A 5G Signal Localization Method Based on Density Clustering

Abstract

The present invention provides a 5G signal positioning method based on density clustering, including: Step S1, obtaining base station communication parameters, and preprocessing the base station communication parameters to obtain preprocessed communication parameters, where 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 to obtain a number of first positioning coordinates; Step S3, based on the density clustering method, screening the number of first positioning results by using the preset conditions of core points to obtain a number of core points and the positioning coordinate clusters to which the core points belong, and obtaining all positioning coordinate clusters; Step S4, determining the coordinate information of the user equipment (UE) based on each positioning coordinate cluster. The 5G signal positioning method based on density clustering provided by the present invention has strong anti-noise ability, high positioning accuracy, and low deployment cost.

Description

Technical Field

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

[0002] In 5G positioning technology, due to the large influence of environmental factors during the acquisition of sampling points, a large amount of resources are required to establish and maintain a fingerprint database; while technologies such as TOA and AOA require high-precision positioning measurements for devices, and it is not easy to obtain existing devices. The positioning method based on received signal strength or signal quality has the advantages of low cost, convenient data acquisition, and easy implementation in actual deployment.

[0003] However, in actual positioning applications, the signal strength information emitted by the user equipment usually cannot be accurately obtained, the actual distance between the user equipment and the base station cannot be obtained according to the signal propagation model, and a large amount of measurement data is required to obtain a relatively accurate value for the signal strength information at the reference point. At the same time, during the measurement period, the usage environment of the user equipment cannot change greatly, otherwise it will affect the positioning accuracy. Differential received signal strength can eliminate the signal strength at the reference point to be measured and can reduce the equipment requirements between different base stations.

[0004] However, due to errors in equipment 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 position of the user equipment. Currently, the methods for dealing with noise include outlier detection, establishing regression models, etc. However, in practice, it is not possible to directly determine whether the received signal strength is an outlier, so it is not easy to distinguish noise points; the method of establishing a regression model based on data can establish a model between simple coordinates and received signal strength, but it is difficult to fit a non-linear model.

[0005] In addition, in 5G positioning technology, it is usually necessary to first obtain relevant information of the user equipment. In this context, protecting user privacy is also an important technical problem to be overcome in 5G positioning technology.

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

[0007] Aiming at the scenario of large non-line-of-sight positioning error and low accuracy in 5G scenarios in the prior art, the present invention provides a 5G signal positioning method based on density clustering, including:

[0008] Step S1: Obtain the base station communication parameters, and preprocess the base station communication parameters to obtain preprocessed communication parameters, where the preprocessed communication parameters at least include the reference signal received power (RSRP) and / or the reference signal received quality (RSRQ).

[0009] Step S2: Obtain a positioning model, input the preprocessed communication parameters into the positioning model, and obtain a number of first positioning coordinates.

[0010] Step S3: Based on the density clustering method, use the preset conditions of the core points to screen the number of first positioning results, obtain a number of core points and the positioning coordinate clusters to which the core points belong, and obtain all the positioning coordinate clusters.

[0011] Step S4: Determine the coordinate information of the user equipment (UE) based on each of the positioning coordinate clusters.

[0012] In a specific embodiment of the present invention, the preprocessed communication parameters at least further include the serving cell communication parameters and the neighboring cell communication parameters. The serving cell communication parameters include: the serving cell base station signal strength information and the serving cell base station number information. The neighboring cell communication parameters include: the neighboring cell base station signal strength information and the neighboring cell base station number information.

[0013] In a specific embodiment of the present invention, 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 distances between the UE and the serving cell base station and the neighboring cell base station respectively.

[0014] In a specific embodiment of the present invention, the signal propagation model includes: , where represents the signal strength of the base station received by the UE, represents the received signal strength at 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 subject to a Gaussian distribution.

[0015] In a specific embodiment of the present invention, the signal propagation differential positioning model includes: , where is the difference between the signal strength of the neighboring cell base station received by the UE and the signal strength of the serving cell base station, 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 subject to a Gaussian distribution, that is , where represents a Gaussian distribution, the mean of the Gaussian distribution is 0, and the variance of the Gaussian distribution is , represents the variance of the 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, and m is the number of neighboring cell base stations received by the UE.

