CN103987118A - Access point k-means clustering method based on received signal strength signal ZCA whitening - Google Patents
Access point k-means clustering method based on received signal strength signal ZCA whitening Download PDFInfo
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Abstract
The invention discloses an access point k-means clustering method based on received signal strength signal ZCA whitening. Each position fingerprint in RM is represented through a received signal strength vector at a corresponding reference point, and mean values are subjected to normalization; then by received signal strength mean value whitening, correlation is removed; in k-means clustering in the whole RM, k fingerprints are selected to be used as initial clustering centers; for other received signal strength mean values except the k clustering centers, according to the Euclidean distances between the mean values and the clustering centers, the mean values are distributed to clusters with the minimum Euclidean distances respectively; after all the fingerprints are processed, new clusters are obtained, and the mean value of all fingerprints of the new clusters are used as a new clustering center; and the third step and the fourth step are carried out repeatedly until the k clustering centers do not change any longer, and iteration is stopped. According to the method, the correlation of received signal strength signals is fully lowered, the accuracy of clustering is improved, and accordingly system locating accuracy is further improved.
Description
Technical field
The invention belongs to indoor positioning region clustering technical field, relate in particular to a kind of access point k-means clustering method based on received signal strength signal ZCA albefaction for indoor positioning.
Background technology
For larger localizing objects region, the statistical property of received signal strength (RSS) changes greatly, for based on learning-oriented location algorithm, if whole locating area is learnt, to increase algorithm complex, the location model of setting up is not optimum, thereby is unfavorable for improving the positioning precision of system.Therefore need to carry out clustering block to locating area, larger locating area is divided into several little locating areas, and modeling respectively, the object that reduce computation complexity to reach, improves positioning precision.Existing clustering algorithm is not all considered the relativity problem between received signal strength (RSS) signal, thereby causes the precision that nicety of grading is not high enough, limited location compute.
About locating area cluster, mainly contain following several method:
1) the explicit clustering algorithm that the people such as Youssef proposes.This algorithm is divided into different bunches by the location fingerprint of RM, and every cluster is shared the same access point receiving (AP) set, and this set is called to the key assignments of this bunch.It is little that the method is applicable to localizing objects region, in the few situation of access point (AP) quantity, for large localizing objects region and more access point (AP), exist in situation, often exist in some reference point sensing less than the situation of some access point (AP), this can make mobile terminal for online positioning stage because can not find correct bunch and mistake maybe cannot be resolved positional information.
2) Region Segmentation Algorithm that the people such as Borenovic proposes.This algorithm need to be divided into locating area several locator regions that area is suitable artificially.This algorithm weak point is to adopt artificial method that locating area is divided into subregion of the same size, is difficult to respect to the difference of indoor framework and layout and the characteristics such as similitude of signal, and algorithm application is limited.
3) k average (k-means) algorithm that the people such as Chen proposes.Its technical deficiency part is exactly the relativity problem of not considering between received signal strength (RSS) signal, has limited the accuracy of cluster.
Summary of the invention
The object of the embodiment of the present invention is to provide a kind of access point k-means clustering method based on received signal strength signal ZCA albefaction, is intended to solve due to correlation between received signal strength signal, and the accuracy of cluster is not high enough, restriction positioning precision problem.
The embodiment of the present invention is achieved in that a kind of access point k-means clustering method based on received signal strength signal ZCA albefaction, should comprise by the access point k-means clustering method based on received signal strength signal ZCA albefaction:
Step 1, by the equal value representation of received signal strength vector in corresponding reference point for each location fingerprint in RM, and by average normalization; Then by albefaction received signal strength average, remove correlation;
Step 2, k-means cluster is chosen k fingerprint as initial cluster center in whole RM;
Step 3, for other all received signal strength averages except k cluster centre,, according to the Euclidean distance of they and these cluster centres, distributes to them respectively the cluster nearest with its Euclidean distance;
Step 4, executes after all fingerprints, obtains new cluster, using the mean value of all fingerprints of new cluster as new cluster centre;
Step 5, continuous repeating step three and four, until k cluster centre no longer change, termination of iterations.
Further, cluster finishes, and each location fingerprint is converged to nearest with it cluster centre, just each cluster is considered as to a locator region; Off-line phase, each cluster and corresponding location fingerprint data form an independently sub-fingerprint database; Online positioning stage, the received signal strength newly recording is the Euclidean distance with cluster centre by calculating first, obtains nearest cluster centre, then by mapping function corresponding to this cluster centre, is drawn user's locator region.
