CN108566675B - WiFi indoor positioning method based on multiple access point selection - Google Patents

WiFi indoor positioning method based on multiple access point selection Download PDF

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CN108566675B
CN108566675B CN201711262019.3A CN201711262019A CN108566675B CN 108566675 B CN108566675 B CN 108566675B CN 201711262019 A CN201711262019 A CN 201711262019A CN 108566675 B CN108566675 B CN 108566675B
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黄鹏宇
赵豪杰
刘伟
盛敏
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Xidian University
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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W4/00Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02Services making use of location information
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
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    • H04W84/02Hierarchically pre-organised networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
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Abstract

The invention discloses a WiFi indoor positioning method based on multiple access point selection, which mainly solves the problem of inaccurate positioning result of the access point in the prior art, and adopts the technical scheme that: 1) collecting access point data; 2) according to the access point data, multiple access point selections are carried out, and an access point set with stable signals and strong resolving power is selected to form a position fingerprint database; 3) grouping the positions according to the fingerprints of the positions; 4) reselecting an access point for each position cluster, and selecting a characteristic access point set capable of better expressing the position cluster; 5) establishing a decision tree model for each position group; 6) determining the position grouping of the target position of the positioning sample by the given positioning sample; 7) and determining the specific position of the positioning sample by using the positioning model of the position grouping of the positioning sample. The invention can effectively overcome the influence of unstable access points in the environment on the positioning precision, and can be used in various complex positioning environments.

Description

基于多重接入点选择的WiFi室内定位方法WiFi indoor positioning method based on multiple access point selection

技术领域technical field

本发明通信技术领域,特别涉及一种WiFi室内定位方法,可用于对室内用户的定位、导航等基于位置的服务。The present invention relates to the field of communication technologies, in particular, to a WiFi indoor positioning method, which can be used for location-based services such as positioning and navigation for indoor users.

背景技术Background technique

随着移动通信技术和移动互联网技术的蓬勃发展,移动智能终端应运而生,并迅速普及,基于移动互联网的各类服务呈现爆炸式增长。其中基于位置的服务LBS在过去十年也经历了快速的发展,被广泛应用到人们的生活中,极大地改变了人们的生活方式,为人们的生活带来了便利。而定位技术是LBS应用中必不可少的底层支持技术。在室外环境下,全球定位系统GPS是当前最为成熟的定位系统。但在室内,由于墙体阻隔,室内环境复杂多变,干扰众多,无法使用GPS信号进行室内定位。With the vigorous development of mobile communication technology and mobile Internet technology, mobile smart terminals have emerged and become popular rapidly, and various services based on mobile Internet have shown explosive growth. Among them, the location-based service LBS has also experienced rapid development in the past ten years, and has been widely used in people's lives, which has greatly changed people's lifestyles and brought convenience to people's lives. The positioning technology is an essential underlying support technology in LBS applications. In the outdoor environment, the global positioning system GPS is the most mature positioning system. However, indoors, due to the wall barrier, the indoor environment is complex and changeable, and there are many interferences, so it is impossible to use GPS signals for indoor positioning.

随着IEEE 802.11协议的不断完善,WiFi得到了普及。WiFi虽不是为定位而生,但却因为其传输速度快,覆盖率高,公共场合可免费使用,无需安装特殊设备,且部署成本低这些自身的特点,成为室内定位的首选。在WiFi定位技术中,目前主要的研究方向是基于信号强度RSSI的定位,其中基于位置指纹识别的WiFi室内定位方法是一种很流行的方法。With the continuous improvement of the IEEE 802.11 protocol, WiFi has become popular. Although WiFi is not born for positioning, but because of its fast transmission speed, high coverage, free use in public places, no need to install special equipment, and low deployment costs, it has become the first choice for indoor positioning. In the WiFi positioning technology, the current main research direction is the positioning based on the signal strength RSSI, and the WiFi indoor positioning method based on the location fingerprint identification is a very popular method.

基于位置指纹识别的WiFi室内定位方法就是对待定位环境中观测到的场景特征进行抽象和形式化描述,利用信号强度RSSI与物理位置之间的关联性进行定位。在不同的物理位置上,信号强度RSSI的表现力是不一样的,也就是说各点接收到的AP的信号强度不同。通过提前在各采样点检测定位环境中布置的AP点的信号强度,提取该信号强度作为定位特征值,将其训练成与物理位置的映射关系,构建相应的位置指纹数据库。然后,通过特定的匹配方法,将在待定位点实时测量到的信号强度RSSI指纹数据与位置指纹数据库中的指纹数据进行匹配,以相似度比较大的若干个指纹对应的坐标位置估计待定位用户的位置。The WiFi indoor positioning method based on location fingerprinting is to abstract and formally describe the scene features observed in the environment to be located, and use the correlation between the signal strength RSSI and the physical location to locate. In different physical locations, the expressiveness of the signal strength RSSI is different, that is to say, the signal strength of the AP received by each point is different. By detecting the signal strength of AP points arranged in the positioning environment at each sampling point in advance, extracting the signal strength as a positioning feature value, training it into a mapping relationship with the physical location, and constructing a corresponding location fingerprint database. Then, through a specific matching method, the signal strength RSSI fingerprint data measured in real time at the point to be located is matched with the fingerprint data in the location fingerprint database, and the coordinate positions corresponding to several fingerprints with relatively large similarity are used to estimate the user to be located. s position.

