CN109739830A - A rapid construction method of location fingerprint database based on crowdsourced data - Google Patents
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Abstract
本发明公开了一种基于众包数据的位置指纹数据库快速构建方法,S1、基于PDR、粒子滤波和地图约束的裂变建库;S2、基于MDS纠正非显式地标点区域的PDR累积误差。本发明基于裂变方式和设置路径有效时间阈值构建位置指纹数据库,并采用MDS结合隐式地标点纠正PDR累计误差,同时,基于加权多维WiFi数值特征构建短距离位置指纹关联模型;相对于现有技术可以快速构建位置指纹数据库,减少传感器的累计误差,提高位置和指纹的匹配精度,有效地解决了现有技术中频繁打扰用户,位置和指纹匹配精度低的问题。
The invention discloses a method for rapidly constructing a location fingerprint database based on crowdsourced data. S1, fission building based on PDR, particle filtering and map constraints; S2, correcting PDR accumulated errors in non-explicit landmark point areas based on MDS. The present invention constructs a location fingerprint database based on the fission method and the set path effective time threshold, and uses MDS combined with implicit landmark points to correct the PDR cumulative error, and at the same time, constructs a short-distance location fingerprint correlation model based on weighted multi-dimensional WiFi numerical features; compared with the prior art The location fingerprint database can be quickly constructed, the accumulative error of the sensor is reduced, the matching accuracy of the location and the fingerprint is improved, and the problems of frequently disturbing the user and low matching accuracy of the location and the fingerprint in the prior art are effectively solved.
Description
技术领域technical field
本发明属于通信的技术领域,具体涉及一种基于众包数据的位置指纹数据库快速构建方法。The invention belongs to the technical field of communications, and in particular relates to a method for rapidly constructing a location fingerprint database based on crowdsourcing data.
背景技术Background technique
现有的基于众包构建位置指纹数据库方法包括两种:一种是显示众包方式,该方法的特点是可以得到相对准确的位置指纹信息,但是会频繁打扰用户,造成用户的体验下降,用户不准确操作时会造成数据受到污染,阻碍实际推广应用;另一种是隐式众包方式,该方法可避免用户被频繁打扰的现象,但是需要考虑位置和指纹的匹配问题,现有的基于隐式众包的位置指纹数据库构建方法,只能对位置和指纹进行粗略的匹配。There are two existing methods for building a location fingerprint database based on crowdsourcing: one is the display crowdsourcing method, which is characterized in that it can obtain relatively accurate location fingerprint information, but it will frequently disturb users, resulting in a decrease in user experience, and user experience. Inaccurate operation will cause data pollution, hindering the actual promotion and application; the other is the implicit crowdsourcing method, which can avoid the phenomenon that users are frequently disturbed, but the matching problem of location and fingerprint needs to be considered. The implicit crowdsourced location fingerprint database construction method can only roughly match location and fingerprint.
现有的基于PDR结合隐式众包构建位置指纹数据库方式可以实现位置和指纹的匹配问题,可快速构建相对完整的位置指纹数据库。但PDR存在较大的累计误差,造成位置和指纹的匹配精度不高,现有采用特征地标点纠正PDR累计误差可以提高部分区域的匹配精度,但是特征地标点数目有限,匹配精度不能得到有效提高。The existing method of building a location fingerprint database based on PDR combined with implicit crowdsourcing can realize the matching problem of location and fingerprint, and can quickly build a relatively complete location fingerprint database. However, there is a large cumulative error in PDR, resulting in a low matching accuracy between position and fingerprint. The existing use of characteristic landmarks to correct the cumulative error of PDR can improve the matching accuracy of some areas, but the number of characteristic landmarks is limited, and the matching accuracy cannot be effectively improved. .
发明内容SUMMARY OF THE INVENTION
本发明的目的在于针对现有技术中的上述不足,提供一种基于众包数据的位置指纹数据库快速构建方法,以解决现有技术中频繁打扰用户,位置和指纹匹配精度低的问题。The purpose of the present invention is to provide a method for quickly constructing a location fingerprint database based on crowdsourced data to solve the problems of frequently disturbing users and low matching accuracy of location and fingerprint in the prior art.
