle with the smallest absolute value; in, D d The specific calculation process is: in, is the vertex coordinate of the error triangle of the circle related to UWB base stations u and v in the positioning system under the d-th iteration, is the vertex coordinate of the error triangle of the circle related to UWB base stations u and w in the positioning system under the d-th iteration, is the vertex coordinate of the error triangle of the circle related to UWB base stations v and w in the positioning system under the d-th iteration, is the centroid coordinate of the error triangle at the dth iteration, (u x ,u y ) is the coordinate of the UWB base station u in the positioning system, (v x ,v y ) is the coordinate of the UWB base station v in the positioning system, (w x ,w y ) is the coordinate of the UWB base station w in the positioning system; Step 6.7, draw a circle with the retained new error triangle vertex coordinates as the center and the distance from the retained new error triangle vertex coordinates to the new error triangle centroid coordinates as the radius, update the corresponding circle, and obtain the coordinates of the intersection points where the three circles intersect after the update, and repeat steps 6.5-6.

6.

2. The UWB positioning method suitable for a mixed LOS and NLOS environment according to claim 1, characterized in that: Step 2 includes the following sub-steps: Step 2.1, use each UWB base station in the positioning environment to perform multiple ranging of the UWB tag, and calculate the ranging mean and standard deviation of each UWB base station; Step 2.2, sum up the ranging averages of any three UWB base stations, and select the three UWB base stations corresponding to the maximum summed values; Step 2.3, taking the triangle formed by the three UWB base stations corresponding to the maximum accumulated value as the bottom surface, taking the sum of the ranging mean value and three times the standard deviation corresponding to the three UWB base stations as the side edge length, to form a tetrahedron; Step 2.4: construct a Cayley-Menger determinant according to the distances between the three UWB base stations corresponding to the maximum accumulated value, the ranging mean and the standard deviation, calculate the volume of the tetrahedron by the Cayley-Menger determinant, and calculate the height on the bottom surface of the tetrahedron by the volume of the tetrahedron and the area of ​​the bottom surface as the LOS environment threshold.

3. The UWB positioning method suitable for a mixed LOS and NLOS environment according to claim 2, characterized in that: The process of obtaining the volume of the tetrahedron by the Cayley-Menger determinant in step 2.4 is specifically as follows: Among them, V lyz represents the volume of a tetrahedron; s ly represents the distance between UWB base station l and UWB base station y corresponding to the maximum accumulated value, s yz Indicates the distance between UWB base station y and UWB base station z corresponding to the maximum accumulated value, s lz represents the distance between UWB base station l and UWB base station z corresponding to the maximum accumulated value; μ l represents the mean distance measurement value of UWB base station l, σ l represents the ranging standard deviation of UWB base station l; μ y represents the mean distance measurement value of UWB base station y, σ y represents the ranging standard deviation of UWB base station y; μ z represents the mean distance measurement value of UWB base station z, σ z Represents the ranging standard deviation of UWB base station z.

4. The UWB positioning method applicable to a mixed LOS and NLOS environment according to claim 3, characterized in that: The calculation process of the area of ​​the bottom surface of the tetrahedron described in step 2.4 is: Among them, S lyz represents the area of ​​the base of the tetrahedron.

5. The UWB positioning method applicable to a mixed LOS and NLOS environment according to claim 4, characterized in that: Step 4 includes the following sub-steps: Step 4.1, using the triangle formed by the three UWB base stations in the positioning system as the bottom surface, and using the ranging values ​​of the three UWB base stations in the positioning system at the current moment as the length of the side edges to form a new tetrahedron; Step 4.2, constructing the Cayley-Menger determinant of the positioning system according to the distances between the three UWB base stations in the positioning system and the ranging value at the current moment, calculating the volume of the new tetrahedron by the Cayley-Menger determinant of the positioning system, and calculating the height on the bottom surface of the new tetrahedron by the volume and bottom surface area of ​​the new tetrahedron; Step 4.3: If the height on the bottom surface of the new tetrahedron is less than the LOS environment threshold, the positioning system is in a LOS environment; otherwise, the positioning system is in a NLOS environment.

