CN114424635A - Signal map construction method, device, equipment and readable storage medium - Google Patents

Signal map construction method, device, equipment and readable storage medium Download PDF

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Publication number
CN114424635A
CN114424635A CN202080027035.8A CN202080027035A CN114424635A CN 114424635 A CN114424635 A CN 114424635A CN 202080027035 A CN202080027035 A CN 202080027035A CN 114424635 A CN114424635 A CN 114424635A
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fingerprint
original
signal
cluster
road network
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陈尊裕
胡斯洋
方雄
吴珏其
陈欣
吴沛谦
张仲文
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Fengtuzhi Technology Holding Co ltd
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Fengtuzhi Technology Holding Co ltd
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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

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Navigation (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

A signal map construction method, a signal map construction device, a signal map construction equipment and a readable storage medium are provided. The method comprises the following steps: acquiring an original Wi-Fi fingerprint (S101); clustering the original Wi-Fi fingerprints to obtain Wi-Fi fingerprint clusters (S102); obtaining a new Wi-Fi fingerprint by using the original Wi-Fi fingerprint in the Wi-Fi fingerprint cluster (S103); a Wi-Fi signal map is constructed using the new Wi-Fi fingerprint (S104). Due to clustering and new Wi-Fi fingerprint acquisition, the data volume of the constructed Wi-Fi signal map can be greatly reduced, data caching and calculation overhead are reduced, on the other hand, the difference of signal characteristics among Wi-Fi fingerprints can be enhanced, and the positioning precision can be improved.

Description

Signal map construction method, device, equipment and readable storage medium Technical Field
The present application relates to the field of positioning technologies, and in particular, to a method, an apparatus, a device, and a readable storage medium for constructing a signal map.
Background
Real-time location technology has become the fundamental technology for a plurality of high-level applications such as transportation, business, logistics, individual services, and the like. In outdoor environments, global navigation satellite systems have been developed for a long time to provide good positioning services.
In indoor environments, the gps system cannot provide reliable services due to weak satellite signals reaching the ground, inability to penetrate buildings, and multipath effects. Therefore, in recent years, indoor positioning technology has become a popular research direction in the navigation field. Indoor positioning is performed by utilizing an indoor Wi-Fi signal map, and the indoor positioning method has become one of the most widely applied indoor positioning technologies due to the characteristics of relatively high positioning accuracy, easiness in deployment, strong transportability and the like.
Indoor positioning is carried out by utilizing an indoor Wi-Fi signal map, and the Wi-Fi signal map is firstly constructed. If the Wi-Fi fingerprints in the Wi-Fi signal map are too sparse, the positioning precision is inevitably reduced; on one hand, the Wi-Fi fingerprints are too dense, so that the data volume of the signal map is large, the data caching and calculation overhead is increased, and on the other hand, the difference of signal characteristics among the Wi-Fi fingerprints is weakened, and the positioning accuracy is influenced.
In summary, how to solve the problems of Wi-Fi signal map construction and the like is a technical problem which needs to be solved urgently by those skilled in the art at present.
Disclosure of Invention
The method, the device and the equipment for constructing the signal map and the readable storage medium are used for clustering original Wi-Fi fingerprints, reconstructing a new Wi-Fi fingerprint by using the original Wi-Fi fingerprint in a Wi-Fi fingerprint cluster, and then constructing the Wi-Fi signal map with light weight and larger signal characteristic difference based on the new Wi-Fi fingerprint.
In order to solve the technical problem, the application provides the following technical scheme:
a signal mapping method, comprising:
acquiring an original Wi-Fi fingerprint;
clustering the original Wi-Fi fingerprints to obtain Wi-Fi fingerprint clusters;
obtaining a new Wi-Fi fingerprint by using the original Wi-Fi fingerprint in the Wi-Fi fingerprint cluster;
and constructing a Wi-Fi signal map by using the new Wi-Fi fingerprint.
Preferably, obtaining a new Wi-Fi fingerprint using the original Wi-Fi fingerprint in the Wi-Fi fingerprint cluster comprises:
acquiring a geographical coordinate corresponding to the cluster center of the Wi-Fi fingerprint cluster;
acquiring all Wi-Fi access points appearing in all the original Wi-Fi fingerprints in the Wi-Fi fingerprint cluster, and calculating the average value and the variance value of the received signal strength of the Wi-Fi access points;
and constructing the new Wi-Fi fingerprint by using the geographic coordinates, the average received signal strength value and the variance value of the received signal strength of each Wi-Fi access point.
Preferably, clustering the original Wi-Fi fingerprint to obtain a Wi-Fi fingerprint cluster includes:
calculating the horizontal distance from each original Wi-Fi fingerprint to each road network node;
and clustering the original Wi-Fi fingerprints by using the horizontal distance to obtain the Wi-Fi fingerprint cluster.
