WO2021243504A1 - 一种信号地图构建方法、装置、设备及可读存储介质 - Google Patents

一种信号地图构建方法、装置、设备及可读存储介质 Download PDF

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Publication number
WO2021243504A1
WO2021243504A1 PCT/CN2020/093681 CN2020093681W WO2021243504A1 WO 2021243504 A1 WO2021243504 A1 WO 2021243504A1 CN 2020093681 W CN2020093681 W CN 2020093681W WO 2021243504 A1 WO2021243504 A1 WO 2021243504A1
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Prior art keywords
fingerprint
original
fingerprints
signal map
road network
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PCT/CN2020/093681
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English (en)
French (fr)
Inventor
陈尊裕
胡斯洋
方雄
吴珏其
陈欣
吴沛谦
张仲文
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蜂图志科技控股有限公司
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Application filed by 蜂图志科技控股有限公司 filed Critical 蜂图志科技控股有限公司
Priority to PCT/CN2020/093681 priority Critical patent/WO2021243504A1/zh
Priority to JP2022563236A priority patent/JP7446642B2/ja
Priority to CN202080027035.8A priority patent/CN114424635A/zh
Publication of WO2021243504A1 publication Critical patent/WO2021243504A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Definitions

  • This application relates to the field of positioning technology, and in particular to a method, device, equipment and readable storage medium for building a signal map.
  • Real-time positioning technology has become the basic technology for many high-level applications such as transportation, commerce, logistics, and personalized services.
  • the global navigation satellite system In the outdoor environment, the global navigation satellite system has been able to provide good positioning services after long-term development.
  • indoor positioning technology has become a hot research direction in the navigation field.
  • indoor Wi-Fi signal maps for indoor positioning has become one of the most widely used indoor positioning technologies due to its relatively high positioning accuracy, easy deployment, and strong portability.
  • a Wi-Fi signal map For indoor positioning using indoor Wi-Fi signal maps, a Wi-Fi signal map must first be constructed. If the Wi-Fi fingerprints in the Wi-Fi signal map are too sparse, it will inevitably lead to a decrease in positioning accuracy; while the Wi-Fi fingerprints are too dense, on the one hand, the data volume of the signal map will be larger, and the data cache and calculation overhead will increase. On the one hand, it weakens the difference in signal characteristics between Wi-Fi fingerprints and affects positioning accuracy.
  • the purpose of this application is to provide a method, device, equipment and readable storage medium for constructing a signal map, by clustering the original Wi-Fi fingerprints, and reconstructing from the original Wi-Fi fingerprints in the Wi-Fi fingerprint cluster New Wi-Fi fingerprints, and then based on the new Wi-Fi fingerprints, a lightweight Wi-Fi signal map with greater difference in signal characteristics can be constructed.
  • a method for constructing a signal map including:
  • the new Wi-Fi fingerprint is used to construct a Wi-Fi signal map.
  • using the original Wi-Fi fingerprint in the Wi-Fi fingerprint cluster to obtain a new Wi-Fi fingerprint includes:
  • the new Wi-Fi fingerprint is constructed by using the geographic coordinates, the average value of the received signal strength of each Wi-Fi access point, and the variance value of the received signal strength.
  • performing clustering processing on the original Wi-Fi fingerprints to obtain Wi-Fi fingerprint clusters includes:
  • said obtaining the original Wi-Fi fingerprint includes:
  • the road network includes a sampling path, and the sampling path has road network nodes;
  • the original Wi-Fi fingerprint is calculated by using the Wi-Fi data and the geographic coordinates of the road network node.
  • using the Wi-Fi data and the geographic coordinates of the road network node to calculate the original Wi-Fi fingerprint includes:
  • the original Wi-Fi fingerprint is constructed.
  • using the sampling start and end time to determine the timestamp of the road network node includes:
  • the method further includes:
  • the Wi-Fi signal map is updated by using the new Wi-Fi fingerprint obtained by recalculation.
  • a Wi-Fi signal map construction device including:
  • Original Wi-Fi fingerprint acquisition module used to acquire the original Wi-Fi fingerprint
  • the clustering module is used to perform clustering processing on the original Wi-Fi fingerprints to obtain Wi-Fi fingerprint clusters
  • the Wi-Fi fingerprint merging module is configured to use the original Wi-Fi fingerprint in the Wi-Fi fingerprint cluster to obtain a new Wi-Fi fingerprint
  • the signal map construction module is used to construct a Wi-Fi signal map by using the new Wi-Fi fingerprint.
  • a signal map construction equipment including:
  • Memory used to store computer programs
  • the processor is used to implement the steps of the above-mentioned signal map construction method when the computer program is executed.
  • the original Wi-Fi fingerprints are first clustered to obtain fewer Wi-Fi fingerprints than the original Wi-Fi fingerprint data. cluster.
  • the original Wi-Fi fingerprint is no longer used, but a new Wi-Fi fingerprint is obtained based on the original Wi-Fi fingerprint in the Wi-Fi fingerprint cluster.
  • a Wi-Fi signal map can be constructed based on the new Wi-Fi fingerprint corresponding to each Wi-Fi fingerprint cluster. Due to clustering and new Wi-Fi fingerprint acquisition, it can greatly reduce the size of the built Wi-Fi signal map data, reduce data caching and reduce computational overhead. On the other hand, it can also enhance the signal characteristics between Wi-Fi fingerprints. The difference can improve the positioning accuracy.
  • the embodiments of the present application also provide devices, equipment and readable storage media corresponding to the signal map construction method, which have the above technical effects, and will not be repeated here.
  • Fig. 1 is an implementation flow chart of a method for constructing a signal map in an embodiment of the application
  • Figure 2 is a schematic diagram of a road network in an embodiment of the application.
  • FIG. 3 is a schematic structural diagram of a signal map construction device in an embodiment of the application.
  • FIG. 4 is a schematic structural diagram of a signal map construction device in an embodiment of this application.
  • Fig. 5 is a schematic diagram of a specific structure of a signal map construction device in an embodiment of the application.
  • the signal map construction method uses a single floor in a building as a basic unit to construct a Wi-Fi signal map. Therefore, the Wi-Fi signal characteristics in a certain building need to be represented by several single-floor signal maps.
  • Wi-Fi Access Point Wi-Fi Access Point (Wi-Fi AP), a hardware device that allows mobile devices to access the Internet through wireless signals, multi-finger wireless routers, mobile hotspots, etc.
  • Wi-Fi fingerprint Wi-Fi fingerprint, at a certain location in space, multiple scanned Wi-Fi access points and their signal strengths are formed by a data collection that is different from the scan results of other locations, and is different from the current one.
  • a data pair of ⁇ signal, location> composed of coordinate values (such as latitude and longitude geographic coordinates) of the location in a certain coordinate system.
  • MAC address refers to the physical address or hardware address of the Wi-Fi access point, and is the only address that distinguishes the Wi-Fi access point.
  • SSID Service Set Identifier, that is, the service set identifier. This article refers to the name of the wireless LAN broadcasted by the Wi-Fi access point, which is customized by the LAN owner and is not unique.
  • RSSI Received Signal Strength Indicator
  • Indoor digital map Under a certain coordinate system, an orderly collection of data that can be identified by a computer and can be summarized on a storage medium is assigned to ground elements such as the internal structure and layout of a building with determined coordinates and attributes.
  • Indoor Wi-Fi signal map Based on the coordinate system of the indoor digital map, an orderly data set formed by marking the geographical coordinate position of the Wi-Fi fingerprint on the indoor digital map, providing comparison Wi-Fi for indoor positioning calculations -Fi data and reference position.
  • Road network road network, a network structure with geographic coordinates formed by marking indoor walkable passages or areas with line segments on an indoor digital map.
  • Road network nodes The intersections and connection points between line segments in the road network, and points with geographic coordinates inserted at a certain interval on the line segments are all defined as road network nodes.
  • FIG. 1 is a flowchart of a method for constructing a signal map in an embodiment of this application. The method includes the following steps:
  • the original Wi-Fi fingerprint refers to the Wi-Fi fingerprint for which the signal and position in the data pair ⁇ signal, location> have not been processed, that is, the original state generated after sampling.
