A positioning method, apparatus, device and medium in a mixed environment

Abstract

This application relates to the technical field of target positioning, and in particular to a positioning method, apparatus, device, and medium in a mixed environment. The method includes: calculating the distance between nodes based on flight speed and the flight time corresponding to each anchor node to obtain a distance measurement value between each anchor node and the target node; then, determining a first relationship between measurement noise information, non-line-of-sight error information, and true distance information based on the distance measurement value; further, determining a joint error entropy relationship based on the noise probability distribution, the non-line-of-sight probability distribution, and the first relationship; finally, performing positioning calculations based on the position information corresponding to each anchor node and the joint error entropy relationship to determine the target position information of the target node. The positioning method of this application comprehensively considers non-line-of-sight errors and measurement noise in a mixed environment, and performs positioning calculations based on the minimum joint error entropy criterion, thereby improving the positioning accuracy of the positioning method in a mixed environment.

Description

Technical Field

[0001] This application relates to the technical field of target positioning, and in particular to a positioning method, apparatus, device and medium in a mixed environment. Background Technology

[0002] High-precision wireless positioning is essential in location-aware applications, such as target tracking, navigation, disaster management, rescue, and intelligent transportation. Early target localization methods primarily studied line-of-sight (LOS) environments, assuming a straight-line communication between the target node and anchor node without any obstructions. However, in indoor, underground, or other harsh environments, communication between the target node and anchor node is often blocked by numerous obstacles. This means the signal propagation path between the target node and anchor node is a non-line-of-sight (NLOS) path, resulting in the anchor node measuring a greater signal propagation distance than the actual distance between the target node and anchor node. This creates an NLOS error, leading to lower positioning accuracy in NLOS environments.

[0003] To improve positioning accuracy in NLOS environments, various solutions have been proposed. One approach, in a hybrid environment, involves extracting only measurement data from the LOS transmission path for positioning calculations to determine the target node's position. This hybrid environment involves both LOS and NLOS propagation between the target and anchor nodes. However, extracting only LOS transmission path measurement data is extremely challenging, and missed detections can lead to significant performance degradation. Another approach uses prior information about NLOS errors to calculate the target node's position in a hybrid environment. This prior information includes probability distributions, upper bound constraints, and sparsity. However, the accuracy of this positioning method is highly dependent on the accuracy of the prior information. If the prior information about NLOS errors is significantly flawed, the improvement effect of this method is poor, and it may even reduce positioning accuracy.

[0004] Therefore, how to provide a high-precision positioning method in mixed environments is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] The purpose of this application is to provide a positioning method, apparatus, device, and medium in a mixed environment to solve at least one of the above-mentioned technical problems.

[0006] The above-mentioned inventive objective of this application is achieved through the following technical solutions:

[0007] Firstly, this application provides a positioning method in a mixed environment, employing the following technical solution:

[0008] A positioning method in a hybrid environment includes:

[0009] Acquire the location information of each of the multiple anchor nodes, and obtain the flight time of the signal from the target node to each anchor node;

[0010] Based on the flight speed and the flight time corresponding to each anchor node, the distance between nodes is calculated to obtain the distance measurement value between each anchor node and the target node.

[0011] Based on the distance measurement values, a first relationship is determined, wherein the first relationship is the relationship between measurement noise information, non-line-of-sight error information, and true distance information. The measurement noise information is the error information generated during the instrument's measurement of flight time. The non-line-of-sight error information is the error information caused by obstruction between the target node and each anchor node. The true distance information is determined by the target position information of the target node and the position information of each anchor node. The noise probability distribution corresponding to the measurement noise information and the non-line-of-sight probability distribution corresponding to the non-line-of-sight error information are obtained. Based on the noise probability distribution, the non-line-of-sight probability distribution, and the first relationship, a joint error entropy relationship is determined, wherein the joint error entropy relationship is used to characterize the relationship between the joint error entropy and the position information of the target node.

[0012] Based on the location information corresponding to each anchor node and the joint error entropy relationship, a positioning calculation is performed to determine the target location information of the target node, wherein the target location information of the target node is the location information corresponding to the minimum joint error entropy.

[0013] By adopting the above technical solution, the distance between nodes is calculated based on the flight speed and the flight time corresponding to each anchor node, obtaining the distance measurement value between each anchor node and the target node. Then, based on the distance measurement value, a first relationship is determined between measurement noise information, non-line-of-sight error information, and true distance information. Furthermore, based on the noise probability distribution, non-line-of-sight probability distribution, and the first relationship, a joint error entropy relationship is determined. Finally, based on the position information corresponding to each anchor node and the joint error entropy relationship, positioning calculation is performed, and the position information corresponding to the minimum joint error entropy is determined as the target position information of the target node. The positioning method of this application embodiment comprehensively considers non-line-of-sight error and measurement noise in a mixed environment, and performs positioning calculation based on the minimum joint error entropy criterion, thereby improving the positioning accuracy of the positioning method in a mixed environment.

[0014] In a preferred embodiment, this application can be further configured such that: determining the joint error entropy relationship based on the noise probability distribution, the non-line-of-sight probability distribution, and the first relationship includes:

[0015] Determine the joint probability distribution based on the noise probability distribution and the non-line-of-sight probability distribution;

[0016] Based on the joint probability distribution and the relationship between the joint error entropy and the probability distribution, a second relationship is determined, wherein the second relationship is the relationship between the non-line-of-sight error entropy, the noise error entropy and the joint error entropy;

[0017] Based on the first relationship and the second relationship, the joint error entropy relationship is determined.

[0018] In a preferred embodiment, this application can be further configured such that the relationship between the joint error entropy and the probability distribution is: J(n,e)=-log∫∫p 2 (n,e)dnde, where p(n,e) is the joint probability distribution and J(n,e) is the joint error entropy;

[0019] The second relationship is: J(n,e)=J(n)+J(e), where J(n) is the noise error entropy and J(e) is the non-line-of-sight error entropy.

[0020] In a preferred embodiment, this application may be further configured such that, before determining the joint error entropy relationship based on the first relationship and the second relationship, it further includes:

[0021] Obtain the balance parameters corresponding to multiple virtual anchor nodes, wherein the virtual anchor nodes represent virtual nodes with a mean of 0 and a variance that is the minimum noise variance;

[0022] The non-line-of-sight error entropy is adjusted based on the balance parameter to obtain the adjusted non-line-of-sight error entropy;

[0023] Based on the adjusted non-line-of-sight error entropy, an adjusted second relationship is determined, wherein the adjusted second relationship is the relationship between the adjusted non-line-of-sight error entropy, the noise error entropy, and the joint error entropy;

[0024] Accordingly, determining the joint error entropy relationship based on the first relationship and the second relationship includes:

[0025] Based on the first relationship and the adjusted second relationship, the joint error entropy relationship is determined.