Preferably, the acquiring of the original Wi-Fi fingerprint comprises:
acquiring an indoor digital map and a road network; the road network comprises a sampling path, and road network nodes are arranged on the sampling path;
determining the geographic coordinates of the road network nodes by utilizing the indoor digital map;
triggering Wi-Fi scanning and recording Wi-Fi data according to fixed frequency by using the sampling path;
and calculating the original Wi-Fi fingerprint by using the Wi-Fi data and the geographic coordinates of the road network nodes.
Preferably, calculating the original Wi-Fi fingerprint using the Wi-Fi data and the geographical coordinates of the routing node comprises:
acquiring sampling start-stop time of a sampling path;
determining the time stamp of the road network node by using the sampling start-stop time;
calculating the geographical coordinates of the Wi-Fi data by using the timestamp of the road network node;
and constructing the original Wi-Fi fingerprint by utilizing the Wi-Fi data and the geographic coordinates of the Wi-Fi data.
Preferably, the determining the time stamp of the road network node by using the sampling start-stop time includes:
determining a total sampling time length of the sampling path by using the sampling start-stop time;
calculating a timestamp of passing through the road network node during sampling by using the total sampling duration and combining the relative position of the road network node in the sampling path;
and marking the time stamp for the road network node.
Preferably, after building the Wi-Fi signal map by using the new Wi-Fi fingerprint, the method further comprises:
receiving and analyzing a map updating request to obtain a target Wi-Fi fingerprint;
determining a target Wi-Fi fingerprint cluster which is closest to the target Wi-Fi fingerprint level distance in the Wi-Fi signal map;
adding the target Wi-Fi fingerprint in the target Wi-Fi fingerprint cluster;
recalculating a new Wi-Fi fingerprint of the target Wi-Fi fingerprint cluster by using all Wi-Fi fingerprints in the target Wi-Fi fingerprint cluster;
and updating the Wi-Fi signal map by using the new Wi-Fi fingerprint obtained by recalculation.
A Wi-Fi signal mapping apparatus, comprising:
the original Wi-Fi fingerprint acquisition module is used for acquiring an original Wi-Fi fingerprint;
the clustering module is used for clustering the original Wi-Fi fingerprints to obtain Wi-Fi fingerprint clusters;
the Wi-Fi fingerprint merging module is used for acquiring a new Wi-Fi fingerprint by using the original Wi-Fi fingerprint in the Wi-Fi fingerprint cluster;
and the signal map building module is used for building a Wi-Fi signal map by utilizing the new Wi-Fi fingerprint.
A signal mapping apparatus comprising:
a memory for storing a computer program;
a processor for implementing the steps of the signal mapping method as described above when executing the computer program.
A readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the signal mapping method as described above.
The method provided by the embodiment of the application is applied to obtain the original Wi-Fi fingerprint; clustering the original Wi-Fi fingerprints to obtain Wi-Fi fingerprint clusters; obtaining a new Wi-Fi fingerprint by using the original Wi-Fi fingerprint in the Wi-Fi fingerprint cluster; and constructing a Wi-Fi signal map by using the new Wi-Fi fingerprint.
After the original Wi-Fi fingerprints are acquired, in order to reduce the data volume of the Wi-Fi signal map, the original Wi-Fi fingerprints are firstly clustered, and Wi-Fi fingerprint clusters which are smaller than the original Wi-Fi fingerprint data are acquired. In order to improve the signal characteristic difference of the Wi-Fi fingerprints, the original Wi-Fi fingerprints are not adopted any more, and a new Wi-Fi fingerprint is obtained based on the original Wi-Fi fingerprints in the Wi-Fi fingerprint cluster. And then, 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, the data volume of the constructed Wi-Fi signal map can be greatly reduced, data caching and calculation overhead are reduced, on the other hand, the difference of signal characteristics among Wi-Fi fingerprints can be enhanced, and the positioning precision can be improved.
Accordingly, embodiments of the present application further provide a device, an apparatus, and a readable storage medium corresponding to the signal map construction method, which have the above technical effects and are not described herein again.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of an implementation of a signal map construction method in an embodiment of the present application;
FIG. 2 is a schematic diagram of a road network according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a signal map construction apparatus in an embodiment of the present application;
fig. 4 is a schematic structural diagram of a signal map building apparatus in an embodiment of the present application;
fig. 5 is a schematic structural diagram of a signal map building apparatus in an embodiment of the present application.
Detailed Description
In order that those skilled in the art will better understand the disclosure, the following detailed description will be given with reference to the accompanying drawings. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the signal map construction method provided by the embodiment of the present application is to construct a Wi-Fi signal map with a single floor in a building as a basic unit. Therefore, Wi-Fi signal features within a building need to be represented by several single floor signal maps.
