CN115413026B - Base station selection method, system, device and storage medium based on clustering algorithm - Google Patents
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Abstract
The invention provides a base station selection method, a system, equipment and a storage medium based on a clustering algorithm, wherein the method comprises the steps of presetting the number h of base stations in each group in an initial base station set of a target to be positioned, respectively obtaining corresponding positioning coordinates, establishing a positioning coordinate set, sequentially clustering the positioning coordinate set based on the number k of clusters, then carrying out classification suitability evaluation to obtain the optimal number k of clusters, matching the coordinate mean value of the cluster with the largest number of positioning coordinates in the cluster with the nearest positioning coordinates in the cluster according to the clustering of the optimal number k, and combining the positioning coordinates of the target to be positioned obtained according to the positioning coordinates with the corresponding positioning base stations. The invention can select the realization scheme with proper number and position from the alternative base stations, and improves the precision and accuracy of the 5G positioning coordinates while improving the utilization rate of network element resources of the positioning management function.
Description
Technical Field
The invention relates to the field of communication positioning, in particular to a base station selection method, a system, equipment and a storage medium based on a clustering algorithm.
Background
Because 5G communications are characterized by high speed, low latency, large connections, etc., its key technologies include large-scale antenna arrays, ultra-dense networking, new multiple access, full spectrum access, new network architecture, etc., in the continuous standard evolution, 5G itself also adds high precision positioning functions, such as industrial AGV, asset tracking, etc., especially indoor precision positioning, but satellite positioning cannot be used indoors, LTE and WiFi positioning technologies are not precise (it is actually provided with high precision positioning in the room already with bluetooth AOA, UWB, etc.), for which, 5G adds positioning functions in the R16 version, which utilizes MIMO multi-beam characteristics, defining indoor positioning technologies based on cell signal Round Trip Time (RTT), signal arrival Time Difference (TDOA), angle of arrival measurement (AOA), angle of departure measurement (AoD), etc.
The 5G positioning is to measure a reference signal through a measured terminal, calculate a measurement result by a positioning management function network element (Location Management Function, LMF), and calculate the position information of the measured terminal according to the stored known base station position information. According to the positioning principle, the two-dimensional positioning can finish position calculation by at least 3 positioning base stations, but in practical application, the number of commonly measured base stations is large, a large amount of redundant base station information exists, for example, calculation is carried out on a large amount of base station data, so that calculation amount is large, LMF (local mean time function) resource waste is caused, and if only 3 base stations are selected for calculation, insufficient precision is caused, and a large error is generated in positioning.
Therefore, how to select a proper number of base stations with proper positions from the alternative base stations is an important problem to be solved, determines the precision and accuracy of the subsequent positioning coordinates, and has important value for positioning local or limited areas and dividing positioning areas.
In view of the above, the present invention provides a base station selection method, system, device and storage medium based on clustering algorithm.
It should be noted that the information disclosed in the foregoing background section is only for enhancement of understanding of the background of the invention and thus may include information that does not form the prior art that is already known to those of ordinary skill in the art.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to provide a base station selection method, a system, equipment and a storage medium based on a clustering algorithm, which overcome the difficulty in the prior art, and can select a proper number and a proper position implementation scheme from alternative base stations, thereby improving the accuracy and the precision of 5G positioning coordinates while improving the utilization rate of network element resources of a positioning management function.
The embodiment of the invention provides a base station selection method based on a clustering algorithm, which comprises the following steps:
Presetting the quantity h of base stations of each group in an initial base station set of a target to be positioned, wherein h is more than or equal to 3, arranging and combining the initial base stations, respectively obtaining corresponding positioning coordinates, and establishing a positioning coordinate set;
clustering the positioning coordinate set based on the number k of clusters in sequence, and then carrying out classification suitability evaluation to obtain the optimal number k of clusters;
and in the clusters according to the optimal cluster number k, matching the coordinate mean value of the cluster with the largest number of positioning coordinates in the cluster with the nearest positioning coordinates, and combining the positioning coordinates of the target to be positioned and the corresponding positioning base stations according to the positioning coordinates.
Preferably, in the initial base station set of the target to be positioned, the number h of base stations in each group is preset to be greater than or equal to 3, the initial base stations are arranged and combined, corresponding positioning coordinates are obtained respectively, and the following steps are further included before the positioning coordinate set is established:
An initial base station set is obtained based on the position of a target to be positioned, an isolated forest algorithm is adopted, the isolated base stations in the initial base station set are filtered according to the strength of reference signals of the initial base stations, if no redundant base stations exist, positioning coordinates and corresponding positioning base station combinations are obtained, and if the redundant base stations still exist, the subsequent steps are executed.
Preferably, in the initial base station set of the target to be positioned, the number h of base stations in each group is preset to be greater than or equal to 3, the initial base stations are arranged and combined, corresponding positioning coordinates are obtained respectively, and a positioning coordinate set is established, including:
based on the preset base station number h of each group, the value range of the base station number is [3, M ], and the corresponding initial base stations are obtained according to different values of the base station number h for permutation and combination;
Obtaining the sum W of permutation and combination types obtained by different numbers h of the base stations;
And each permutation and combination obtains positioning coordinates based on the target to be positioned, and establishes a mapping relation between the positioning coordinates and the permutation and combination.