为达到上述目的,本发明采取的技术方案是:In order to achieve the above object, the technical scheme that the present invention takes is:
一种基于众包数据的位置指纹数据库快速构建方法,其包括:A method for rapidly constructing a location fingerprint database based on crowdsourced data, comprising:
S1、基于PDR、粒子滤波和地图约束的裂变建库,其步骤包括S1. Fission library building based on PDR, particle filter and map constraints, the steps include
S11、训练短距离位置指纹关联模型;S11. Train a short-distance location fingerprint association model;
S12、构建室内地图;S12. Build an indoor map;
S13、标记显式地标点并采集相应位置指纹数据;S13, marking explicit landmark points and collecting fingerprint data of corresponding positions;
S14、判断众包路径数据是否启效;S14. Determine whether the crowdsourcing path data is effective;
S15、基于PDR、粒子滤波和地图约束多重条件约束关联指纹和位置点;S15, based on PDR, particle filter and map constraint multiple condition constraints to associate fingerprints and location points;
S16、设置众包路径的有效时间阈值,在所述有效时间阈值内,若传感器数据累积误差属于可容忍范围内时,则进入步骤S15,否则,众包路径失效,进入步骤S17;S16, setting the effective time threshold of the crowdsourcing path, within the effective time threshold, if the cumulative error of the sensor data falls within the tolerable range, then proceed to step S15, otherwise, the crowdsourcing path is invalid, and proceed to step S17;
S17、基于裂变方式分段依次建库;S17. Build the database in sequence based on the fission method;
S2、基于MDS纠正非显式地标点区域的PDR累积误差,其步骤包括S2. Correct the cumulative error of the PDR in the non-explicit landmark region based on MDS, and the steps include:
S21、基于指纹相似度计算隐式地标点区域范围;S21. Calculate the area range of the implicit landmark point based on the fingerprint similarity;
S22、统计隐式地标点区域之间的共有路径;S22. Count common paths between implicit landmark point regions;
S23、搜索相对有效的众包路径;S23. Search for a relatively effective crowdsourcing path;
S24、重新绘制隐式地标点区域之间的新路径;S24. Redraw the new path between the implicit landmark point regions;
S25、计算隐式地标点区域的相对坐标;S25. Calculate the relative coordinates of the implicit landmark point area;
S26、基于MDS精确计算隐式地标点区域的绝对坐标;S26, accurately calculate the absolute coordinates of the implicit landmark point area based on MDS;
S27、纠正非显式地标点区域的PDR累积误差。S27. Correct the accumulated PDR error of the non-explicit landmark region.
优选地,步骤S11中训练短距离位置指纹关联模型的方法包括:Preferably, the method for training the short-distance location fingerprint association model in step S11 includes:
A1、基于多维WiFi数值特征指纹距离建模;A1. Based on multi-dimensional WiFi numerical feature fingerprint distance modeling;
A2、短距离位置指纹关联模型的训练。A2. Training of short-distance location fingerprint association model.
优选地,步骤A1基于多维WiFi数值特征指纹距离建模的方法为:Preferably, the method of step A1 based on multi-dimensional WiFi numerical feature fingerprint distance modeling is:
A11、构建由相对子序列构成的相对指纹;A11. Construct a relative fingerprint composed of relative subsequences;
A12、计算相对子序列对的相似性;A12. Calculate the similarity of relative subsequence pairs;
A13、计算相对指纹对中指纹相对子序列对的相似度,并采用遍历计算得到相对指纹对的相似度矩阵;A13. Calculate the similarity of the relative subsequence pairs of fingerprints in the relative fingerprint pair, and obtain the similarity matrix of the relative fingerprint pair by traversal calculation;
A14、采用动态规划算法在相似度矩阵中搜索查找最佳匹配的相对指纹对。A14. Use the dynamic programming algorithm to search the similarity matrix to find the best matching relative fingerprint pair.
优选地,步骤A2短距离位置指纹关联模型的训练方法为:Preferably, the training method of the short-distance location fingerprint association model in step A2 is:
A21、获取已知地标点周围的众包数据;A21. Obtain crowdsourced data around known landmarks;
A22、根据指纹相似度提取地标点辐射区域的指纹数据;A22. Extract the fingerprint data of the radiation area of the landmark point according to the fingerprint similarity;
A23、计算所述地标点辐射区域指纹数据的指纹距离;A23. Calculate the fingerprint distance of the fingerprint data of the radiation area of the landmark point;
A24、基于MDS确定短距离位置指纹关联模型。A24. Determine a short-distance location fingerprint association model based on the MDS.
优选地,步骤S14判断众包路径数据是否启效的方法为:Preferably, the method for judging whether the crowdsourcing path data is effective in step S14 is:
当数据库中只存在显式地标点数据时,设定当用户行走到显式地标点附近时,众包路径才开始有效,并利用短距离位置指纹关联模型计算出众包路径开始有效时的起始位置点物理坐标;When there is only explicit landmark data in the database, it is set that the crowdsourcing route becomes valid only when the user walks near the explicit landmark, and the short-distance location fingerprint correlation model is used to calculate the starting point when the crowdsourcing route becomes valid. physical coordinates of the location point;
根据WiFi指纹相似度判断用户是否已经行走到显式地标点周围。Determine whether the user has walked around the explicit landmark point according to the WiFi fingerprint similarity.
优选地,步骤S15基于PDR、粒子滤波和地图约束多重条件约束关联指纹和位置点的方法为:Preferably, in step S15, the method for constraining the associated fingerprint and location point based on multiple conditions of PDR, particle filtering and map constraint is:
结合PDR、粒子滤波和地图约束多重技术构建位置指纹数据库;Combining PDR, particle filtering and map constraint multiple techniques to build a location fingerprint database;
根据智能手机内置传感器数据推导行人步数、步长和航向,得到用户行走路径,获得众包路径上数据采集点的物理坐标,并获得位置和指纹的匹配;According to the built-in sensor data of the smartphone, the pedestrian steps, step length and heading are deduced, the user's walking path is obtained, the physical coordinates of the data collection points on the crowdsourcing path are obtained, and the matching of the position and the fingerprint is obtained;
采用粒子滤波和地图约束双重约束条件获得精准的众包路径。Accurate crowdsourcing paths are obtained by using the dual constraints of particle filtering and map constraints.