[0026] In a preferred embodiment, this application can be further configured such that: adjusting the non-line-of-sight error entropy based on the balance parameter to obtain the adjusted non-line-of-sight error entropy includes:

[0027] Based on the balance parameters, the non-line-of-sight error entropy is adjusted according to the following formula to obtain the adjusted non-line-of-sight error entropy, wherein the formula is:

[0028] J λ (e) represents the adjusted non-line-of-sight error entropy, λ is the balance parameter, N is the number of anchor nodes, and e i Let e ​​be the non-line-of-sight error between the target node and the i-th anchor node. j Let σ be the non-line-of-sight error between the target node and the j-th anchor node. e G is the non-line-of-sight variance, and G is the Gaussian kernel function.

[0029] In a preferred embodiment, this application can be further configured as follows: the positioning calculation based on each of the location information and the joint error entropy relationship to determine the target location information of the target node includes:

[0030] A non-exponential relaxation transformation is performed on the joint error entropy relationship to obtain the first relaxed joint error entropy relationship.

[0031] Based on the first relaxed joint error entropy relationship, a positive semidefinite relaxation is performed to obtain the second relaxed joint error entropy relationship;

[0032] Based on each of the aforementioned location information and the second relaxed joint error entropy relationship, a positioning calculation is performed to determine the target location information of the target node.

[0033] In a preferred embodiment, this application can be further configured as follows: the positioning calculation based on each of the location information and the second relaxed joint error entropy relationship to determine the target location information of the target node includes:

[0034] Obtain the preset constraints;

[0035] Based on the preset constraint relationship, each of the location information, and the second relaxed joint error entropy relationship, a positioning calculation is performed to determine the target location information of the target node.

[0036] The preset constraints include:

[0037] g≥0, where g is a pre-defined auxiliary vector;

[0038] A[x T z] T ≤f, where A is a preset coefficient matrix, x is the coordinate to be solved, and z is defined as x T x and f are preset condition matrices;

[0039] H is a pre-defined auxiliary matrix, and is defined as gT g;

[0040] I 2×2 It is an identity matrix with dimension 2;

[0041] H ii Let s be the element in the i-th row and i-th column of matrix H. i This provides the position information of the i-th anchor node;

[0042] H ij Let s be the element in the i-th row and j-th column of matrix H. j This provides the location information for the j-th anchor node.

[0043] H N+i,N+i Let H be the element in the (N+i)th row and (N+i)th column of matrix H. i,N+i Let r be the element in the i-th row and N+i-th column of matrix G. i The distance measurement between the i-th anchor node and the target node, σ i To measure the noise variance.

[0044] Secondly, this application provides a positioning device in a mixed environment, which adopts the following technical solution:

[0045] A positioning device for use in a mixed environment, comprising:

[0046] The acquisition module is used to acquire the position information of each of the multiple anchor nodes and to acquire the flight time of the signal from the target node to each anchor node.

[0047] The distance calculation module is used to calculate the distance between nodes based on the flight speed and the flight time corresponding to each anchor node, so as to obtain the distance measurement value between each anchor node and the target node.

[0048] A relationship determination module is used to determine a first relationship based on the distance measurement value, wherein the first relationship is the relationship between measurement noise information, non-line-of-sight error information and true distance information, the measurement noise information is error information generated during the instrument's measurement of flight time, the non-line-of-sight error information is error information caused by obstruction between the target node and each anchor node, and the true distance information is determined by the target position information of the target node and the position information of each anchor node;

[0049] The joint error entropy relationship determination module is used to obtain the noise probability distribution corresponding to the measurement noise information and the non-line-of-sight probability distribution corresponding to the non-line-of-sight error information, and determine the joint error entropy relationship based on the noise probability distribution, the non-line-of-sight probability distribution and the first relationship, wherein the joint error entropy relationship is used to characterize the relationship between the joint error entropy and the position information of the target node;

[0050] The positioning calculation module is used to perform positioning calculations based on the location information corresponding to each anchor node and the joint error entropy relationship to determine the target location information of the target node, wherein the target location information of the target node is the location information corresponding to the minimum joint error entropy.

[0051] Thirdly, this application provides an electronic device that adopts the following technical solution:

[0052] At least one processor;

[0053] Memory;

[0054] At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: perform the positioning method described above in a hybrid environment.

[0055] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0056] A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the aforementioned positioning method in a hybrid environment.

[0057] In summary, this application includes at least one of the following beneficial technical effects:

[0058] 1. Based on flight speed and flight time corresponding to each anchor node, the distance between nodes is calculated to obtain the distance measurement value between each anchor node and the target node. Then, based on the distance measurement value, a first relationship is determined between measurement noise information, non-line-of-sight error information, and true distance information. Furthermore, based on the noise probability distribution, non-line-of-sight probability distribution, and the first relationship, a joint error entropy relationship is determined. Finally, based on the position information corresponding to each anchor node and the joint error entropy relationship, a positioning calculation is performed, and the position information corresponding to the minimum joint error entropy is determined as the target position information of the target node. The positioning method of this application embodiment comprehensively considers non-line-of-sight error and measurement noise in a mixed environment, and performs positioning calculation based on the minimum joint error entropy criterion, thereby improving the positioning accuracy of the positioning method in a mixed environment.

[0059] 2. The non-line-of-sight error entropy is adjusted based on the balance parameters corresponding to multiple virtual anchor nodes to obtain the adjusted non-line-of-sight error entropy. Based on the adjusted non-line-of-sight error entropy, an adjusted second relationship is determined. Correspondingly, based on the first relationship and the adjusted second relationship, a joint error entropy relationship is determined. This embodiment of the application improves the sensitivity of the non-line-of-sight entropy to the mean of the non-line-of-sight error by adding a reference zero point to the non-line-of-sight error, i.e., introducing virtual anchor nodes to generate additional measurement data. Attached Figure Description

[0060] Figure 1 This is a flowchart illustrating a positioning method in a mixed environment according to one embodiment of this application;

[0061] Figure 2 This is a comparison chart of positioning performance in a non-line-of-sight dense scene according to one embodiment of this application;

[0062] Figure 3 This is a performance comparison chart of one embodiment of this application under the condition of non-line-of-sight link change;

[0063] Figure 4 This is a performance comparison chart of one embodiment of this application under non-line-of-sight error variation conditions;

[0064] Figure 5 This is a schematic diagram of the structure of a positioning device in a mixed environment according to one embodiment of this application;

[0065] Figure 6 This is a schematic diagram of the structure of an electronic device according to one embodiment of this application. Detailed Implementation

[0066] The following combination Figures 1 to 6 This application will be described in further detail.