Preferably, the value range of the base station number is [3, m ] based on the preset base station number h of each group, and the initial base stations corresponding to the different values of the base station number h are obtained for permutation and combination, including:
Where M represents the initial total number of base stations and P represents the total number of isolated base stations filtered.
Preferably, the clustering is sequentially performed on the positioning coordinate set based on the cluster number k, and then classification suitability evaluation is performed to obtain an optimal cluster number k, which includes:
selecting a first initial clustering center from the positioning coordinate set according to the density peak value;
calculating the distance from each of the rest of the positioning coordinates in the positioning coordinate set to the initial clustering center and the sum of the distances;
Taking the positioning coordinate farthest from the initial clustering center as the next initial clustering center, wherein the value range of the cluster number k is [2, T ], and distributing each positioning coordinate to the nearest cluster, wherein T is the minimum natural number of the root opening result which is larger than W;
And carrying out classification suitability evaluation on the clustering result, iteratively selecting the clustering result with the best evaluation result as a final clustering result, and taking the value of the cluster number k corresponding to the final clustering result as the optimal cluster number.
Preferably, the selecting a first initial cluster center in the positioning coordinate set according to the density peak value includes:
obtaining localized density formula positioning Ρ i represents the number of points contained in a circle with a preset radius d c with i points as the center, d ij is the distance from j points to i points in the positioning coordinate set, χ (x) represents the number of data objects with a distance to the data object i smaller than the preset radius d c, when x <0, χ (x) =1, when x is greater than or equal to 0, χ (x) =0;
and taking the positioning coordinate with the maximum local density as the first initial clustering center.
Preferably, the classifying suitability evaluation is performed on the clustering result, the clustering result with the best evaluation result is selected as a final clustering result through iteration, and the value of the cluster number k corresponding to the final clustering result is regarded as the optimal cluster number, including:
the evaluation index DBI is evaluated by a clustering algorithm,
Wherein avg (C i) is the average Euclidean distance from the C i sample to the center of the sample, avg (C j) is the average Euclidean distance from the C j sample to the center of the sample, and d cen(ui,uj) is the Euclidean distance from the centers of the C i and the C j sample;
when the DBI value is smaller, the dispersion degree is lower, and the clustering result is better;
Iteration is carried out through each round of k=k+1, and an evaluation result of the number k of clusters under different values is obtained;
and when k=T, selecting the clustering result with the best evaluation result as a final clustering result, and taking the value of the cluster number k corresponding to the final clustering result as the optimal cluster number.
Preferably, in the clustering according to the optimal cluster number k, the coordinate mean of the cluster with the largest number of positioning coordinates in the cluster matches the nearest positioning coordinate, and the combination of the positioning coordinate of the target to be positioned and the corresponding positioning base station obtained according to the positioning coordinate includes:
clustering is carried out according to the number of the optimal clusters, and the cluster with the largest number of positioning coordinates in the clusters is selected as the optimal cluster;
Obtaining an average value of all positioning coordinates in the optimal cluster;
And taking the positioning coordinate closest to the average value as the positioning coordinate of the target to be positioned.
Preferably, in the clustering according to the optimal cluster number k, the coordinate mean of the cluster with the largest number of positioning coordinates in the cluster matches the nearest positioning coordinate, and the combination of the positioning coordinate of the target to be positioned and the corresponding positioning base station obtained according to the positioning coordinate further includes:
and obtaining the arrangement combination of the initial base stations corresponding to the positioning coordinates according to the mapping relation.
The embodiment of the invention also provides a base station selection system based on the clustering algorithm, which is used for realizing the base station selection method based on the clustering algorithm, and comprises the following steps:
the arrangement and combination module is used for presetting the quantity h of each group of base stations in an initial base station set of a target to be positioned, wherein h is more than or equal to 3, arranging and combining the initial base stations, respectively obtaining corresponding positioning coordinates, and establishing a positioning coordinate set;
The coordinate clustering module is used for sequentially clustering the positioning coordinate set based on the cluster number k, and then carrying out classification suitability evaluation to obtain the optimal cluster number k;
And the positioning coordinate module is used for matching the nearest positioning coordinate according to the coordinate mean value of the cluster with the largest number of positioning coordinates in the cluster in the optimal cluster number k, and combining the positioning coordinate of the target to be positioned and the corresponding positioning base station according to the positioning coordinate.
The embodiment of the invention also provides base station selection equipment based on a clustering algorithm, which comprises the following steps:
A processor;
A memory having stored therein executable instructions of the processor;
wherein the processor is configured to perform the steps of the above-described clustering algorithm based base station selection method via execution of the executable instructions.
The embodiment of the invention also provides a computer readable storage medium for storing a program which when executed implements the steps of the above base station selection method based on a clustering algorithm.
The invention aims to provide a base station selection method, a system, equipment and a storage medium based on a clustering algorithm, which can select a proper number and a proper position implementation scheme from alternative base stations, and improve the accuracy and the precision of 5G positioning coordinates while improving the utilization rate of network element resources of a positioning management function.