For the sake of understanding, some terms in the embodiments of the present application are explained below:
Wi-Fi access point: Wi-Fi Access Point (Wi-Fi AP), a hardware device which allows a mobile device to Access the Internet through a wireless signal, a multi-finger wireless router, a mobile hotspot and the like.
Wi-Fi fingerprint: the Wi-Fi finger print is a data set which is formed by a plurality of scanned Wi-Fi access points and signal intensity thereof and is different from scanning results of other positions and a < signal, position > data pair which is formed by coordinate values (such as longitude and latitude geographic coordinates) of the current position under a certain coordinate system at a certain position in space.
MAC address: the physical address or the hardware address of the Wi-Fi access point is a unique address for distinguishing the Wi-Fi access point.
SSID: service Set Identifier, i.e. Service Set Identifier. The name of the wireless local area network broadcasted by the Wi-Fi access point is customized by a local area network owner and is not unique.
RSSI: the Received Signal Strength Indicator is used for indicating the Strength of a wireless network Signal Received by the mobile terminal equipment and sent by a Wi-Fi access point.
Indoor digital map: under a certain coordinate system, the ground elements of the internal structure, arrangement and the like of the building are assigned to an ordered data set which can be identified by a computer, can be summarized on a storage medium and can determine the coordinates and the attributes.
Indoor Wi-Fi signal maps: based on a coordinate system of the indoor digital map, the Wi-Fi fingerprints are marked on the indoor digital map by the geographic coordinate positions of the Wi-Fi fingerprints to form an ordered data set, and compared Wi-Fi data and reference positions are provided for indoor positioning calculation.
Road network: the road network is a network structure which is formed by marking indoor walkable channels or areas with line segments on an indoor digital map and has geographic coordinates.
Road network node: intersection points and connection points between line segments in the road network, and points with geographic coordinates inserted into the line segments at certain intervals are defined as road network nodes.
Referring to fig. 1, fig. 1 is a flowchart of a signal map construction method according to an embodiment of the present application, the method including the following steps:
s101, acquiring an original Wi-Fi fingerprint.
The original Wi-Fi fingerprint refers to a Wi-Fi fingerprint in which neither a signal nor a position in a < signal, position > data pair is processed, i.e., an original state generated after sampling.
In the embodiment, the original Wi-Fi fingerprint can be directly obtained from the Wi-Fi signal map with relatively dense Wi-Fi fingerprints; the original Wi-Fi fingerprint stored in advance can be directly read from the readable storage medium; the original Wi-Fi fingerprint can also be obtained by directly sampling in a static sampling (Point-to-Point, P2P) or walking sampling (walk surfey) mode.
The static sampling requires that a certain sampling point with known space coordinates stays for several seconds to several minutes to record the Wi-Fi access point and the signal strength scanned in the time period, namely, two elements of a signal and a position are obtained at the same time to form a Wi-Fi fingerprint. The collection process needs to be repeated from one sampling point to another until the sampling point covers the whole building space with the positioning requirement.
The walking sampling is divided into two stages of acquisition and calculation. And in the acquisition stage, the key points (such as a starting point, an inflection point and an end point) of the walking track, the Wi-Fi access points scanned along the way and the signal intensity are recorded. In the calculation stage, the position of the Wi-Fi data is obtained through interpolation on a path by using data such as coordinate values of all key points and timestamps of walking records, and accordingly a < signal, position > data pair, namely a Wi-Fi fingerprint, is constructed.
S102, clustering the original Wi-Fi fingerprints to obtain Wi-Fi fingerprint clusters.
In order to reduce the number of Wi-Fi fingerprints and improve the signal difference among the Wi-Fi fingerprints, after the original Wi-Fi fingerprints are obtained, clustering processing can be carried out on the original Wi-Fi fingerprints to obtain a Wi-Fi fingerprint cluster.
Since at least one original Wi-Fi fingerprint is included in one Wi-Fi fingerprint cluster, and the smaller the number of Wi-Fi fingerprint clusters is, the more original Wi-Fi fingerprints in the Wi-Fi fingerprint clusters are.
In the clustering process, clustering can be carried out according to the relative relation among the positions in the original Wi-Fi fingerprints, so that the original Wi-Fi fingerprints with similar geographic positions are gathered into a Wi-Fi fingerprint cluster. When clustering is carried out, the number of clusters (or the positions of the centers of all fixed clusters) can be preset, and the number of Wi-Fi fingerprint clusters obtained by final clustering is limited; the number of clusters does not need to be set (the cluster center position is not limited), and the original Wi-Fi fingerprints with close positions can be gathered in one Wi-Fi fingerprint cluster to the maximum extent.