Access point k-means clustering method based on received signal strength signal ZCA albefaction provided by the invention, received signal strength (RSS) signal is first carried out to albefaction, and then in conjunction with k-means cluster, can fully reduce the correlation between received signal strength (RSS) signal, improve the accuracy of cluster, thereby further improve the positioning precision of system.Method of the present invention is simple, easy to operate, has solved preferably due to correlation between received signal strength signal, and the accuracy of the cluster causing is low, the problem that positioning precision is low.
Accompanying drawing explanation
Fig. 1 is the flow chart of the access point k-means clustering method based on received signal strength signal ZCA albefaction that provides of the embodiment of the present invention;
Fig. 2 is the flow chart of the embodiment 1 of the access point k-means clustering method based on received signal strength signal ZCA albefaction that provides of the embodiment of the present invention;
Fig. 3 WLAN indoor positioning environmental structure figure;
Fig. 4 has shown that cluster centre number k is from 1 during to 8 variation, the clustering precision comparison diagram of the k-means clustering algorithm before and after the albefaction of RSS signal;
Fig. 5 is k system accuracy probability cumulative distribution figure while getting different value;
K-means clustering algorithm schematic diagram to the comparison of positioning precision before and after RSS signal has or not albefaction when Fig. 6 is k=4;
Fig. 7 is maximum, minimum, the mean error of system position error before and after the albefaction of RSS signal, and position error standard variance statistical chart.
Embodiment
In order to make object of the present invention, technical scheme and advantage clearer, below in conjunction with embodiment, the present invention is further elaborated.Should be appreciated that specific embodiment described herein, only in order to explain the present invention, is not intended to limit the present invention.
Below in conjunction with drawings and the specific embodiments, application principle of the present invention is further described.
As shown in Figure 1, the access point k-means clustering method based on received signal strength signal ZCA albefaction of the embodiment of the present invention comprises the following steps:
S101: by the equal value representation of received signal strength vector in corresponding reference point for each location fingerprint in RM, and by average normalization.Then by albefaction received signal strength average, remove correlation;
S102:k-means cluster is chosen k fingerprint as initial cluster center in whole RM;
S103: for other all received signal strength averages except k cluster centre,, according to the Euclidean distance of they and these cluster centres, respectively they are distributed to the cluster nearest with its Euclidean distance;
S104: execute after all fingerprints, obtain new cluster, using the mean value of all fingerprints of new cluster as new cluster centre;
S105: constantly repeating step S103 and S104, until k cluster centre no longer change, termination of iterations.
Concrete steps of the present invention are:
1) by the equal value representation of the received signal strength in corresponding reference point for each location fingerprint (RSS) vector in RM, and by average normalization.Then by albefaction received signal strength (RSS) average, remove correlation.
2) k-means cluster is chosen k fingerprint and (for fear of the initial cluster center of random selection, is caused concentrations or the deployment conditions of cluster as initial cluster center in whole RM, in locator region, choose equably k initial cluster center as far as possible, to realize as far as possible according to the consistency in physical location space, carry out cluster).
3), for other all received signal strengths (RSS) average except k cluster centre,, according to the Euclidean distance of they and these cluster centres, respectively they are distributed to the cluster nearest with its Euclidean distance.
4) execute after all fingerprints, obtain new cluster, using the mean value of all fingerprints of new cluster as new cluster centre.
5) continuous repeating step (3) and (4).Until k cluster centre no longer change, termination of iterations.
Cluster finishes, and each location fingerprint is converged to nearest with it cluster centre, just each cluster is considered as to a locator region.Off-line phase, each cluster and corresponding location fingerprint data form an independently sub-fingerprint database.Online positioning stage, the received signal strength newly recording (RSS) first calculates nearest cluster centre by the Euclidean distance with cluster centre, then by mapping function corresponding to this cluster centre, is drawn user's locator region.
Embodiments of the invention:
Experimental situation of the present invention is that the typical office block 5 layers of buildings completes, fragment of brick body of wall, metal window, glass and timber.This building is covered by 802.11 WLAN (wireless local area network), at most visible 46 access points.Specific experiment region is the 4th layer of building, general 400 square metres of area.Whole floor has computer room laboratory, corridor, hall, hourly halt and a lavatory, and some offices.In this experiment, lay altogether 68 reference points (RP) and 35 random test points (TestPoint, TP).At 40 RSS samples of the general reception of each RP, each TP receives general 5～20 RSS measured values.All data acquisitions all complete on same working day.The location algorithm of on-line stage adopts WKNN (K=3).As shown in Figure 3, redness "+" is wherein RP position to experimental situation.