[0067] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they are within the scope of this application.

[0068] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0069] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0070] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0071] In environments with non-line-of-sight (NLOS) errors, positioning accuracy is low. To improve positioning accuracy in mixed environments (where signals propagate along both line-of-sight and non-line-of-sight paths), various solutions have been proposed. One approach involves extracting only LOS transmission path measurement data for positioning calculations to determine the target node's position. However, extracting only LOS transmission path measurement data is extremely challenging, and missed detections can lead to significant performance loss, failing to adequately address the low positioning accuracy issue in mixed environments. Another approach uses prior information based on NLOS errors to calculate the target node's position in a mixed environment. This prior information includes probability distribution, upper bound constraints, and sparsity. However, this method requires pre-determining the non-line-of-sight error prior information, making the operation complex. Furthermore, the accuracy of this method is highly dependent on the accuracy of the prior information; significant deviations in the non-line-of-sight error prior information result in poor improvement effects or even reduced positioning accuracy.

[0072] To address the aforementioned technical problems, this application provides a positioning method in a mixed environment. This method comprehensively considers non-line-of-sight (NOS) errors in the mixed environment, but does not perform positioning calculations using prior information. Instead, it introduces a minimum joint error entropy criterion for positioning calculations. This criterion provides a constraint on NOS errors, eliminating the need for prior knowledge. Therefore, by comprehensively considering NOS errors during positioning calculations, this application improves positioning accuracy in mixed environments.

[0073] This application provides a positioning method in a hybrid environment, executed by an electronic device, which can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be any anchor node, but is not limited thereto; this application does not impose such limitations. Figure 1As shown, the method includes steps S101, S102, S103, S104, and S105, wherein:

[0074] Step S101: Obtain the position information corresponding to each of the multiple anchor nodes, and obtain the flight time of the signal from the target node to each anchor node;

[0075] Step S102: Calculate the distance between nodes based on the flight speed and the flight time corresponding to each anchor node, and obtain the distance measurement value between each anchor node and the target node.

[0076] In this embodiment, the position information of all anchor nodes and target nodes is in coordinate form, that is, the position information of anchor nodes and target nodes is represented by position coordinates in the same reference coordinate system. The time elapsed from the target node transmitting the wireless signal to each anchor node receiving the wireless signal is recorded as the flight time of each anchor node. Then, the distance measurement between each anchor node and the target node is obtained by multiplying the flight speed by the corresponding flight time of each anchor node, where the flight speed is the speed of light. When the electronic device is a physical server, the electronic device obtains the position information of multiple anchor nodes through wireless communication, and can obtain the transmission time of the signal from the target node and the reception time of the signal received by the anchor node, and determines the flight time based on the transmission time and reception time. When the electronic device is an anchor node, the electronic device can obtain the transmission time of the signal from the target node, and determine the flight time based on the transmission time and reception time.

[0077] Step S103: Based on the distance measurement value, determine the first relationship, wherein the first relationship is the relationship between measurement noise information, non-line-of-sight error information and true distance information. Measurement noise information is the error information generated during the instrument's measurement of flight time. Non-line-of-sight error information is the error information generated due to the obstruction between the target node and each anchor node. True distance information is determined by the target position information of the target node and the position information of each anchor node.

[0078] In the embodiments of this application, during the actual propagation of the wireless signal, obstructions exist between the target node and the anchor node, causing a significant error between the distance measurement value calculated based on flight time and flight speed and the actual distance. This error is denoted as non-line-of-sight (NLS) error, where the actual distance is the actual distance between the target node and the anchor node. Simultaneously, the instrument also incurs errors during flight time measurement, denoted as measurement noise. In practical applications, if the effects of NLS error and measurement noise are not considered, the positioning accuracy of the positioning method will be low. However, the embodiments of this application comprehensively consider both NLS error and measurement noise when calculating the positioning information of the target node, greatly improving the positioning accuracy of the positioning method.

[0079] Specifically, based on the distance measurement values, the relationship between measurement noise information, non-line-of-sight error information, and true distance information is determined, that is, the first relationship is determined. The first relationship is shown in the following formula (1):

[0080] r i =d i +e i +n i d i =‖xs i ‖,i=1,…,N; (1)

[0081] Where, r i The distance measurement between the target node and the i-th anchor node, d i Let e ​​be the true distance between the target node and the i-th anchor node. i Let n be the non-line-of-sight error between the target node and the i-th anchor node. i Let x be the measurement noise of the target node and the i-th anchor node, and s be the target location information of the target node. i Let be the location information of the i-th anchor node, and d i x and s i A relationship exists, and the symbol "‖‖" represents the Euclidean norm. It's important to note that there is a non-line-of-sight error between the target node and each anchor node, hence e i ≥0, because the wireless signal propagation path is different, therefore, for each anchor node, e i They are all different; measurement noise is due to errors generated by the measuring instrument, and each measurement is different, therefore, n i They are all different.

[0082] Step S104: Obtain the noise probability distribution corresponding to the measurement noise information and the non-line-of-sight probability distribution corresponding to the non-line-of-sight error information, and determine the joint error entropy relationship based on the noise probability distribution, the non-line-of-sight probability distribution and the first relationship. The joint error entropy relationship is used to characterize the relationship between the joint error entropy and the position information of the target node.

[0083] For the embodiments of this application, the noise probability distribution and the non-line-of-sight probability distribution are both determined using the Parzen windows method. The non-line-of-sight probability distribution is shown in the following formula (2):

[0084]

[0085] The noise probability distribution is shown in the following formula (3):

[0086]

[0087] Where p(e) is the non-line-of-sight probability distribution, p(n) is the noise probability distribution, N is the number of anchor nodes, and σ e The variance of non-line-of-sight error, σ i Let G be the noise variance and G be the Gaussian kernel function, where...

[0088] Furthermore, based on the noise probability distribution, the non-line-of-sight probability distribution, and the first relationship, the joint error entropy relationship is determined. Specifically, in the embodiments of this application, the joint error entropy relationship is determined based on the noise probability distribution, the non-line-of-sight probability distribution, and the first relationship. This may include: determining the joint probability distribution based on the noise probability distribution and the non-line-of-sight probability distribution; then, based on the joint probability distribution and the relationship between the joint error entropy and the probability distribution, determining the relationship between the non-line-of-sight error entropy, the noise error entropy, and the joint error entropy, denoted as the second relationship; and then, based on the first relationship and the second relationship, determining the joint error entropy relationship, wherein the joint error entropy relationship is as shown in the following formula (4):

[0089] J(n,e)=J(n)+J(e); (4)

[0090] Where J(n,e) is the joint error entropy, J(n) is the noise error entropy, and J(e) is the non-line-of-sight error entropy.

