5G signal positioning method based on density clustering

Abstract

The invention provides a 5G signal positioning method based on density clustering, and the method comprises the steps: S1, obtaining a base station communication parameter, carrying out the preprocessing of the base station communication parameter, and obtaining a preprocessed communication parameter, the preprocessed communication parameter at least comprising reference signal receiving power (RSRP) and / or reference signal receiving quality (RSRQ); s2, obtaining a positioning model, inputting the preprocessed communication parameters into the positioning model, and obtaining a plurality of first positioning coordinates; s3, based on a density clustering method, screening the plurality of first positioning results by using a core point preset condition to obtain a plurality of core points and positioning coordinate clusters to which the core points belong, and obtaining all positioning coordinate clusters; and S4, determining coordinate information of user equipment (UE) based on each positioning coordinate cluster. The 5G signal positioning method based on density clustering provided by the invention is high in anti-noise capability, high in positioning precision and low in deployment cost.

Description

Technical Field

[0001] The present invention belongs to the field of positioning, and in particular relates to a 5G signal positioning method based on density clustering. Background Art

[0002] In 5G positioning technology, fingerprint database technology is greatly affected by environmental factors when collecting sampling points, which requires a lot of resources to build and maintain the fingerprint database; and the positioning measurement required by TOA, AOA and other technologies requires high accuracy of the equipment, which is not easy to obtain with existing equipment. The positioning method based on received signal strength or signal quality has the advantages of low cost, convenient data collection, and easy implementation of actual deployment.

[0003] However, in actual positioning applications, it is usually impossible to accurately obtain the signal strength information transmitted by the user equipment, and it is impossible to obtain the actual distance between the user equipment and the base station based on the signal propagation model. In addition, obtaining the signal strength information at the reference point requires a large amount of measurement data to obtain a more accurate value. At the same time, during the measurement, the user equipment's operating environment cannot change significantly, otherwise it will affect the positioning accuracy. The differential received signal strength can eliminate the signal strength at the reference point that needs to be measured, and can reduce the equipment requirements between different base stations.

[0004] However, due to errors in device measurement and the influence of various noises in the environment, the received data will fluctuate, the positioning accuracy will be affected, and the positioning result may deviate greatly from the actual user device location. At present, there are methods for dealing with noise, such as outlier detection and regression model establishment, but in practice, it is not possible to directly determine whether the received signal strength is an outlier, and it is difficult to distinguish noise points; the method of establishing a regression model based on data can establish a simple model between coordinates and received signal strength, but it is difficult to fit nonlinear models.

[0005] In addition, 5G positioning technology usually needs to obtain relevant information of user devices first. In this scenario, protecting user privacy is also an important technical difficulty to overcome in 5G positioning technology.

[0006] Based on the above, this application provides a technical solution to solve the above technical problems. Summary of the invention

[0007] Aiming at the scenario in the prior art where the non-line-of-sight positioning error is large and the accuracy is low in the 5G scenario, the present invention provides a 5G signal positioning method based on density clustering, comprising: Step S1, acquiring base station communication parameters, and preprocessing the base station communication parameters to obtain preprocessed communication parameters, wherein the preprocessed communication parameters at least include reference signal received power RSRP and / or reference signal received quality RSRQ; Step S2, obtaining a positioning model, inputting the preprocessed communication parameters into the positioning model, and obtaining a plurality of first positioning coordinates; Step S3: Based on the density clustering method, the plurality of first positioning results are screened using the preset conditions of the core points to obtain a plurality of core points and the positioning coordinate clusters to which the core points belong, and obtain all the positioning coordinate clusters; Step S4: Determine the coordinate information of the user equipment UE based on each of the positioning coordinate clusters.

[0008] In a specific embodiment of the present invention, the pre-processed communication parameters also include at least service cell communication parameters and neighboring cell communication parameters, the service cell communication parameters include: service cell base station signal strength information, service cell base station number information, the neighboring cell communication parameters include: neighboring cell base station signal strength information, neighboring cell base station number information.

[0009] In a specific embodiment of the present invention, the positioning model includes a signal propagation model and a signal propagation differential positioning model, wherein the signal propagation model is used to determine the distance between the UE and the serving cell base station based on the received signal strength information; the signal propagation differential positioning model is used to convert the received signal strength information into the ratio of the distance between the UE and the serving cell and the neighboring cell base station respectively.

[0010] In a specific embodiment of the present invention, the signal propagation model includes: ,in, Indicates the signal strength of the base station received by the UE. Indicates the reference distance The received signal strength at is the distance between the UE and the current cell base station, is the path loss exponent, is the shadow fading coefficient, which is a random variable obeying the Gaussian distribution.