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Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings.
Fig. 1 is a flow chart of a base station selection method based on a clustering algorithm of the present invention.
Fig. 2 is a flowchart of step S120 in an embodiment of the base station selection method based on the clustering algorithm of the present invention.
Fig. 3 is a flowchart of step S130 in an embodiment of the base station selection method based on the clustering algorithm of the present invention.
Fig. 4 is a flowchart of step S140 in an embodiment of the base station selection method based on the clustering algorithm of the present invention.
Fig. 5 is a flowchart of specific steps of a base station selection method based on a clustering algorithm embodying the present invention.
Fig. 6, 7, 8, 9 are schematic diagrams of steps of a base station selection method based on a clustering algorithm embodying the present invention.
Fig. 10 is a block diagram of a system implementing the clustering algorithm-based base station selection method of the present invention.
Fig. 11 is a block diagram of an arrangement combination module in an embodiment of a base station selection system based on a clustering algorithm of the present invention.
Fig. 12 is a block diagram of a coordinate clustering module in an embodiment of a base station selection system based on a clustering algorithm of the present invention.
Fig. 13 is a block diagram of a positioning coordinate module in an embodiment of a base station selection system based on a clustering algorithm of the present invention.
Fig. 14 is a schematic diagram of a base station selection device based on a clustering algorithm of the present invention.
Detailed Description
Other advantages and effects of the present application will be readily apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present application by way of specific examples. The application may be practiced or carried out in other embodiments and with various details, and various modifications and alterations may be made to the details of the application from various points of view and applications without departing from the spirit of the application. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.
The embodiments of the present application will be described in detail below with reference to the attached drawings so that those skilled in the art to which the present application pertains can easily implement the present application. This application may be embodied in many different forms and is not limited to the embodiments described herein.
In the context of the present description, reference to the terms "one embodiment," "some embodiments," "examples," "particular examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. Furthermore, the particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples, as well as features of various embodiments or examples, presented herein may be combined and combined by those skilled in the art without conflict.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the context of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
For the purpose of clarity of explanation of the present application, components that are not related to the explanation are omitted, and the same or similar components are given the same reference numerals throughout the description.
Throughout the specification, when a device is said to be "connected" to another device, this includes not only the case of "direct connection" but also the case of "indirect connection" with other elements interposed therebetween. In addition, when a certain component is said to be "included" in a certain device, unless otherwise stated, other components are not excluded, but it means that other components may be included.
When a device is said to be "on" another device, this may be directly on the other device, but may also be accompanied by other devices therebetween. When a device is said to be "directly on" another device in contrast, there is no other device in between.
Although the terms first, second, etc. may be used herein to connote various elements in some instances, the elements should not be limited by the terms. These terms are only used to distinguish one element from another element. For example, a first interface, a second interface, etc. Furthermore, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes," and/or "including" specify the presence of stated features, steps, operations, elements, components, items, categories, and/or groups, but do not preclude the presence, presence or addition of one or more other features, steps, operations, elements, components, items, categories, and/or groups. The terms "or" and/or "as used herein are to be construed as inclusive, or meaning any one or any combination. Thus, "A, B or C" or "A, B and/or C" means "any of A, B, C, A and B, A and C, B and C, A, B and C". An exception to this definition will occur only when a combination of elements, functions, steps or operations are in some way inherently mutually exclusive.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the language clearly indicates the contrary. The meaning of "comprising" in the specification is to specify the presence of stated features, regions, integers, steps, operations, elements, and/or components, but does not preclude the presence or addition of other features, regions, integers, steps, operations, elements, and/or components.
Although not differently defined, including technical and scientific terms used herein, all have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The term addition defined in the commonly used dictionary is interpreted as having a meaning conforming to the contents of the related art document and the current hint, so long as no definition is made, it is not interpreted as an ideal or very formulaic meaning too much.
Fig. 1 is a flow chart of a base station selection method based on a clustering algorithm of the present invention. As shown in fig. 1, the base station selection method based on the clustering algorithm of the present invention includes:
s120, presetting the quantity h of base stations of each group in an initial base station set of a target to be positioned, wherein h is more than or equal to 3, arranging and combining the initial base stations, respectively obtaining corresponding positioning coordinates, and establishing a positioning coordinate set.
And S130, clustering the positioning coordinate set based on the number k of the clusters in sequence, and then performing classification suitability evaluation to obtain the optimal number k of the clusters.
And S140, matching the coordinate mean value of the cluster with the largest number of positioning coordinates in the cluster with the nearest positioning coordinates in the cluster according to the number k of the optimal clusters, and combining the positioning coordinates of the target to be positioned obtained according to the positioning coordinates with the corresponding positioning base station.