随着WiFi的快速普及,无线接入点也变得随处可见。这样在进行数据采集的时候,在定位环境中可以检测到数以百计的接入点。而这些接入点中存着一些距离定位环境较远的不理想接入点,其信号不稳定,波动大,且携带较大的噪声,几乎不会给定位提供有价值的信息,甚至还有可能会降低定位精度。另外,当使用所有的接入点时,这会大大增加指纹库的维度,增大定位复杂度。With the rapid spread of WiFi, wireless access points have become ubiquitous. In this way, during data collection, hundreds of access points can be detected in the positioning environment. Among these access points, there are some unsatisfactory access points that are far away from the positioning environment. Their signals are unstable, fluctuate greatly, and carry a lot of noise. They hardly provide valuable information for positioning, and even some Positioning accuracy may be reduced. In addition, when all access points are used, this will greatly increase the dimension of the fingerprint database and increase the complexity of positioning.

发明内容SUMMARY OF THE INVENTION

本发明的目的在于提出一种基于多重接入点选择的WiFi室内定位方法,以选择出更为稳定且分辨能力强的接入点集合来表示位置指纹,减少计算复杂度、降低指纹库的维度、去除信号质量差的接入点对定位结果的影响,提高定位精度。The purpose of the present invention is to propose a WiFi indoor positioning method based on multiple access point selection, so as to select a set of access points that are more stable and capable of distinguishing to represent the location fingerprint, reduce the computational complexity and reduce the dimension of the fingerprint database , Remove the influence of the access point with poor signal quality on the positioning result, and improve the positioning accuracy.

本发明的技术思路是:通过多重接入点选择,选出信号稳定且分辨能力强的接入点集合,建立一个维度更低且更为稳定的位置指纹库,通过该位置指纹库纹对定位环境中的位置进行分群,将指纹相似度更高位置分到同一个位置分群内。通过对接入点的重新选择,选出可以更好表示各位置分群特征的接入点集合,并建立对位置分群有更好定位效果的决策模型。The technical idea of the present invention is as follows: through multiple access point selection, select a set of access points with stable signals and strong discrimination ability, establish a location fingerprint database with lower dimensions and more stable, and locate the location through the location fingerprint database pattern. The locations in the environment are grouped, and the locations with higher fingerprint similarity are grouped into the same location group. Through the re-selection of access points, a set of access points that can better represent the characteristics of each location grouping is selected, and a decision-making model with better positioning effect for location grouping is established.

根据上述思路,本发明的实现步骤包括如下:According to the above thinking, the implementation steps of the present invention include the following:

1)构建接入点信号强度数据库:1) Build the access point signal strength database:

将定位环境划分为多个大小相同的栅格,并用栅格的中心点位置表示该栅格的位置;再对每一个位置进行数据采集,记录每个位置检测到的各接入点信号强度值,形成一个记录各位置采样数据的接入点信号强度数据库;Divide the positioning environment into multiple grids of the same size, and use the position of the center point of the grid to indicate the position of the grid; then collect data for each position, and record the signal strength value of each access point detected at each position , forming an access point signal strength database recording the sampling data of each location;

2)构建位置指纹库:2) Build the location fingerprint library:

2a)根据定位环境中各接入点的覆盖时间分布,设置接入点至少覆盖各位置的时间初始阈值m,及接入点至少覆盖整个定位空间的时间初始阈值n,根据数据库中接入点对各位置的覆盖时间,以及接入点在整个定位环境的覆盖时间,对接入点进行多重选择,删除覆盖时间小于这两个阈值的接入点,筛选出预选接入点,形成预选接入点集合;2a) According to the coverage time distribution of each access point in the positioning environment, set the time initial threshold m that the access point covers at least each location, and the time initial threshold n that the access point covers at least the entire positioning space, according to the access point in the database. For the coverage time of each location and the coverage time of the access point in the entire positioning environment, perform multiple selections on the access points, delete the access points whose coverage time is less than these two thresholds, filter out the preselected access points, and form a preselected access point. set of entry points;

2b)计算预选接入点集合中各接入点的信息增益,按信息增益从大到小的顺序对接入点进行排序,选择前k个接入点,构成最终的指纹接入点集合,k大于等于10;2b) Calculate the information gain of each access point in the preselected access point set, sort the access points in descending order of the information gain, and select the first k access points to form the final fingerprint access point set, k is greater than or equal to 10;