  • the original Wi-Fi fingerprint can be directly obtained from the relatively dense Wi-Fi signal map of the Wi-Fi fingerprint; the original Wi-Fi fingerprint stored in advance can also be directly read from the readable storage medium; The original Wi-Fi fingerprint can also be obtained by direct sampling through static sampling (Point-to-Point, P2P) or walking sampling (walk survey).
  • static sampling Point-to-Point, P2P
  • walking sampling walking survey
  • static sampling requires staying at a sampling point with known spatial coordinates for a few seconds to a few minutes to record the Wi-Fi access points and signal strength scanned during that time period, that is, to obtain two elements of signal and location at the same time to form one Wi-Fi fingerprint.
  • the collection process needs to be repeated from one sampling point to another until the sampling point covers the entire building space with positioning requirements.
  • Walking sampling is divided into two stages: acquisition and calculation.
  • the key points of the walking track (such as the starting point, the inflection point and the end point) and the Wi-Fi access points and signal strength scanned along the way are recorded.
  • data such as the coordinate value of each key point and the time stamp of the walking record are used to interpolate the location of the Wi-Fi data on the path, thereby constructing a data pair of ⁇ signal, location>, that is, a Wi-Fi fingerprint.
  • clustering of the original Wi-Fi fingerprints can be performed to obtain a Wi-Fi fingerprint cluster.
  • Wi-Fi fingerprint cluster includes at least one original Wi-Fi fingerprint, and the fewer the number of Wi-Fi fingerprint clusters, the more original Wi-Fi fingerprints in the Wi-Fi fingerprint cluster.
  • clustering can be performed according to the relative relationship between the positions in the original Wi-Fi fingerprints, so that the original Wi-Fi fingerprints with similar geographic locations are gathered into a Wi-Fi fingerprint cluster.
  • the number of clusters (or the position of each fixed cluster center) can be preset to limit the number of Wi-Fi fingerprint clusters obtained by the final clustering; there is also no need to set the number of clusters (and the location of the cluster center is also not limited. ), which can maximize the original Wi-Fi fingerprints with similar locations to gather in a Wi-Fi fingerprint cluster.
  • the road network node can be used as the cluster center for clustering processing, so that the center of the Wi-Fi fingerprint cluster is on the walking path to improve the positioning accuracy.
  • the specific clustering process includes:
  • Step 1 Calculate the horizontal distance from each original Wi-Fi fingerprint to each road network node
  • Step 2 Use the horizontal distance to cluster the original Wi-Fi fingerprints to obtain Wi-Fi fingerprint clusters.
  • the clustering process Take the road network node as the center of the cluster, and use the horizontal distance between the original Wi-Fi fingerprint and the road network node as the reference, and classify the original Wi-Fi fingerprint into the road network node with the smallest horizontal distance.
  • a Wi-Fi fingerprint cluster is formed at each road network node.
  • a Wi-Fi fingerprint in order to reduce the number of Wi-Fi fingerprints and increase the difference in signal characteristics seen by the Wi-Fi fingerprints, a Wi-Fi fingerprint can be reconstructed for each Wi-Fi fingerprint cluster. In other words, a new Wi-Fi fingerprint replaces all the original Wi-Fi fingerprints in a Wi-Fi fingerprint cluster.
  • the process of obtaining a new Wi-Fi fingerprint includes:
  • Step 1 Obtain the geographic coordinates corresponding to the cluster center of the Wi-Fi fingerprint cluster
  • Step 2 Obtain all Wi-Fi access points that have appeared in the original Wi-Fi fingerprints in the Wi-Fi fingerprint cluster, and calculate the average received signal strength and the received signal strength variance of each access point;
  • Step 3 Use geographic coordinates, the average received signal strength of each Wi-Fi access point and the received signal strength variance value to construct a new Wi-Fi fingerprint.
  • the cluster center can be specified during the clustering process, it can also be determined after clustering by the clustering algorithm. Therefore, in this embodiment, if the cluster center is designated, the geographic coordinates of the designated cluster center can be directly obtained. If the cluster center is not specified, the geometric center of the geographic location of all original Wi-Fi fingerprints in the Wi-Fi fingerprint cluster can be calculated as the geographic coordinates of the cluster center.
  • the average received signal strength of the Wi-Fi access point is the average RSSI value of the Wi-Fi access point
  • the received signal strength variance value is the RSSI variance value of the Wi-Fi access point
  • the position in the new Wi-Fi fingerprint is the geographic coordinates corresponding to the cluster center of the Wi-Fi fingerprint cluster, which can be expressed in the form of longitude and latitude; the signal in the new Wi-Fi fingerprint is all Wi-Fi contained in the original Wi-Fi fingerprint in the cluster. Fi access point and the RSSI mean value and RSSI variance value of each access point. Of course, in other embodiments of the present application, the signal in the Wi-Fi fingerprint may also be in other manifestations.
  • the number of new Wi-Fi fingerprints based on the Wi-Fi fingerprint cluster is much smaller than the number of original Wi-Fi fingerprints.
  • the signal characteristics of a Wi-Fi access point in the range are characterized by the RSSI statistical value on the Wi-Fi fingerprint cluster, which reduces the access point’s multiple original Wi-Fi Information redundancy formed by repeated occurrences in fingerprints.
  • a Wi-Fi signal map can be constructed based on the new Wi-Fi fingerprints.
  • positioning can be performed based on the Wi-Fi signal map. Since the data volume of the Wi-Fi signal map is small, and the signal characteristics of the Wi-Fi fingerprint are more obvious, when positioning is performed, calculations can be reduced and positioning accuracy can be improved.
  • the original Wi-Fi fingerprints are first clustered to obtain Wi-Fi fingerprints with less data than the original Wi-Fi fingerprints. cluster.
  • the original Wi-Fi fingerprint is no longer used, but a new Wi-Fi fingerprint is obtained based on the original Wi-Fi fingerprint in the Wi-Fi fingerprint cluster.
  • a Wi-Fi signal map can be constructed based on the new Wi-Fi fingerprint corresponding to each Wi-Fi fingerprint cluster. Due to clustering and new Wi-Fi fingerprint acquisition, it can greatly reduce the size of the built Wi-Fi signal map data, reduce data caching and reduce computational overhead. On the other hand, it can also enhance the signal characteristics between Wi-Fi fingerprints. The difference can improve the positioning accuracy.
  • the embodiments of the present application also provide corresponding improvement solutions.
  • the same steps as in the above-mentioned embodiments or the corresponding steps can be referred to each other, and the corresponding beneficial effects can also be referred to each other, which will not be repeated in the preferred/improved embodiments herein.
  • the original Wi-Fi fingerprints can also be obtained by specifically performing the following steps:
  • Step 1 Obtain indoor digital maps and road networks; the road network includes sampling paths, and the sampling paths have road network nodes;
  • Step 2 Use the indoor digital map to determine the geographic coordinates of the outlet network node
  • Step 3 Use the sampling path to trigger Wi-Fi scanning and record Wi-Fi data at a fixed frequency
  • Step 4. Use Wi-Fi data and geographic coordinates of road network nodes to calculate the original Wi-Fi fingerprint.
  • a map drawing tool can be used to draw a digital map of the floor.
  • the digital map may include, but is not limited to, corridors, rooms, stairwells, elevators, and fixed ground elements (such as curtain walls, large-scale arrangement of tables and chairs, etc.) that significantly change the walking path.
  • the road network generation program can be used to mark the walkable area with a dot-line structure on the digital map. Then, set the parameters of the road network node spacing according to the characteristics of the building structure, and generate a road network with geographical latitude and longitude with the same distance between adjacent nodes.
  • FIG. 2 is a schematic diagram of a road network in an embodiment of this application, in which black hollow dots are road network nodes.
  • the timestamp of the road network node can be calculated first, and then the geographic coordinates of the Wi-Fi data can be calculated based on the segment, that is, step four can specifically include:
  • Step 4.1 Obtain the sampling start and end time of the sampling path
  • Step 4.2 Determine the timestamp of the road network node by using the sampling start and end time
  • Step 4.3 Use the timestamp of the road network node to calculate the geographic coordinates where the Wi-Fi data is located;
  • Step 4.4 Use the Wi-Fi data and the geographic coordinates where the Wi-Fi data is located to construct the original Wi-Fi fingerprint.