The invention provides a base station selection optimization method aiming at 5G positioning. The method comprises the steps of firstly removing edge base stations with lower measuring signals through an isolated forest algorithm, then combining a certain number of base stations, calculating a positioning result (positioning point) of each combination, obtaining a clustering result by using an improved K-means++ algorithm through the scattered positioning points, and finally reversely determining the base station combination according to the positioning points, so that the aim of selecting the optimal base station combination is achieved. The method can reversely select the base station combination based on the optimal positioning result from the perspective of reducing the positioning error, improves the indoor positioning precision, and has important value for positioning local or limited areas and dividing the positioning areas.
Fig. 2 is a flowchart of step S120 in an embodiment of the base station selection method based on the clustering algorithm of the present invention. Fig. 3 is a flowchart of step S130 in an embodiment of the base station selection method based on the clustering algorithm of the present invention. Fig. 4 is a flowchart of step S140 in an embodiment of the base station selection method based on the clustering algorithm of the present invention. As shown in fig. 2 to 4, in the embodiment of fig. 1, step S120, S130, S140 is further included before step S120, step S120 is replaced by S121, S122, S123, step S130 is replaced by S131, S132, S133, S134, step S140 is replaced by S141, S142, S143, S144, and each step is described below:
S110, acquiring an initial base station set based on the position of a target to be positioned, filtering the isolated base stations in the initial base station set according to the intensity of reference signals of the initial base stations by adopting an isolated forest algorithm, acquiring positioning coordinates and corresponding positioning base station combinations if no redundant base station exists, and executing subsequent steps if the redundant base stations still exist. In this embodiment, more than three base stations in the filtered initial base station set are used as judging conditions for existence of redundant base stations, and if the base stations are less than or equal to three base stations, the base stations are regarded as non-existence of redundant base stations, so that positioning coordinates are directly output. The isolated forest algorithm is an Ensemble-based anomaly detection method, so that the method has linear time complexity. And the accuracy is higher, and the speed is high when processing big data, so the application range in the industry is wider at present. Common scenarios include attack detection in network security, financial transaction fraud detection, disease detection, noise data filtering (data cleansing), etc. For different types of anomalies, different algorithms are used to detect, while the isolated forest algorithm is mainly directed to outliers in the continuous structured data. The precondition for using an isolated forest is that outliers are defined as those "outliers that are easily isolated" -points that are sparsely distributed and are farther from the high density population can be understood. Statistically, if there are only sparse points in a region in the data space, the probability of the data points falling in the region is low, and thus the points in the region can be considered abnormal, but not limited to this.
S121, based on the preset base station quantity h of each group, the value range of the base station quantity is [3, M ], corresponding initial base stations are obtained according to different values of the base station quantity h for permutation and combination, wherein,
Where M represents the initial total number of base stations and P represents the total number of isolated base stations filtered.
S122, obtaining the sum W of permutation and combination types obtained by different base station numbers h.
S123, each permutation and combination obtains positioning coordinates based on the target to be positioned, and a mapping relation between the positioning coordinates and the permutation and combination is established.
S131, selecting a first initial clustering center from the positioning coordinate set according to the density peak value. Step S131, including obtaining a local density formula locationΡ i represents the number of points contained in a circle with a preset radius d c with i points as the center, d ij is the distance from j points to i points in the positioning coordinate set, χ (x) represents the number of data objects with a distance from i points to i points smaller than the preset radius d c, when x <0, χ (x) =1, when x is greater than or equal to 0, χ (x) =0, and the positioning coordinate with the greatest local density is taken as the first initial cluster center, but is not limited thereto.
S132, calculating the distance from each of the rest positioning coordinates in the positioning coordinate set to the initial clustering center and the sum of the distances.
S133, taking the positioning coordinate farthest from the initial clustering center as the next initial clustering center, wherein the value range of the cluster number k is [2, T ], and distributing each positioning coordinate to the nearest cluster, wherein T is the minimum natural number of the root opening result larger than W.
S134, carrying out classification suitability evaluation on the clustering result, iteratively selecting the clustering result with the best evaluation result as a final clustering result, and regarding the value of the cluster number k corresponding to the final clustering result as the optimal cluster number. Step S134, including:
the evaluation index DBI is evaluated by a clustering algorithm,
Wherein avg (C i) is the average Euclidean distance from the C i sample to the center of the sample, avg (C j) is the average Euclidean distance from the C j sample to the center of the sample, and d cen(ui,uj) is the Euclidean distance from the centers of the C i and the C j sample;
when the DBI value is smaller, the dispersion degree is lower, and the clustering result is better;
Iteration is carried out through each round of k=k+1, and an evaluation result of the number k of clusters under different values is obtained;
And when k=T, selecting the clustering result with the best evaluation result as a final clustering result, and taking the value of the cluster number k corresponding to the final clustering result as the optimal cluster number. Davidsenburg Ding Zhishu (DBI), also known as a classification suitability index, is an index for evaluating the merits of clustering algorithms proposed by dainty L-davis and tangnald Bouldin, but not limited thereto.
S141, clustering is carried out according to the number of the optimal clusters, and the cluster with the largest number of positioning coordinates in the clusters is selected as the optimal cluster.
S142, obtaining the average value of all positioning coordinates in the optimal cluster.
S143, taking the positioning coordinate closest to the average value as the positioning coordinate of the target to be positioned.