[0091] Step S105: Based on the location information corresponding to each anchor node and the joint error entropy relationship, perform positioning calculation to determine the target location information of the target node. The target location information of the target node is the location information corresponding to the minimum joint error entropy.

[0092] For the embodiments of this application, the joint error entropy relationship is shown in the following formula (4):

[0093] J(n,e)=J(n)+J(e); (4),

[0094] The noise error entropy is shown in the following formula (5):

[0095]

[0096] The non-line-of-sight error entropy is shown in the following formula (6):

[0097]

[0098] Based on the above formulas (5) and (6), the joint error entropy relationship is determined as shown in the following formula (7):

[0099]

[0100] Among them, the relationship between measurement noise information, non-line-of-sight error information and true distance information is the first relationship, as shown in the following formula (1): r i =d i +e i +n i d i =‖xs i ‖ (1).

[0101] Therefore, the joint error entropy relationship includes the position coordinates of the target node to be determined. Thus, based on the position information corresponding to each anchor node and the joint error entropy relationship, the positioning calculation is performed, and the position information corresponding to the minimum joint error entropy is taken as the target position information of the target node. That is, it is equivalent to solving the following formula (8):

[0102]

[0103] The positioning method in this application comprehensively considers non-line-of-sight errors and measurement noise in mixed environments, and performs positioning calculations based on the minimum joint error entropy criterion, thereby improving positioning accuracy in mixed environments.

[0104] As can be seen, in this embodiment, the distance between nodes is calculated based on the flight speed and the flight time corresponding to each anchor node to obtain the distance measurement value between each anchor node and the target node. Then, based on the distance measurement value, a first relationship is determined between the measurement noise information, non-line-of-sight error information, and the true distance information. Furthermore, based on the noise probability distribution, the non-line-of-sight probability distribution, and the first relationship, a joint error entropy relationship is determined. Finally, based on the position information corresponding to each anchor node and the joint error entropy relationship, a positioning calculation is performed, and the position information corresponding to the minimum joint error entropy is determined as the target position information of the target node. The positioning method in this embodiment comprehensively considers non-line-of-sight errors and measurement noise in a mixed environment and performs positioning calculations based on the minimum joint error entropy criterion, thereby improving the positioning accuracy of the positioning method in a mixed environment.

[0105] Furthermore, to improve the positioning accuracy of the positioning method in mixed environments, in this embodiment of the application, a joint error entropy relationship is determined based on the noise probability distribution, the non-line-of-sight probability distribution, and the first relationship, including:

[0106] Determine the joint probability distribution based on the noise probability distribution and the non-line-of-sight probability distribution;

[0107] Based on the joint probability distribution and the relationship between the joint error entropy and the probability distribution, a second relationship is determined, which is the relationship between the non-line-of-sight error entropy, the noise error entropy, and the joint error entropy.

[0108] Based on the first and second relationships, the joint error entropy relationship is determined.

[0109] In the embodiments of this application, when determining the joint probability distribution, since non-line-of-sight error and measurement noise are two independent error variables, the joint probability distribution is the product of the noise probability distribution and the non-line-of-sight probability distribution, as shown in the following formula (9):

[0110] p(n,e)=p(n)p(e); (9)

[0111] Where p(n,e) is the joint probability distribution. Then, based on the joint probability distribution and the relationship between the joint error entropy and the probability distribution, the second relationship is determined by calculation according to the following formulas (10) and (11).

[0112] J(n,e)=-log∫∫p 2 (n,e)dnde; (10)

[0113] J(n,e)=-log∫∫p 2 (n,e)dnde=J(n)+J(e; (11)

[0114] Therefore, the second relationship between the non-line-of-sight error entropy, the noise error entropy, and the joint error entropy is: J(n,e)=J(n)+J(e).

[0115] As can be seen, in this embodiment, a joint probability distribution is determined based on the noise probability distribution and the non-line-of-sight probability distribution. Based on the joint probability distribution and the relationship between the joint error entropy and the probability distribution, a second relationship is determined between the non-line-of-sight error entropy, the noise error entropy, and the joint error entropy. Finally, based on the first and second relationships, the joint error entropy relationship is determined. This embodiment creatively proposes a joint error entropy and introduces it into the positioning problem of anchor node networks, comprehensively considering non-line-of-sight errors and measurement noise in mixed environments, thus improving the positioning accuracy of the positioning method in mixed environments.

[0116] Furthermore, in this embodiment, the relationship between the joint error entropy and the probability distribution is as follows:

[0117] J(n,e)=-log∫∫p 2 (n,e)dnde, where p(n,e) is the joint probability distribution and J(n,e) is the joint error entropy;

[0118] The second relationship is: J(n,e)=J(n)+J(e), where J(n) is the noise error entropy and J(e) is the non-line-of-sight error entropy.

[0119] For the embodiments of this application, the relationship between the joint error entropy and the probability distribution is: J(n,e)=-log∫∫p 2 (n,e)dnde, and since non-line-of-sight error and measurement noise are two independent error variables, p(n,e)=p(n)p(e), and substituting the joint probability distribution into the relationship between the joint error entropy and the probability distribution, we determine J(n,e)=-log∫∫p 2 (n)p 2 (e)dnde, and further, determine J(n,e)=-log∫∫p 2 (n)p 2 (e)dnde=-log∫∫p 2 (n)dn-log∫∫p 2 (e)de, where, according to the formula for quadratic entropy, J(n)=-log∫∫p 2 (n)dn,J(e)=-log∫∫p 2 (e)de. Therefore, the second relation is determined as: J(n,e)=J(n)+J(e).

[0120] Furthermore, in order to improve the sensitivity of non-line-of-sight entropy to the mean of non-line-of-sight error, in this embodiment of the application, before determining the joint error entropy relationship based on the first relationship and the second relationship, the following steps are also included:

[0121] Obtain the balance parameters corresponding to multiple virtual anchor nodes, where virtual anchor nodes represent virtual nodes with a mean of zero and a variance that is the minimum value of noise variance;

[0122] The non-line-of-sight error entropy is adjusted based on the balance parameter to obtain the adjusted non-line-of-sight error entropy;

[0123] Based on the adjusted non-line-of-sight error entropy, the adjusted second relationship is determined. The adjusted second relationship is the relationship between the adjusted non-line-of-sight error entropy, the noise error entropy, and the joint error entropy.

[0124] Accordingly, based on the first and second relations, the joint error entropy relationship is determined, including:

[0125] Based on the first relation and the adjusted second relation, the joint error entropy relation is determined.