优选地,步骤S17基于裂变方式分段依次建库的方法为:Preferably, in step S17, the method of building the database in sequence based on the fission method is as follows:
当位置指纹数据库中建立了非显式位置点的指纹数据后,其他用户的众包路径数据有效起始位置为任何已知位置点周围,基于裂变方式建库;When the fingerprint data of the non-explicit location points is established in the location fingerprint database, the effective starting location of the crowdsourced path data of other users is around any known location points, and the database is built based on the fission method;
裂变方式建库之后的位置指纹数据库上的每个指纹点均对应一个粗略的物理坐标。Each fingerprint point on the location fingerprint database after the fission method is established corresponds to a rough physical coordinate.
优选地,步骤S21包括设定一个指纹相似度阈值σsim,指纹相似度超过σsim的位置点所在的小区域作为隐式地标点区域;Preferably, step S21 includes setting a fingerprint similarity threshold σ sim , and the small area where the location point where the fingerprint similarity exceeds σ sim is located is used as the implicit landmark point area;
步骤S22包括对连续路径进行编号,得到同时经过两个地标点区域之间的路径数;Step S22 includes numbering the continuous paths to obtain the number of paths passing between the two landmark point regions simultaneously;
步骤S23包括依次在两隐式地标点区域之间的共有路径中,选取磁力计数据波动最小的路径作为两地标点区域之间的有效路径;Step S23 includes sequentially selecting the path with the smallest fluctuation of the magnetometer data as the effective path between the two landmark point regions in the common path between the two implicit landmark point regions;
步骤S24包括根据隐式地标点区域之间有效路径的加速度传感器和磁力计传感器数据,重新绘制出隐式地标点区域之间的新路径;Step S24 includes redrawing a new path between the implicit landmark point regions according to the acceleration sensor and magnetometer sensor data of the effective path between the implicit landmark point regions;
步骤S25包括从显式地标节点出发,依次计算各个隐式地标点区域相对显式地标点的坐标。Step S25 includes, starting from the explicit landmark nodes, sequentially calculating the coordinates of each implicit landmark point area relative to the explicit landmark points.
优选地,步骤S26基于MDS精确计算隐式地标点区域的绝对坐标的方法为:Preferably, the method for accurately calculating the absolute coordinates of the implicit landmark point region in step S26 based on the MDS is:
根据各隐式地标点区域和显式地标点的相对坐标,构建相对距离矩阵D,According to the relative coordinates of each implicit landmark point area and the explicit landmark point, a relative distance matrix D is constructed,
其中,dij为显式地标点、隐式地标点和中心位置点之间的相对距离,i=1,2,3,…,m;j=1,2,3…,m;Among them, d ij is the relative distance between the explicit landmark point, the implicit landmark point and the center position point, i=1,2,3,...,m; j=1,2,3...,m;
将中心位置点设置为原点,根据MDS计算出显式和隐式地标点与原点的相对坐标,更新隐式地标点的原始相对坐标;Set the center point as the origin, calculate the relative coordinates of the explicit and implicit landmark points and the origin according to the MDS, and update the original relative coordinates of the implicit landmark point;
根据显式地标点的实际物理坐标求出各个隐式地标点的实际物理坐标。The actual physical coordinates of each implicit landmark point are obtained according to the actual physical coordinates of the explicit landmark points.
优选地,步骤S27纠正非显式地标点区域的PDR累积误差的方法为:Preferably, the method for correcting the PDR accumulation error of the non-explicit landmark region in step S27 is:
将各个隐式地标点作为经过该点周围众包路径的新起始点,根据经过该点的后续部分原始路径信息绘制新的众包路径,纠正PDR的累积误差,提高位置与指纹的匹配精度。Each implicit landmark point is taken as the new starting point of crowdsourcing path around the point, and a new crowdsourcing path is drawn according to the subsequent part of the original path information passing through the point, the accumulated error of PDR is corrected, and the matching accuracy of position and fingerprint is improved.
本发明提供的基于众包数据的位置指纹数据库快速构建方法,具有以下有益效果:The method for quickly constructing a location fingerprint database based on crowdsourcing data provided by the present invention has the following beneficial effects:
本发明基于裂变方式和设置路径有效时间阈值构建位置指纹数据库,并采用MDS结合隐式地标点纠正PDR累计误差,同时,基于加权多维WiFi数值特征构建短距离位置指纹关联模型;相对于现有技术可以快速构建位置指纹数据库,减少传感器的累计误差,提高位置和指纹的匹配精度,有效地解决了现有技术中频繁打扰用户,位置和指纹匹配精度低的问题。The present invention constructs a location fingerprint database based on the fission method and the set path effective time threshold, and uses MDS combined with implicit landmark points to correct the PDR cumulative error, and at the same time, constructs a short-distance location fingerprint correlation model based on weighted multi-dimensional WiFi numerical features; compared with the prior art The location fingerprint database can be quickly constructed, the accumulative error of the sensor is reduced, the matching accuracy of the location and the fingerprint is improved, and the problems of frequently disturbing the user and low matching accuracy of the location and the fingerprint in the prior art are effectively solved.
附图说明Description of drawings
图1为基于众包数据的位置指纹数据库快速构建方法的流程图。Figure 1 is a flow chart of a method for rapidly constructing a location fingerprint database based on crowdsourced data.
图2为训练短距离位置指纹关联模型流程图。Figure 2 is a flowchart of training a short-distance location fingerprint association model.
图3为基于裂变方式建库示意图。Fig. 3 is a schematic diagram of building a database based on fission.
图4为计算隐式地标点区域的相对坐标示意图。FIG. 4 is a schematic diagram of calculating the relative coordinates of the implicit landmark point area.