S144, obtaining the arrangement combination of the initial base stations corresponding to the positioning coordinates according to the mapping relation.
The method can remove the influence of the isolated base station on the clustering result through the isolated forest algorithm. Based on initial measurement results, preliminary screening is performed, the distance between an individual base station and a target to be measured is far, and the measurement results of reference signals are low, so that the positioning accuracy can be affected. The isolated forest algorithm can quickly separate out P base stations with sparse distribution through super cutting.
The invention obtains the clustering result and the positioning coordinates based on the improved K-means++ algorithm. The basic idea in which the k-means++ algorithm selects the initial seed points is that the mutual distance between the initial cluster centers is as far as possible. 1. A point is randomly selected from the input set of data points as the first cluster center ("seed point"). 2. For each point x in the dataset, its distance D (x) from the nearest cluster center (referring to the selected cluster center) is calculated. 3. A new data point is selected as a new cluster center, and the selection principle is that the point with larger D (x) is selected as the cluster center with larger probability. 4. Repeat 2 and 3 until k cluster centers are selected. 5. The standard k-means algorithm is run with these k initial cluster centers.
In the present invention, first, candidate base stations are combined into a plurality of base station combinations (commonCombinations) and calculating a positioning result based on the measurement results of each combination to form a plurality of scattered positioning points. And then, the initial point is selected, namely a first initial clustering center is selected according to a density peak value method, and the first clustering center is ensured to be generated in the integral core. Next, the number of clusters is determined, wherein the range of the number of clusters isThe first round is preset to be 2, and the number of clusters is increased to the upper limit along with the increase of the number of clusters. And selecting the rest initial clustering centers by using the sum criterion of the furthest distances proposed by K-means++. And searching a more suitable clustering center through an iterative algorithm. And finally, evaluating the clustering result through the DBI evaluation index to obtain an evaluation result. Repeatedly executing the step ②-⑤, wherein the number of clusters set in the ③ step is increased, evaluating the clustering results corresponding to the number of clusters by using the ⑤ step, comparing the clustering results of different preset clusters by using a DBI evaluation method, and selecting the clustering result with the best evaluation.
And, the invention pushes back the base station according to the optimal positioning coordinates. According to the characteristic that the positioning result is generated around the real position, the cluster with the most coordinates in the clustering result is selected, the coordinate mean value in the class is calculated, the coordinate closest to the coordinate mean value is selected as the final positioning coordinate, and the combination of the positioning base stations can be reversely deduced according to the coordinate, so that the selected base station is determined.
The base station selection method based on the clustering algorithm can select the realization scheme with proper number and positions from the alternative base stations, and improves the accuracy and the precision of the 5G positioning coordinates while improving the utilization rate of network element resources of the positioning management function.
Fig. 5 is a flowchart of specific steps of a base station selection method based on a clustering algorithm embodying the present invention. The specific step flow of the base station selection method based on the clustering algorithm, which is implemented by the template shown in fig. 5, comprises the following steps:
And determining the terminal to be positioned.
And obtaining an initial measurement result of the terminal to be positioned, and simultaneously obtaining positioning auxiliary data.
Generating initial positioning coordinates, eliminating the isolated base stations by using an isolated forest algorithm, and judging whether the number of the rest base stations is more than 3.
If the number of the positioning coordinates is less than or equal to 3, the initial positioning coordinates are directly output to serve as the positioning coordinates of the terminal to be positioned.
If the number of the isolated points is more than 3, clustering the positioning results after eliminating the isolated points based on a density peak value and a K-means++ algorithm. And evaluating the clustering quality through an internal effectiveness index DBI, and outputting a clustering result. And (3) selecting the class with the largest number as class C by recording the number of default coordinates of each cluster. By calculating the mean value of class C coordinatesTo select the coordinates closest thereto as final positioning coordinates. And outputting the positioning coordinates of the terminal to be positioned, and reversely pushing the base station combination according to the positioning coordinates.
Fig. 6, 7, 8, 9 are schematic diagrams of steps of a base station selection method based on a clustering algorithm embodying the present invention. Referring to fig. 5, 6, 7, 8 and 9, the present invention uses 5G positioning to measure a reference signal through a measured terminal, a positioning management function network element (Location Management Function, LMF) calculates the measurement result, and the LMF calculates the location information of the measured terminal 12 according to the stored known base station location information. However, the number of the measured positioning base stations 11 is large, and a large amount of redundant base station information exists, for example, the calculation of a large amount of base station data causes a large calculation amount and causes the resource waste of the LMF, and if only 3 base stations are selected for calculation, the precision is insufficient and the positioning generates a large error. After adding the 5G positioning base station selection function:
Step 1 lmf screens based on initial reference signal measurements. P isolated base stations 10 relatively far from the target to be measured are eliminated through an isolated forest algorithm (IForest). IForest mainly comprises two parts, a training phase and a testing phase.
The training stage comprises the steps of firstly, randomly selecting n sample points from a training data set, putting the sample points into a root node, secondly, randomly selecting one dimension, cutting in a data range, and finally, repeating the steps until each node has only one data or reaches a preset height.