[0126] In the embodiments of this application, the smaller the mean value of the non-line-of-sight error during positioning calculation, the higher the positioning accuracy. However, according to formula (6), the non-line-of-sight error entropy is sensitive to the difference in non-line-of-sight errors, but not sensitive to the mean value of the non-line-of-sight errors. That is, the non-line-of-sight error entropy is as shown in the following formula (6):

[0127]

[0128] The value of J(e) and e j -e i Therefore, the non-line-of-sight error entropy is more sensitive to the difference in non-line-of-sight errors, but not to the mean of non-line-of-sight errors. Thus, this embodiment of the application improves the sensitivity of the non-line-of-sight entropy to the mean of non-line-of-sight errors by adding a reference zero point to the non-line-of-sight error, i.e., by introducing virtual anchor nodes to generate additional measurement data.

[0129] Specifically, virtual anchor nodes are introduced to generate additional measurement data. These virtual anchor nodes are artificially added, and their distance from the signal source transmitting the wireless signal is zero. Therefore, the non-line-of-sight (NLS) error of the virtual anchor nodes is zero, with a mean of zero, and the noise variance is the minimum noise variance. Adding multiple virtual anchor nodes affects the NLS error entropy. Therefore, the NLS error entropy is adjusted based on a balancing parameter to obtain the adjusted NLS error entropy. The magnitude of the balancing parameter is related to the number of virtual anchor nodes. The principle for selecting the balancing parameter is: in sparse cases where a few links are non-line-of-sight, the balancing parameter is set to 1; in dense cases where most links are non-line-of-sight, the balancing parameter is set to 0.5. Furthermore, based on the adjusted NLS error entropy, an adjusted second relationship is determined. Correspondingly, based on the first relationship and the adjusted second relationship, a joint error entropy relationship is determined. Introducing virtual anchor nodes to generate additional measurement data improves the sensitivity of non-line-of-sight entropy to the mean of non-line-of-sight error. Furthermore, positioning calculations are performed based on the joint error entropy relationship determined by the first relationship and the adjusted second relationship, thereby improving positioning accuracy in mixed environments.

[0130] As can be seen, in this embodiment, the non-line-of-sight error entropy is adjusted based on the balance parameters corresponding to multiple virtual anchor nodes to obtain the adjusted non-line-of-sight error entropy. Based on the adjusted non-line-of-sight error entropy, an adjusted second relationship is determined. Correspondingly, based on the first relationship and the adjusted second relationship, a joint error entropy relationship is determined. This embodiment improves the sensitivity of the non-line-of-sight entropy to the mean of the non-line-of-sight error by adding a reference zero point to the non-line-of-sight error, i.e., introducing virtual anchor nodes to generate additional measurement data.

[0131] Furthermore, in this embodiment, the non-line-of-sight error entropy is adjusted based on the balance parameter to obtain the adjusted non-line-of-sight error entropy, including:

[0132] The non-line-of-sight error entropy is adjusted based on the balance parameters according to the following formula to obtain the adjusted non-line-of-sight error entropy, where the formula is:

[0133] J λ (e) represents the adjusted non-line-of-sight error entropy, λ is the balance parameter, N is the number of anchor nodes, and ei Let e ​​be the non-line-of-sight error between the target node and the i-th anchor node. j Let σ be the non-line-of-sight error between the target node and the j-th anchor node. e G is the non-line-of-sight variance, and G is the Gaussian kernel function.

[0134] For the embodiments of this application, after inserting a virtual anchor node, the non-line-of-sight error entropy is transformed from formula (6) to the following formula (12):

[0135]

[0136] Furthermore, when multiple virtual anchor nodes are inserted, the non-line-of-sight error entropy is adjusted based on the balance parameter to obtain the adjusted non-line-of-sight error entropy. The user can select the magnitude of the balance parameter according to the specific circumstances of the environment. That is, the adjusted non-line-of-sight error entropy is shown in the following formula (13):

[0137]

[0138] Furthermore, to alleviate the thorny exponential optimization problem and make the solution more convenient during positioning calculations, in this embodiment, positioning calculations are performed based on each location information and the joint error entropy relationship to determine the target location information of the target node, including:

[0139] A non-exponential relaxation transformation is performed on the joint error entropy relationship to obtain the first relaxed joint error entropy relationship.

[0140] Based on the first relaxation joint error entropy relationship, a positive semidefinite relaxation is performed to obtain the second relaxation joint error entropy relationship;

[0141] Based on each location information and the second relaxed joint error entropy relationship, the positioning calculation is performed to determine the target location information of the target node.

[0142] For the embodiments of this application, based on formulas (7) and (13), the joint error entropy relationship after adding virtual anchor nodes is determined as shown in the following formula (14):

[0143]

[0144] Furthermore, based on each location information and the relationship between the joint error entropy, a positioning calculation is performed, and the location information corresponding to the minimum joint error entropy is taken as the target location information of the target node, as shown in the following formula (15):

[0145] Formula (15) contains a large number of exponential and logarithmic operations. It would be difficult to perform the positioning calculation directly based on Formula (15). Therefore, in order to simplify the calculation process, the minimization problem is equivalently transformed into a maximization problem, and the constant term is removed, as shown in the following formula (16):

[0146]

[0147] Then, in order to make Equation (16) easier to solve, this application performs a two-step relaxation on the joint error entropy relationship, transforming the original exponential non-convex optimization problem into an SDP problem that can be solved effectively. First, a non-exponential relaxation transformation is performed on Equation (16) to obtain the first relaxed joint error entropy relationship, which is shown in Equation (17) below:

[0148]

[0149] Specifically, when performing a non-exponential relaxation transformation, Jessen's inequality is used to transform equation (16) into... Relaxed to: In formula (16) Relaxed to: The relaxed two are then summed to obtain the first relaxed joint error entropy relationship. It can be proven through compression mapping and fixed point theorem that formula (17) and formula (16) have the same optimal solution.

[0150] Furthermore, since the first relaxation joint error entropy relationship is still a non-convex problem and inconvenient to solve, the positive semidefinite relaxation (SDR) technique is used to transform the first relaxation joint error entropy relationship into an SDP problem with an affine objective function and two composite optimization terms. Then, during the positive semidefinite relaxation, a preset auxiliary vector g, a preset auxiliary matrix H, and C are introduced to determine the second relaxation joint error entropy relationship, which is shown in the following formula (18):

[0151]

[0152] Where g is a preset auxiliary vector, defined as: g = [d1, d2, ..., d N ,e1,e2,...,e N ] T H is a pre-defined auxiliary matrix, defined as g T g; C is a predefined auxiliary matrix, defined as: B = [I] N ,I N ], I N The i-th row of the identity matrix r is the vector of distance measurements; tr is the trace of the matrix.