2c)根据指纹接入点集合对步骤1)得到的接入点信号强度数据库进行筛选,仅保留指纹接入点集合中包含的接入点数据,得到各位置的指纹,形成最终的位置指纹库;2c) Screen the access point signal strength database obtained in step 1) according to the fingerprint access point set, retain only the access point data contained in the fingerprint access point set, obtain the fingerprints of each location, and form the final location fingerprint database ;

3)使用k-means算法对环境位置进行分群,并对每个位置分群进行接入点的重新选择,选择出能够更好地表示该位置分群自己的特征接入点集合;3) Use the k-means algorithm to group the environmental locations, and re-select the access points for each location group, and select a feature access point set that can better represent the location group itself;

4)使用C4.5决策树方法为每个分群建立决策树模型,得到一个位置未知信息减少最快的判决模型;4) Use the C4.5 decision tree method to establish a decision tree model for each grouping, and obtain a decision model with the fastest reduction of location unknown information;

5)定位阶段5) Positioning stage

5a)给定需要定位的样本数据,计算定位样本指纹到各位置分群之间的欧氏距离,选择与其欧氏距离最小的位置分群,将该分群作为其目标位置所在的分群;5a) Given the sample data that needs to be positioned, calculate the Euclidean distance between the positioning sample fingerprint and each position cluster, select the position cluster with the smallest Euclidean distance from it, and use the cluster as the cluster where its target position is located;

5b)定位样本沿着该分群的判决树模型从根节点向下移动,直到移动到判决树的叶子节点,该叶子节点即为最终的定位样本的位置。5b) The positioning sample moves down from the root node along the grouped decision tree model until it moves to the leaf node of the decision tree, and the leaf node is the position of the final positioning sample.

本发明与现有技术相比具有以下优点:Compared with the prior art, the present invention has the following advantages:

本发明通过多重接入点选择可以删除不理想接入点、信号不稳定的接入点,能选择出更为稳定且分辨能力强的接入点集合,从而减少定位计算复杂度、降低指纹库的维度,提高定位精度,可以应用到更加复杂的定位环境;The present invention can delete unsatisfactory access points and access points with unstable signals through multiple access point selection, and can select a set of access points that are more stable and have strong distinguishing ability, thereby reducing the complexity of positioning calculation and the fingerprint database. It can improve the positioning accuracy and can be applied to more complex positioning environments;

本发明通过对位置分群接入点的重新选择,可以选择出更好地表示该位置分群的特征接入点集合,从而能够为各分群建立更合理的定位模型,进一步提高定位精度。The present invention can select a set of characteristic access points that better represent the location group by re-selection of the location grouping access points, so that a more reasonable positioning model can be established for each grouping, and the positioning accuracy can be further improved.

附图说明Description of drawings

图1是本发明的实施流程图;Fig. 1 is the implementation flow chart of the present invention;

图2是本发明实施的定位场景示意图;2 is a schematic diagram of a positioning scenario implemented by the present invention;

图3是本发明在定位场景中不同定位误差下的定位结果示意图;3 is a schematic diagram of the positioning results of the present invention under different positioning errors in a positioning scene;

图4是定位误差在2m之内,本发明与基于信息增益法的定位方法在定位场景中的定位结果对比图;Fig. 4 is the positioning result comparison diagram of the present invention and the positioning method based on the information gain method in the positioning scene within 2m of positioning error;

图5是位置分群的决策树模型。Figure 5 is a decision tree model for location clustering.

具体实施方式Detailed ways

参照图1,本发明的实现步骤如下:1, the implementation steps of the present invention are as follows:

步骤1,构建接入点信号强度数据库。Step 1, build the access point signal strength database.

如图2,本实例的定位场景是西安电子科技大学主楼4楼I区走廊,面积为340m2As shown in Figure 2, the location scene of this example is the corridor of Zone I, 4th floor, the main building of Xidian University, with an area of 340m 2 ;

本步骤是先将所述定位环境划分成177个边长为0.8m的正方形栅格,用栅格的中心点位置表示该栅格的位置,并对各位置进行编号,用Gj表示第j个位置;再对每一个位置进行数据采集,记录每个位置检测到的各接入点信号强度RSSI值,形成一个记录各位置采样数据的RSSI数据库。In this step, the positioning environment is firstly divided into 177 square grids with a side length of 0.8m, the position of the grid is represented by the position of the center point of the grid, and each position is numbered, and G j is used to represent the jth each location; then collect data for each location, record the RSSI value of the signal strength of each access point detected at each location, and form an RSSI database that records the sampling data of each location.

步骤2,接入点选择。Step 2, access point selection.

