CN109462814A - Locating base station selection method based on minimum spanning tree clustering algorithm - Google Patents
Locating base station selection method based on minimum spanning tree clustering algorithm Download PDFInfo
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- CN109462814A CN109462814A CN201811431127.3A CN201811431127A CN109462814A CN 109462814 A CN109462814 A CN 109462814A CN 201811431127 A CN201811431127 A CN 201811431127A CN 109462814 A CN109462814 A CN 109462814A
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- base station
- spanning tree
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
- H04W64/003—Locating users or terminals or network equipment for network management purposes, e.g. mobility management locating network equipment
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- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The present invention provides the locating base station selection methods based on minimum spanning tree clustering algorithm.The present invention the following steps are included: obtain the time of arrival (toa) (TOA) of base station location and base station measurement first；Then base station is divided by k initial clustering by minimum spanning tree clustering algorithm, and using base station location mean value in each class as such data center；It is iterated later, selects base station nearest apart from cluster centre in every one kind as representing, judge whether mobile terminal location precision meets the requirements, using this k base station as initial base station group if meeting, otherwise expand k and re-start cluster；Finally the group of base stations of acquisition is ranked up from small to large by the TOA value of measurement, and gradually decreases the big base station of TOA value, until exporting minimum base station number.The present invention can choose suitable k base station in all N number of base stations and participate in positioning, use number of base stations as few as possible to realize, realize the positioning accuracy of near-optimization.
Description
Technical field
The present invention relates to locating base station selection, it is related specifically to the locating base station selection based on minimum spanning tree clustering algorithm
Method.
Background technique
Mobile terminal technology based on base station is a kind of wireless network location technology, while being also a kind of common position letter
Breath service (LBS) technology is a kind of emerging location technology for not needing to support by hardware modules such as GPS.This location technology is then
It is, in the location information for combining base station itself, to be calculated using relevant location algorithm using the information of base station in mobile network
The geographical location information of mobile terminal.
Wherein the location algorithm based on time of arrival (toa) (TOA) is a kind of common location algorithm, and principle is to pass through
Measure between several base stations and terminal corresponding signal and reach time TOA, by the propagation time of electric signal be converted into communication base station and
The distance between mobile terminal value, then establishes range equation group using the distance value that calculating is found out, solves mobile terminal.Reason
By upper, as the increase of number of base stations can improve the positioning accuracy of mobile terminal, but increase number of base stations and undoubtedly also will increase
Added cost power consumption reduces the performance of positioning, thus should comprehensively consider in practical applications base station setup cost problem and
Positioning accuracy request.The present invention provides a kind of efficient locating base station under the premise of the position of each base station known and TOA value
Selection method is with regard to necessary.
Summary of the invention
The purpose of the present invention is to provide the locating base station selection method based on minimum spanning tree clustering algorithm, this can be
Suitable k base station is chosen in all N number of base stations and participates in positioning, uses number of base stations as few as possible to realize, is realized approximate
Optimal positioning accuracy.
The present invention provides the locating base station selection method based on minimum spanning tree clustering algorithm, comprising: S1, obtains base station position
Set time of arrival (toa) (TOA) data of data and base station measurement；S2, base station is divided by minimum spanning tree clustering algorithm
K initial clustering cluster, and using the mean value of base station position data in each initial clustering cluster as such position data center；
S3, the k position data center that S2 is obtained is iterated poly- k as the initial cluster center of K-means algorithm, and to base station
Class selects base station nearest apart from cluster centre in every one kind as representative；S4, using the k base station selected to mobile terminal
Positioning, if meeting positioning accuracy request, using this k base station as initial base station group, otherwise expansion k and return S2 again into
Row cluster；S5, initial base station group is ranked up from small to large by the TOA value of measurement, and gradually removes the big base station of TOA value,
Until exporting minimum base station number.