Preferably, in practical application, when the original Wi-Fi fingerprint acquisition is performed, the walking path of the user under the practical application environment is considered. Therefore, when clustering is carried out, the road network nodes can be used as cluster centers to carry out clustering processing, so that the centers of the Wi-Fi fingerprint clusters are on a walking path, and the positioning accuracy is improved. Specifically, the specific clustering process includes:
step one, calculating the horizontal distance from each original Wi-Fi fingerprint to each road network node;
and step two, clustering the original Wi-Fi fingerprints by using the horizontal distance to obtain a Wi-Fi fingerprint cluster.
For convenience of description, the above two steps will be described in combination.
That is, the clustering process: and taking the road network node as the center of the cluster, taking the horizontal distance between the original Wi-Fi fingerprint and the road network node as a reference, classifying the original Wi-Fi fingerprint into the road network node with the minimum horizontal distance to the original Wi-Fi fingerprint, and forming a Wi-Fi fingerprint cluster at each road network node.
S103, obtaining a new Wi-Fi fingerprint by using the original Wi-Fi fingerprint in the Wi-Fi fingerprint cluster.
In the embodiment of the application, in order to reduce the number of Wi-Fi fingerprints and increase the signal characteristic difference seen by the Wi-Fi fingerprints, one Wi-Fi fingerprint can be reconstructed for each Wi-Fi fingerprint cluster. That is, a new Wi-Fi fingerprint replaces all the original Wi-Fi fingerprints in a cluster of original Wi-Fi fingerprints.
Specifically, the process of acquiring a new Wi-Fi fingerprint includes:
acquiring a geographical coordinate corresponding to a cluster center of a Wi-Fi fingerprint cluster;
step two, obtaining Wi-Fi access points appearing in all original Wi-Fi fingerprints in the Wi-Fi fingerprint cluster, and calculating the average value and the variance value of the received signal strength of each access point;
and thirdly, constructing a new Wi-Fi fingerprint 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.
For convenience of description, the above three steps will be described in combination.
During clustering, the cluster center can be designated or determined after clustering by a clustering algorithm. Therefore, in this embodiment, if the cluster center is specified, the geographic coordinates of the specified cluster center can be directly acquired. If the cluster center is not specified, the geometric center of the geographic positions of all the original Wi-Fi fingerprints in the Wi-Fi fingerprint cluster can be calculated and used as the geographic coordinate of the cluster center.
The RSSI mean value of the Wi-Fi access point is the RSSI mean value of the Wi-Fi access point, and 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, namely the geographical coordinate corresponding to the cluster center of the Wi-Fi fingerprint cluster, can be specifically in a longitude and latitude representation form; and the signals in the new Wi-Fi fingerprint are the RSSI mean value and the RSSI variance value of all the Wi-Fi access points and all the access points contained in the original Wi-Fi fingerprint in the cluster. Of course, in other embodiments of the present application, the signals in the Wi-Fi fingerprint may also take other forms of presentation.
Obviously, the number of the new Wi-Fi fingerprints based on the Wi-Fi fingerprint cluster is far smaller than that of the original Wi-Fi fingerprints in the same building. In addition, in the peripheral range of a certain node, the signal characteristics of a certain Wi-Fi access point in the range are represented by the RSSI statistical value on the Wi-Fi fingerprint cluster, and the information redundancy formed by the repeated appearance of the access point in a plurality of original Wi-Fi fingerprints is reduced.
And S104, constructing a Wi-Fi signal map by using the new Wi-Fi fingerprint.
After new Wi-Fi fingerprints which are fewer in number and obvious in signal difference among the Wi-Fi fingerprints relative to the original Wi-Fi fingerprints are obtained, a Wi-Fi signal map can be constructed based on the new Wi-Fi fingerprints.
After the Wi-Fi signal map is constructed, positioning can be carried out based on the Wi-Fi signal map. Because the data volume of the Wi-Fi signal map is small and the signal characteristics of Wi-Fi fingerprints are more obvious, the calculation can be reduced and the positioning precision can be improved during positioning.
The method provided by the embodiment of the application is applied to obtain the original Wi-Fi fingerprint; clustering the original Wi-Fi fingerprints to obtain Wi-Fi fingerprint clusters; obtaining a new Wi-Fi fingerprint by using the original Wi-Fi fingerprint in the Wi-Fi fingerprint cluster; and constructing a Wi-Fi signal map by using the new Wi-Fi fingerprint.