本步骤进行接入点选择时,分为如下两步进行:When selecting an access point in this step, it is divided into the following two steps:

2a)该方法基于接入点的覆盖时间对检测到的接入点进行初步筛选,删除那些信号不稳定的接入点,用相对稳定的接入点构成预先接入点集合:2a) The method preliminarily screens the detected access points based on the coverage time of the access points, deletes those access points with unstable signals, and uses relatively stable access points to form a pre-access point set:

2a1)计算每个位置上检测到的接入点在检测时间段内出现的个数,并保留那些在检测时间段内至少80%的时间覆盖该位置的接入点,删除其它的接入点,用各位置保留的接入点集合构成初级的预选接入点的集合;2a1) Calculate the number of access points detected at each location in the detection time period, and keep those access points that cover the location at least 80% of the time in the detection time period, and delete other access points , using the set of access points reserved at each location to form a set of primary pre-selected access points;

2a2)计算初级预选接入点集合中各接入点在定位环境中所有位置出现的次数,保留那些在检测时间段内至少有20%的时间覆盖所有位置的接入点,形成最终的预选接入点集合;2a2) Calculate the number of times that each access point in the primary pre-selected access point set appears at all positions in the positioning environment, and retain those access points that cover all positions at least 20% of the time during the detection period to form the final pre-selected access point. set of entry points;

2b)基于信息增益方法计算预选接入点集合中接入点的信息增益,按信息增益的降序对接入点排序,选择信息增益最大的15个接入点构成接入点集合,接入点的信息增益计算过程如下:2b) Calculate the information gain of the access points in the pre-selected access point set based on the information gain method, sort the access points in descending order of the information gain, and select 15 access points with the largest information gain to form the access point set. The calculation process of the information gain is as follows:

2b1)计算定位环境中位置信息的不确定度H(G):2b1) Calculate the uncertainty H(G) of the position information in the positioning environment:

Figure BDA0001493835760000041
Figure BDA0001493835760000041

其中,G表示定位环境中的位置,Gi表示第i个位置,P(Gi)表示位置Gi出现的概率,177为定位环境中位置的个数;Among them, G represents the position in the positioning environment, G i represents the ith position, P(G i ) represents the probability of the occurrence of the position G i , and 177 is the number of positions in the positioning environment;

2b2)计算在已知接入点的条件下,定位环境中位置信息的不确定度H(G|APi):2b2) Calculate the uncertainty H(G|AP i ) of the location information in the positioning environment under the condition of known access points:

Figure BDA0001493835760000042
Figure BDA0001493835760000042

其中,APi表示第i个接入点,vj表示APi的信号强度取值,N表示APi信号强度的取值个数,H(G|APi=vj)表示在已知APi的信号强度取值为vj的条件下,定位环境中位置的信息熵,其计算公式与H(G)相同;Among them, AP i represents the i-th access point, v j represents the value of the signal strength of AP i , N represents the number of values of the signal strength of AP i , and H(G|AP i =v j ) represents the value of the known AP i Under the condition that the signal strength of i is v j , the information entropy of the location in the positioning environment, its calculation formula is the same as H(G);

2b3)根据2b1)和2b2)的结果计算接入点的信息熵Gain(APi):2b3) Calculate the information entropy Gain(AP i ) of the access point according to the results of 2b1) and 2b2):

Gain(APi)=H(G)-H(G|APi),Gain(AP i )=H(G)-H(G|AP i ),

其中,Gain(APi)表示在已知接入点APi的条件下,定位环境中位置信息不确定度的减少量,信息增益越大表示位置不确定信息的减少量越大,且接入点对位置的分辨能力越强。Among them, Gain(AP i ) represents the reduction of the uncertainty of the location information in the positioning environment under the condition of known access point AP i . The greater the information gain, the greater the reduction of the location uncertainty information, and the The point-to-position resolution is stronger.

步骤3,构建位置指纹库。Step 3, build a location fingerprint library.

根据指纹接入点集合对步骤1得到的接入点信号强度数据库进行筛选,即仅保留指纹接入点集合中包含的接入点数据,得到各位置的指纹,形成最终的位置指纹库。The access point signal strength database obtained in step 1 is screened according to the fingerprint access point set, that is, only the access point data contained in the fingerprint access point set is retained, and the fingerprints of each location are obtained to form the final location fingerprint database.

步骤4,位置分群。Step 4, location grouping.

本步骤使用经典的k-means分群方法根据各位置的指纹对位置进行分群,其过程如下:This step uses the classical k-means grouping method to group the positions according to the fingerprints of each position, and the process is as follows:

4a)确定分群数量k,本实例在图2所示的定位环境下确定分群数为5,任选5个位置作为这5个群的群中心,并将位置的指纹做为群指纹;4a) determine the number of groups k, this example determines that the number of groups is 5 under the positioning environment shown in Figure 2, optionally 5 positions are used as the group center of these 5 groups, and the fingerprint of the position is used as the group fingerprint;

4b)计算所有位置到这5个群中心的欧氏距离,将其分配到距离最小的群,所有位置分配完之后,求位置分群内所有群元素指纹的均值,作为新的群中心;4b) Calculate the Euclidean distance from all positions to the 5 cluster centers, and assign it to the group with the smallest distance. After all positions are assigned, find the mean value of the fingerprints of all the group elements in the position cluster as the new cluster center;

4c)重复步骤4b,直到群中心指纹不再发生变化,即为位置分群结束。4c) Repeat step 4b until the fingerprint of the cluster center no longer changes, that is, the location grouping ends.