  • step 4.2 may specifically include:
  • Step 4.2.1 using the sampling start and end time to determine the total sampling time of the sampling path
  • Step 4.2.2 using the total sampling time, combined with the relative position of the road network node on the sampling path, calculate the time stamp of the road network node when sampling;
  • Step 4.2.3 Mark the time stamp for the road network node.
  • the sampling program can be used to load the indoor digital map of the sampled floor and display the road network distribution in a prominent dotted line shape.
  • the sampler uses the sampling program to plan a sampling path containing at least 4 path nodes (including the starting point) with the connection node of the road network as the starting point. After determining the path, the sampling program records all path nodes and their geographic latitudes and longitudes that the path passes.
  • the sampler (can be an intelligent robot or a sampler) confirms the start of walking on the sampling program and walks along the planned path at a constant speed until the end of the path and confirms the end of the walk. Specifically, the sampler manually triggers and ends the sampling process, and records the corresponding sampling start and end time; during the sampling process, Wi-Fi scanning is triggered at a fixed frequency and recorded: the timestamp of each scan (one-to-one with the physical location) Correspondence), the SSID, MAC address and RSSI value of each Wi-Fi access point scanned.
  • the fixed frequency can be set according to the accuracy required by the map. If the positioning accuracy is required, a higher fixed frequency can be set; if the positioning accuracy is low, a lower fixed frequency can be set.
  • sampling process of each floor can be divided into multiple sampling paths for execution.
  • the sampler walks and samples each sampling path until the sampling path covers all the road networks on the floor, then the sampling ends.
  • the acquisition of the original Wi-Fi fingerprint is divided into two steps: the first step is to mark the time stamp for the road network node on the sampling path. Since the sampler walks at a constant speed, the length of time from the beginning of walking to passing a node is proportional to the length of the path between the node and the starting point; the geographic latitude and longitude of each node on the path is known, and the straight path between adjacent nodes can be calculated Length and the total length of the sampling path.
  • the second step is to calculate the Wi-Fi data based on the timestamp corresponding to the road network node Geographical coordinates of the location; first, according to the time stamp of the Wi-Fi data, it can be determined that the data exists on a certain straight path determined by two adjacent road network nodes; also based on the condition of constant speed, the current paragraph start time stamp
  • the length of time to the Wi-Fi data timestamp is proportional to the length of the path from the beginning of the paragraph to the location of the Wi-Fi data; it can be interpolated under the premise that the latitude and longitude coordinates of the start and end points of the paragraph and the timestamp of the Wi-Fi data are known.
  • Obtain the latitude and longitude value of the corresponding location of the Wi-Fi data and then obtain the data pair of ⁇ signal, location>, that is, the original Wi-Fi fingerprint.
  • the Wi-Fi signal map may be updated.
  • the Wi-Fi signal map update can be achieved by performing the following steps:
  • Step 1 Receive and parse the map update request to obtain the target Wi-Fi fingerprint
  • Step 2 Determine the target Wi-Fi fingerprint cluster with the closest horizontal distance to the target Wi-Fi fingerprint in the Wi-Fi signal map
  • Step 3 Add the target Wi-Fi fingerprint to the target Wi-Fi fingerprint cluster
  • Step 4 Use all Wi-Fi fingerprints in the target Wi-Fi fingerprint cluster to recalculate the new Wi-Fi fingerprint of the target Wi-Fi fingerprint cluster;
  • Step 5 Use the new Wi-Fi fingerprint obtained by recalculation to update the Wi-Fi signal map.
  • the target Wi-Fi fingerprint can be a newly collected Wi-Fi fingerprint that needs a key modification location.
  • the specific classification method can be based on which Wi-Fi fingerprint cluster has the closest horizontal distance to which Wi-Fi fingerprint cluster.
  • the updated Wi-Fi signal map already contains the signal characteristics of the target Wi-Fi fingerprint. It not only maintains the light weight of Wi-Fi signal map, but also guarantees the difference of signal characteristics between Wi-Fi fingerprints.
  • the embodiment of the present application also provides a signal map construction device.
  • the signal map construction device described below and the signal map construction method described above can be referenced correspondingly.
  • the device includes:
  • the original Wi-Fi fingerprint acquisition module 101 is used to acquire the original Wi-Fi fingerprint
  • the clustering module 102 is used to perform clustering processing on the original Wi-Fi fingerprints to obtain Wi-Fi fingerprint clusters;
  • the Wi-Fi fingerprint merging module 103 is used to obtain a new Wi-Fi fingerprint by using the original Wi-Fi fingerprint in the Wi-Fi fingerprint cluster;
  • the signal map construction module 104 is used to construct a Wi-Fi signal map using the new Wi-Fi fingerprint.
  • the original Wi-Fi fingerprints are first clustered to obtain Wi-Fi fingerprints with less data than the original Wi-Fi fingerprints. cluster.
  • the original Wi-Fi fingerprint is no longer used, but a new Wi-Fi fingerprint is obtained based on the original Wi-Fi fingerprint in the Wi-Fi fingerprint cluster.
  • a Wi-Fi signal map can be constructed based on the new Wi-Fi fingerprint corresponding to each Wi-Fi fingerprint cluster. Due to clustering and new Wi-Fi fingerprint acquisition, it can greatly reduce the size of the built Wi-Fi signal map data, reduce data caching and reduce computational overhead. On the other hand, it can also enhance the signal characteristics between Wi-Fi fingerprints. The difference can improve the positioning accuracy.
  • the Wi-Fi fingerprint merging module 103 is specifically used to obtain the geographic coordinates corresponding to the cluster center of the Wi-Fi fingerprint cluster; to obtain all the original Wi-Fi fingerprints in the Wi-Fi fingerprint cluster Wi-Fi access points that have appeared, and calculate the average received signal strength and the received signal strength variance value of the Wi-Fi access points; use geographic coordinates and the average received signal strength of each Wi-Fi access point Value and received signal strength variance value to construct a new Wi-Fi fingerprint.
  • the clustering module 102 is specifically used to calculate the horizontal distance from each original Wi-Fi fingerprint to each road network node; use the horizontal distance to cluster the original Wi-Fi fingerprints , Get Wi-Fi fingerprint cluster.
  • the original Wi-Fi fingerprint acquisition module 101 includes:
  • the road network acquisition unit is used to acquire indoor digital maps and road networks;
  • the road network includes sampling paths, and the sampling paths have road network nodes;
  • the geographic coordinate calculation unit is used to determine the geographic coordinates of the road network node by using the indoor digital map
  • the sampling unit is used to use the sampling path to trigger Wi-Fi scanning and record Wi-Fi data at a fixed frequency
  • the fingerprint calculation unit is used to calculate the original Wi-Fi fingerprint using Wi-Fi data and the geographic coordinates of the road network node.
  • the fingerprint calculation unit is specifically configured to obtain the sampling start and end time of the sampling path; use the sampling start and end time to determine the timestamp of the road network node; use the time of the road network node Stamp, calculate the geographic coordinates of the Wi-Fi data; use the Wi-Fi data and the geographic coordinates of the Wi-Fi data to construct the original Wi-Fi fingerprint.
  • the fingerprint calculation unit is specifically configured to use the sampling start and end time to determine the total sampling time of the sampling path; use the total sampling time, combined with the relative position of the road network node on the sampling path, Calculate the time stamp of the road network node when sampling; mark the time stamp for the road network node.
  • the map update module is used to receive and parse the map update request after constructing the Wi-Fi signal map by using the new Wi-Fi fingerprint to obtain the target Wi-Fi fingerprint; determine the level of the Wi-Fi signal map and the target Wi-Fi fingerprint The closest target Wi-Fi fingerprint cluster; add the target Wi-Fi fingerprint to the target Wi-Fi fingerprint cluster; use all Wi-Fi fingerprints in the target Wi-Fi fingerprint cluster to recalculate the new target Wi-Fi fingerprint cluster Wi-Fi fingerprint: Use the recalculated new Wi-Fi fingerprint to update the Wi-Fi signal map.