[0153] Then, based on each location information and the second relaxed joint error entropy relationship, the positioning calculation is performed, and the final solved x is used as the target location information of the target node. The positioning calculation is shown in the following formula (19):

[0154]

[0155] As can be seen, in this embodiment, a non-exponential relaxation transformation is performed based on the joint error entropy relationship to obtain a first relaxed joint error entropy relationship, and a positive semidefinite relaxation is performed based on the first relaxed joint error entropy relationship to obtain a second relaxed joint error entropy relationship. Finally, positioning calculations are performed based on each location information and the second relaxed joint error entropy relationship to determine the target location information of the target node. This embodiment utilizes non-exponential relaxation transformation and positive semidefinite relaxation to alleviate the thorny exponential optimization problem, making the solution more convenient during positioning calculations.

[0156] Furthermore, to further improve the positioning accuracy of the positioning method, in this embodiment, positioning calculations are performed based on each location information and the target location information of the target node to determine the target location information of the target node, including:

[0157] Obtain the preset constraints;

[0158] Based on the preset constraint relationship, each location information, and the second relaxed joint error entropy relationship, the positioning calculation is performed to determine the target location information of the target node.

[0159] The preset constraints include:

[0160] g≥0, where g is a pre-defined auxiliary vector;

[0161] A[x T z] T ≤f, where A is a preset coefficient matrix, x is the coordinate to be solved, and z is defined as x T x and f are preset condition matrices;

[0162] H is a pre-defined auxiliary matrix, and is defined as g T g;

[0163] I 2×2 It is an identity matrix with dimension 2;

[0164] H ii Let s be the element in the i-th row and i-th column of matrix H. i This provides the position information of the i-th anchor node;

[0165] H ij Let s be the element in the i-th row and j-th column of matrix H. j This provides the location information for the j-th anchor node.

[0166] H ii +H N+i,N+i +2H i,N+i ≤r i 2 +4r i σ i i = 1, ..., N, H N+i,N+i Let H be the element in the (N+i)th row and (N+i)th column of matrix H. i,N+i Let r be the element in the i-th row and N+i-th column of matrix G. i The distance measurement between the i-th anchor node and the target node, σ i To measure the noise variance.

[0167] In this embodiment of the application, adding some effective constraints during the positioning calculation can further improve the positioning accuracy of the positioning method. Therefore, positioning calculations are performed based on preset constraint relationships, each location information, and the second relaxed joint error entropy relationship to determine the target location information of the target node. The preset constraint conditions include: g≥0, A[x T z] T ≤f,

[0168]

[0169] Specifically, for the constraint g≥0, g is a pre-defined auxiliary vector, defined as: g=[d1,d2,...,d N ,e1,e2,...,e N ] T The length of vector g is 2N, where e i Let d be the non-line-of-sight error between the target node and the i-th anchor node. i Let A[x] be the true distance between the target node and the i-th anchor node; for A[x] T z] T For the ≤f constraint, A is a pre-defined coefficient matrix, defined as: s i Let f be the position information of the i-th anchor node, and f be a preset condition matrix, defined as: r i This is the distance measurement between the target node and the i-th anchor node. For d... i =‖xs i Squaring both sides yields the following constraint: Based on the triangle inequality, the following side length constraint conditions are obtained: Based on the first relation, we know di +e i =r i -n i Furthermore, the following inequality can be derived: d i +e i ≤r i Let i = 1, ..., N. Squaring both sides of the inequality, we obtain the following initial constraints: However, when measuring noise n i When the value is negative, the initial constraint is invalid; therefore, a positive constant u is added to the right-hand side of the initial constraint. i This makes the initial constraints slightly more relaxed, where, Therefore, based on the initial constraints and the constant u i The final constraints are determined as follows:

[0170] As can be seen, in this embodiment, the target location information of the target node is determined by performing positioning calculation based on the preset constraint relationship, each location information, and the second relaxed joint error entropy relationship. In the positioning calculation, this embodiment adds some effective constraints, which can further improve the positioning accuracy of the positioning method.

[0171] This application's embodiments present data simulation tests on a method for solving positioning information based on minimizing joint error entropy. The simulation results show that the joint error entropy minimization positioning method has strong robustness and outperforms various positioning methods based on non-line-of-sight error prior information. Simulation data is shown below. Figures 2 to 4 As shown. Figure 2 This is a comparison chart of positioning performance in dense non-line-of-sight scenes. The horizontal axis σ represents the measurement noise variance, and the vertical axis RMSE is defined as the mean square error value. Figure 3 This is a performance comparison chart under varying non-line-of-sight link conditions, with the horizontal axis being N. n The number of non-line-of-sight links is defined as the number of links in a total of 8 links tested, and the RMSE on the vertical axis is defined as the mean square error. Figure 4 This is a performance comparison chart under conditions of non-line-of-sight error variation, with the horizontal axis B. max Defined as the maximum non-visual error used in the test, the ordinate RMSE is defined as the mean square error. Figures 2 to 4 In the text, AML represents the localization method of the original maximum likelihood method, BL represents the localization method of the balance factor method, SPA represents the localization method of the sparse regularization method, SOCPR represents the localization method of the robust optimization method based on the upper bound, and SMEE represents the localization method of the minimum joint error entropy in the embodiments of this application. Figures 2 to 4 As can be seen, the localization method of minimum joint error entropy in the embodiments of this application has good performance advantages and good robustness to adapt to various scenarios.

[0172] The above embodiments describe a positioning method in a hybrid environment from the perspective of process flow. The following embodiments describe a positioning device in a hybrid environment from the perspective of virtual modules or virtual units. For details, please refer to the following embodiments.

[0173] This application provides a positioning device 200 in a mixed environment, such as... Figure 5 As shown, the positioning device 200 in this mixed environment may specifically include:

[0174] The acquisition module 210 is used to acquire the position information of each of the multiple anchor nodes and to acquire the flight time of the signal from the target node to each anchor node.

[0175] The distance calculation module 220 is used to calculate the distance between nodes based on the flight speed and the flight time corresponding to each anchor node, and to obtain the distance measurement value between each anchor node and the target node.

[0176] The relationship determination module 230 is used to determine a first relationship based on the distance measurement value. The first relationship is the relationship between measurement noise information, non-line-of-sight error information and true distance information. Measurement noise information is the error information generated during the instrument's measurement of flight time. Non-line-of-sight error information is the error information generated due to the obstruction between the target node and each anchor node. True distance information is determined by the target position information of the target node and the position information of each anchor node.