步骤5,接入点的重新选择。Step 5, re-selection of the access point.

本步骤使用信息增益法对接入点进行重新选择,分别计算预选接入点集合中各接入点对各群群元素的分辨能力,为每一个位置分群选择一组接入点集合,且重新选择分群的接入点集合时,只考虑接入点对该分群内位置的分辨能力,不考虑该接入点对其他分群元素的分辨能力,得到对于每个分群最优的接入点集合;In this step, the information gain method is used to re-select the access points, and the discrimination capability of each access point in the pre-selected access point set for each group element is calculated respectively, and a set of access point sets is selected for each location grouping, and the When selecting a clustered access point set, only the access point's ability to distinguish the position within the group is considered, and the access point's ability to distinguish other grouping elements is not considered, and the optimal access point set for each group is obtained;

通过各分群最优的接入点集合对步骤1得到的接入点信号强度数据库进行重新筛选,得到位置分群内各位置新的指纹,接入点重现选择之前各位置的指纹是由步骤2中选择出的接入点集合组成的,且步骤2中选择出的接入点集合是对整个定位环境来说最优的;Re-screen the access point signal strength database obtained in step 1 through the optimal access point set for each group, and obtain new fingerprints for each location in the location group. It consists of the set of access points selected in step 2, and the set of access points selected in step 2 is optimal for the entire positioning environment;

本步骤的目的是选择出对于每个位置分群来说最优的接入点,即局部最优,因为对于整个定位环境最优的接入点集合,不一定是每个位置分群的最优接入点集合,故新的位置指纹比接入点重现选择之前位置指纹更好的表示各位置的属性,即新的位置指纹可以更好地表示各位置的特征。The purpose of this step is to select the optimal access point for each location grouping, that is, the local optimum, because the optimal access point set for the entire positioning environment is not necessarily the optimal access point for each location grouping. Therefore, the new location fingerprint can better represent the attributes of each location than the location fingerprint before the access point re-selection, that is, the new location fingerprint can better represent the characteristics of each location.

步骤6,建立定位决策模型。Step 6, establishing a positioning decision model.

本步骤基于C4.5算法为每个位置分群建立决策树模型,该算法基于接入点的信息增益率来选择决策树各节点的接入点,建立一个位置未知信息减少最快的判决模型,过程如下:In this step, a decision tree model is established for each location grouping based on the C4.5 algorithm. The algorithm selects the access points of each node of the decision tree based on the information gain rate of the access point, and establishes a decision model with the fastest reduction of location unknown information. The process is as follows:

6a)对各接入点的信号强度取值进行离散化,即将接入点的取值范围划分成几个连续的取值范围,本实例将接入点取值范围分为但不限于两段,并计算出各接入点离散化后对应的信息增益率,选择信息增益率最大的接入点作为根节点;6a) Discretize the signal strength value of each access point, that is, divide the value range of the access point into several continuous value ranges. In this example, the value range of the access point is divided into but not limited to two sections , and calculate the corresponding information gain rate of each access point after discretization, and select the access point with the largest information gain rate as the root node;

6b)将接入点的每一个取值范围对应一个树枝,并将该取值范围作为该树枝的判决条件;6b) Corresponding each value range of the access point to a branch, and using the value range as the judgment condition of the branch;

6c)重复以上过程,进一步确定各树枝连接点子节点,直到最终各树枝的子节点均为叶子结点,即完成了决策树的建立。6c) Repeat the above process to further determine the sub-nodes of each branch connection point, until finally the sub-nodes of each branch are all leaf nodes, that is, the establishment of the decision tree is completed.

步骤7,判断定为样本所在位置分群。Step 7: It is determined that the location of the sample is grouped.

根据需要定位的样本数据,计算该数据到各位置分群之间的欧氏距离,本实例在图2所示的定位环境中得到5个欧氏距离,选择与定位样本欧氏距离最小的位置分群,将该分群作为其目标位置所在的分群,其欧氏距离计算如下:According to the sample data to be located, calculate the Euclidean distance between the data and each location group. In this example, 5 Euclidean distances are obtained in the positioning environment shown in Figure 2, and the location group with the smallest Euclidean distance from the positioning sample is selected. , take this group as the group where its target position is located, and its Euclidean distance is calculated as follows:

Figure BDA0001493835760000061
Figure BDA0001493835760000061

其中,D(T,Cj)表示定位样本T与第j个位置分群的欧氏距离,Cj是第j个位置分群的群中心,SSj(T)表示定位样本中第i个接入点的信号强度,SSj(Cj)表示第j个位置分群的第i个接入点的信号强度。Among them, D(T, C j ) represents the Euclidean distance between the positioning sample T and the j-th location cluster, C j is the cluster center of the j-th location cluster, and SS j (T) represents the i-th access in the positioning sample The signal strength of the point, SS j (C j ) represents the signal strength of the ith access point of the jth location grouping.

步骤8,判断定位样本的具体位置。Step 8: Determine the specific location of the positioning sample.