  • the embodiment of the present application also provides a signal map construction device.
  • the signal map construction device described below and the signal map construction method described above can be referred to each other.
  • the signal map construction equipment includes:
  • the memory 332 is used to store computer programs
  • the processor 322 is configured to implement the steps of the signal map construction method described in the foregoing method embodiment when the computer program is executed.
  • FIG. 5 is a schematic diagram of the specific structure of a signal map building device provided by this embodiment.
  • the signal map building device may have relatively large differences due to different configurations or performances, and may include one or more processes.
  • a central processing unit (CPU) 322 for example, one or more processors
  • the memory 332 stores one or more computer application programs 342 or data 344.
  • the memory 332 may be short-term storage or persistent storage.
  • the program stored in the memory 332 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the data processing device.
  • the central processing unit 322 may be configured to communicate with the memory 332, and execute a series of instruction operations in the storage medium 330 on the signal map construction device 301.
  • the signal map construction device 301 may also include one or more power supplies 326, one or more wired or wireless network interfaces 350, one or more input and output interfaces 358, and/or one or more operating systems 341.
  • the steps in the signal map construction method described above can be implemented by the structure of the signal map construction device.
  • the embodiments of the present application also provide a readable storage medium.
  • the readable storage medium described below and the method for constructing a signal map described above may correspond to each other with reference to each other.
  • a readable storage medium in which a computer program is stored, and when the computer program is executed by a processor, the steps of the signal map construction method described in the above method embodiment are realized.
  • the readable storage medium can specifically be a U disk, a mobile hard disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk, or an optical disk that can store program codes. Readable storage medium.

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Abstract

一种信号地图构建方法、装置、设备及可读存储介质。该方法包括以下步骤：获取原始Wi-Fi指纹（S101）；对原始Wi-Fi指纹进行聚类处理，获得Wi-Fi指纹簇（S102）；利用Wi-Fi指纹簇中的原始Wi-Fi指纹，获得新Wi-Fi指纹（S103）；利用新Wi-Fi指纹构建Wi-Fi信号地图（S104）。由于经过聚类以及新Wi-Fi指纹获取，能够大大降低所构建的Wi-Fi信号地图数据体积较大，降低数据缓存与减少计算开销，另一方面，还可增强Wi-Fi指纹间信号特征的差异性，能够提高定位精度。

Description

一种信号地图构建方法、装置、设备及可读存储介质 技术领域
本申请涉及定位技术领域，特别是涉及一种信号地图构建方法、装置、设备及可读存储介质。
背景技术
实时定位技术已经成为交通、商业、物流、个性服务等多个高层次应用的基础技术。在室外环境下，全球导航卫星系统经过长期的发展，已经可以提供很好的定位服务。
而在室内环境中，由于卫星信号到达地面时较弱、不能穿透建筑物，以及多径效应等问题，全球定位系统无法提供可靠的服务。因此，近年来室内定位技术已经成为导航领域的一个热门研究方向。利用室内Wi-Fi信号地图进行室内定位，由于其定位精度相对较高、易于部署、可移植性强等特点，已经成为应用最为广泛的室内定位技术之一。
进行利用室内Wi-Fi信号地图进行室内定位，首先需构建出Wi-Fi信号地图。如果Wi-Fi信号地图中的Wi-Fi指纹过于稀疏，必然导致定位精度下降；而Wi-Fi指纹过于稠密，一方面会使信号地图的数据体积较大，增大数据缓存与计算开销，另一方面，削弱Wi-Fi指纹间信号特征的差异性，影响定位精度。
综上所述，如何解决Wi-Fi信号地图构建等问题，是目前本领域技术人员急需解决的技术问题。
发明内容
本申请的目的是提供一种信号地图构建方法、装置、设备及可读存储介质，通过对原始Wi-Fi指纹进行聚类，并以Wi-Fi指纹簇中的原始Wi-Fi指纹重新构建出新Wi-Fi指纹，然后基于新Wi-Fi指纹可以构建出一个轻量级且信号特征差异更大的Wi-Fi信号地图。
为了解决上述技术问题，本申请提出以下技术方案：
一种信号地图构建方法，包括：
获取原始Wi-Fi指纹；
对所述原始Wi-Fi指纹进行聚类处理，获得Wi-Fi指纹簇；
利用所述Wi-Fi指纹簇中的所述原始Wi-Fi指纹，获得新Wi-Fi指纹；
利用所述新Wi-Fi指纹构建Wi-Fi信号地图。
优选地，利用所述Wi-Fi指纹簇中的所述原始Wi-Fi指纹，获得新Wi-Fi指纹，包括：
获取所述Wi-Fi指纹簇的簇中心对应的地理坐标；
获取对所述Wi-Fi指纹簇中所有所述原始Wi-Fi指纹中出现过的所有Wi-Fi接入点，并计算所述Wi-Fi接入点的接收信号强度平均值和接收信号强度方差值；