[0016] In a specific embodiment of the present invention, the positioning model further includes a likelihood function, and a plurality of first positioning coordinates are obtained by maximizing the likelihood function through the Gauss-Newton method. The likelihood function includes:

[0017] ,

[0018] ,

[0019] where represents the coordinates of the UE, is the likelihood function with respect to , is the standard deviation of the noise subject to a Gaussian distribution, .

[0020] In a specific embodiment of the present invention, the step S3 includes:

[0021] Step S3.1: Randomly select a coordinate point from all the first positioning coordinates;

[0022] Step S3.2: If the coordinate point meets the core point preset condition, then use the coordinate point as the core point;

[0023] Step S3.3: Traverse all the first positioning coordinates that meet the core point preset condition around the core point until the number of core points cannot increase. All the core points that meet the core point preset condition form a positioning coordinate cluster;

[0024] Step S3.4: Randomly select a coordinate point that has not formed a positioning coordinate cluster from the first positioning coordinates, and repeat steps S3.2 to S3.4 until all the first positioning coordinates form positioning coordinate clusters.

[0025] In a specific embodiment of the present invention, the preset conditions for the core points 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 sampling points, then this first positioning coordinate is used as a core point, and the core point and all the first positioning coordinates within the neighborhood radius form 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 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 this first positioning coordinate is a noise point.

[0026] In a specific embodiment of the present invention, step S3 further includes: calculating the average values of the abscissas and ordinates of all the core points in each positioning coordinate cluster as the central coordinates of each positioning coordinate cluster.

[0027] In a specific embodiment of the present invention, step S4 includes: calculating the average values of the abscissas and ordinates of the central coordinates of all the positioning coordinate clusters as the coordinate information of the UE.

[0028] 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, it obtains the RSRP and / or RSRQ and preprocesses the parameters, then inputs the preprocessed communication parameters into the positioning model to obtain a number of first positioning coordinates. According to the density clustering method and the preset conditions for the core points, each core point and the positioning coordinate cluster to which the core point belongs are screened to obtain all the positioning coordinate clusters, and then the coordinate information of the user equipment UE is determined. This method only needs to obtain the RSRP and / or RSRQ of the base station during the UE reporting time, and can exclude the interference of noise points based on the density clustering method to obtain the coordinates of the UE. It neither needs to obtain the environmental factors of the area to be located nor the information of the user equipment, and can obtain accurate coordinates 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

[0029] The following will further illustrate the above characteristics, technical features, advantages and their implementation manners in a clear and understandable manner in combination with the drawings in the preferred embodiments.

[0030] Figure 1 It is a step schematic diagram of the 5G signal positioning method based on density clustering proposed in the embodiment of the present invention;

[0031] Figure 2 It is an application scenario schematic diagram of the 5G signal positioning method based on density clustering proposed in the embodiment of the present invention;

[0032] Figure 3 It is a schematic diagram showing the distribution of core points and noise points of an actual application example of the 5G signal positioning method based on density clustering proposed by the present invention. Detailed implementation manners

[0033] The following further details each aspect of the present invention.

[0034] Unless otherwise defined or described, all professional and scientific terms used herein have the same meaning as those familiar to persons skilled in the art. In addition, any methods and materials similar or equivalent to the described content can be applied to the method of the present invention.

[0035] The following explains the terms.

[0036] Unless otherwise clearly specified and limited, the "or" described 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".

[0037] It can be understood that although terms such as "first", "second", etc. can 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. Therefore, the first element can be called the second element without departing from the teachings of the inventive concept.

[0038] In the present invention, the terms "contain", "include" or "comprise" mean that various components can be applied together to the mixture or composition of the present invention. Therefore, the term "consisting essentially of..." is included in the terms "contain", "include" or "comprise".