具体实施方式Detailed ways
下面对本发明的具体实施方式进行描述,以便于本技术领域的技术人员理解本发明,但应该清楚,本发明不限于具体实施方式的范围,对本技术领域的普通技术人员来讲,只要各种变化在所附的权利要求限定和确定的本发明的精神和范围内,这些变化是显而易见的,一切利用本发明构思的发明创造均在保护之列。The specific embodiments of the present invention are described below to facilitate those skilled in the art to understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Such changes are obvious within the spirit and scope of the present invention as defined and determined by the appended claims, and all inventions and creations utilizing the inventive concept are within the scope of protection.
根据本申请的一个实施例,参考图1,本方案的基于众包数据的位置指纹数据库快速构建方法,包括:According to an embodiment of the present application, referring to FIG. 1 , the method for rapidly constructing a location fingerprint database based on crowdsourced data in this solution includes:
S1、基于PDR、粒子滤波和地图约束的裂变建库,其步骤包括:S1. Fission building library based on PDR, particle filter and map constraints, the steps include:
S11、训练短距离位置指纹关联模型;S11. Train a short-distance location fingerprint association model;
参考图2,其中,步骤S11具体包括以下两个步骤Referring to FIG. 2, wherein step S11 specifically includes the following two steps
A1、基于多维WiFi数值特征指纹距离建模;A1. Based on multi-dimensional WiFi numerical feature fingerprint distance modeling;
A2、短距离位置指纹关联模型的训练。A2. Training of short-distance location fingerprint association model.
步骤A1基于多维WiFi数值特征指纹距离建模的具体方法包括:The specific method of step A1 based on multi-dimensional WiFi numerical feature fingerprint distance modeling includes:
A11、构建由相对子序列构成的相对指纹;A11. Construct a relative fingerprint composed of relative subsequences;
设有两组指纹和将指纹中每个AP的rss值扩展一组rss向量,构成相对子序列其中i,j∈{1,…,Nn}。由相对子序列构成相对指纹:和 With two sets of fingerprints and Extend the rss value of each AP in the fingerprint to a set of rss vectors to form relative subsequences where i,j∈{1,…,N n }. Constitute relative fingerprints from relative subsequences: and
A12、计算相对子序列对的相似性;A12. Calculate the similarity of relative subsequence pairs;
将两组不同的相对指纹和组成指纹对分别在和中取指纹相对子序列和其中,j∈{1,…,Nn},i∈{1,…,Nm},即组成指纹的相对子序列对基于多维WiFi数值特征加权统计得到相对指纹对中的相似度可表示为:Two sets of different relative fingerprints and make up fingerprint pairs Respectively and fingerprint relative subsequence and Among them, j∈{1,…,N n }, i∈{1,…,N m }, namely pairs of relative subsequences that make up the fingerprint Relative fingerprint alignment based on weighted statistics of multi-dimensional WiFi numerical features similarity of can be expressed as:
其中,wi为加权系数(0≤wi≤1,i=1,2,3,4),是rss等级匹配代价,是之间的斯皮尔曼相关系数,N为Nm和Nn中所有不相同的AP总个数,是中共有AP数,是中不相关AP数。Among them, wi is the weighting coefficient (0≤wi ≤1, i =1,2,3,4), is the rss level matching cost, Yes Spearman correlation coefficient between , N is the total number of all different APs in N m and N n , Yes The total number of APs in China, Yes The number of irrelevant APs in the middle.
A13、计算相对指纹对中指纹相对子序列对的相似度,并采用遍历计算得到相对指纹对的相似度矩阵;A13. Calculate the similarity of the relative subsequence pairs of fingerprints in the relative fingerprint pair, and obtain the similarity matrix of the relative fingerprint pair by traversal calculation;
相对指纹对的相似矩阵表示的是在相对指纹对中,由构成的矩阵;先计算中指纹相对子序列对的相似度通过遍历计算中的相似度即可得到的相似度矩阵 The similarity matrix of the relative fingerprint pair represents the relative fingerprint pair in, by formed matrix; compute first medium fingerprint relative subsequence pair similarity of Calculate by traversal middle similarity can be obtained The similarity matrix of
其中, in,
A14、采用动态规划算法在相似度矩阵中搜索查找最佳匹配的相对指纹对;A14. Use the dynamic programming algorithm to search the similarity matrix to find the best matching relative fingerprint pair;
获得到相似度矩阵后,使用动态规划算法搜索在中查找最佳匹配的路径C;根据两个相对指纹对的最佳匹配路径C,累加得到指纹对的相似度(或指纹距离)为:get the similarity matrix After that, use the dynamic programming algorithm to search exist Find the best matching path C in ; according to the best matching path C of the two relative fingerprint pairs, the similarity (or fingerprint distance) of the fingerprint pair is accumulated as:
采用上述的构建相对指纹距离方法可以降低设备异构性对定位结果的影响。Using the above method of constructing relative fingerprint distance can reduce the influence of equipment heterogeneity on the positioning results.