利用所述地理坐标、各个所述Wi-Fi接入点的所述接收信号强度平均值和所述接收信号强度方差值，构建所述新Wi-Fi指纹。
优选地，对所述原始Wi-Fi指纹进行聚类处理，获得Wi-Fi指纹簇，包括：
计算每个所述原始Wi-Fi指纹到每个路网节点的水平距离；
利用所述水平距离对所述原始Wi-Fi指纹进行聚类处理，得到所述Wi-Fi指纹簇。
优选地，所述获取原始Wi-Fi指纹，包括：
获取室内数字地图和路网；所述路网中包括采样路径，所述采样路径上具有路网节点；
利用所述室内数字地图确定出所述路网节点的地理坐标；
利用所述采样路径，按照固定频率触发Wi-Fi扫描并记录Wi-Fi数据；
利用所述Wi-Fi数据和所述路网节点的地理坐标，计算出所述原始Wi-Fi指纹。
优选地，利用所述Wi-Fi数据和所述路网节点的地理坐标，计算出所述原始Wi-Fi指纹，包括：
获取采样路径的采样起止时间；
利用所述采样起止时间确定所述路网节点的时间戳；
利用所述路网节点的时间戳，计算出所述Wi-Fi数据所在的地理坐标；
利用所述Wi-Fi数据，以及所述Wi-Fi数据所在的地理坐标，构建出所述原始Wi-Fi指纹。
优选地，利用所述采样起止时间确定所述路网节点的时间戳，包括：
利用所述采样起止时间确定所述采样路径的采样总时长；
利用所述采样总时长，结合所述路网节点在所述采样路径的相对位置，计算出采样时经过所述路网节点的时间戳；
为所述路网节点标记所述时间戳。
优选地，在利用所述新Wi-Fi指纹构建Wi-Fi信号地图之后，还包括：
接收并解析地图更新请求，获得目标Wi-Fi指纹；
确定出所述Wi-Fi信号地图中与所述目标Wi-Fi指纹水平距离最近的目标Wi-Fi指纹簇；
在所述目标Wi-Fi指纹簇中添加所述目标Wi-Fi指纹；
利用所述目标Wi-Fi指纹簇中的全部Wi-Fi指纹，重新计算所述目标Wi-Fi指纹簇的新Wi-Fi指纹；
利用重新计算得到的新Wi-Fi指纹，对所述Wi-Fi信号地图进行更新。
一种Wi-Fi信号地图构建装置，包括：
原始Wi-Fi指纹获取模块，用于获取原始Wi-Fi指纹；
聚类模块，用于对所述原始Wi-Fi指纹进行聚类处理，获得Wi-Fi指纹簇；
Wi-Fi指纹合并模块，用于利用所述Wi-Fi指纹簇中的所述原始Wi-Fi指纹，获得新Wi-Fi指纹；
信号地图构建模块，用于利用所述新Wi-Fi指纹构建Wi-Fi信号地图。
一种信号地图构建设备，包括：
存储器，用于存储计算机程序；
处理器，用于执行所述计算机程序时实现如上述的信号地图构建方法的步骤。
一种可读存储介质，所述可读存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现如上述的信号地图构建方法的步骤。
应用本申请实施例所提供的方法，获取原始Wi-Fi指纹；对原始Wi-Fi指纹进行聚类处理，获得Wi-Fi指纹簇；利用Wi-Fi指纹簇中的原始Wi-Fi指纹，获得新Wi-Fi指纹；利用新Wi-Fi指纹构建Wi-Fi信号地图。
在获取到原始Wi-Fi指纹之后，为了降低Wi-Fi信号地图的数据量， 首先对原始Wi-Fi指纹进行聚类处理，得到相较于原始Wi-Fi指纹数据更少的Wi-Fi指纹簇。为了提高Wi-Fi指纹的信号特征差异，不再继续采用原始Wi-Fi指纹，而是基于Wi-Fi指纹簇中的原始Wi-Fi指纹，获取一个新Wi-Fi指纹。然后，便可基于每一个Wi-Fi指纹簇对应的新Wi-Fi指纹，构建出Wi-Fi信号地图。由于经过聚类以及新Wi-Fi指纹获取，能够大大降低所构建的Wi-Fi信号地图数据体积较大，降低数据缓存与减少计算开销，另一方面，还可增强Wi-Fi指纹间信号特征的差异性，能够提高定位精度。
相应地，本申请实施例还提供了与信号地图构建方法相对应的装置、设备和可读存储介质，具有上述技术效果，在此不再赘述。
附图说明
为了更清楚地说明本申请实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为本申请实施例中一种信号地图构建方法的实施流程图；
图2为本申请实施例中一种路网示意图；
图3为本申请实施例中一种信号地图构建装置的结构示意图；
图4为本申请实施例中一种信号地图构建设备的结构示意图；
图5为本申请实施例中一种信号地图构建设备的具体结构示意图。
具体实施方式
为了使本技术领域的人员更好地理解本申请方案，下面结合附图和具体实施方式对本申请作进一步的详细说明。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
需要说明的是，本申请实施例所提供的信号地图构建方法是以建筑物内单一楼层为基本单位构建Wi-Fi信号地图。因此，某个建筑物内的Wi-Fi信号特征需由数个单楼层信号地图来表述。
为了便于理解，下面对本申请实施例中的部分术语进行解释说明：
Wi-Fi接入点：Wi-Fi Access Point(Wi-Fi AP)，通过无线信号允许移动设备接入互联网的硬件设备，多指无线路由器、移动热点等。
Wi-Fi指纹：Wi-Fi fingerprint，在空间某位置上，多个被扫描到的Wi-Fi接入点及其信号强度所形成的且有别于其他位置扫描结果的数据集合，与当前该位置在一定坐标体系下的坐标值(如经纬度地理坐标)所组成的<信号，位置>数据对。
MAC地址：指Wi-Fi接入点的物理地址或硬件地址，是区分Wi-Fi接入点的唯一位址。
SSID：Service Set Identifier，即服务集标识。本文指Wi-Fi接入点广播出来的无线局域网的名称，由局域网所有者自定义，并没有唯一性。
RSSI：Received Signal Strength Indicator，本文指是由Wi-Fi接入点发出的、移动端设备接受到的无线网络信号的强度指示。
室内数字地图：在一定坐标系统下，将建筑物内部结构、布置等地面要素赋予确定坐标和属性的、可由计算机识别的、可在存储介质上概括的、有序的数据集合。
室内Wi-Fi信号地图：基于室内数字地图的坐标系统，以Wi-Fi指纹的地理坐标位置将其标记在室内数字地图上所形成的有序的数据集合，为室内定位计算提供比对的Wi-Fi数据和参考位置。
路网：road network，在室内数字地图上以线段标记室内可行走的通道或区域而形成的且具有地理坐标的网络结构。
路网节点：路网中线段之间的交点、连接点，以及在线段上按一定间距插入的具有地理坐标的点均定义为路网节点。
请参考图1，图1为本申请实施例中一种信号地图构建方法的流程图，该方法包括以下步骤：
S101、获取原始Wi-Fi指纹。
其中，原始Wi-Fi指纹即指<信号，位置>数据对中的信号以及位置均未进行处理的Wi-Fi指纹，即采样后生成的原始状态。
在本实施例中，可直接从Wi-Fi指纹相对较为稠密的Wi-Fi信号地图中获取原始Wi-Fi指纹；也可从可读存储介质中直接读取预先存储的原始 Wi-Fi指纹；还可通过静态采样(Point-to-Point，P2P)或行走采样(walk survey)的方式，直接采样得到原始Wi-Fi指纹。
其中，静态采样要求在已知空间坐标的某采样点停留数秒到数分钟来记录该时间段内扫描到的Wi-Fi接入点及信号强度，即同时获得信号和位置两个元素来构成一个Wi-Fi指纹。采集过程需从一个采样点到另一采样点重复操作，直至采样点覆盖整个有定位需求的建筑物空间。
行走采样则分为采集和计算两个阶段。采集阶段记录行走轨迹的关键点(如起点、拐点和终点)和沿途扫描到的Wi-Fi接入点和信号强度。计算阶段利用各关键点的坐标值、行走记录的时间戳等数据，在路径上插值得到Wi-Fi数据的位置，从而构建<信号，位置>数据对，即Wi-Fi指纹。
S102、对原始Wi-Fi指纹进行聚类处理，获得Wi-Fi指纹簇。
为了降低Wi-Fi指纹的数量以及提高Wi-Fi指纹间的信号差异，在获取到原始Wi-Fi指纹之后，可对原始Wi-Fi指纹进行聚类处理，得到Wi-Fi指纹簇。
由于一个Wi-Fi指纹簇中至少包括一个原始Wi-Fi指纹，且Wi-Fi指纹簇的数量越少，Wi-Fi指纹簇中的原始Wi-Fi指纹便越多。
在聚类处理中，可按照原始Wi-Fi指纹中的位置之间的相对关系进行聚类，使得地理位置相近的原始Wi-Fi指纹聚集为一个Wi-Fi指纹簇。在进行聚类时，可预先设置簇的数量(或各个固定簇中心的位置)，限制最终聚类得到的Wi-Fi指纹簇的数量；也可无需设置簇的数量(也不限制簇中心位置)，能够最大限度的使得位置相近的原始Wi-Fi指纹聚集在一个Wi-Fi指纹簇中。