After the original Wi-Fi fingerprints are acquired, in order to reduce the data volume of the Wi-Fi signal map, the original Wi-Fi fingerprints are firstly clustered, and Wi-Fi fingerprint clusters which are smaller than the original Wi-Fi fingerprint data are acquired. In order to improve the signal characteristic difference of the Wi-Fi fingerprints, the original Wi-Fi fingerprints are not adopted any more, and a new Wi-Fi fingerprint is obtained based on the original Wi-Fi fingerprints in the Wi-Fi fingerprint cluster. And then, 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, the data volume of the constructed Wi-Fi signal map can be greatly reduced, data caching and calculation overhead are reduced, on the other hand, the difference of signal characteristics among Wi-Fi fingerprints can be enhanced, and the positioning precision can be improved.
It should be noted that, based on the above embodiments, the embodiments of the present application also provide corresponding improvements. In the preferred/improved embodiment, the same steps as those in the above embodiment or corresponding steps may be referred to each other, and corresponding advantageous effects may also be referred to each other, which are not described in detail in the preferred/improved embodiment herein.
Preferably, in order to improve the sampling efficiency of the original Wi-Fi fingerprint, the original Wi-Fi fingerprint may be further obtained by specifically performing the following steps:
step one, acquiring an indoor digital map and a road network; the road network comprises a sampling path, and road network nodes are arranged on the sampling path;
determining the geographic coordinates of the road network nodes by utilizing the indoor digital map;
triggering Wi-Fi scanning according to fixed frequency by using a sampling path and recording Wi-Fi data;
and fourthly, calculating the original Wi-Fi fingerprint by utilizing the Wi-Fi data and the geographic coordinates of the road network nodes.
For convenience of description, the above four steps will be described in detail below in combination.
In particular, a digital map of a floor may be drawn using a mapping tool. The digital map may include, but is not limited to, corridors, rooms, stairwells, elevator cabs, and fixed ground elements (e.g., curtain walls, widely deployed tables and chairs, etc.) that significantly alter the path of travel. The walkable regions may be marked with a point-line structure on the digital map using a road network generation program. Then, setting road network node distance parameters according to the building structure characteristics, and generating a road network with basically consistent adjacent node distance and geographical latitude and longitude. For example, referring to fig. 2, fig. 2 is a schematic diagram of a road network according to an embodiment of the present application, wherein black hollow dots are road network nodes.
Preferably, in order to make the constructed Wi-Fi signal map location more accurate, the timestamp of the routing network node may be preferentially calculated, and then the geographic coordinate where the Wi-Fi data is located is calculated based on the segments, that is, the fourth step may specifically include:
step 4.1, acquiring the sampling start-stop time of the sampling path;
step 4.2, determining the time stamp of the road network node by using the sampling start-stop time;
4.3, calculating the geographical coordinates of the Wi-Fi data by using the time stamps of the road network nodes;
and 4.4, constructing an original Wi-Fi fingerprint by using the Wi-Fi data and the geographic coordinates of the Wi-Fi data.
Specifically, step 4.2 may specifically include:
step 4.2.1, determining the total sampling time length of the sampling path by using the sampling start-stop time;
4.2.2, calculating a timestamp of passing through the road network node during sampling by using the total sampling time length and combining the relative position of the road network node in the sampling path;
and 4.2.3, marking the time stamp for the road network node.
In practical application, 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 a sampling program to plan a sampling path at least comprising 4 path nodes (which may comprise starting points) by taking the connecting nodes of the road network as the starting points. After the path is determined, the sampling program records all path nodes passed by the path and the geographical latitude and longitude of the path nodes.
And the sampler (which 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 walking. Specifically, a sampler manually triggers and ends a sampling process, and records corresponding sampling start-stop time; during sampling, Wi-Fi scanning is triggered at a fixed frequency and recorded: a timestamp for each scan (one-to-one correspondence with the physical location), SSID, MAC address, and RSSI values for each scanned Wi-Fi access point. The fixed frequency can be set according to the precision required by the map, and if the positioning precision requirement is higher, a higher fixed frequency can be set; if the positioning accuracy requirement is low, a low fixed frequency can be set.
And the obtained geographical longitude and latitude of each road network node on the sampling path, the start and end time of walking sampling and Wi-Fi data recorded for multiple times in the sampling process form a sampling record. In general, the sampling process for each floor can be performed by splitting the sampling process into a plurality of sampling paths. And the sampler performs walking sampling on each sampling path until the sampling path covers all the road networks of the floor, and the sampling is finished.