[0177] The joint error entropy relationship determination module 240 is used to obtain the noise probability distribution corresponding to the measurement noise information and the non-line-of-sight probability distribution corresponding to the non-line-of-sight error information, and determine the joint error entropy relationship based on the noise probability distribution, the non-line-of-sight probability distribution and the first relationship. The joint error entropy relationship is used to characterize the relationship between the joint error entropy and the position information of the target node. The positioning calculation module 250 is used to perform positioning calculation based on the position information corresponding to each anchor node and the joint error entropy relationship to determine the target position information of the target node. The target position information of the target node is the position information corresponding to the minimum joint error entropy.

[0178] In this embodiment, the distance calculation module 220 calculates the distance between nodes based on the flight speed and the flight time corresponding to each anchor node, obtaining the distance measurement value between each anchor node and the target node. Then, the relationship determination module 230 determines the first relationship between the measurement noise information, non-line-of-sight error information, and the true distance information based on the distance measurement value. Furthermore, the joint error entropy relationship determination module 240 determines the joint error entropy relationship based on the noise probability distribution, the non-line-of-sight probability distribution, and the first relationship. Finally, the positioning calculation module 250 performs positioning calculation based on the position information corresponding to each anchor node and the joint error entropy relationship, determining the position information corresponding to the minimum joint error entropy as the target position information of the target node. The positioning method in this embodiment comprehensively considers the non-line-of-sight error and measurement noise in the mixed environment and performs positioning calculation based on the minimum joint error entropy criterion, improving the positioning accuracy of the positioning method in the mixed environment.

[0179] In one possible implementation of this application embodiment, the joint error entropy relationship determination module 240, when performing the determination of the joint error entropy relationship based on the noise probability distribution, the non-line-of-sight probability distribution, and the first relationship, is configured to:

[0180] Determine the joint probability distribution based on the noise probability distribution and the non-line-of-sight probability distribution;

[0181] Based on the joint probability distribution and the relationship between the joint error entropy and the probability distribution, a second relationship is determined, which is the relationship between the non-line-of-sight error entropy, the noise error entropy, and the joint error entropy.

[0182] Based on the first and second relationships, the joint error entropy relationship is determined.

[0183] In one possible implementation of this application, the relationship between the joint error entropy and the probability distribution is: J(n,e)=-log∫∫p 2 (n,e)dnde, where p(n,e) is the joint probability distribution and J(n,e) is the joint error entropy;

[0184] The second relationship is: J(n,e)=J(n)+J(e), where J(n) is the noise error entropy and J(e) is the non-line-of-sight error entropy.

[0185] In one possible implementation of this application embodiment, the positioning device 200 in a mixed environment further includes:

[0186] The non-line-of-sight error entropy adjustment module is used to obtain the balance parameters corresponding to multiple virtual anchor nodes. The virtual anchor nodes are represented by virtual nodes with a mean of zero and a variance that is the minimum noise variance.

[0187] The non-line-of-sight error entropy is adjusted based on the balance parameter to obtain the adjusted non-line-of-sight error entropy;

[0188] Based on the adjusted non-line-of-sight error entropy, the adjusted second relationship is determined. The adjusted second relationship is the relationship between the adjusted non-line-of-sight error entropy, the noise error entropy, and the joint error entropy.

[0189] Accordingly, when performing the determination of the joint error entropy relationship based on the first and second relationships, the joint error entropy relationship determination module 240 is used for:

[0190] Based on the first relation and the adjusted second relation, the joint error entropy relation is determined.

[0191] In one possible implementation of this application embodiment, when the non-line-of-sight error entropy adjustment module performs adjustment of the non-line-of-sight error entropy based on balance parameters to obtain the adjusted non-line-of-sight error entropy, it is used to:

[0192] The non-line-of-sight error entropy is adjusted based on the balance parameters according to the following formula to obtain the adjusted non-line-of-sight error entropy, where the formula is:

[0193] J λ (e) represents the adjusted non-line-of-sight error entropy, λ is the balance parameter, N is the number of anchor nodes, and e i Let e ​​be the non-line-of-sight error between the target node and the i-th anchor node. j Let σ be the non-line-of-sight error between the target node and the j-th anchor node. e G is the non-line-of-sight variance, and G is the Gaussian kernel function.

[0194] In one possible implementation of this application embodiment, the non-line-of-sight error entropy adjustment module, when performing positioning calculations based on each location information and the joint error entropy relationship to determine the target location information of the target node, is used for:

[0195] A non-exponential relaxation transformation is performed on the joint error entropy relationship to obtain the first relaxed joint error entropy relationship.

[0196] Based on the first relaxation joint error entropy relationship, a positive semidefinite relaxation is performed to obtain the second relaxation joint error entropy relationship;

[0197] Based on each location information and the second relaxed joint error entropy relationship, the positioning calculation is performed to determine the target location information of the target node.

[0198] In one possible implementation of this application embodiment, when the non-line-of-sight error entropy adjustment module performs positioning calculations based on each location information and the second relaxed joint error entropy relationship to determine the target location information of the target node, it is used to:

[0199] Obtain the preset constraints;

[0200] Based on the preset constraint relationship, each location information, and the second relaxed joint error entropy relationship, the positioning calculation is performed to determine the target location information of the target node.

[0201] The preset constraints include:

[0202] g≥0, where g is a pre-defined auxiliary vector;

[0203] A[x T z] T ≤f, where A is a preset coefficient matrix, x is the coordinate to be solved, and z is defined as x T x and f are preset condition matrices;

[0204] H is a pre-defined auxiliary matrix, and is defined as g T g;

[0205] I 2×2 It is an identity matrix with dimension 2;

[0206] H ii Let s be the element in the i-th row and i-th column of matrix H. i This provides the position information of the i-th anchor node;

[0207] H ij Let s be the element in the i-th row and j-th column of matrix H. j This provides the location information for the j-th anchor node.

[0208] H N+i,N+i Let H be the element in the (N+i)th row and (N+i)th column of matrix H. i,N+i Let r be the element in the i-th row and N+i-th column of matrix G. i The distance measurement between the i-th anchor node and the target node, σ i To measure the noise variance.

[0209] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the positioning device 200 in a mixed environment described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0210] This application provides an electronic device, such as... Figure 6 As shown, Figure 6The illustrated electronic device 300 includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in practical applications, the transceiver 304 is not limited to one type, and the structure of this electronic device 300 does not constitute a limitation on the embodiments of this application.