优选地，考虑到实际应用中，在进行原始Wi-Fi指纹采集时，会考虑实际应用环境下，用户的行走路径。因此，在进行聚类时，可采用将路网节点作为簇中心进行聚类处理，使得Wi-Fi指纹簇的中心在行走路径上，以提高定位精度。具体的，具体聚类处理过程，包括：
步骤一、计算每个原始Wi-Fi指纹到每个路网节点的水平距离；
步骤二、利用水平距离对原始Wi-Fi指纹进行聚类处理，得到Wi-Fi指纹簇。
为便于描述，下面将上述两个步骤结合起来进行说明。
也就是说，聚类过程：以路网节点作为簇的中心，以原始Wi-Fi指纹与路网节点的水平距离作为基准，将原始Wi-Fi指纹归入与其水平距离最小的路网节点，在每一个路网节点处形成一个Wi-Fi指纹簇。
S103、利用Wi-Fi指纹簇中的原始Wi-Fi指纹，获得新Wi-Fi指纹。
在本申请实施例中，为了降低Wi-Fi指纹的数量以及增大Wi-Fi指纹见的信号特征差异，可针对每一个Wi-Fi指纹簇，重新构建一个Wi-Fi指纹。也就是说，一个新Wi-Fi指纹将原本的一个Wi-Fi指纹簇内的全部原始Wi-Fi指纹进行替代。
具体的，新Wi-Fi指纹的获取过程，包括：
步骤一、获取Wi-Fi指纹簇的簇中心对应的地理坐标；
步骤二、获取Wi-Fi指纹簇中所有原始Wi-Fi指纹中出现过的Wi-Fi接入点，并计算每个接入点接收信号强度平均值和接收信号强度方差值；
步骤三、利用地理坐标、各Wi-Fi接入点的接收信号强度平均值和接收信号强度方差值，构建新Wi-Fi指纹。
为便于描述，下面将上述三个步骤结合起来进行说明。
由于聚类处理时，簇中心可指定，也可由聚类算法聚类后确定。因此，在本实施例中，若簇中心是指定的，则可直接获取指定簇中心的地理坐标。若簇中心未指定，可计算出Wi-Fi指纹簇内的全部原始Wi-Fi指纹的地理位置的几何中心作为簇中心的地理坐标。
其中，Wi-Fi接入点接收信号强度平均值即Wi-Fi接入点的RSSI均值，接收信号强度方差值即Wi-Fi接入点的RSSI方差值。
新Wi-Fi指纹中的位置即Wi-Fi指纹簇的簇中心对应的地理坐标，可具体为经纬度表示形式；新Wi-Fi指纹中的信号即簇中原始Wi-Fi指纹包含的所有Wi-Fi接入点及各接入点的RSSI均值和RSSI方差值。当然，在本申请的其他实施例中，Wi-Fi指纹中的信号还可为其他表现形式。
显然，在同一建筑物内，基于Wi-Fi指纹簇的新Wi-Fi指纹个数要远小于原始的Wi-Fi指纹个数。此外，在某节点周边范围内，某个Wi-Fi接入点于该范围内的信号特征由Wi-Fi指纹簇上的RSSI统计值来表征，减少了接入点在多个原始Wi-Fi指纹中重复出现而形成的信息冗余。
S104、利用新Wi-Fi指纹构建Wi-Fi信号地图。
得到相对于原始Wi-Fi指纹数量更少且Wi-Fi指纹间的信号区别更明显的新Wi-Fi指纹后，便可基于新Wi-Fi指纹构建出Wi-Fi信号地图。
构建出Wi-Fi信号地图之后，便可基于Wi-Fi信号地图进行定位。由于该Wi-Fi信号地图的数据量少，且Wi-Fi指纹的信号特征间更明显，因而进行定位时，可减少计算，提高定位精度。
应用本申请实施例所提供的方法，获取原始Wi-Fi指纹；对原始Wi-Fi指纹进行聚类处理，获得Wi-Fi指纹簇；利用Wi-Fi指纹簇中的原始Wi-Fi指纹，获得新Wi-Fi指纹；利用新Wi-Fi指纹构建Wi-Fi信号地图。
在获取到原始Wi-Fi指纹之后，为了降低Wi-Fi信号地图的数据量，首先对原始Wi-Fi指纹进行聚类处理，得到相较于原始Wi-Fi指纹数据更少的Wi-Fi指纹簇。为了提高Wi-Fi指纹的信号特征差异，不再继续采用原始Wi-Fi指纹，而是基于Wi-Fi指纹簇中的原始Wi-Fi指纹，获取一个新Wi-Fi指纹。然后，便可基于每一个Wi-Fi指纹簇对应的新Wi-Fi指纹，构建出Wi-Fi信号地图。由于经过聚类以及新Wi-Fi指纹获取，能够大大降低所构建的Wi-Fi信号地图数据体积较大，降低数据缓存与减少计算开销，另一方面，还可增强Wi-Fi指纹间信号特征的差异性，能够提高定位精度。
需要说明的是，基于上述实施例，本申请实施例还提供了相应的改进方案。在优选/改进实施例中涉及与上述实施例中相同步骤或相应步骤之间可相互参考，相应的有益效果也可相互参照，在本文的优选/改进实施例中不再一一赘述。
优选地，为了提高原始Wi-Fi指纹的采样效率，还可具体通过执行以下步骤，获取原始Wi-Fi指纹：
步骤一、获取室内数字地图和路网；路网中包括采样路径，采样路径上具有路网节点；
步骤二、利用室内数字地图确定出路网节点的地理坐标；
步骤三、利用采样路径，按照固定频率触发Wi-Fi扫描并记录Wi-Fi数据；
步骤四、利用Wi-Fi数据和路网节点的地理坐标，计算出原始Wi-Fi指纹。
为了便于描述，下面将上述四个步骤结合起来进行详细说明。
具体的，可以利用地图绘制工具绘制楼层的数字地图。该数字地图可包括但不限于走廊、房间、楼梯间、电梯间及明显改变行走路径的固定的地面要素(如幕墙、大范围布置的桌椅等)。可使用路网生成程序在数字地图上以点-线结构标记可行走区域。然后，根据建筑结构特点设定路网节点间距参数，生成相邻节点间距基本一致的、具有地理经纬度的路网。例如，请参考图2，图2为本申请实施例中一种路网示意图，其中，黑色空心圆点为路网节点。
优选地，为了使得构建的Wi-Fi信号地图定位更准确，可优先计算出路网节点的时间戳，然后基于分段计算出Wi-Fi数据所在的地理坐标，即步骤四可具体包括：
步骤4.1、获取所述采样路径的采样起止时间；
步骤4.2、利用所述采样起止时间确定所述路网节点的时间戳；
步骤4.3、利用路网节点的时间戳，计算出Wi-Fi数据所在的地理坐标；
步骤4.4、利用Wi-Fi数据，以及该Wi-Fi数据所在的地理坐标，构建出原始Wi-Fi指纹。
具体的，步骤4.2可具体包括：
步骤4.2.1、利用所述采样起止时间确定所述采样路径的采样总时长；
步骤4.2.2、利用采样总时长，结合路网节点在采样路径的相对位置，计算出采样时经过路网节点的时间戳；
步骤4.2.3、为路网节点标记时间戳。
在实际应用中，可利用采样程序加载被采样楼层的室内数字地图并以显著的点线形状显示路网分布。采样者使用采样程序，以路网的连接节点为起始点，规划一段至少包含4个路径节点(可包括起始点)的采样路径。确定路径后，采样程序记录下路径经过的所有路径节点及其地理经纬度。
采样者(可为智能机器人或采样人员)于采样程序上确认行走开始并沿规划的路径匀速行走，直至路径终点并确认行走结束。具体的，由采样者手动触发和结束采样过程，并记录对应的采样起止时间；在采样过程中，以固定频率触发Wi-Fi扫描并记录：每次扫描的时间戳(与物理定位位置一一对应)、扫描到的各个Wi-Fi接入点的SSID、MAC地址和RSSI值。 其中，固定频率可根据地图所需精度进行设置，若定位精度要求较高，则可设置较高的固定频率；若定位精度要求较低，则可设置较低的固定频率。
得到的采样路径上的各个路网节点地理经纬度、以及行走采样开始及结束时间、采样过程中记录多次Wi-Fi数据构成一条采样记录。一般情况下，每个楼层的采样过程可拆分成多条采样路径来执行。采样者对每一条采样路径进行行走采样，直至采样路径覆盖该楼层所有路网，则采样结束。
原始Wi-Fi指纹的获取分为两步：第一步，为采样路径上的路网节点标记时间戳。由于采样者以匀速行走，自开始行走到路过某节点的时间长度与该节点与起点间的路径长度成正比；路径上各节点地理经纬度已知，则可计算得到相邻节点间呈直线的路径长度和采样路径的总长度。采样路径的采样起止时间已知，则可得到总的行走时长；那么路径上的任意节点对应的时间戳均可插值得到；第二步，以路网节点对应时间戳为基础计算Wi-Fi数据所在位置的地理坐标；首先，根据Wi-Fi数据的时间戳可确定该数据存在于某两相邻路网节点所确定的某段直线路径上；同样基于匀速行走的条件，当前段落起点时间戳到Wi-Fi数据时间戳的时间长度与段落起点到Wi-Fi数据所在位置之路径长度成正比；在已知段落起止点经纬度坐标和时间戳、Wi-Fi数据时间戳的前提下，可以插值得到Wi-Fi数据对应位置的经纬度值，进而得到<信号，位置>数据对，即原始的Wi-Fi指纹。
优选地，考虑到实际应用环境中，Wi-Fi的数量以及位置可能会发生变动，为了提高定位的精准度，可对Wi-Fi信号地图进行更新。相较于直接添加Wi-Fi指纹，基于本申请实施例所提供的信号地图构建方法，提出了对Wi-Fi信号地图中已有的Wi-Fi指纹进行更新，来保持地图的轻量级以及信号之间的特征差异。具体的，即在利用新Wi-Fi指纹构建Wi-Fi信号地图之后，可通过执行以下步骤来实现Wi-Fi信号地图更新：