The acquisition of the original Wi-Fi fingerprint comprises two steps: in the first step, a timestamp is marked for a network node on the sampling path. Because the sampler walks at a constant speed, the time length from the beginning to the time when the sampler walks through a certain node is in direct proportion to the path length between the node and the starting point; if the geographical longitude and latitude of each node on the path are known, the length of the path which is in a straight line between adjacent nodes and the total length of the sampling path can be calculated. If the sampling start-stop time of the sampling path is known, the total walking time length can be obtained; then the time stamp corresponding to any node on the path can be obtained by interpolation; secondly, calculating the geographical coordinates of the position of the Wi-Fi data based on the corresponding time stamp of the road network node; firstly, determining that the data exists on a certain straight path determined by two adjacent road network nodes according to the timestamp of the Wi-Fi data; based on the condition of uniform walking, the time length from the starting point timestamp of the current paragraph to the Wi-Fi data timestamp is in direct proportion to the path length from the starting point of the paragraph to the position of the Wi-Fi data; on the premise of knowing longitude and latitude coordinates and timestamps of starting and stopping points of the paragraph and the Wi-Fi data timestamp, the longitude and latitude values of the position corresponding to the Wi-Fi data can be obtained through interpolation, and then < signal, position > data pairs, namely original Wi-Fi fingerprints, are obtained.
Preferably, in order to improve the positioning accuracy, the Wi-Fi signal map is updated, considering that the number and the position of Wi-Fi may vary in the actual application environment. Compared with the method for directly adding Wi-Fi fingerprints, the method for constructing the signal map provided by the embodiment of the application is used for updating the existing Wi-Fi fingerprints in the Wi-Fi signal map so as to keep the light weight of the map and the characteristic difference between signals. Specifically, after the Wi-Fi signal map is built by using the new Wi-Fi fingerprint, the Wi-Fi signal map can be updated by executing the following steps:
the method comprises the steps of firstly, receiving and analyzing a map updating request to obtain a target Wi-Fi fingerprint;
determining a target Wi-Fi fingerprint cluster which is closest to the target Wi-Fi fingerprint level distance in the Wi-Fi signal map;
adding a target Wi-Fi fingerprint in the target Wi-Fi fingerprint cluster;
step four, recalculating a new Wi-Fi fingerprint of the target Wi-Fi fingerprint cluster by using all Wi-Fi fingerprints in the target Wi-Fi fingerprint cluster;
and step five, updating the Wi-Fi signal map by using the new Wi-Fi fingerprint obtained by recalculation.
The target Wi-Fi fingerprint can be a Wi-Fi fingerprint which is newly acquired and needs to be subjected to emphasis position modification.
After the target Wi-Fi fingerprint is obtained, it is first determined which Wi-Fi fingerprint cluster should be attributed to. The specific classification method can be based on which Wi-Fi fingerprint cluster the horizontal distance from which Wi-Fi fingerprint cluster is closest, so that the classification method is convenient to which Wi-Fi fingerprint cluster.
And then, reconstructing a new Wi-Fi fingerprint corresponding to the target Wi-Fi fingerprint cluster. After the new Wi-Fi fingerprint is obtained through recalculation, only the corresponding Wi-Fi fingerprint in the Wi-Fi signal map can be replaced.
Therefore, after the updating is completed, the number of the Wi-Fi fingerprints in the Wi-Fi signal map is unchanged, and the updated Wi-Fi signal map already contains the signal characteristics of the target Wi-Fi fingerprint. The lightweight of the Wi-Fi signal map is kept, and the difference of signal characteristics among Wi-Fi fingerprints is guaranteed.
Corresponding to the above method embodiment, the present application further provides a signal map construction device, and a signal map construction device described below and a signal map construction method described above may be referred to in correspondence with each other.
Referring to fig. 3, the apparatus includes:
an original Wi-Fi fingerprint acquisition module 101, configured to acquire an original Wi-Fi fingerprint;
the clustering module 102 is configured to perform clustering processing on the original Wi-Fi fingerprint to obtain a Wi-Fi fingerprint cluster;
the Wi-Fi fingerprint merging module 103 is used for acquiring a new Wi-Fi fingerprint by using the original Wi-Fi fingerprint in the Wi-Fi fingerprint cluster;
and the signal map building module 104 is used for building a Wi-Fi signal map by using the new Wi-Fi fingerprint.
The method comprises the steps of obtaining an original Wi-Fi fingerprint by applying the device provided by the embodiment of the application; clustering the original Wi-Fi fingerprints to obtain Wi-Fi fingerprint clusters; obtaining a new Wi-Fi fingerprint by using the original Wi-Fi fingerprint in the Wi-Fi fingerprint cluster; and constructing a Wi-Fi signal map by using the new Wi-Fi fingerprint.