[0011] In a specific embodiment of the present invention, the signal propagation differential positioning model includes: ,in, It is the difference between the signal strength of the neighboring cell base station and the signal strength of the serving cell base station received by the UE. is the distance between the UE and the neighboring cell base station, is the distance between the UE and the serving cell base station, is a random variable that obeys a Gaussian distribution, that is, ,in, represents a Gaussian distribution, the mean of which is 0, and the variance of which is , represents the variance of shadow fading of each neighboring cell base station, represents the variance of the shadow fading of the serving cell base station, i=1,2,…,m, where m is the number of neighboring cell base stations received by the UE.

[0012] In a specific embodiment of the present invention, the positioning model further includes a likelihood function, and the likelihood function is maximized by the Gauss-Newton method to obtain a plurality of first positioning coordinates, and the likelihood function includes: , , in, represents the coordinates of the UE, About The likelihood function of is the standard deviation of the noise that follows a Gaussian distribution, .

[0013] In a specific embodiment of the present invention, step S3 includes: Step S3.1, randomly select a coordinate point from all first positioning coordinates; Step S3.2: If the coordinate point meets the preset conditions of the core point, the coordinate point is used as the core point; Step S3.3, traverse all first positioning coordinates around the core point that meet the preset conditions of the core point until the number of core points cannot be increased, and all core points that meet the preset conditions of the core point form a positioning coordinate cluster; Step S3.4: randomly select a coordinate point that does not form a positioning coordinate cluster from the first positioning coordinates, and repeat steps S3.2 to S3.4 until all the first positioning coordinates form a positioning coordinate cluster.

[0014] In a specific embodiment of the present invention, the core point preset conditions include: if the number of first positioning coordinates within the neighborhood radius of a randomly selected coordinate point is greater than or equal to the minimum number of sampling points, then the first positioning coordinate is used as the core point, and the core point and all the first positioning coordinates within the neighborhood radius are a positioning coordinate cluster; if the selected first positioning coordinate is within the neighborhood radius of other core points, but the number of first positioning coordinates within the neighborhood radius of the selected first positioning coordinate is less than the minimum number of sampling points, then the selected first positioning coordinate is a boundary coordinate; if a first positioning coordinate is neither a core point nor a boundary coordinate, then the first positioning coordinate is a noise point.

[0015] In a specific implementation of the present invention, step S3 further includes: calculating the average values ​​of the horizontal coordinates and the vertical coordinates of all the core points in each positioning coordinate cluster as the center coordinates of each positioning coordinate cluster.

[0016] In a specific implementation of the present invention, step S4 includes: calculating the average values ​​of the horizontal coordinates and the vertical coordinates of the center coordinates of all positioning coordinate clusters as the coordinate information of the UE.

[0017] The present invention can bring at least one of the following beneficial effects: The present invention provides a 5G signal positioning method based on density clustering, first obtaining RSRP and / or RSRQ, and preprocessing the parameters, and then inputting the preprocessed communication parameters into the positioning model to obtain a number of first positioning coordinates, and screening each of the core points and the positioning coordinate clusters to which the core points belong according to the density clustering method and the preset conditions of the core points, and then obtaining all the positioning coordinate clusters, and then determining the coordinate information of the user equipment UE. This method only needs to obtain the RSRP and / or RSRQ of the base station within the UE reporting time, and can eliminate the interference of noise points based on the density clustering method to obtain the coordinates of the UE. It does not need to obtain the environmental factors of the area to be positioned, nor does it need to obtain the information of the user equipment. Accurate coordinates can be obtained through a simple positioning algorithm. This positioning method can effectively avoid the influence of noise points, has strong anti-interference ability, can protect user privacy, and has low computational complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The preferred implementation scheme will be described below in a clear and understandable manner with reference to the accompanying drawings to further illustrate the above-mentioned characteristics, technical features, advantages and their implementation methods.

[0019] Figure 1 A schematic diagram of the steps of a 5G signal positioning method based on density clustering proposed in an embodiment of the present invention; Figure 2 A schematic diagram of an application scenario of a 5G signal positioning method based on density clustering proposed in an embodiment of the present invention; Figure 3 This is a schematic diagram of the distribution of core points and noise points in a practical application example of the 5G signal positioning method based on density clustering proposed in the present invention. DETAILED DESCRIPTION

[0020] Various aspects of the present invention are described in further detail below.

[0021] Unless otherwise defined or indicated, all professional and scientific terms used herein have the same meanings as those familiar to those skilled in the art. In addition, any methods and materials similar or equivalent to those described herein can be applied to the method of the present invention.