步骤一、接收并解析地图更新请求，获得目标Wi-Fi指纹；
步骤二、确定出Wi-Fi信号地图中与目标Wi-Fi指纹水平距离最近的目标Wi-Fi指纹簇；
步骤三、在目标Wi-Fi指纹簇中添加目标Wi-Fi指纹；
步骤四、利用目标Wi-Fi指纹簇中的全部Wi-Fi指纹，重新计算目标 Wi-Fi指纹簇的新Wi-Fi指纹；
步骤五、利用重新计算得到的新Wi-Fi指纹，对Wi-Fi信号地图进行更新。
其中，目标Wi-Fi指纹可以为新采集需要重点修改位置的Wi-Fi指纹。
得到目标Wi-Fi指纹之后，首先确定出应当归属于哪一个Wi-Fi指纹簇。具体的归类方式可以基于其与哪个Wi-Fi指纹簇的水平距离最近，便归于哪个Wi-Fi指纹簇。
然后，重新构建该目标Wi-Fi指纹簇对应的新Wi-Fi指纹。在重新计算得到新Wi-Fi指纹后，便可仅更替Wi-Fi信号地图中对应的Wi-Fi指纹即可。
可见，更新完成后，Wi-Fi信号地图中的Wi-Fi指纹的数量无变化，且更新后的Wi-Fi信号地图中已经容纳了目标Wi-Fi指纹的信号特征。即保持了Wi-Fi信号地图的轻量化，又保障了Wi-Fi指纹间信号特征的差异性。
相应于上面的方法实施例，本申请实施例还提供了一种信号地图构建装置，下文描述的一种信号地图构建装置与上文描述的一种信号地图构建方法可相互对应参照。
请参考图3，该装置包括：
原始Wi-Fi指纹获取模块101，用于获取原始Wi-Fi指纹；
聚类模块102，用于对原始Wi-Fi指纹进行聚类处理，获得Wi-Fi指纹簇；
Wi-Fi指纹合并模块103，用于利用Wi-Fi指纹簇中的原始Wi-Fi指纹，获得新Wi-Fi指纹；
信号地图构建模块104，用于利用新Wi-Fi指纹构建Wi-Fi信号地图。
应用本申请实施例所提供的装置，获取原始Wi-Fi指纹；对原始Wi-Fi指纹进行聚类处理，获得Wi-Fi指纹簇；利用Wi-Fi指纹簇中的原始Wi-Fi指纹，获得新Wi-Fi指纹；利用新Wi-Fi指纹构建Wi-Fi信号地图。
在获取到原始Wi-Fi指纹之后，为了降低Wi-Fi信号地图的数据量，首先对原始Wi-Fi指纹进行聚类处理，得到相较于原始Wi-Fi指纹数据更少的Wi-Fi指纹簇。为了提高Wi-Fi指纹的信号特征差异，不再继续采用原始Wi-Fi指纹，而是基于Wi-Fi指纹簇中的原始Wi-Fi指纹，获取一个新 Wi-Fi指纹。然后，便可基于每一个Wi-Fi指纹簇对应的新Wi-Fi指纹，构建出Wi-Fi信号地图。由于经过聚类以及新Wi-Fi指纹获取，能够大大降低所构建的Wi-Fi信号地图数据体积较大，降低数据缓存与减少计算开销，另一方面，还可增强Wi-Fi指纹间信号特征的差异性，能够提高定位精度。
在本申请的一种具体实施方式中，Wi-Fi指纹合并模块103，具体用于获取Wi-Fi指纹簇的簇中心对应的地理坐标；获取Wi-Fi指纹簇中所有原始Wi-Fi指纹中出现过的Wi-Fi接入点，并计算所述Wi-Fi接入点的接收信号强度平均值和接收信号强度方差值；利用地理坐标、各个Wi-Fi接入点的接收信号强度平均值和接收信号强度方差值，构建新Wi-Fi指纹。
在本申请的一种具体实施方式中，聚类模块102，具体用于计算每个原始Wi-Fi指纹到每个路网节点的水平距离；利用水平距离对原始Wi-Fi指纹进行聚类处理，得到Wi-Fi指纹簇。
在本申请的一种具体实施方式中，原始Wi-Fi指纹获取模块101，包括：
路网获取单元，用于获取室内数字地图和路网；路网中包括采样路径，采样路径上具有路网节点；
地理坐标计算单元，用于利用室内数字地图确定出路网节点的地理坐标；
采样单元，用于利用采样路径，按照固定频率触发Wi-Fi扫描并记录Wi-Fi数据；
指纹计算单元，用于利用Wi-Fi数据和路网节点的地理坐标，计算出原始Wi-Fi指纹。
在本申请的一种具体实施方式中，指纹计算单元，具体用于获取所述采样路径的采样起止时间；利用所述采样起止时间确定所述路网节点的时间戳；利用路网节点的时间戳，计算出Wi-Fi数据所在的地理坐标；利用Wi-Fi数据，以及该Wi-Fi数据所在的地理坐标，构建出原始Wi-Fi指纹。
在本申请的一种具体实施方式中，指纹计算单元，具体用于利用所述采样起止时间确定所述采样路径的采样总时长；利用采样总时长，结合路网节点在采样路径的相对位置，计算出采样时经过路网节点的时间戳；为路网节点标记时间戳。
在本申请的一种具体实施方式中，还包括：
地图更新模块，用于在利用新Wi-Fi指纹构建Wi-Fi信号地图之后，接收并解析地图更新请求，获得目标Wi-Fi指纹；确定出Wi-Fi信号地图中与目标Wi-Fi指纹水平距离最近的目标Wi-Fi指纹簇；在目标Wi-Fi指纹簇中添加目标Wi-Fi指纹；利用目标Wi-Fi指纹簇中的全部Wi-Fi指纹，重新计算目标Wi-Fi指纹簇的新Wi-Fi指纹；利用重新计算得到的新Wi-Fi指纹，对Wi-Fi信号地图进行更新。
相应于上面的方法实施例，本申请实施例还提供了一种信号地图构建设备，下文描述的一种信号地图构建设备与上文描述的一种信号地图构建方法可相互对应参照。
参见图4所示，该信号地图构建设备包括：
存储器332，用于存储计算机程序；
处理器322，用于执行计算机程序时实现如上述方法实施例所描述的信号地图构建方法的步骤。
具体的，请参考图5，为本实施例提供的一种信号地图构建设备的具体结构示意图，该信号地图构建设备可因配置或性能不同而产生比较大的差异，可以包括一个或一个以上处理器(central processing units，CPU)322(例如，一个或一个以上处理器)和存储器332，该存储器332中存储一个或一个以上的计算机应用程序342或数据344。其中，存储器332可以是短暂存储或持久存储。存储在存储器332的程序可以包括一个或一个以上模块(图示没标出)，每个模块可以包括对数据处理设备中的一系列指令操作。更进一步地，中央处理器322可以设置为与存储器332通信，在信号地图构建设备301上执行存储介质330中的一系列指令操作。
信号地图构建设备301还可以包括一个或一个以上电源326，一个或一个以上有线或无线网络接口350，一个或一个以上输入输出接口358，和/或，一个或一个以上操作系统341。
上文所描述的信号地图构建方法中的步骤可以由信号地图构建设备的结构实现。
相应于上面的方法实施例，本申请实施例还提供了一种可读存储介质，下文描述的一种可读存储介质与上文描述的一种信号地图构建方法可相互对应参照。
一种可读存储介质，可读存储介质上存储有计算机程序，计算机程序被处理器执行时实现上述方法实施例所描述的信号地图构建方法的步骤。
该可读存储介质具体可以为U盘、移动硬盘、只读存储器(Read-Only Memory，ROM)、随机存取存储器(Random Access Memory，RAM)、磁碟或者光盘等各种可存储程序代码的可读存储介质。
本领域的技术人员还可以进一步意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、计算机软件或者二者的结合来实现，为了清楚地说明硬件和软件的可互换性，在上述说明中已经按照功能一般性地描述了各示例的组成及步骤。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。本领域的技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。

Claims (10)

  1. 一种信号地图构建方法，其特征在于，包括：
    获取原始Wi-Fi指纹；
    对所述原始Wi-Fi指纹进行聚类处理，获得Wi-Fi指纹簇；
    利用所述Wi-Fi指纹簇中的所述原始Wi-Fi指纹，获得新Wi-Fi指纹；
    利用所述新Wi-Fi指纹构建Wi-Fi信号地图。
  2. 根据权利要求1所述的信号地图构建方法，其特征在于，利用所述Wi-Fi指纹簇中的所述原始Wi-Fi指纹，获得新Wi-Fi指纹，包括：
    获取所述Wi-Fi指纹簇的簇中心对应的地理坐标；
    获取所述Wi-Fi指纹簇中所有所述原始Wi-Fi指纹中出现过的Wi-Fi接入点，并计算所述Wi-Fi接入点的接收信号强度平均值和接收信号强度方差值；
    利用所述地理坐标、各个所述Wi-Fi接入点的所述接收信号强度平均值和所述接收信号强度方差值，构建所述新Wi-Fi指纹。
  3. 根据权利要求1所述的信号地图构建方法，其特征在于，对所述原始Wi-Fi指纹进行聚类处理，获得Wi-Fi指纹簇，包括：