步骤A2短距离位置指纹关联模型的训练的具体方法包括:The specific method of the training of the short-distance location fingerprint association model in step A2 includes:
A21、获取已知地标点周围的众包数据;A21. Obtain crowdsourced data around known landmarks;
在室内待定位区域上设置一些已知地标点并采集对应位置的坐标和rss信息,用户手持设备在地标点周围采集rss数据同时利用PDR记录行走路径;该过程中,为了避免训练过程存在的设备异构性问题,在已知地标点采集数据的手机设备和用户在该点附近采集众包数据时所用的手机设备一致,这些数据不是真正建库过程的众包数据,而是为训练短距离估计模型专门采集的众包数据。Set some known landmark points on the indoor area to be located and collect the coordinates and rss information of the corresponding position. The user's handheld device collects rss data around the landmark points and uses PDR to record the walking path; in this process, in order to avoid the existence of equipment in the training process Heterogeneity problem, the mobile phone equipment that collects data at a known landmark point is the same as the mobile phone equipment that users use to collect crowdsourced data near this point. These data are not crowdsourced data in the real database building process, but are used for training short distances. Crowdsourced data collected specifically for estimation models.
A22、根据指纹相似度提取地标点辐射区域的指纹数据;A22. Extract the fingerprint data of the radiation area of the landmark point according to the fingerprint similarity;
根据指纹相似度提取地标点辐射区域的指纹数据,由于训练数据采集时避免了设备异构性的影响,则可根据指纹之间的rss欧式距离进行指纹相似度计算。According to the fingerprint similarity, the fingerprint data of the radiation area of the landmark point is extracted. Since the influence of equipment heterogeneity is avoided when the training data is collected, the fingerprint similarity can be calculated according to the rss Euclidean distance between the fingerprints.
A23、计算所述地标点辐射区域指纹数据的指纹距离;A23. Calculate the fingerprint distance of the fingerprint data of the radiation area of the landmark point;
将提取出来的地标点周围指纹数据按照同一众包路径进行划分,构成训练数据;训练数据中包含了两两匹配的数据采集点,匹配的数据采集点位于同一众包路径上,其相对物理距离和指纹数据均已知。利用指纹距离模型计算出各匹配数据采集点之间的相对指纹距离,构成相对指纹距离矩阵Dwifi。Divide the extracted fingerprint data around the landmark points according to the same crowdsourcing path to form training data; the training data contains paired data collection points, and the matched data collection points are located on the same crowdsourcing path, and their relative physical distances and fingerprint data are known. The relative fingerprint distance between each matching data collection point is calculated by using the fingerprint distance model to form a relative fingerprint distance matrix D wifi .
A24、基于MDS确定短距离位置指纹关联模型A24. Determine short-distance location fingerprint association model based on MDS
短距离位置指纹关联模型是由指纹特征推导出来的,可以用于估计实际物理距离。此模型可以根据两点之间指纹信息和其中一个点的物理坐标信息来推断出另外一个点的物理坐标信息。The short-range location fingerprint association model is derived from fingerprint features and can be used to estimate actual physical distances. This model can infer the physical coordinate information of another point based on the fingerprint information between two points and the physical coordinate information of one point.
短距离位置指纹关联模型是在指纹距离模型的基础上进一步得到的,通过训练最优的权值0≤wi≤1(i=1,2,3,4)来获得该模型;通过MDS(Multidimensional ScalingAnalysis,MDS)算法根据相对指纹距离矩阵Dwifi生成各数据采集点之间的相对坐标,根据相对坐标求得数据采集点之间的相对距离L,由PDR得到数据采集点之间的物理相对距离d,假设d和L近似为正比关系d=kL,该式子中包含了0≤wi≤1(i=1,2,3,4)和k四个未知参数,通过最小二乘法LS(Least Square)求出最优的未知参数求得短距离位置指纹关联模型,其中将同一众包路径位置点数据作为最小二乘法的训练数据,最后采用已知地标点的精确数据验证该模型的准确性,求出模型的误差。The short-distance location fingerprint association model is further obtained on the basis of the fingerprint distance model, and the model is obtained by training the optimal weights 0≤wi ≤1 ( i =1,2,3,4); through MDS ( The Multidimensional ScalingAnalysis, MDS) algorithm generates the relative coordinates between the data collection points according to the relative fingerprint distance matrix D wifi , obtains the relative distance L between the data collection points according to the relative coordinates, and obtains the physical relative distance between the data collection points from the PDR. Distance d, assuming that d and L are approximately proportional to d=kL, this formula includes 0≤wi≤1( i =1,2,3,4) and k four unknown parameters, through the least squares method LS (Least Square) find the optimal unknown parameters to obtain the short-distance location fingerprint correlation model, in which the same crowdsourced path location data is used as the training data of the least squares method, and finally the accurate data of the known landmarks are used to verify the model's performance. Accuracy, find the error of the model.
S12、构建室内地图;S12. Build an indoor map;
将待定位区域按照一定的比例绘制相应的平面地图,用于粒子滤波的约束条件,进一步提高建库过程位置和指纹的匹配精度。The corresponding plane map is drawn according to a certain proportion of the area to be located, which is used for the constraints of particle filtering, and further improves the matching accuracy of the location and fingerprint in the process of building the database.
S13、标记显式地标点并采集相应位置指纹数据;S13, marking explicit landmark points and collecting fingerprint data of corresponding positions;
显式地标节点一般设置在特殊的物理位置点,走廊交叉位置、楼梯口位置以及区域入口等行人容易经过的区域。众包建库前,提前采集显式地标点的物理坐标和指纹数据存入位置指纹数据库。Explicit landmark nodes are generally set at special physical locations, corridor intersections, stairway openings, and areas where pedestrians are easy to pass through. Before crowdsourcing the database, collect the physical coordinates and fingerprint data of explicit landmarks in advance and store them in the location fingerprint database.