6. The UWB positioning method applicable to a mixed LOS and NLOS environment according to claim 5, characterized in that: The process of obtaining the volume of the new tetrahedron by the Cayley-Menger determinant of the positioning system in step 4.2 is specifically as follows: Among them, V uvw represents the volume of the new tetrahedron, u t 、v t and w t They represent the distance values ​​of the three UWB base stations in the positioning system at the current moment, s uv 、s vw and uw Respectively represent the distances between the three UWB base stations in the positioning system.

7. The UWB positioning method applicable to a mixed LOS and NLOS environment according to claim 6, characterized in that: The process of calculating the base area of ​​the new tetrahedron in step 4.2 is: Among them, S uvw represents the base area of ​​the new tetrahedron.

8. The UWB positioning method applicable to a mixed LOS and NLOS environment according to claim 6, characterized in that: Step 5 includes the following sub-steps: Step 5.1, if the positioning system is in a LOS environment, a polynomial curve fitting method integrating cyclic iteration and Bayesian maximum a posteriori probability is used to suppress the ranging errors of the three UWB base stations in the positioning system, and the ranging correction values ​​of the three UWB base stations in the positioning system are obtained; Step 5.2: If the positioning system is in a NLOS environment, the GMM-EM-IMM method is used to suppress the ranging errors of the three UWB base stations in the positioning system, and the ranging correction values ​​of the three base stations in the positioning system are obtained.

9. The UWB positioning method applicable to a mixed LOS and NLOS environment according to claim 8, characterized in that: Step 5.1 includes the following sub-steps: Step 5.1.1, in the LOS environment, place the UWB tag at several positions in the positioning environment, use any UWB base station to measure the distance of the UWB tag at each position, and obtain the ranging set of the UWB base station; at the same time, obtain the real distance from the UWB base station to the UWB tag at each position, and obtain the real data set of the UWB base station; Step 5.1.2, according to the ranging value of each UWB base station in the positioning system, establish a polynomial curve fitting function to solve the ranging correction value of the corresponding UWB base station; The polynomial curve fitting function is: Among them, x t,LOS is the ranging value of a UWB base station in the positioning system at the current time t under the LOS environment, W is the polynomial coefficient vector, W = (w0, w1, ..., w M ) T ; M is the polynomial order, M=f+2, f is the number of iterations of the polynomial curve fitting function; Step 5.1.3, in the single iterative solution process of the ranging correction value corresponding to the UWB base station, according to the ranging set of the UWB base station and the real data set in step 5.1.1, the Bayesian maximum a posteriori probability polynomial curve fitting method is used to determine the accuracy of the optimal solution of the polynomial coefficient vector and the ranging correction value corresponding to the UWB base station; Step 5.1.4: If the variance of the Gaussian distribution obeyed by the ranging correction value corresponding to the UWB base station does not converge, adjust the polynomial order according to the number of iterations, and repeat step 5.1.3 until the variance of the Gaussian distribution obeyed by the ranging correction value corresponding to the UWB base station converges, and obtain the ranging correction value corresponding to the UWB base station in the positioning system.

10. The UWB positioning method applicable to a mixed LOS and NLOS environment according to claim 9, characterized in that: The calculation process of the ranging correction value corresponding to the UWB base station in the positioning system in step 5.1.4 is as follows: in, is the ranging correction value of the corresponding UWB base station in the positioning system at the current time t in the LOS environment, β MAP is the accuracy of the optimal solution of the polynomial coefficient vector, φ(x t,LOS ) is the first parameter to be solved for the ranging correction value, φ(x n,LOS ) is the second parameter for solving the ranging correction value, x n,LOS is the distance measurement value of any UWB base station to the UWB tag at the nth position; N is the number of positions where the UWB tag is placed in the positioning environment, S is the third parameter for solving the distance correction value, α is the precision parameter of the prior probability distribution of the polynomial coefficient vector, and I is the M+1 order identity matrix; is the actual distance from any UWB base station to the UWB tag at the nth position.