Fig. 4 has shown that cluster centre number k is from 1 during to 8 variation,, the clustering precision comparison diagram of the k-means clustering algorithm before and after the albefaction of RSS signal.
As can be seen from Figure 4 the effect of the k-means algorithm after RSS albefaction before being better than albefaction aspect cluster accuracy first.Especially along with the increase of cluster centre k value, this raising is further obvious, this be because, the in the situation that of certain in localizing objects region, cluster centre value is larger, illustrates that the locator region being divided is more, and between corresponding neighborhood, the correlation between signal strength signal intensity is just larger, by albefaction, remove the redundancy that signal correlation brings, thereby can make the clustering block accuracy before and after albefaction have larger lifting.Secondly, in this experiment, when the value of k gets 4, the accuracy of cluster can reach 97%, certainly, is less than in 4 the accuracy rate of cluster higher at k, but when k value is too small, easily occur that class internal information similarity is not high, be unfavorable for reducing computation complexity and improve positioning precision.In addition, from figure, it can also be seen that, increase along with clusters number, the piecemeal precision of the k-means clustering algorithm before and after all albefactions is all at fast-descending, as being more than or equal to after 5 when k value, due to too much piecemeal, each locator regional extent is reduced, cause similarity between class to increase, not only reduced piecemeal accuracy, also can reduce positioning precision simultaneously.
System position error probability cumulative distribution corresponding when the value of k gets 8 by 1 as shown in Figure 5.
As can be seen from Figure 5, when the value of k is 4, the positioning precision of system is better than the situation that k gets other value on the whole, although be not the highest in the cumulative probability that is less than or equal to 3 meters of, this does not affect its whole positioning performance advantage.In fact, cumulative probability during k=4 is 77.1% herein, and maximum is herein 80% (k=5), although can realize 3 meters of positioning precisioies with interior 80% fiducial probability when k value is 5, but because clustering precision now only has 93.9%, be starkly lower than k 97% when getting 4, and cluster centre value be 5 o'clock at 2 meters with interior and 4 meters not high with interior positioning precision, so consider the performance of clustering block accuracy and positioning precision two aspects, we think that cluster centre number is to be conducive to the reliability of positioning of system at 4 o'clock most.From Fig. 5, we it can also be seen that, cluster is conducive to improve positioning precision, and for example, when cluster centre number gets respectively 2,3,4,5, the cumulative probability of system accuracy distributes and is all better than the situation without cluster (k=1).But also see, when the value of k is 6,7 and 8, the good behaviour of this cluster has disappeared again simultaneously, that is to say aspect the fiducial probability of positioning precision will be lower than without cluster situation.
Above-mentioned analysis shows, the numerical value of cluster centre has direct relation for the performance quality of system, so in order to dwindle location fingerprint space, improve positioning precision, can effectively divide locator interval again, guarantee higher cluster accuracy, avoid because of the harmful effect of cluster error band to positioning precision, will get cluster centre k value herein and be taken as 4.When getting k value when being 4, we have compared the comparison to positioning precision before and after RSS has or not albefaction of k-means clustering algorithm, as shown in Figure 6.
In Fig. 6 optimum cluster situation, the impact of signal albefaction on positioning precision
As can be seen from Figure 6, to carrying out again cluster after signal albefaction, can obviously improve the positioning precision of system.The probability of cluster positioning precision after albefaction in 2 meters is 60%, compared with RSS, without the cluster location of albefaction, improved 39.7%; Positioning precision probability in 3 meters is 77.1%, than the cluster location without albefaction, has improved 12.4%.In addition, the positioning precision probability in 1 meter also has comparatively significantly increases.In order to be illustrated more clearly in the effect of albefaction, Fig. 7 has contrasted maximum, minimum, the mean error of albefaction front and back system position errors, and position error standard variance.As can be seen from Figure 7, the system location mean error after albefaction is 2.21 meters, and is 2.74 meters without the location mean error of albefaction, has improved 24%; And maximum positioning error is also by dropping to 6.27 meters without 7.91 meters of albefaction, has declined 1.64 meters; In addition, the standard variance of position error is also than becoming 1.67 meters without 2.85 meters of albefaction, this degree that illustrates that error in each test point departs from average position error is reducing, from the variation of position error scope, also this point can be described, error range before albefaction is 0.93～7.91 meter, and the cluster position error scope of albefaction is 0.61～6.27 meter.Because orientation range is less, the degree that specification error departs from average is just less, so standard variance just can diminish.