S14、判断众包路径数据是否启效;S14. Determine whether the crowdsourcing path data is effective;
由于PDR存在较大的累积误差,若长时间未能进行纠正,所采集的众包数据可靠性很低。为了得到相对准确的众包数据,当数据库中只存在显式地标点数据时,设定当用户行走到显式地标点附近时,众包路径才开始有效,并利用短距离位置指纹关联模型计算出该众包路径开始有效时的起始位置点物理坐标。根据WiFi指纹相似度(公式2所示)判断用户是否已经行走到显式地标点周围。Due to the large cumulative error of PDR, if it is not corrected for a long time, the reliability of the collected crowdsourcing data is very low. In order to obtain relatively accurate crowdsourcing data, when there is only explicit landmark data in the database, it is set that the crowdsourcing path becomes effective when the user walks near the explicit landmark, and the short-distance location fingerprint correlation model is used to calculate The physical coordinates of the starting point when the crowdsourcing route becomes effective. According to the WiFi fingerprint similarity (shown in Equation 2), it is determined whether the user has walked around the explicit landmark.
S15、基于PDR、粒子滤波和地图约束多重条件约束关联指纹和位置点;S15, based on PDR, particle filter and map constraint multiple condition constraints to associate fingerprints and location points;
众包路径开始有效后,结合PDR、粒子滤波和地图约束多重技术构建位置指纹数据库。根据智能手机内置传感器(陀螺仪、磁力计和加速度计传感器)数据推导行人的步数、步长和航向,得到用户行走的路径;After the crowdsourcing route becomes effective, a location fingerprint database is constructed by combining PDR, particle filtering and map constraint multiple techniques. According to the data of the built-in sensors of the smartphone (gyroscope, magnetometer and accelerometer sensors), the pedestrian's steps, step length and heading are deduced, and the user's walking path is obtained;
由于众包路径开始有效时的起始位置点物理坐标已知,因此可得到众包路径上数据采集点的物理坐标,从而获得位置和指纹的匹配;同时采用粒子滤波和地图约束双重约束条件获得更加精准的众包路径。Since the physical coordinates of the starting point when the crowdsourcing path is effective is known, the physical coordinates of the data collection points on the crowdsourcing path can be obtained, so as to obtain the matching of position and fingerprint; at the same time, the dual constraints of particle filtering and map constraints are used to obtain More accurate crowdsourcing routes.
S16、设置众包路径的有效时间阈值,在所述有效时间阈值内,若传感器数据累积误差属于可容忍范围内时,则进入步骤S15,否则,众包路径失效,进入步骤S17;S16, setting the effective time threshold of the crowdsourcing path, within the effective time threshold, if the cumulative error of the sensor data falls within the tolerable range, then proceed to step S15, otherwise, the crowdsourcing path is invalid, and proceed to step S17;
由于传感器具有累积误差,为了减少累积误差,提前训练出传感器累积误差时间阈值,一般在5-10分钟左右,在该时间阈值内,传感器数据累积误差属于可容忍范围。超出该时间阈值时,该众包路径失效,采集的数据不存入位置指纹数据库。Since the sensor has accumulated error, in order to reduce the accumulated error, the accumulated error time threshold of the sensor is trained in advance, which is generally about 5-10 minutes. Within this time threshold, the accumulated error of the sensor data belongs to the tolerable range. When the time threshold is exceeded, the crowdsourcing route is invalid, and the collected data is not stored in the location fingerprint database.
S17、基于裂变方式分段依次建库;S17. Build the database in sequence based on the fission method;
参考3,裂变方式指的是从一个已知位置点出发得到的一系列有效路径位置点,又可以作为经过这些点的众包路径的有效起始位置点。当位置指纹数据库中建立了非显式位置点的指纹数据后,其他用户的众包路径数据有效起始位置可以是任何已知位置点周围(包括显式位置点和有效的众包路径片段)。经过裂变方式建库之后,位置指纹数据库上的每个指纹点均对应一个粗略的物理坐标。Referring to 3, the fission method refers to a series of valid path position points obtained from a known position point, which can also be used as an effective starting position point for crowdsourcing paths passing through these points. After the fingerprint data of the non-explicit location points is established in the location fingerprint database, the effective starting position of the crowdsourced path data of other users can be around any known location points (including explicit location points and valid crowdsourced path segments) . After the database is established by fission, each fingerprint point on the location fingerprint database corresponds to a rough physical coordinate.
步骤S2、基于MDS纠正非显式地标点区域的PDR累积误差,其步骤包括:Step S2, correcting the PDR cumulative error of the non-explicit landmark area based on MDS, the steps include:
S21、基于指纹相似度计算隐式地标点区域范围;S21. Calculate the area range of the implicit landmark point based on the fingerprint similarity;
隐式地标点是由众包路径计算出来的,多个众包路径交叉位置可作为一个隐式地标点。因此当待定位区域上采集了较多的众包路径后,可以计算出较多的隐式地标点。由于同一个位置点周围的指纹相似度很高,因此可设定一个指纹相似度阈值σsim,指纹相似度超过该阈值的位置点所在的小区域作为隐式地标点区域,例如图3中的区域V。为了降低异构性的影响,可先对指纹求其相对指纹后才计算指纹相似度。Implicit landmark points are calculated by crowdsourcing paths, and the intersection of multiple crowdsourcing paths can be used as an implicit landmark point. Therefore, when more crowdsourcing paths are collected in the to-be-located area, more implicit landmark points can be calculated. Since the similarity of fingerprints around the same location point is very high, a fingerprint similarity threshold σ sim can be set, and the small area where the fingerprint similarity exceeds the threshold is used as the implicit landmark point area, such as in Figure 3. area V. In order to reduce the influence of heterogeneity, the fingerprint similarity can be calculated after the relative fingerprint of the fingerprint is obtained.