Further, the step S2 may comprise steps of:
S21, N number of base station for acquisition construct the adjacency matrix of complete graph according to the distance between base station；
S22, the minimum spanning tree that distance matrix is found by minimum spanning tree prim algorithm；
S23, k-1 side for deleting maximum weight in minimum spanning tree, obtain k stalk tree, and every stalk tree represents one
Initial clustering cluster, and using the mean value of base station position data in each initial clustering cluster as such position data center, consider
Influence to base station distribution to positioning, can take biggish value for k value in advance, and base station is divided into more classes, is made as far as possible
The base station of terminal surrounding can be selected, and k value is by the present invention To be rounded symbol downwards.
Further, the step S3 may include:
Theoretically, all N number of base stations are involved in the positions calculations of mobile terminal, and positioning accuracy will be very high, therefore this hair
The bright position location using using all N number of base stations is as reference standard, it is assumed that the position of N number of base station location is L0, and applying k
The position that a base station is positioned is Lk, define the relative positioning error of k base stationWhen RPE very little
When, it is believed that meet positioning accuracy request, when RPE then thinks to be unsatisfactory for positioning accuracy request very much greatly.
Further, the step S5 may comprise steps of:
S51, a possibility that considering that base station and distance of mobile terminal are remoter, encountering barrier is bigger, and non line of sight degree is got over
Greatly, error level is also bigger, therefore can preferentially select some base stations closer away from the target terminal, and the present invention considers to survey
For the TOA value of amount as the standard for measuring base station and distance of mobile terminal, smaller TOA value, error is smaller, therefore first will be first
Beginning group of base stations is ranked up from small to large by the TOA value of base station measurement；
S52, the analysis according to S51, the selection measurement the smallest k-1 base station of TOA value position terminal；
S53, judge whether the positioning accuracy of k-1 base station meets positioning requirements, if satisfied, k-1, which is assigned to k, returns to S52,
Otherwise, minimum base station number is exported.
Detailed description of the invention
Fig. 1 is the flow diagram of the locating base station selection method based on minimum spanning tree K-means clustering algorithm.
Specific embodiment
Locating base station selection method of the present invention based on minimum spanning tree clustering algorithm, can be in all N number of bases
Suitable k base station is chosen in standing and participates in positioning, is used number of base stations as few as possible to realize, is realized the positioning of near-optimization
Precision, the specific steps are as follows:
1, the present invention obtains time of arrival (toa) (TOA) data of base station position data and base station measurement first；
2, assume there is N number of base station, for N number of base station of acquisition, constructed according to the distance between base station assign power completely first
Figure, and the adjacency matrix of base station distance is obtained, the minimum of adjacency matrix is then found by minimum spanning tree prim algorithm and is generated
Tree, finally deletes k-1 side of maximum weight in minimum spanning tree, obtains k stalk tree, and every stalk tree represents one and initially gathers
Class cluster, and using the mean value of base station position data in each initial clustering cluster as such position data center, it is contemplated that base station
It is distributed the influence to positioning, k value biggish value can be taken into advance, base station is divided into more classes, makes terminal four as far as possible
The base station in week can be selected, and k value is by the present invention To be rounded symbol downwards, this step not only can be right
Base station position data determines an initial division, can also exclude isolated point and noise data in set；
3, k position data center for obtaining step 2 be as the initial cluster center of K-means algorithm, and to base station
Position data is iterated poly- k class, selects base station nearest apart from cluster centre in every one kind as representing, the present invention passes through this
The clustering method of sample can obtain the base station of terminal surrounding different location, can eliminate to a certain extent some in Same Scene
Similar noise；
4, using the k base station selected to mobile terminal location, if meeting positioning accuracy request, by this k base station
As initial base station group, otherwise expands k and return step 2 re-starts cluster；
5, a possibility that considering that base station and distance of mobile terminal are remoter, encountering barrier is bigger, and non line of sight degree is bigger,
Error is also bigger, therefore can preferentially select some base stations closer away from the target terminal, so the present invention considers to measure
TOA value as measure base station and distance of mobile terminal standard, smaller TOA value, error is smaller, be based on such think of
Think, steps are as follows for concrete implementation:
(1) initial base station group will be obtained to be ranked up from small to large by the TOA value of base station measurement；
(2) the selection measurement the smallest k-1 base station of TOA value positions terminal；
(3) judge whether the positioning accuracy of k-1 base station meets positioning requirements, if satisfied, k-1, which is assigned to k, returns to S52,
Otherwise, minimum base station number is exported.