Embodiment 1:
As shown in Figure 2, embodiments of the invention specifically comprise the following steps:
The first step, hard clustering number k, calculates RSS average normalization from all reference points;
Second step, albefaction normalization data;
The 3rd step, an initialization k cluster centre;
The 4th step, the normalization RSS average that calculating RP is corresponding and the distance of k cluster centre;
The 5th step, is divided into the class at nearest cluster centre place with reference to point, suppress to upgrade each cluster centre;
The 6th step, whether cluster centre immobilizes, no, returns to the 4th step, is to carry out next step;
The 7th step, exports k cluster centre and corresponding RP set.
The present invention first carries out albefaction by received signal strength (RSS) signal, and then in conjunction with k-means cluster, can fully reduce the correlation between received signal strength (RSS) signal, improve the accuracy of cluster, thereby further improve the positioning precision of system.
The foregoing is only preferred embodiment of the present invention, not in order to limit the present invention, all any modifications of doing within the spirit and principles in the present invention, be equal to and replace and improvement etc., within all should being included in protection scope of the present invention.
Claims (2)
1. the access point k-means clustering method based on received signal strength signal ZCA albefaction, is characterized in that, should comprise by the access point k-means clustering method based on received signal strength signal ZCA albefaction:
Step 1, by the equal value representation of received signal strength vector in corresponding reference point for each location fingerprint in RM, and by average normalization; Then by albefaction received signal strength average, remove correlation;
Step 2, k-means cluster is chosen k fingerprint as initial cluster center in whole RM;
Step 3, for other all received signal strength averages except k cluster centre,, according to the Euclidean distance of they and these cluster centres, distributes to them respectively the cluster nearest with its Euclidean distance;
Step 4, executes after all fingerprints, obtains new cluster, using the mean value of all fingerprints of new cluster as new cluster centre;
Step 5, continuous repeating step three and four, until k cluster centre no longer change, termination of iterations.
2. the access point k-means clustering method based on received signal strength signal ZCA albefaction as claimed in claim 1, it is characterized in that, cluster finishes, and each location fingerprint is converged to nearest with it cluster centre, just each cluster is considered as to a locator region; Off-line phase, each cluster and corresponding location fingerprint data form an independently sub-fingerprint database; Online positioning stage, first the received signal strength newly recording calculates nearest cluster centre by the Euclidean distance with cluster centre, then by the corresponding mapping function of this cluster centre, is drawn user's locator region.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106643736A (en) * | 2017-01-06 | 2017-05-10 | 中国人民解放军信息工程大学 | Indoor positioning method and system |
| CN106643736B (en) * | 2017-01-06 | 2020-05-22 | 中国人民解放军信息工程大学 | Indoor positioning method and system |
| CN108540926A (en) * | 2017-03-02 | 2018-09-14 | 中国移动通信有限公司研究院 | A kind of construction method and device of wireless signal fingerprint |
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| CN107148002A (en) * | 2017-05-27 | 2017-09-08 | 柳州天艺科技有限公司 | Primary user's localization method of RSSI based on cluster |
| CN110837112A (en) * | 2018-08-16 | 2020-02-25 | 中国石油化工股份有限公司 | Data preprocessing method and system for seismic channel editing |
| CN110691319A (en) * | 2019-09-03 | 2020-01-14 | 东南大学 | Method for realizing high-precision indoor positioning of heterogeneous equipment in self-adaption mode in use field |
| CN110691319B (en) * | 2019-09-03 | 2021-06-01 | 东南大学 | Method for realizing high-precision indoor positioning of heterogeneous equipment in self-adaption mode in use field |
| CN110636466A (en) * | 2019-09-06 | 2019-12-31 | 联泰集群（北京）科技有限责任公司 | WiFi indoor positioning system based on channel state information under machine learning |
| CN111488941A (en) * | 2020-04-15 | 2020-08-04 | 烽火通信科技股份有限公司 | Video user grouping method and device based on improved Kmeans algorithm |
| CN111488941B (en) * | 2020-04-15 | 2022-05-13 | 烽火通信科技股份有限公司 | Video user grouping method and device based on improved Kmeans algorithm |
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Application publication date: 20140813 Assignee: Zhejiang Fengshou e-commerce Co.,Ltd. Assignor: ZHEJIANG NORMAL University Contract record no.: X2022980008007 Denomination of invention: K-means clustering method of access points based on zca whitening of received signal strength Granted publication date: 20170905 License type: Common License Record date: 20220623 |