After the original Wi-Fi fingerprints are acquired, in order to reduce the data volume of the Wi-Fi signal map, the original Wi-Fi fingerprints are firstly clustered, and Wi-Fi fingerprint clusters which are smaller than the original Wi-Fi fingerprint data are acquired. In order to improve the signal characteristic difference of the Wi-Fi fingerprints, the original Wi-Fi fingerprints are not adopted any more, and a new Wi-Fi fingerprint is obtained based on the original Wi-Fi fingerprints in the Wi-Fi fingerprint cluster. And then, 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, the data volume of the constructed Wi-Fi signal map can be greatly reduced, data caching and calculation overhead are reduced, on the other hand, the difference of signal characteristics among Wi-Fi fingerprints can be enhanced, and the positioning precision can be improved.
In a specific embodiment of the present application, the Wi-Fi fingerprint merging module 103 is specifically configured to obtain a geographic coordinate corresponding to a cluster center of a Wi-Fi fingerprint cluster; acquiring Wi-Fi access points appearing in all original Wi-Fi fingerprints in a Wi-Fi fingerprint cluster, and calculating the average value and the variance value of the received signal strength of the Wi-Fi access points; and constructing a new Wi-Fi fingerprint 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.
In a specific embodiment of the present application, the clustering module 102 is specifically configured to calculate a horizontal distance from each original Wi-Fi fingerprint to each road network node; and clustering the original Wi-Fi fingerprints by using the horizontal distance to obtain a Wi-Fi fingerprint cluster.
In a specific embodiment of the present application, the original Wi-Fi fingerprint obtaining module 101 includes:
the road network acquisition unit is used for acquiring an indoor digital map and a road network; the road network comprises a sampling path, and road network nodes are arranged on the sampling path;
the geographic coordinate calculation unit is used for determining the geographic coordinates of the road network nodes by utilizing the indoor digital map;
the sampling unit is used for triggering Wi-Fi scanning according to fixed frequency by using a sampling path and recording Wi-Fi data;
and the fingerprint calculation unit is used for calculating the original Wi-Fi fingerprint by utilizing the Wi-Fi data and the geographic coordinates of the road network nodes.
In an embodiment of the present application, the fingerprint calculation unit is specifically configured to obtain a sampling start-stop time of the sampling path; determining the time stamp of the road network node by using the sampling start-stop time; calculating the geographical coordinates of the Wi-Fi data by using the time stamps of the road network nodes; and constructing an original Wi-Fi fingerprint by using the Wi-Fi data and the geographic coordinates of the Wi-Fi data.
In an embodiment of the present application, the fingerprint calculation unit is specifically configured to determine a total sampling duration of the sampling path by using the sampling start-stop time; calculating a timestamp of passing through the road network node during sampling by using the total sampling duration and combining the relative position of the road network node in the sampling path; the routing nodes are time stamped.
In one embodiment of the present application, the method further includes:
the map updating module is used for receiving and analyzing a map updating request after a Wi-Fi signal map is constructed by using the new Wi-Fi fingerprint to obtain a target Wi-Fi fingerprint; determining a target Wi-Fi fingerprint cluster which is closest to the target Wi-Fi fingerprint horizontal distance in the Wi-Fi signal map; adding a target Wi-Fi fingerprint in the target Wi-Fi fingerprint cluster; recalculating a new Wi-Fi fingerprint of the target Wi-Fi fingerprint cluster by using all Wi-Fi fingerprints in the target Wi-Fi fingerprint cluster; and updating the Wi-Fi signal map by using the new Wi-Fi fingerprint obtained by recalculation.
Corresponding to the above method embodiment, the present application further provides a signal map construction device, and a signal map construction device described below and a signal map construction method described above may be referred to in correspondence with each other.
Referring to fig. 4, the signal mapping apparatus includes:
a memory 332 for storing a computer program;
a processor 322 for implementing the steps of the signal mapping method as described in the above method embodiments when executing the computer program.
Specifically, referring to fig. 5, a specific structural diagram of a signal mapping device provided in this embodiment is shown, where the signal mapping device may generate relatively large differences due to different configurations or performances, and may include one or more processors (CPUs) 322 (e.g., one or more processors) and a memory 332, where the memory 332 stores one or more computer applications 342 or data 344. Memory 332 may be, among other things, transient or persistent storage. The program stored in memory 332 may include one or more modules (not shown), each of which may include a sequence of instructions operating on a data processing device. Still further, the central processor 322 may be configured to communicate with the memory 332 to perform a series of instruction operations in the storage medium 330 on the signal mapping apparatus 301.
The signal mapping apparatus 301 may also include one or more power sources 326, one or more wired or wireless network interfaces 350, one or more input-output interfaces 358, and/or one or more operating systems 341.
The steps in the signal mapping method described above may be implemented by the structure of the signal mapping apparatus.
Corresponding to the above method embodiment, the present application further provides a readable storage medium, and a readable storage medium described below and a signal map construction method described above may be referred to correspondingly.
A readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the signal mapping method described in the above-mentioned method embodiments.
The readable storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various other readable storage media capable of storing program codes.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

Claims (10)

  1. A signal mapping method, comprising:
    acquiring an original Wi-Fi fingerprint;
    clustering the original Wi-Fi fingerprints to obtain Wi-Fi fingerprint clusters;
    obtaining a new Wi-Fi fingerprint by using the original Wi-Fi fingerprint in the Wi-Fi fingerprint cluster;
    and constructing a Wi-Fi signal map by using the new Wi-Fi fingerprint.
  2. The signal mapping method of claim 1, wherein obtaining a new Wi-Fi fingerprint using the original Wi-Fi fingerprint in the Wi-Fi fingerprint cluster comprises:
    acquiring a geographical coordinate corresponding to the cluster center of the Wi-Fi fingerprint cluster;
    acquiring Wi-Fi access points appearing in all the original Wi-Fi fingerprints in the Wi-Fi fingerprint cluster, and calculating the average value and the variance value of the received signal strength of the Wi-Fi access points;
    and constructing the new Wi-Fi fingerprint by using the geographic coordinates, the average received signal strength value and the variance value of the received signal strength of each Wi-Fi access point.
  3. The signal map construction method according to claim 1, wherein clustering the original Wi-Fi fingerprints to obtain Wi-Fi fingerprint clusters comprises:
    calculating the horizontal distance from each original Wi-Fi fingerprint to each road network node;
    and clustering the original Wi-Fi fingerprints by using the horizontal distance to obtain the Wi-Fi fingerprint cluster.
  4. The signal mapping method of claim 1, wherein the obtaining of the raw Wi-Fi fingerprint comprises:
    acquiring an indoor digital map and a road network; the road network comprises a sampling path, and road network nodes are arranged on the sampling path;
    determining the geographic coordinates of the road network nodes by utilizing the indoor digital map;
    triggering Wi-Fi scanning and recording Wi-Fi data according to fixed frequency by using the sampling path;
    and calculating the original Wi-Fi fingerprint by using the Wi-Fi data and the geographic coordinates of the road network nodes.
  5. The signal mapping method of claim 4, wherein computing the original Wi-Fi fingerprint using the Wi-Fi data and the geographic coordinates of the routing nodes comprises:
    acquiring sampling start-stop time of the sampling path;
    determining the time stamp of the road network node by using the sampling start-stop time;
    calculating the geographical coordinates of the Wi-Fi data by using the timestamp of the road network node;
    and constructing the original Wi-Fi fingerprint by utilizing the Wi-Fi data and the geographic coordinates of the Wi-Fi data.
  6. The signal mapping method according to claim 5, wherein determining the time stamp of the road network node using the sampling start and stop time comprises:
    determining a total sampling time length of the sampling path by using the sampling start-stop time;
    calculating a timestamp of passing through the road network node during sampling by using the total sampling duration and combining the relative position of the road network node in the sampling path;
    and marking the time stamp for the road network node.
  7. The signal mapping method according to any one of claims 1 to 6, further comprising, after building a Wi-Fi signal map using the new Wi-Fi fingerprint:
    receiving and analyzing a map updating request to obtain a target Wi-Fi fingerprint;
    determining a target Wi-Fi fingerprint cluster which is closest to the target Wi-Fi fingerprint level distance in the Wi-Fi signal map;
    adding the target Wi-Fi fingerprint in the target Wi-Fi fingerprint cluster;
    recalculating a new Wi-Fi fingerprint of the target Wi-Fi fingerprint cluster by using all Wi-Fi fingerprints in the target Wi-Fi fingerprint cluster;
    and updating the Wi-Fi signal map by using the new Wi-Fi fingerprint obtained by recalculation.
  8. A Wi-Fi signal mapping apparatus, comprising:
    the original Wi-Fi fingerprint acquisition module is used for acquiring an original Wi-Fi fingerprint;
    the clustering module is used for clustering the original Wi-Fi fingerprints to obtain Wi-Fi fingerprint clusters;
    the Wi-Fi fingerprint merging module is used for acquiring a new Wi-Fi fingerprint by using the original Wi-Fi fingerprint in the Wi-Fi fingerprint cluster;
    and the signal map building module is used for building a Wi-Fi signal map by utilizing the new Wi-Fi fingerprint.
  9. A signal mapping apparatus, comprising:
    a memory for storing a computer program;
    a processor for implementing the steps of the signal mapping method according to any of claims 1 to 7 when executing the computer program.
  10. A readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the signal mapping method according to any one of claims 1 to 7.
CN202080027035.8A 2020-06-01 2020-06-01 Signal map construction method, device, equipment and readable storage medium Pending CN114424635A (en)

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