The test stage comprises the steps of traversing training data x through each iTree, calculating that x falls on the first layer of the tree, calculating the average height of x on each tree, and finally judging as an isolated point when the anomaly score s is larger than a set threshold value and the signal intensity from the base station to the target to be tested is smaller than the average value of all the base stations.
s(x,n)=2^(-E(h(x))/c(n))
For example, the object to be measured can perform data transmission with 10 nearby base stations (M1, M2, M3, M4, M5, M6, M7, M8, M9, M10), and the signal intensity is [ -44-42-52-78-30-36-54-36-72-47], and the unit is dbm. The "-78 and-72" in the one-dimensional array can be identified as abnormal values through an isolated forest algorithm, so that two isolated base stations (M4 and M9) are eliminated, and the influence on the positioning accuracy is avoided.
And step 2, if redundant base stations still exist after the isolated base stations are removed, continuing to execute the subsequent steps. First, a plurality of base station combinations are constructed, each combination containing h (3.ltoreq.h.ltoreq.M) base stations. Traversal can obtainSeed base station combination and correspondingAnd (5) positioning results. Wherein M represents the total number of initial base stations, P represents the number of removed base stations, h represents the number of h base stations as a group, and a positioning point can be correspondingly calculated) (positioning results 13 of a plurality of combinations obtained in the step are coordinate points scattered in a positioning area, and are sample sources clustered in the subsequent step).
For example, after 2 base stations are eliminated by the isolated forest, there are 8 candidate base stations (M1, M2, M3, M5, M6, M7, M8, M10). Traversal can obtainAnd a base station combination mode, wherein each combination mode can obtain a corresponding positioning result.
And 3, clustering by using a modified K-means++ algorithm. The specific steps of the improved algorithm are as follows:
(1) And selecting a first initial cluster center according to the density peak value. Localized density formula positioning Χ (x) represents the number of data objects having a distance to data object i less than truncated distance dist c.
(2) And calculating the sum of the distances from each coordinate point to the existing clustering center. And selecting the coordinate with the largest value as the next initial clustering center. (initial cluster number wheel set to 2)
(3) And distributing each positioning result to the cluster with the highest similarity, and searching a reasonable cluster center through iteration.
(4) The clustering results were evaluated using the modified DBI method. Using the following formulaThe clustering results in this range are evaluated.
After the first round of steps (2) - (4) are completed, the set number of clusters is increased (cluster number range)) Steps (2) - (4) are performed again, and the clustering evaluation result DBI (i) of each round is output. And comparing DBI (i) of each round, selecting the best evaluation result as a final clustering result, and regarding the number of clusters corresponding to the round as the optimal number of clusters.
For example, the cluster number range is set to [2,15] based on the total number of positioning results being 219. The first round sets the number of clusters equal to 2, executes the modified K-means++ algorithm, and evaluates cluster quality by DBI. The above steps are performed again within the range of the number of clusters until the number of clusters reaches 15. It is assumed that the minimum value of DBI in the evaluation result is 0.5, and the number of clusters is 6. That is, when the number of clusters is 6, the clustering result is optimal.
And 4, selecting the cluster with the largest number of coordinates in the cluster as the optimal cluster according to the clustering result. Calculating the mean value of the inner coordinates of the classThe positioning coordinate closest to the average coordinate 15 is the final positioning coordinate 16, and the base station combinations [ base station 141, base station 142, base station 143, base station 144] are back-deduced based on the positioning result.
For example, among 6 clusters, the cluster having the largest number of coordinates in the cluster is selected as the optimal cluster. Suppose that the mean value of the intra-class coordinates is (15.74,20.23). And assume that the coordinate closest to the coordinate (15.74,20.23) among 219 positioning results is (13, 17). The final positioning coordinates are (13, 17). Assuming that the positioning coordinates (13, 17) are generated by four positioning base stations M1, M2, M5, M6, the final 5G positioning base station selected is M1, M2, M5, M6.
Compared with the prior art, the main advantages of this patent lie in:
1. The scheme designs a selection algorithm of the base station in the 5G positioning, can select the optimal base station combination based on the initial measurement result, and improves the positioning precision.
2. And adopting an isolated forest algorithm (IForest), removing P isolated base stations relatively far from the target to be measured according to the reference signal size measured for the first time, and avoiding affecting the positioning accuracy.
3. The clustering result and the positioning coordinates are obtained by adopting an improved K-means++ algorithm:
(1) The initial point selection is improved. The point with the maximum density peak value is selected as the initial clustering point, and the maximum density indicates that the probability of the tested terminal at the position is maximum, so that the method is more reasonable than random point selection.
(2) The k value selection is improved. In the scheme, k values are selected to be not fixed, a DBI evaluation method is introduced to evaluate the clustering result corresponding to each selected k value, and a final clustering result is determined according to the evaluation result.
(3) And generating clustered sample points. The scheme can set a plurality of base station combinations, and each combination can calculate a positioning result, namely a coordinate point, so that the number of base stations in the combination is not required to be preset, and the scheme is more reasonable.