[0022] The following is an explanation of the terms.

[0023] Unless otherwise clearly specified and limited, the "or" mentioned in the present invention includes the relationship of "and". The "and" is equivalent to the Boolean logic operator "AND", the "or" is equivalent to the Boolean logic operator "OR", and "AND" is a subset of "OR".

[0024] It is to be understood that although the terms "first", "second", etc. may be used herein to describe different elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. Therefore, the first element may be referred to as the second element without departing from the teachings of the present invention.

[0025] In the present invention, the terms "comprising", "including" or "comprising" indicate that various components can be applied together in the mixture or composition of the present invention. Therefore, the term "consisting mainly of..." is included in the terms "comprising", "including" or "comprising".

[0026] Unless otherwise clearly specified and limited, the terms "connected", "connected" and "connection" in the present invention should be understood in a broad sense, for example, it can be a fixed connection, or it can be connected through an intermediary medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0027] For example, if an element (or component) is referred to as being on, coupled to, or connected to another element, the element may be directly formed on, coupled to, or connected to the other element, or there may be one or more intervening elements between them. In contrast, if the expressions "directly on," "directly coupled to," and "directly connected to" are used herein, then no intervening elements are indicated. Other words used to describe the relationship between elements should be similarly interpreted, such as "between" and "directly between," "attached" and "directly attached," "adjacent" and "directly adjacent," etc.

[0028] It should also be noted that the words "front", "rear", "left", "right", "upper" and "lower" used in the following description refer to directions in the drawings. The words "inner" and "outer" used refer to directions toward or away from the geometric center of a particular component, respectively. It can be understood that these terms are used here to describe the relationship of one element, layer or region relative to another element, layer or region as shown in the drawings. In addition to the orientations described in the drawings, these terms should also include other orientations of the device.

[0029] Other aspects of the present invention will be apparent to those skilled in the art in view of the disclosure herein.

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings and other implementation methods can be obtained based on these drawings without creative work.

[0031] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application. The drawings only show the components related to the present application and are not drawn according to the number, shape and size of the components in actual implementation. The type, quantity and proportion of each component in actual implementation may be changed arbitrarily, and the component layout may also be more complicated. For example, the thickness of the components in the drawings may be exaggerated for the sake of clarity.

[0032] Example For the existing technology of 5G scenarios with large non-line-of-sight positioning errors and low accuracy, see Figure 1 The present invention provides a 5G signal positioning method based on density clustering, comprising: Step S1, acquiring base station communication parameters, and preprocessing the base station communication parameters to obtain preprocessed communication parameters, wherein the preprocessed communication parameters at least include reference signal received power RSRP and / or reference signal received quality RSRQ; Step S2, obtaining a positioning model, inputting the preprocessed communication parameters into the positioning model, and obtaining a plurality of first positioning coordinates; Step S3: Based on the density clustering method, the plurality of first positioning results are screened using the preset conditions of the core points to obtain a plurality of core points and the positioning coordinate clusters to which the core points belong, and obtain all the positioning coordinate clusters; Step S4: Determine the coordinate information of the user equipment UE based on each of the positioning coordinate clusters.

[0033] Preferably, the preprocessing includes: removing communication data with less than three total base stations, and performing average filtering on the communication data. Specifically, the average filtering includes: performing signal strength average processing on every three data according to the base station number.

[0034] In a preferred embodiment of the present invention, the application scenario diagram of the 5G signal positioning method based on density clustering of the present invention is as follows: Figure 2As shown, the pre-processed communication parameters also include at least serving cell communication parameters and neighboring cell communication parameters. The serving cell communication parameters include: serving cell base station signal strength information and serving cell base station number information. The neighboring cell communication parameters include: neighboring cell base station signal strength information and neighboring cell base station number information.

[0035] Preferably, the positioning model includes a signal propagation model and a signal propagation differential positioning model, and the signal propagation model is used to determine the distance between the UE and the serving cell base station based on the received signal strength information, including: ,in, Indicates the first The signal strength of each base station, Indicates the reference distance The received signal strength at is the distance between the UE and the current cell base station, is the path loss exponent, is the shadow fading coefficient, which is a random variable obeying the Gaussian distribution, that is, , used to characterize the current user's usage environment.

[0036] Preferably, the signal propagation differential positioning model is used to convert the received signal strength information into the ratio of the distance between the UE and the serving cell and the neighboring cell base station, respectively, including: ,in, It is the difference between the signal strength of the neighboring cell base station and the signal strength of the serving cell base station received by the UE. is the distance between the UE and the neighboring cell base station, is the distance between the UE and the serving cell base station, is a Gaussian random variable, , i=1,2,…,m, m is the number of neighboring cell base stations received by the UE.