定位样本沿着定位样本所在分群的判决树模型从根节点向下移动,并根据定位样本的特征接入点取值对决策树支路的判决条件进行匹配,即沿着定位样本满足判决条件的那条支路向下移动,直到移动到叶子节点,该叶子节点即为定位样本最终的位置;The positioning sample moves down from the root node along the decision tree model of the cluster where the positioning sample is located, and matches the decision condition of the decision tree branch according to the value of the characteristic access point of the positioning sample, that is, along the path that satisfies the judgment condition along the positioning sample. The branch moves down until it moves to the leaf node, which is the final position of the positioning sample;

例如表1为一个定位样本,图5为定位样本所在分群的决策树模型,利用该决策树模型对样本的定位过程如下:For example, Table 1 is a positioning sample, and Figure 5 is the decision tree model of the cluster where the positioning sample is located. The positioning process of the sample using this decision tree model is as follows:

8a)该决策树模型的根节点为AP4,故首先判断定位样本中AP4的取值满足哪条树枝的判决条件,显然该样本满足AP4节点最右端树枝的判决条件,故沿着AP4最右端树枝向下移动;8a) The root node of the decision tree model is AP4, so first determine which branch’s judgment condition the value of AP4 in the positioning sample satisfies. Obviously, this sample satisfies the judgment condition of the rightmost branch of the AP4 node, so along the rightmost branch of AP4 Move Downward;

8b)移动到节点AP1,该定位样本AP1的取值为63,满足该节点最右支的判决条件,则沿AP1最右端树枝向下移动;8b) Move to the node AP1, the value of the positioning sample AP1 is 63, and the judgment condition of the rightmost branch of the node is satisfied, then move down along the rightmost branch of AP1;

8c)当AP1向下移动移动到节点AP6时,该定位样本AP6的取值为57,满足该节点最左支的判决条件,再沿AP6最左端树枝向下移动;8c) When AP1 moves down to the node AP6, the value of the positioning sample AP6 is 57, which satisfies the judgment condition of the leftmost branch of the node, and then moves down along the leftmost branch of AP6;

8d)当AP6移动到节点G6时,该节点为叶子节点,即该定位样本的目标位置为G6。8d) When AP6 moves to node G6, the node is a leaf node, that is, the target position of the positioning sample is G6.

表1Table 1

AP1AP1 AP2AP2 AP3AP3 AP4AP4 AP5AP5 AP6AP6 6363 5656 4545 7070 6161 5757

本发明的优点可以通过以下仿真结果进一步说明:The advantages of the present invention can be further illustrated by the following simulation results:

仿真1,在定位场景中用不同定位误差对随机采集的定位样本进行定位,结果如图3,从图3中可以看出在不同定位误差条件下,最佳选择的接入点个数是一致的,且定位误差在2m内,定位正确率最优时可达到93%,定位误差在0.8m内最优时也可以有超过60%的正确率,表明本发明具有良好的定位效果。Simulation 1, in the positioning scenario, the randomly collected positioning samples are positioned with different positioning errors. The results are shown in Figure 3. It can be seen from Figure 3 that the number of optimally selected access points is the same under different positioning error conditions. and the positioning error is within 2m, the positioning accuracy can reach 93% when the positioning error is optimal, and the positioning error can be more than 60% when the positioning error is within 0.8m, indicating that the present invention has a good positioning effect.

仿真2,在定位场景中,用本发明与基于信息增益法的定位方法在定位误差为2m内对随机采集的定位样本进行定位,结果如图4,由图4可见在相同条件下,本发明定位效果明显优于基于信息增益法的定位方法。Simulation 2, in the positioning scene, the present invention and the positioning method based on the information gain method are used to locate the randomly collected positioning samples within a positioning error of 2m. The results are shown in Figure 4. It can be seen from Figure 4 that under the same conditions, the The positioning effect is obviously better than the positioning method based on the information gain method.

Claims (7)