[0039] Unless otherwise clearly specified and limited, the terms "connected", "communicated" and "connected" in the present invention should be understood in a broad sense. For example, it can be a fixed connection, or can be connected through an intermediate medium, can be the internal communication of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0040] For example, if an element (or component) is said to be on another element, coupled to another element, or connected to another element, then the said one element can be formed directly on the said another element, coupled thereto, or connected thereto, or there can be one or more intermediate elements therebetween. Conversely, if the expressions "directly on...", "directly coupled to...", and "directly connected to..." are used herein, it means that there are no intermediate elements. Other words used to describe the relationship between elements should be interpreted similarly, such as "between..." and "directly between...", "attached" and "directly attached", "adjacent" and "directly adjacent", and so on.

[0041] In addition, it should be noted that the words "front", "rear", "left", "right", "upper", and "lower" used in the following description refer to the directions in the drawings. The words "inner" and "outer" respectively refer to the directions towards or away from the geometric center of a specific component. It can be understood that here, these terms are used to describe the relationship of one element, layer, or region relative to another element, layer, or region as shown in the drawings. Except for the orientations described in the drawings, these terms should also include other orientations of the device.

[0042] Other aspects of the present invention will be obvious to those skilled in the art from the present disclosure.

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the specific embodiments of the present invention will be described below with reference to the accompanying drawings. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings, and other embodiments can also be obtained.

[0044] It should also be noted that the diagrams provided in the following embodiments only schematically illustrate the basic concept of the present application. The diagrams 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 ratio of each component in its actual implementation can be arbitrarily changed, and the component layout type may also be more complex. For example, the thickness of the elements in the drawings can be exaggerated for clarity.

[0045] Embodiment

[0046] In view of the large non-line-of-sight positioning error and low accuracy in the 5G scenario in the prior art, see Figure 1 , the present invention provides a 5G signal positioning method based on density clustering, including:

[0047] Step S1: Obtain the base station communication parameters, and preprocess the base station communication parameters to obtain preprocessed communication parameters, where the preprocessed communication parameters at least include the reference signal received power (RSRP) and / or the reference signal received quality (RSRQ).

[0048] Step S2: Obtain the positioning model, input the preprocessed communication parameters into the positioning model, and obtain a number of first positioning coordinates.

[0049] Step S3: Based on the density clustering method, use the preset conditions of the core points to screen the number of first positioning results, obtain a number of core points and the positioning coordinate clusters to which the core points belong, and obtain all the positioning coordinate clusters.

[0050] Step S4: Determine the coordinate information of the user equipment (UE) based on each positioning coordinate cluster.

[0051] Preferably, the preprocessing includes: eliminating the communication data with the total number of base stations less than three, and at the same time performing average filtering processing on the communication data. Specifically, the average filtering processing includes: performing signal strength averaging processing every three pieces of data according to the base station number.

[0052] In a preferred embodiment of the present invention, the application scenario schematic diagram of the 5G signal positioning method based on density clustering of the present invention is as Figure 2 shown. The preprocessed communication parameters at least further include the serving cell communication parameters and the neighboring cell communication parameters. The serving cell communication parameters include: the serving cell base station signal strength information and the serving cell base station number information. The neighboring cell communication parameters include: the neighboring cell base station signal strength information and the neighboring cell base station number information.

[0053] Preferably, 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, including:

[0054] , where represents the signal strength of the th base station received by the UE, represents the received signal strength at 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 subject to a Gaussian distribution, that is , and is used to characterize the usage environment of the current user.

[0055] Preferably, the signal propagation differential positioning model is used to convert the received signal strength information into the ratio of the distances between the UE and the serving cell base station and the neighboring cell base stations respectively, including: , where is the difference between the signal strength of the neighboring cell base station received by the UE and the signal strength of the serving cell base station, 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, where m is the number of neighboring cell base stations received by the UE.

[0056] The signal propagation differential positioning model can overcome the problem that the signal strength information at the reference point cannot be accurately obtained in practice. The signal propagation differential positioning model assumes that the signal strengths transmitted between 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. From this, the ratio of the distances from the user equipment to the serving cell base station and the neighboring cell base stations can be obtained.

[0057] 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 and iterated continuously to obtain a number of first positioning coordinates. The likelihood function includes:

[0058] ,

[0059] ,

[0060] where represents the coordinates of the UE, is the likelihood function with respect to , is the standard deviation of the noise that follows a Gaussian distribution, .