4. And reversely pushing the base station combination according to the clustering result and the positioning coordinates. And selecting a positioning coordinate point according to the rule, wherein the point can correspond to one base station combination, so that the number and the positions of the optimal base station combination can be determined.
In the method, aiming at 5G positioning, under the condition that a large number of redundant base stations exist, a proper positioning base station is selected, so that the method is a real problem to be solved in the positioning calculation of LMF. And the number of the positioning base stations and the positions of the base stations are determined through the algorithm by eliminating the isolated base stations and selecting the base stations through a clustering algorithm, so that the positioning error caused by subjectively determining the number is avoided.
The invention can be applied to local or limited area positioning in 5G positioning and dividing positioning base stations for positioning areas, solves the problem of base station selection in the process of position calculation by LMF, avoids positioning errors caused by subjectively selecting base station combinations, improves positioning precision and accuracy, and can also determine positioning base station combinations corresponding to local positioning areas, divide positioning areas and determine base station combinations corresponding to certain positioning areas.
Fig. 10 is a block diagram of a system implementing the clustering algorithm-based base station selection method of the present invention. As shown in fig. 10, the base station selection system based on the clustering algorithm of the present invention includes, but is not limited to:
the permutation and combination module 52 presets the number h of the base stations of each group to be more than or equal to 3 in the initial base station set of the target to be positioned, permutations and combines the initial base stations, obtains corresponding positioning coordinates respectively, and establishes a positioning coordinate set.
The coordinate clustering module 53 clusters the positioning coordinate set based on the cluster number k in turn, and then performs classification suitability evaluation to obtain the optimal cluster number k.
The positioning coordinate module 54 matches the nearest positioning coordinate according to the coordinate mean value of the cluster with the largest number of positioning coordinates in the cluster with the optimal number k, and combines the positioning coordinate of the target to be positioned obtained according to the positioning coordinate with the corresponding positioning base station.
The implementation principle of the above modules is described in the related description of the base station selection method based on the clustering algorithm, and will not be repeated here.
The base station selection system based on the clustering algorithm can select the realization scheme with proper number and positions from the alternative base stations, and improves the accuracy and the precision of 5G positioning coordinates while improving the utilization rate of network element resources of a positioning management function.
Fig. 11 is a block diagram of an arrangement combination module in an embodiment of a base station selection system based on a clustering algorithm of the present invention. Fig. 12 is a block diagram of a coordinate clustering module in an embodiment of a base station selection system based on a clustering algorithm of the present invention. Fig. 13 is a block diagram of a positioning coordinate module in an embodiment of a base station selection system based on a clustering algorithm of the present invention. As shown in fig. 11 to 13, the base station selection system based on the clustering algorithm of the present invention further includes a module base station filtering module 51, and the permutation and combination module 52 is replaced by a base station grouping module 521, a category summing module 522, and a mapping relation module 523 based on the embodiment of the apparatus of fig. 10. The coordinate clustering module 53 is replaced by an initial clustering module 531, a distance calculation module 532, a coordinate allocation module 533, an iterative clustering module 534. The positioning coordinate module 54 is replaced by the optimal clustering module 541, the coordinate averaging module 542, the coordinate positioning module 543, and the base station combining module 544, and each module is described below:
The base station filtering module 51 is configured to obtain an initial base station set based on the position of the target to be positioned, filter the isolated base stations in the initial base station set according to the strength of the reference signal of the initial base station by adopting an isolated forest algorithm, obtain a combination of the positioning coordinates and the corresponding positioning base stations if no redundant base station exists, and execute the subsequent module if the redundant base station still exists.
The base station grouping module 521 is configured to obtain corresponding initial base stations for permutation and combination according to different values of the base station number h based on the preset base station number h of each group, where the value range of the base station number is [3, m ],
Where M represents the initial total number of base stations and P represents the total number of isolated base stations filtered.
A category summing module 522 configured to obtain a sum W of permutation and combination categories obtained by the different base station numbers h;
The mapping relation module 523 is configured to obtain positioning coordinates based on the target to be positioned for each permutation and combination, and establish a mapping relation between the positioning coordinates and the permutation and combination.
An initial clustering module 531 configured to select a first initial cluster center in the set of positioning coordinates based on the density peak value, including obtaining a local density formula positioningΡ i represents the number of points contained in a circle with a preset radius d c with i points as the center, d ij is the distance from j points to i points in the positioning coordinate set, χ (x) represents the number of data objects with a distance from the data object i smaller than the preset radius d c, when x <0, χ (x) =1, when x is greater than or equal to 0, χ (x) =0, and the positioning coordinate with the greatest local density is taken as the first initial clustering center.