1.基于多重接入点选择的WiFi室内定位方法,包括:1. WiFi indoor positioning method based on multiple access point selection, including: 1)构建接入点信号强度数据库:1) Build the access point signal strength database: 将定位环境划分为多个大小相同的栅格,并用栅格的中心点位置表示该栅格的位置;再对每一个位置进行数据采集,记录每个位置检测到的各接入点信号强度值,形成一个记录各位置采样数据的接入点信号强度数据库;Divide the positioning environment into multiple grids of the same size, and use the position of the center point of the grid to indicate the position of the grid; then collect data for each position, and record the signal strength value of each access point detected at each position , forming an access point signal strength database recording the sampling data of each location; 2)构建位置指纹库:2) Build the location fingerprint library: 2a)根据定位环境中各接入点的覆盖时间分布,设置接入点至少覆盖各位置的时间初始阈值m,及接入点至少覆盖整个定位空间的时间初始阈值n,根据数据库中接入点对各位置的覆盖时间,以及接入点在整个定位环境的覆盖时间,对接入点进行多重选择,删除覆盖时间小于这两个阈值的接入点,筛选出预选接入点,形成预选接入点集合;2a) According to the coverage time distribution of each access point in the positioning environment, set the time initial threshold m that the access point covers at least each location, and the time initial threshold n that the access point covers at least the entire positioning space, according to the access point in the database. For the coverage time of each location and the coverage time of the access point in the entire positioning environment, perform multiple selections on the access points, delete the access points whose coverage time is less than these two thresholds, filter out the preselected access points, and form a preselected access point. set of entry points; 2b)计算预选接入点集合中各接入点的信息增益,按信息增益从大到小的顺序对接入点进行排序,选择前k个接入点,构成最终的指纹接入点集合,k大于等于10;2b) Calculate the information gain of each access point in the preselected access point set, sort the access points in descending order of the information gain, and select the first k access points to form the final fingerprint access point set, k is greater than or equal to 10; 2c)根据指纹接入点集合对步骤1)得到的接入点信号强度数据库进行筛选,仅保留指纹接入点集合中包含的接入点数据,得到各位置的指纹,形成最终的位置指纹库;2c) Screen the access point signal strength database obtained in step 1) according to the fingerprint access point set, retain only the access point data contained in the fingerprint access point set, obtain the fingerprints of each location, and form the final location fingerprint database ; 3)使用k-means算法对环境位置进行分群,并对每个位置分群进行接入点的重新选择,选择出能够更好地表示该位置分群自己的特征接入点集合;3) Use the k-means algorithm to group the environmental locations, and re-select the access points for each location group, and select a feature access point set that can better represent the location group itself; 4)使用C4.5决策树方法为每个分群建立决策树模型,得到一个位置未知信息减少最快的判决模型;4) Use the C4.5 decision tree method to establish a decision tree model for each grouping, and obtain a decision model with the fastest reduction of location unknown information; 5)定位阶段5) Positioning stage 5a)给定需要定位的样本数据,计算定位样本指纹到各位置分群之间的欧氏距离,选择与其欧氏距离最小的位置分群,将该分群作为其目标位置所在的分群;5a) Given the sample data that needs to be positioned, calculate the Euclidean distance between the positioning sample fingerprint and each position cluster, select the position cluster with the smallest Euclidean distance from it, and use the cluster as the cluster where its target position is located; 5b)定位样本沿着该分群的判决树模型从根节点向下移动,直到移动到判决树的叶子节点,该叶子节点即为最终的定位样本的位置。5b) The positioning sample moves down from the root node along the grouped decision tree model until it moves to the leaf node of the decision tree, and the leaf node is the position of the final positioning sample. 2.根据权利要求1所述的方法,其中步骤2b)中计算预选接入点集合中各接入点的信息增益,按如下步骤进行:2. The method according to claim 1, wherein in step 2b), calculating the information gain of each access point in the preselected access point set, is carried out according to the following steps: 2b1)计算定位环境中位置信息的不确定度H(G):2b1) Calculate the uncertainty H(G) of the position information in the positioning environment:
Figure FDA0002471806030000021
Figure FDA0002471806030000021
其中,G表示定位环境中的位置,Gi表示第i个位置,P(Gi)表示位置Gi出现的概率,m表示定位环境中位置的个数;Among them, G represents the position in the positioning environment, G i represents the ith position, P(G i ) represents the probability of the occurrence of the position G i , and m represents the number of positions in the positioning environment; 2b2)计算已知接入点的条件下,定位环境中位置信息的不确定度H(G|APi):2b2) Calculate the uncertainty H(G|AP i ) of the location information in the positioning environment under the condition of known access points:
Figure FDA0002471806030000022
Figure FDA0002471806030000022
其中,APi表示第i个接入点,vj表示APi的信号强度取值,N表示APi信号强度的取值个数,P(APi=vj)表示在参考位置j上检测到接入点APi的信号强度为vj的概率;Among them, AP i represents the ith access point, v j represents the value of the signal strength of AP i , N represents the number of values of the signal strength of AP i , and P(AP i =v j ) represents the detection at the reference position j the probability that the signal strength to the access point AP i is v j ; H(G|APi=vj)表示在已知APi的信号强度取值为vj的条件下,定位环境中位置的信息熵,其计算公式与H(G)相同;H(G|AP i =v j ) represents the information entropy of the location in the positioning environment under the condition that the signal strength of AP i is known to be v j , and its calculation formula is the same as H(G); 2b3)根据2b1)和2b2)的结果计算接入点的信息熵Gain(APi):2b3) Calculate the information entropy Gain(AP i ) of the access point according to the results of 2b1) and 2b2): Gain(APi)=H(G)-H(G|APi),Gain(AP i )=H(G)-H(G|AP i ), 其中,Gain(APi)表示已知接入点APi的条件下,定位环境中位置信息不确定度的减少量,信息增益越大表示位置不确定信息的减少量越大,则该接入点对位置的分辨能力了越强。Among them, Gain(AP i ) represents the reduction of the uncertainty of the location information in the positioning environment under the condition that the access point AP i is known. The greater the information gain, the greater the reduction of the location uncertainty information. The stronger the point-to-position resolution is.