S22、统计隐式地标点区域之间的共有路径;S22. Count common paths between implicit landmark point regions;
经过各个地标点(包括隐式地标点和显式地标点)之间的众包路径数量不一定相同,通过对连续路径进行编号,求得同时经过两个地标点区域之间的路径数。The number of crowdsourcing paths passing through each landmark point (including implicit landmark points and explicit landmark points) is not necessarily the same. By numbering the continuous paths, the number of paths passing between two landmark point areas at the same time is obtained.
S23、搜索相对有效的众包路径;S23. Search for a relatively effective crowdsourcing path;
各众包路径的累积误差不同,需要在地标点区域之间的共有路径中选取累计误差最小的一条路径。The cumulative error of each crowdsourcing path is different, and it is necessary to select the path with the smallest cumulative error among the common paths between the landmark points.
磁力计传感器长时间相对稳定,短时间易受手机自身磁场影响,陀螺仪传感器长时间累积误差大,短时间较准,因此可依次在两隐式地标点区域之间的共有路径中,选取磁力计数据波动最小的路径作为两地标点区域之间的有效路径。The magnetometer sensor is relatively stable for a long time, and is easily affected by the magnetic field of the mobile phone in a short time. The gyroscope sensor has a large accumulated error for a long time and is accurate in a short time. Therefore, the magnetic force can be selected in the common path between the two implicit landmark points in turn. The path with the least fluctuation in the count data is used as the effective path between the two punctuation areas.
S24、重新绘制隐式地标点区域之间的新路径;S24. Redraw the new path between the implicit landmark point regions;
根据隐式地标点区域之间有效路径的加速度传感器和磁力计传感器数据,重新绘制出隐式地标点区域之间的新路径。Based on the accelerometer and magnetometer sensor data of valid paths between implicit landmark point regions, new paths between implicit landmark point regions are redrawn.
S25、计算隐式地标点区域的相对坐标;S25. Calculate the relative coordinates of the implicit landmark point area;
显式地标点的物理坐标准确已知,从显式地标节点出发,依次计算各个隐式地标点区域相对显式地标点的坐标。过程如下:若有众包路径同时经过显式地标点和某个隐式地标点区域,则可以计算出该隐式地标点区域的坐标,进而可以计算出和该隐式地标点区域有众包路径相连的其他隐式地标点区域坐标。The physical coordinates of the explicit landmark points are accurately known. Starting from the explicit landmark nodes, the coordinates of each implicit landmark point region relative to the explicit landmark points are calculated in turn. The process is as follows: If a crowdsourcing path passes through an explicit landmark point and an implicit landmark point area at the same time, the coordinates of the implicit landmark point area can be calculated, and then the crowdsourcing with the implicit landmark point area can be calculated. Additional implicit landmark point area coordinates connected by the path.
参考图4,显式地标点和隐式地标点A、E有路径相连,根据PDR算法和显式地标点坐标可求得A、E两点的坐标,而隐式地标点B与A有路径相连,则B点坐标可根据A点坐标求得,依次类推求出有路径相连的其他隐式地标点坐标。Referring to Figure 4, the explicit landmark points and the implicit landmark points A and E are connected by a path. According to the PDR algorithm and the coordinates of the explicit landmark point, the coordinates of the two points A and E can be obtained, while the implicit landmark point B and A have a path. If they are connected, the coordinates of point B can be obtained from the coordinates of point A, and so on to obtain the coordinates of other implicit landmark points connected by paths.
S26、基于MDS精确计算隐式地标点区域的绝对坐标;S26, accurately calculate the absolute coordinates of the implicit landmark point area based on MDS;
根据各隐式地标点区域和显式地标点的相对坐标,构建相对距离矩阵D,假设由众包路径连接起来的显式地标点和隐式地标点数量共m-1个,求这m-1个地标点的中心位置点根据坐标求各位置点(包括显式地标点,隐式地标点和中心位置点)之间相对距离dij,其中i=1,2,3,...,m,j=1,2,3,...,m则相对距离矩阵为:According to the relative coordinates of each implicit landmark point area and the explicit landmark point, the relative distance matrix D is constructed. Assuming that the number of explicit landmark points and implicit landmark points connected by the crowdsourcing path is m-1 in total, find this m- The center position point of 1 landmark point Find the relative distance d ij between each location point (including explicit landmark point, implicit landmark point and central location point) according to the coordinates, where i=1,2,3,...,m, j=1,2, 3,...,m then the relative distance matrix is:
将中心位置点设置为原点,根据MDS计算出显式和隐式地标点与原点的相对坐标,更新隐式地标点的原始相对坐标。进一步根据显式地标点的实际物理坐标精确求出各个隐式地标点的实际物理坐标。由于隐式地标点的原始相对坐标是根据单个路径计算得到的,误差可能较大,根据MDS算法可使得各隐式地标点之间相互校正,减少隐式地标点的坐标误差。Set the center point as the origin, calculate the relative coordinates of the explicit and implicit landmark points and the origin according to the MDS, and update the original relative coordinates of the implicit landmark point. Further, the actual physical coordinates of each implicit landmark point are accurately obtained according to the actual physical coordinates of the explicit landmark points. Since the original relative coordinates of the implicit landmarks are calculated according to a single path, the error may be large. According to the MDS algorithm, the implicit landmarks can be mutually corrected to reduce the coordinate errors of the implicit landmarks.