    计算每个所述原始Wi-Fi指纹到每个路网节点的水平距离；
    利用所述水平距离对所述原始Wi-Fi指纹进行聚类处理，得到所述Wi-Fi指纹簇。
  4. 根据权利要求1所述的信号地图构建方法，其特征在于，所述获取原始Wi-Fi指纹，包括：
    获取室内数字地图和路网；所述路网中包括采样路径，所述采样路径上具有路网节点；
    利用所述室内数字地图确定出所述路网节点的地理坐标；
    利用所述采样路径，按照固定频率触发Wi-Fi扫描并记录Wi-Fi数据；
    利用所述Wi-Fi数据和所述路网节点的地理坐标，计算出所述原始Wi-Fi指纹。
  5. 根据权利要求4所述的信号地图构建方法，其特征在于，利用所述Wi-Fi数据和所述路网节点的地理坐标，计算出所述原始Wi-Fi指纹，包括：
    获取所述采样路径的采样起止时间；
    利用所述采样起止时间确定所述路网节点的时间戳；
    利用所述路网节点的时间戳，计算出所述Wi-Fi数据所在的地理坐标；
    利用所述Wi-Fi数据，以及所述Wi-Fi数据所在的地理坐标，构建出所述原始Wi-Fi指纹。
  6. 根据权利要求5所述的信号地图构建方法，其特征在于，利用所述采样起止时间确定所述路网节点的时间戳，包括：
    利用所述采样起止时间确定所述采样路径的采样总时长；
    利用所述采样总时长，结合所述路网节点在所述采样路径的相对位置，计算出采样时经过所述路网节点的时间戳；
    为所述路网节点标记所述时间戳。
  7. 根据权利要求1至6任一项所述的信号地图构建方法，其特征在于，在利用所述新Wi-Fi指纹构建Wi-Fi信号地图之后，还包括：
    接收并解析地图更新请求，获得目标Wi-Fi指纹；
    确定出所述Wi-Fi信号地图中与所述目标Wi-Fi指纹水平距离最近的目标Wi-Fi指纹簇；
    在所述目标Wi-Fi指纹簇中添加所述目标Wi-Fi指纹；
    利用所述目标Wi-Fi指纹簇中的全部Wi-Fi指纹，重新计算所述目标Wi-Fi指纹簇的新Wi-Fi指纹；
    利用重新计算得到的新Wi-Fi指纹，对所述Wi-Fi信号地图进行更新。
  8. 一种Wi-Fi信号地图构建装置，其特征在于，包括：
    原始Wi-Fi指纹获取模块，用于获取原始Wi-Fi指纹；
    聚类模块，用于对所述原始Wi-Fi指纹进行聚类处理，获得Wi-Fi指纹簇；
    Wi-Fi指纹合并模块，用于利用所述Wi-Fi指纹簇中的所述原始Wi-Fi指纹，获得新Wi-Fi指纹；
    信号地图构建模块，用于利用所述新Wi-Fi指纹构建Wi-Fi信号地图。
  9. 一种信号地图构建设备，其特征在于，包括：
    存储器，用于存储计算机程序；
    处理器，用于执行所述计算机程序时实现如权利要求1至7任一项所述的信号地图构建方法的步骤。
  10. 一种可读存储介质，其特征在于，所述可读存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现如权利要求1至7任一项所述的信号地图构建方法的步骤。
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170134909A1 (en) * 2015-11-06 2017-05-11 International Business Machines Corporation Wifi-fingerprint based indoor localization map
CN109286900A (zh) * 2018-08-29 2019-01-29 桂林电子科技大学 一种Wi-Fi样本数据优化方法
CN110049549A (zh) * 2019-01-29 2019-07-23 上海无线通信研究中心 基于WiFi指纹的多融合室内定位方法及其系统
CN110933631A (zh) * 2019-12-03 2020-03-27 浙江科技学院 基于wifi位置指纹的室内定位方法
CN111179634A (zh) * 2019-12-04 2020-05-19 浙江科技学院 基于Wi-Fi位置指纹的室内停车系统

Family Cites Families (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012011624A1 (ko) * 2010-07-21 2012-01-26 (주)브이아이소프트 위치 추정에 사용자 이동성을 활용하는 와이파이 라디오 맵 기반 실내 네비게이션 시스템 및 방법
CN105143909A (zh) 2012-06-26 2015-12-09 多伦多大学理事会 用于无线电地图的动态生成的系统、方法和计算机程序
JP2015064232A (ja) 2013-09-24 2015-04-09 Kddi株式会社 判定装置、ネットワークノード、判定方法、及びプログラム
WO2015184961A1 (en) 2014-06-06 2015-12-10 The Hong Kong University Of Science And Technology Mitigating signal noise for fingerprint-based indoor localization
CN105208651A (zh) * 2015-08-17 2015-12-30 上海交通大学 基于地图结构的Wi-Fi位置指纹非监督训练方法
CN105242239B (zh) 2015-10-19 2017-06-16 华中科技大学 一种基于众包指纹分簇和匹配的室内子区域定位方法
CN106093852A (zh) * 2016-05-27 2016-11-09 东华大学 一种提高WiFi指纹定位精度与效率的方法
CN105916202A (zh) * 2016-06-20 2016-08-31 天津大学 一种概率性的WiFi室内定位指纹库构建方法
GB201704216D0 (en) * 2017-03-16 2017-05-03 Ranplan Wireless Network Design Ltd WIFI multi-band fingerprint-based indoor positioning
CN107087256A (zh) * 2017-03-17 2017-08-22 上海斐讯数据通信技术有限公司 一种基于WiFi室内定位的指纹聚类方法及装置
US11039414B2 (en) 2017-11-21 2021-06-15 International Business Machines Corporation Fingerprint data pre-process method for improving localization model
CN109856596B (zh) * 2019-02-21 2021-05-07 上海迹寻科技有限公司 一种高速移动状态下搜集无线节点信号位置指纹的方法、计算机可读存储介质及其设备
CN110602651B (zh) * 2019-09-20 2022-02-01 北京智芯微电子科技有限公司 基于wifi位置指纹的定位方法以及机器人的定位系统

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170134909A1 (en) * 2015-11-06 2017-05-11 International Business Machines Corporation Wifi-fingerprint based indoor localization map
CN109286900A (zh) * 2018-08-29 2019-01-29 桂林电子科技大学 一种Wi-Fi样本数据优化方法
CN110049549A (zh) * 2019-01-29 2019-07-23 上海无线通信研究中心 基于WiFi指纹的多融合室内定位方法及其系统
CN110933631A (zh) * 2019-12-03 2020-03-27 浙江科技学院 基于wifi位置指纹的室内定位方法
CN111179634A (zh) * 2019-12-04 2020-05-19 浙江科技学院 基于Wi-Fi位置指纹的室内停车系统

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