[0037] The signal propagation differential positioning model can overcome the problem of not being able to accurately obtain signal strength information at the reference point in practice. The signal propagation differential positioning model assumes that the signal strengths sent by the base stations used for user equipment positioning are the same, so the signal reception strengths at the same reference distance are also the same, thereby obtaining the ratio of the distances from the user equipment to the serving cell base station and the neighboring cell base station.

[0038] In a specific embodiment of the present invention, the positioning model further includes a likelihood function, and the likelihood function is maximized and iterated continuously by the Gauss-Newton method to obtain a plurality of first positioning coordinates, and the likelihood function includes: , , in, represents the coordinates of the UE, About The likelihood function of is the standard deviation of the noise that follows a Gaussian distribution, .

[0039] In a preferred embodiment of the present invention, the first positioning coordinates can be expressed as: , k represents the number of preprocessing communication parameters.

[0040] Preferably, step S3 comprises: Step S3.1, randomly select a coordinate point from all first positioning coordinates; Step S3.2: If the coordinate point meets the preset conditions of the core point, the coordinate point is used as the core point; Step S3.3, traverse all first positioning coordinates around the core point that meet the preset conditions of the core point until the number of core points cannot be increased, and all core points that meet the preset conditions of the core point form a positioning coordinate cluster; Step S3.4: randomly select a coordinate point that does not form a positioning coordinate cluster from the first positioning coordinates, and repeat steps S3.2 to S3.4 until all the first positioning coordinates form a positioning coordinate cluster.

[0041] Preferably, the core point preset conditions include: if the number of first positioning coordinates within the neighborhood radius of the randomly selected coordinate point (including the coordinate point itself) is greater than or equal to the minimum number of sampling points (minpoints), then the first positioning coordinate is used as the core point, and the core point and all the first positioning coordinates within the neighborhood radius are a positioning coordinate cluster; if the selected first positioning coordinate is within the neighborhood radius of other core points, but the number of first positioning coordinates within the neighborhood radius of the selected first positioning coordinate (including the coordinate point itself) is less than the minimum number of sampling points, then the selected first positioning coordinate is a boundary coordinate; if a first positioning coordinate is neither a core point nor a boundary coordinate, then the first positioning coordinate is a noise point. In an actual application example, the distribution of core points and noise points is as follows: Figure 3 By this method, all the first positioning coordinates can be decomposed into several positioning coordinate clusters. , M represents the number of positioning coordinate clusters.

[0042] In a preferred embodiment of the present invention, step S3 further includes: calculating the average values ​​of the horizontal coordinates and the vertical coordinates of all the core points in each positioning coordinate cluster as the center coordinates of each positioning coordinate cluster, that is, the center coordinates of the jth cluster are expressed as . Further, the center coordinates of the M positioning coordinate clusters are expressed as .

[0043] Preferably, step S4 includes: calculating the average values ​​of the horizontal coordinates and the vertical coordinates of the center coordinates of all positioning coordinate clusters , as the coordinate information of UE.

[0044] In summary, the present invention has achieved the following effects: The present invention provides a 5G signal positioning method based on density clustering, which first obtains RSRP and / or RSRQ, and pre-processes the parameters, then inputs the pre-processed communication parameters into the positioning model, obtains a number of first positioning coordinates, and screens each of the core points and the positioning coordinate clusters to which they belong according to the density clustering method and the preset conditions of the core points, and then obtains all the positioning coordinate clusters, and then determines the coordinate information of the user equipment UE. This method only needs to obtain the RSRP and / or RSRQ of the base station within the UE reporting time, and can eliminate the interference of noise points based on the density clustering method to obtain the coordinates of the UE. It does not need to obtain the environmental factors of the area to be positioned, nor does it need to obtain the information of the user equipment. Accurate coordinates can be obtained through a simple positioning algorithm. This positioning method can effectively avoid the influence of noise points, has strong anti-interference ability, can protect user privacy, and has low computational complexity.

[0045] Based on this application, it should be understood by those skilled in the art that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number and aspect described herein can be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this device and / or practice this method.

[0046] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0047] It should be noted that the above embodiments can be freely combined as needed. The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention.

[0048] All documents mentioned in the present invention are cited as references in this application, just as each document is cited as reference separately. In addition, it should be understood that after reading the above content of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the claims attached to this application.