[0061] In a preferred embodiment of the present invention, the first positioning coordinates can be expressed as: , where k represents the number of preprocessed communication parameters.

[0062] Preferably, the step S3 includes:

[0063] Step S3.1: Randomly select a coordinate point from all the first positioning coordinates;

[0064] Step S3.2: If the coordinate point meets the preset conditions of the core point, then use this coordinate point as the core point;

[0065] Step S3.3: Traverse all the first positioning coordinates around the core point that meet the preset conditions of the core point until the number of core points cannot increase. All the core points that meet the preset conditions of the core point form a positioning coordinate cluster.

[0066] Step S3.4: Randomly select a coordinate point from the first positioning coordinates that has not formed a positioning coordinate cluster, and repeat Step S3.2 to Step S3.4 until all the first positioning coordinates form positioning coordinate clusters.

[0067] Preferably, the preset conditions of the core point include: if the number of first positioning coordinates (including the coordinate point itself) within the neighborhood radius of the randomly selected coordinate point is greater than or equal to the minimum sampling points (minpoints), then this first positioning coordinate is used as a core point, and the core point and all the first positioning coordinates within the neighborhood radius form 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 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 this first positioning coordinate is a noise point. In a practical application example, the distribution of core points and noise points is as Figure 3 shown. Through this method, all the first positioning coordinates can be decomposed into several positioning coordinate clusters , where M represents the number of positioning coordinate clusters.

[0068] In a preferred embodiment of the present invention, Step S3 further includes: calculating the average values of the abscissas and ordinates 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 represented as . Further, the center coordinates of the M positioning coordinate clusters are represented as .

[0069] Preferably, Step S4 includes: calculating the average values of the abscissas and ordinates of the center coordinates of all the positioning coordinate clusters , as the coordinate information of the UE.

[0070] In summary, the present invention has obtained the following effects:

[0071] The present invention provides a 5G signal positioning method based on density clustering. First, RSRP and / or RSRQ are obtained and the parameters are preprocessed. Then, the preprocessed communication parameters are input into the positioning model to obtain a number of first positioning coordinates. According to the density clustering method and the preset conditions of the core points, each core point and its affiliated positioning coordinate cluster are screened to obtain all positioning coordinate clusters, and then the coordinate information of the user equipment UE is determined. This method only needs to obtain the RSRP and / or RSRQ of the base station during the UE reporting time, and can exclude the interference of noise points based on the density clustering method to obtain the coordinates of the UE. It neither needs to obtain the environmental factors of the area to be located nor the information of the user equipment, and can obtain accurate coordinates 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.

[0072] Based on this application, those skilled in the art should understand that an 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 aspects described herein can be used to implement the device and / or practice the method. In addition, this device and / or practice this method can be implemented using other structures and / or functions in addition to one or more of the aspects described herein.

[0073] Those skilled in the art know that in addition to implementing the system and its various devices, modules, and units provided by the present invention in the form of pure computer-readable program code, the method steps can be logically programmed to enable the system and its various devices, modules, and units provided by the present invention to be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers, etc. to achieve the same function. 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 implementing various functions can also be regarded as the structures within the hardware component; the devices, modules, and units for implementing various functions can also be regarded as both software modules for implementing the method and structures within the hardware component.

[0074] It should be noted that the above embodiments can be freely combined as needed. The above are only the preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

[0075] All documents mentioned in this invention are cited herein by reference as if each individual document was cited by reference. In addition, it should be understood that after reading the above content of this invention, those skilled in the art can make various changes or modifications to this invention, and these equivalent forms also fall within the scope defined by the appended claims of 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: determining the coordinate information of the user equipment UE based on each of the positioning coordinate clusters; wherein: 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 selecting a coordinate point that does not form a positioning coordinate cluster from the first positioning coordinates, and repeating steps S3.2 to S3.4 until all the first positioning coordinates form a positioning coordinate cluster; 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.

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 claim 6 is 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.

8. The 5G signal positioning method based on density clustering according to claim 7 is 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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