A distance calculation module 532 configured to calculate a distance from each of the remaining location coordinates in the set of location coordinates to the initial cluster center and a sum of the distances;
A coordinate allocation module 533 configured to take a positioning coordinate farthest from the initial cluster center as a next initial cluster center, where the range of the number k of clusters is [2, T ], and allocate each positioning coordinate to a nearest cluster, where T is a minimum natural number of the root result greater than W;
the iterative clustering module 534 is configured to perform classification suitability evaluation on the clustering results, iteratively select the clustering result with the best evaluation result as a final clustering result, and regard the value of the cluster number k corresponding to the final clustering result as the optimal cluster number. Comprising the following steps:
the evaluation index DBI is evaluated by a clustering algorithm,
Wherein avg (C i) is the average Euclidean distance from the C i sample to the center of the sample, avg (C j) is the average Euclidean distance from the C j sample to the center of the sample, and d cen(ui,uj) is the Euclidean distance from the centers of the C i and the C j sample;
when the DBI value is smaller, the dispersion degree is lower, and the clustering result is better;
Iteration is carried out through each round of k=k+1, and an evaluation result of the number k of clusters under different values is obtained;
And when k=T, selecting a clustering result with the best evaluation result (a clustering result with the smallest DBI value) as a final clustering result, and taking the value of the number k of clusters corresponding to the final clustering result as the optimal number of clusters.
And an optimal clustering module 541 configured to perform clustering according to the number of optimal clusters, and select, as the optimal cluster, a cluster with the largest number of positioning coordinates in the clusters.
The coordinate averaging module 542 is configured to obtain an average of all positioning coordinates in the optimal cluster.
The coordinate positioning module 543 is configured to take the positioning coordinate closest to the average value as the positioning coordinate of the target to be positioned.
The base station combination module 544 is configured to obtain, according to the mapping relationship, an permutation and combination of the initial base stations corresponding to the positioning coordinates.
The implementation principle of the above steps is referred to the related description in the base station selection method based on the clustering algorithm, and will not be repeated here.
The embodiment of the invention also provides base station selection equipment based on the clustering algorithm, which comprises a processor. A memory having stored therein executable instructions of a processor. Wherein the processor is configured to perform the steps of the clustering algorithm based base station selection method via execution of the executable instructions.
As shown above, the base station selection system based on the clustering algorithm of the embodiment of the invention can select a proper number and a proper position implementation scheme from the alternative base stations, and improves the accuracy and the precision of the 5G positioning coordinates while improving the utilization rate of network element resources of the positioning management function.
Those skilled in the art will appreciate that the various aspects of the invention may be implemented as a system, method, or program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects that may be referred to herein collectively as a "circuit," module, "or" platform.
Fig. 14 is a schematic diagram of a base station selection device based on a clustering algorithm of the present invention. An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 14. The electronic device 600 shown in fig. 14 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 14, the electronic device 600 is in the form of a general purpose computing device. The components of electronic device 600 may include, but are not limited to, at least one processing unit 610, at least one storage unit 620, a bus 630 connecting the different platform components (including storage unit 620 and processing unit 610), a display unit 640, and the like.
Wherein the storage unit stores program code executable by the processing unit 610 such that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above-described electronic prescription flow processing method section of the present specification. For example, the processing unit 610 may perform the steps as shown in fig. 1.
The storage unit 620 may include readable media in the form of volatile storage units, such as Random Access Memory (RAM) 6201 and/or cache memory unit 6202, and may further include Read Only Memory (ROM) 6203.
The storage unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to, a processing system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 630 may be a local bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or using any of a variety of bus architectures.
The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and/or any device (e.g., router, modem, etc.) that enables the electronic device 600 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 650. Also, electronic device 600 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 over the bus 630. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 600, including, but not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, among others.
The embodiment of the invention also provides a computer readable storage medium for storing a program, and the steps of the base station selection method based on the clustering algorithm are realized when the program is executed. In some possible embodiments, the aspects of the present invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the invention as described in the electronic prescription stream processing method section of this specification, when the program product is run on the terminal device.
As shown above, the base station selection system based on the clustering algorithm of the embodiment of the invention can select a proper number and a proper position implementation scheme from the alternative base stations, and improves the accuracy and the precision of the 5G positioning coordinates while improving the utilization rate of network element resources of the positioning management function.
The program product 800 for implementing the above-described method according to an embodiment of the present invention may employ a portable compact disc read-only memory (CD-ROM) and include program code and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of a readable storage medium include an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out processes of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
In summary, the invention aims to provide a base station selection method, a system, equipment and a storage medium based on a clustering algorithm, which can select a proper number and a proper position implementation scheme from alternative base stations, and improve the accuracy and the precision of 5G positioning coordinates while improving the utilization rate of network element resources of a positioning management function.
The foregoing is a further detailed description of the invention in connection with the preferred embodiments, and it is not intended that the invention be limited to the specific embodiments described. It will be apparent to those skilled in the art that several simple deductions or substitutions may be made without departing from the spirit of the invention, and these should be considered to be within the scope of the invention.
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Non-Patent Citations (3)
| Title |
|---|
| 5G基站的改进加权质心室内定位算法;闫子春等;全球定位系统;20211215;第46卷(第6期);第44-48页 * |
| 一种面向室内定位的基站选择优化方法;陈诗军等;计算机科学;20181015;第45卷(第10期);第115-119页 * |
| 通过密度思想和聚类有效性指标改进的K-means算法;马钰等;现代电子技术;20210901;第44卷(第17期);第120-123页 * |
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