[0211] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0212] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The symbol is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0213] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0214] The memory 303 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0215] Electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Servers can also be included. Figure 6 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0216] This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0217] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0218] The above are only some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A positioning method in a mixed environment, characterized in that, include: Acquire the location information of each of the multiple anchor nodes, and obtain the flight time of the signal from the target node to each anchor node; Based on the flight speed and the flight time corresponding to each anchor node, the distance between nodes is calculated to obtain the distance measurement value between each anchor node and the target node. Based on the distance measurement value, a first relationship is determined, wherein the first relationship is the relationship between measurement noise information, non-line-of-sight error information and true distance information. The measurement noise information is the error information generated during the instrument's measurement of flight time. The non-line-of-sight error information is the error information caused by the obstruction between the target node and each anchor node. The true distance information is determined by the target position information of the target node and the position information of each anchor node. Obtain the noise probability distribution corresponding to the measurement noise information and the non-line-of-sight probability distribution corresponding to the non-line-of-sight error information, and determine the joint error entropy relationship based on the noise probability distribution, the non-line-of-sight probability distribution and the first relationship, wherein the joint error entropy relationship is used to characterize the relationship between the joint error entropy and the position information of the target node; Based on the location information corresponding to each anchor node and the joint error entropy relationship, a positioning calculation is performed to determine the target location information of the target node, wherein the target location information of the target node is the location information corresponding to the minimum joint error entropy.

2. The method of positioning in a mixed environment of claim 1, wherein, The step of determining the joint error entropy relationship based on the noise probability distribution, the non-line-of-sight probability distribution, and the first relationship includes: Determine the joint probability distribution based on the noise probability distribution and the non-line-of-sight probability distribution; Based on the joint probability distribution and the relationship between the joint error entropy and the probability distribution, a second relationship is determined, wherein the second relationship is the relationship between the non-line-of-sight error entropy, the noise error entropy and the joint error entropy; Based on the first relationship and the second relationship, the joint error entropy relationship is determined.

3. The method of claim 2, wherein, The relationship between the joint error entropy and the probability distribution is as follows: J(n, e) = -log∫∫p 2 (n, e) dnde, where p(n, e) is a joint probability distribution, J(n, e) is a joint error entropy; The second relationship is: J(n,e)=J(n)+J(e), where J(n) is the noise error entropy and J(e) is the non-line-of-sight error entropy.

4. The method of positioning in a mixed environment of claim 2, wherein, Before determining the joint error entropy relationship based on the first relationship and the second relationship, the method further includes: Obtain the balance parameters corresponding to multiple virtual anchor nodes, wherein the virtual anchor nodes represent virtual nodes with a mean of zero and a variance that is the minimum noise variance; The non-line-of-sight error entropy is adjusted based on the balance parameter to obtain the adjusted non-line-of-sight error entropy; Based on the adjusted non-line-of-sight error entropy, an adjusted second relationship is determined, wherein the adjusted second relationship is the relationship between the adjusted non-line-of-sight error entropy, the noise error entropy, and the joint error entropy; Accordingly, determining the joint error entropy relationship based on the first relationship and the second relationship includes: Based on the first relationship and the adjusted second relationship, the joint error entropy relationship is determined.

5. The method of positioning in a mixed environment of claim 4, wherein, The adjustment of the non-line-of-sight error entropy based on the balance parameter to obtain the adjusted non-line-of-sight error entropy includes: Based on the balance parameters, the non-line-of-sight error entropy is adjusted according to the following formula to obtain the adjusted non-line-of-sight error entropy, wherein the formula is: J λ (e) represents the adjusted non-line-of-sight error entropy, λ is the balance parameter, N is the number of anchor nodes, and e i Let e ​​be the non-line-of-sight error between the target node and the i-th anchor node. j Let σ be the non-line-of-sight error between the target node and the j-th anchor node. e G is the non-line-of-sight variance, and G is the Gaussian kernel function.

6. The method of claim 4, wherein, The step of performing positioning calculations based on each of the location information and the joint error entropy relationship to determine the target location information of the target node includes: A non-exponential relaxation transformation is performed on the joint error entropy relationship to obtain the first relaxed joint error entropy relationship. Based on the first relaxed joint error entropy relationship, a positive semidefinite relaxation is performed to obtain the second relaxed joint error entropy relationship; Based on each of the aforementioned location information and the second relaxed joint error entropy relationship, a positioning calculation is performed to determine the target location information of the target node.

7. The method of claim 6, wherein, The step of performing positioning calculations based on each of the location information and the second relaxed joint error entropy relationship to determine the target location information of the target node includes: Obtain the preset constraints; Based on the preset constraint relationship, each of the location information, and the second relaxed joint error entropy relationship, a positioning calculation is performed to determine the target location information of the target node. The preset constraints include: g≥0, where g is a pre-defined auxiliary vector; A[x T z] T ≤f, where A is a preset coefficient matrix, x is the coordinate to be solved, and z is defined as x T x and f are preset condition matrices; H is a preset auxiliary matrix, and is defined as g T g; I 2×2 It is an identity matrix with dimension 2; H ii Let s be the element in the i-th row and i-th column of matrix H. i This provides the position information of the i-th anchor node; H ij Let s be the element in the i-th row and j-th column of matrix H. j This provides the location information for the j-th anchor node. H N+i,N+i Let H be the element in the (N+i)th row and (N+i)th column of matrix H. i,N+i Let r be the element in the i-th row and N+i-th column of matrix G. i The distance measurement between the i-th anchor node and the target node, σ i To measure the noise variance.

8. A positioning device in a mixed environment, characterized by include: The acquisition module is used to acquire the position information of each of the multiple anchor nodes and to acquire the flight time of the signal from the target node to each anchor node. The distance calculation module is used to calculate the distance between nodes based on the flight speed and the flight time corresponding to each anchor node, so as to obtain the distance measurement value between each anchor node and the target node. A relationship determination module is used to determine a first relationship based on the distance measurement value, wherein the first relationship is the relationship between measurement noise information, non-line-of-sight error information and true distance information, the measurement noise information is error information generated during the instrument's measurement of flight time, the non-line-of-sight error information is error information caused by obstruction between the target node and each anchor node, and the true distance information is determined by the target position information of the target node and the position information of each anchor node; The joint error entropy relationship determination module is used to obtain the noise probability distribution corresponding to the measurement noise information and the non-line-of-sight probability distribution corresponding to the non-line-of-sight error information, and determine the joint error entropy relationship based on the noise probability distribution, the non-line-of-sight probability distribution and the first relationship, wherein the joint error entropy relationship is used to characterize the relationship between the joint error entropy and the position information of the target node; The positioning calculation module is used to perform positioning calculations based on the location information corresponding to each anchor node and the joint error entropy relationship to determine the target location information of the target node, wherein the target location information of the target node is the location information corresponding to the minimum joint error entropy.

9. An electronic device, comprising: include: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, said at least one application being configured to: perform the positioning method in a hybrid environment as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed in a computer, causes the computer to perform the positioning method in a mixed environment as described in any one of claims 1 to 7.

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