11. The UWB positioning method applicable to a mixed LOS and NLOS environment according to claim 8, characterized in that: Step 5.2 includes the following sub-steps: Step 5.2.1: In the NLOS environment, the probability distribution of the ranging value of each UWB base station in the positioning system at the current time t is represented by the Gaussian mixture model GMM: Among them, K is the number of single Gaussian models in the Gaussian mixture model GMM, k is the index of K, and α k is the weight coefficient of the kth single Gaussian model, is the probability distribution of the kth single Gaussian model, η k , are the mean and variance of the kth single Gaussian model, x t,NLOS is the distance measurement value of any base station in the positioning system at the current time t in the NLOS environment, p(x t,NLOS |θ) represents the value of x under a given parameter vector θ t,NLOS The probability distribution of Step 5.2.2: Use the expected maximum EM algorithm to solve the parameter vectors θ and x of the Gaussian mixture model GMM t,NLOS The probability distribution of Step 5.2.3: x established based on Gaussian mixture model GMM t,NLOS The probability distribution of x is determined by the interactive multi-model IMM algorithm, and the interactive multi-model IMM algorithm is used to reconstruct x t,NLOS , obtain the ranging correction value of the corresponding UWB base station in the positioning system at time t.

12. The UWB positioning method applicable to a mixed LOS and NLOS environment according to claim 11, characterized in that: Step 5.2.3 includes the following sub-steps: Step 5.2.3.1: x established based on Gaussian mixture model GMM t,NLOS The probability distribution of the UWB base station and the UWB tag in the positioning system is decomposed into Y sub-models that obey the Gaussian distribution. The state equation and measurement equation are established for each sub-model: X t,y =φX t-1,y +G a t Among them, X t,y is the state of the y-th sub-model at the current time t, x t,y is the distance measurement value of the y-th sub-model at the current time t, v t,y is the speed of the y-th sub-model at the current time t, y is the index of Y; φ is the state transfer matrix, a t is the state noise of the y-th sub-model at the current time t, Γ is the noise driving matrix, η t,k is the mean of the kth single Gaussian model at the current time t obtained by the expected maximum EM algorithm, is the observation matrix, σ t,k The standard deviation of the k-th single Gaussian model at the current time t obtained by the expected maximum EM algorithm; Step 5.2.3.2: Perform Kalman filtering on each sub-model: in, It represents the result of Kalman filtering of the y-th sub-model at time t-1. Indicates based on The predicted state of the y-th sub-model at the current time t, P t-1,y Represents the state covariance matrix of the Kalman filter of the y-th sub-model at time t-1, According to P t-1,y The predicted state covariance matrix of the y-th sub-model at the current time t, Q t Represents the state noise covariance matrix of each sub-model from time t-1 to the current time t; Step 5.2.3.3: According to the predicted state covariance matrix of the y-th sub-model at the current time t Calculate the Kalman filter gain K t,y , and combined with the predicted state of the y-th sub-model at the current time t Update the Kalman filter result of the yth sub-model at the current time t Among them, I 2×2 represents the 2×2 unit matrix, P t,y Represents the state covariance matrix of the Kalman filter of the y-th sub-model at the current time t; Step 5.2.3.4: According to the predicted state covariance matrix of the y-th sub-model at the current time t and the predicted state of the y-th sub-model at the current time t Get the maximum likelihood function Λ that best matches the y-th sub-model t,y : Among them, S t,y is the first parameter to be solved for the maximum likelihood function, d t,y The second parameter to be solved for the maximum likelihood function; Step 5.2.3.5: According to the maximum likelihood function Λ that best matches the y-th sub-model t,y Update the weight of the y-th sub-model: Among them, α t,k is the weight of the kth single Gaussian model solved by the expected maximum EM algorithm at the current time t; Step 5.2.3.6, fusing the updated Kalman filter results of each sub-model and the updated weights to obtain the optimal state of the ranging model at the current time t, wherein the optimal state is composed of the ranging correction value and the speed correction value of the UWB base station in the ranging model at the current time t; The calculation process of the optimal state of the ranging model at the current time t is:

13. A computer-readable storage medium storing a computer program, characterized in that: The computer program enables a computer to execute the UWB positioning method applicable to a mixed environment of LOS and NLOS as described in any one of claims 1 to 12.

14. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the UWB positioning method applicable to a mixed environment of LOS and NLOS as described in any one of claims 1 to 12 is implemented.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the UWB positioning method applicable to a mixed LOS and NLOS environment as described in any one of claims 1 to 12 is implemented.

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