Claims

1. A 5G signal positioning method based on density clustering, characterized in that: include: Step S1, acquiring base station communication parameters, and preprocessing the base station communication parameters to obtain preprocessed communication parameters, wherein the preprocessed communication parameters at least include reference signal received power RSRP and / or reference signal received quality RSRQ; Step S2, obtaining a positioning model, inputting the preprocessed communication parameters into the positioning model, and obtaining a plurality of first positioning coordinates; Step S3: Based on the density clustering method, the plurality of first positioning results are screened using the preset conditions of the core points to obtain a plurality of core points and the positioning coordinate clusters to which the core points belong, and obtain all the positioning coordinate clusters; Step S4: Determine the coordinate information of the user equipment UE based on each of the positioning coordinate clusters.

2. The 5G signal positioning method based on density clustering according to claim 1 is characterized in that: The pre-processed communication parameters at least include serving cell communication parameters and neighboring cell communication parameters. The serving cell communication parameters include: serving cell base station signal strength information and serving cell base station number information. The neighboring cell communication parameters include: neighboring cell base station signal strength information and neighboring cell base station number information.

3. The 5G signal positioning method based on density clustering according to claim 2 is characterized in that: The positioning model includes a signal propagation model and a signal propagation differential positioning model. The signal propagation model is used to determine the distance between the UE and the serving cell base station based on the received signal strength information; the signal propagation differential positioning model is used to convert the received signal strength information into the ratio of the distance between the UE and the serving cell and the neighboring cell base station respectively.

4. The 5G signal positioning method based on density clustering according to claim 3 is characterized in that: The signal propagation model includes: ,in, Indicates the signal strength of the base station received by the UE. Indicates the reference distance The received signal strength at is the reference distance, is the distance between the UE and the current cell base station, is the path loss exponent, is the shadow fading coefficient, which is a random variable obeying the Gaussian distribution.

5. The 5G signal positioning method based on density clustering according to claim 4 is characterized in that: The signal propagation differential positioning model includes: ,in, It is the difference between the signal strength of the neighboring cell base station and the signal strength of the serving cell base station received by the UE. is the distance between the UE and the neighboring cell base station, is the distance between the UE and the serving cell base station, is a random variable that obeys a Gaussian distribution, that is, ,in, represents a Gaussian distribution, the mean of which is 0 and the variance of which is , represents the variance of shadow fading of each neighboring cell base station, represents the variance of the shadow fading of the serving cell base station, i=1,2,…,m, where m is the number of neighboring cell base stations received by the UE.

6. The 5G signal positioning method based on density clustering according to claim 5 is characterized in that: The positioning model also includes a likelihood function, and the likelihood function is maximized by the Gauss-Newton method to obtain a number of first positioning coordinates. The likelihood function includes: ， ， in, represents the coordinates of the UE, About The likelihood function of is the standard deviation of the noise that follows a Gaussian distribution, .

7. The 5G signal positioning method based on density clustering according to any one of claims 1 to 6, characterized in that: The step S3 comprises: Step S3.1, randomly select a coordinate point from all first positioning coordinates; Step S3.2: If the coordinate point meets the preset conditions of the core point, the coordinate point is used as the core point; Step S3.3, traverse all first positioning coordinates around the core point that meet the preset conditions of the core point until the number of core points cannot be increased, and all core points that meet the preset conditions of the core point form a positioning coordinate cluster; Step S3.4: randomly select a coordinate point that does not form a positioning coordinate cluster from the first positioning coordinates, and repeat steps S3.2 to S3.4 until all the first positioning coordinates form a positioning coordinate cluster.

8. The 5G signal positioning method based on density clustering according to claim 7 is characterized in that: The core point preset conditions include: if the number of first positioning coordinates within the neighborhood radius of the randomly selected coordinate point is greater than or equal to the minimum number of sampling points, then the first positioning coordinate is used as the core point; if the selected first positioning coordinate is within the neighborhood radius of other core points, but the number of first positioning coordinates within the neighborhood radius of the selected first positioning coordinate is less than the minimum number of sampling points, then the selected first positioning coordinate is a boundary coordinate; if a first positioning coordinate is neither a core point nor a boundary coordinate, then the first positioning coordinate is a noise point.

9. The 5G signal positioning method based on density clustering according to claim 8, characterized in that: Step S3 also includes: calculating the average values ​​of the horizontal coordinates and the vertical coordinates of all the core points in each positioning coordinate cluster as the center coordinates of each positioning coordinate cluster.

10. The 5G signal positioning method based on density clustering according to claim 9, characterized in that: Step S4 includes: calculating the average values ​​of the horizontal coordinates and the vertical coordinates of the center coordinates of all positioning coordinate clusters as the coordinate information of the UE.

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