Claims (4)
1. the locating base station selection method based on minimum spanning tree clustering algorithm, it is characterised in that comprise the steps of:
S1, time of arrival (toa) (TOA) data for obtaining base station position data and base station measurement；
S2, base station is divided by k initial clustering cluster by minimum spanning tree clustering algorithm, and by base in each initial clustering cluster
Position data center of the mean value of station location data as such；
S3, the k position data center that S2 is obtained is changed as the initial cluster center of K-means algorithm, and to base station
For poly- k class, base station nearest apart from cluster centre in every one kind is selected as representative；
S4, using the k base station selected to mobile terminal location, if meeting positioning accuracy request, using this k base station as
Otherwise initial base station group expands k and returns to S2 and re-start cluster；
S5, initial base station group is ranked up from small to large by the TOA value of measurement, and gradually removes the big base station of TOA value, until
Export minimum base station number.
2. as described in claim 1 based on the locating base station selection method of minimum spanning tree clustering algorithm, the step S2 into
One step the following steps are included:
S21, N number of base station for acquisition construct the adjacency matrix of complete graph according to the distance between base station；
S22, the minimum spanning tree that distance matrix is found by minimum spanning tree prim algorithm；
S23, k-1 side for deleting maximum weight in minimum spanning tree, obtain k stalk tree, and every stalk tree represents one initially
Clustering cluster, and using the mean value of base station position data in each initial clustering cluster as such position data center, it is contemplated that base
It stands influence of the distribution to positioning, k value can be taken into biggish value in advance, base station is divided into more classes, makes terminal as far as possible
The base station of surrounding can be selected, and k value is by the present invention To be rounded symbol downwards.
3. as described in claim 1 based on the locating base station selection method of minimum spanning tree clustering algorithm, the step S3 into
One step includes:
Theoretically, all N number of base stations are involved in the positions calculations of mobile terminal, and positioning accuracy will be very high, therefore the present invention will
Using the position location of all N number of base stations as reference standard, it is assumed that the position of N number of base station location is L0, and applying k base
The position that station is positioned is Lk, define the relative positioning error of k base stationWhen RPE very little
It waits, it is believed that meet positioning accuracy request, when RPE then thinks to be unsatisfactory for positioning accuracy request very much greatly.
4. as described in claim 1 based on the locating base station selection method of minimum spanning tree clustering algorithm, the step S5 into
One step includes:
S51, a possibility that considering that base station and distance of mobile terminal are remoter, encountering barrier is bigger, and non line of sight degree is bigger, accidentally
It is poor horizontal also bigger, therefore some base stations closer away from the target terminal can be preferentially selected, the present invention considers measurement
TOA value is as the standard for measuring base station and distance of mobile terminal, and smaller TOA value, error is smaller, therefore first by first primordium
Group of standing is ranked up from small to large by the TOA value of base station measurement；
S52, the analysis according to S51, the selection measurement the smallest k-1 base station of TOA value position terminal；
S53, judge whether the positioning accuracy of k-1 base station meets positioning requirements, if satisfied, k-1, which is assigned to k, returns to S52, it is no
Then, minimum base station number is exported.
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| CN115334643A (en) * | 2022-08-11 | 2022-11-11 | 广州爱浦路网络技术有限公司 | Method and system for determining positioning measurement node |
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