3.根据权利要求1所述的方法,其中步骤3)中使用k-means算法对环境位置进行分群,按如下步骤进行:3. method according to claim 1, wherein in step 3), use k-means algorithm to carry out grouping to environmental position, carry out according to the following steps: 3a)确定位置分群数量k,任选k个位置作为这k个群的群中心,并将位置的指纹作为群指纹,其中k大于等于2;3a) Determine the number k of position groups, select k positions as the group centers of the k groups, and use the fingerprints of the positions as the group fingerprints, where k is greater than or equal to 2; 3b)计算所有位置到这k个群中心的欧氏距离,将其分配到距离最小的群,所有位置分配完之后,求位置分群内所有群元素指纹的均值,作为新的群中心;3b) Calculate the Euclidean distances from all positions to the k group centers, and assign them to the group with the smallest distance. After all positions are allocated, find the mean value of the fingerprints of all group elements in the position group as the new group center; 3c)重复步骤3b),直到群中心不再发生变化,即为位置分群结束。3c) Repeat step 3b) until the group center no longer changes, that is, the location grouping ends. 4.根据权利要求1所述的方法,其中步骤3)中对每个位置分群进行接入点的重新选择,是使用信息增益法分别计算预选接入点集合中各接入点对各群群元素的分辨能力,为每一个位置分群选择一组接入点集合,且重新选择分群的接入点集合时,只考虑接入点对该分群内位置的分辨能力,不考虑该接入点对其他分群元素的分辨能力,得到对于每个分群最优的接入点集合,这些重新选择出来的接入点集合能更好地表示各位置分群的特征。4. The method according to claim 1 , wherein in step 3), the re-selection of access points for each location grouping is to use an information gain method to calculate, respectively, each access point in the preselected access point set to each group. The discrimination capability of the element, select a set of access points for each location group, and when re-selecting the access point set of the group, only the discrimination ability of the access point for the location in the group is considered, and the pair of access points is not considered. The discriminating ability of other grouping elements is used to obtain the optimal access point set for each grouping, and these reselected access point sets can better represent the characteristics of each location grouping. 5.根据权利要求1所述的方法,其中步骤4)中使用C4.5决策树方法为每个分群建立决策树模型,按如下步骤进行:5. method according to claim 1, wherein in step 4), use C4.5 decision tree method to establish decision tree model for each grouping, carry out according to the following steps: 5a)对各接入点的信号强度取值进行离散化,即将接入点的取值范围划分成几个连续的取值范围,并计算出各接入点离散化后对应的信息增益率,选择信息增益率最大的接入点作为根节点;5a) Discretize the signal strength value of each access point, that is, divide the value range of the access point into several continuous value ranges, and calculate the corresponding information gain rate of each access point after discretization, Select the access point with the largest information gain rate as the root node; 5b)将接入点的每一个取值范围对应一个树枝,并将该取值范围作为该树枝的判决条件;5b) Corresponding each value range of the access point to a branch, and using the value range as the judgment condition of the branch; 5c)重复以上过程,进一步确定各树枝连接点子节点,直到最终的各树枝的子节点均为叶子结点,即完成了决策树的建立。5c) Repeat the above process to further determine the child nodes of each branch connection point until the final child nodes of each branch are leaf nodes, that is, the establishment of the decision tree is completed. 6.根据权利要求1所述的方法,其中步骤5a)中计算定位样本指纹到各位置分群之间的欧氏距离,按如下公式计算:6. method according to claim 1, wherein in step 5a), calculate the Euclidean distance between positioning sample fingerprint to each position grouping, calculate according to the following formula:
Figure FDA0002471806030000031
Figure FDA0002471806030000031
其中D(T,Cj)表示定位样本T与第j个位置分群的欧氏距离,Cj是第j个位置分群的群中心,k表示步骤2b)中选择的接入点个数,SSj(T)表示定位样本中第i个接入点的信号强度,SSj(Cj)表示第j个位置分群的第i个接入点的信号强度。where D(T,C j ) represents the Euclidean distance between the positioning sample T and the jth location cluster, C j is the cluster center of the jth location cluster, k represents the number of access points selected in step 2b), SS j (T) represents the signal strength of the ith access point in the positioning sample, and SS j (C j ) represents the signal strength of the ith access point of the jth location grouping.
7.根据权利要求1所述的方法,其中步骤5b)中定位样本沿着该分群的判决树模型从根节点向下移动,是根据定位样本的特征接入点的取值判断其满足决策树哪条支路的判决条件来决定其移动方向,直到移动到叶子节点,该叶子节点即为定位样本最终的位置。7. The method according to claim 1, wherein in step 5b), the positioning sample moves down from the root node along the decision tree model of this grouping, is to judge that it satisfies the decision tree according to the value of the feature access point of the positioning sample The decision condition of which branch determines its moving direction until it moves to a leaf node, which is the final position of the positioning sample.
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