S27、纠正非显式地标点区域的PDR累积误差。S27. Correct the accumulated PDR error of the non-explicit landmark region.
将各个隐式地标点作为经过该点周围众包路径的新起始点,根据经过该点的后续部分原始路径信息绘制新的众包路径,从而进一步纠正了PDR的累积误差,提高位置与指纹的匹配精度。Taking each implicit landmark point as the new starting point of the crowdsourcing path around the point, and drawing a new crowdsourcing path according to the subsequent part of the original path information passing through the point, the accumulated error of the PDR is further corrected, and the relationship between the position and the fingerprint is improved. Matching accuracy.
本发明基于裂变方式和设置路径有效时间阈值构建位置指纹数据库,并采用MDS结合隐式地标点纠正PDR累计误差,同时,基于加权多维WiFi数值特征构建短距离位置指纹关联模型;相对于现有技术可以快速构建位置指纹数据库,减少传感器的累计误差,提高位置和指纹的匹配精度,有效地解决了现有技术中频繁打扰用户,位置和指纹匹配精度低的问题。The present invention constructs a location fingerprint database based on the fission method and the set path effective time threshold, and uses MDS combined with implicit landmark points to correct the PDR cumulative error, and at the same time, constructs a short-distance location fingerprint correlation model based on weighted multi-dimensional WiFi numerical features; compared with the prior art The location fingerprint database can be quickly constructed, the accumulative error of the sensor is reduced, the matching accuracy of the location and the fingerprint is improved, and the problems of frequently disturbing the user and low matching accuracy of the location and the fingerprint in the prior art are effectively solved.
虽然结合附图对发明的具体实施方式进行了详细地描述,但不应理解为对本专利的保护范围的限定。在权利要求书所描述的范围内,本领域技术人员不经创造性劳动即可做出的各种修改和变形仍属本专利的保护范围。Although the specific embodiments of the invention have been described in detail with reference to the accompanying drawings, they should not be construed as limiting the protection scope of this patent. Within the scope described in the claims, various modifications and deformations that can be made by those skilled in the art without creative work still belong to the protection scope of this patent.
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Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110351666A (en) * | 2019-07-31 | 2019-10-18 | 燕山大学 | A kind of ambiguous method of elimination wireless fingerprint map |
CN111735458A (en) * | 2020-08-04 | 2020-10-02 | 西南石油大学 | A navigation and positioning method of petrochemical inspection robot based on GPS, 5G and vision |
CN113899368A (en) * | 2021-09-15 | 2022-01-07 | 武汉大学 | A Pedestrian Heading Correction Method Based on Topological Relationship of Indoor Fingerprint Points |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106714110A (en) * | 2017-01-19 | 2017-05-24 | 深圳大学 | Auto building method and system of Wi-Fi position fingerprint map |
CN107948930A (en) * | 2017-12-31 | 2018-04-20 | 电子科技大学 | Indoor positioning optimization method based on location fingerprint algorithm |
CN105263113B (en) * | 2015-10-26 | 2018-08-21 | 深圳大学 | A kind of WiFi location fingerprints map constructing method and its system based on crowdsourcing |
US20180348334A1 (en) * | 2017-06-02 | 2018-12-06 | Apple Inc. | Compressing radio maps |
CN109005503A (en) * | 2018-08-13 | 2018-12-14 | 电子科技大学 | A kind of fusion and positioning method based on WiFi and PDR |
-
2019
- 2019-02-28 CN CN201910149853.4A patent/CN109739830B/en active Active
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105263113B (en) * | 2015-10-26 | 2018-08-21 | 深圳大学 | A kind of WiFi location fingerprints map constructing method and its system based on crowdsourcing |
CN106714110A (en) * | 2017-01-19 | 2017-05-24 | 深圳大学 | Auto building method and system of Wi-Fi position fingerprint map |
US20180348334A1 (en) * | 2017-06-02 | 2018-12-06 | Apple Inc. | Compressing radio maps |
CN107948930A (en) * | 2017-12-31 | 2018-04-20 | 电子科技大学 | Indoor positioning optimization method based on location fingerprint algorithm |
CN109005503A (en) * | 2018-08-13 | 2018-12-14 | 电子科技大学 | A kind of fusion and positioning method based on WiFi and PDR |
Non-Patent Citations (4)
Title |
---|
H. LEPPAKOSKI, J. COLLIN, J. TAKALA: "Pedestrian Navigation Based on Inertial Sensors,Indoor Map, and WLAN Signals", 《JOURNAL OF SIGNAL PROCESSING SYSTEMS》 * |
周瑞: "基于粒子滤波和地图匹配的融合室内定位", 《电子科技大学学报》 * |
宋红丽: "基于航位推算的室内定位系统研究与实现", 《中国优秀硕士学位论文全文数据库信息科技辑》 * |
梁溪: "基于智能手机的室内定位关键技术研究与应用", 《中国优秀硕士学位论文全文数据库信息科技辑》 * |
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