CN103152823B - A kind of wireless indoor location method - Google Patents
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- CN103152823B CN103152823B CN201310060708.1A CN201310060708A CN103152823B CN 103152823 B CN103152823 B CN 103152823B CN 201310060708 A CN201310060708 A CN 201310060708A CN 103152823 B CN103152823 B CN 103152823B
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Abstract
The present invention proposes a kind of wireless indoor location method, comprises step: utilize smart mobile phone automatically to gather wireless fingerprint data and user's Mobile data, formed fingerprint collection F and Distance matrix D ', and carry out preliminary treatment; According to pretreated fingerprint collection F and Distance matrix D ', build fingerprint space; Generate unstressed plane graph, carry out key feature extraction, and carry out space coordinate conversion, wherein, key feature extracts and comprises corridor recognition, room identification and reference point coupling, and space coordinate conversion comprises the conversion of floor-level and the conversion of room-level.Wireless indoor location method of the present invention is without the need to carrying out artificial on-site land survey to locating area, finger print information can be provided easily by the multiple user collaboratives in system, its positioning result precision is high, logicality is good, and the redundant information produced when inquiring about and moving at ordinary times all can be used as upgrade information.
Description
Technical field
The invention belongs to wireless indoor positioning field, relate to a kind of wireless indoor location method.
Background technology
The popularization that is universal and general fit calculation of mobile phone has expedited the emergence of a large amount of research about wireless indoor location.Current most indoor orientation method utilizes received signal strength (ReceivedSignalStrength, RSS) to determine position.And RSS fingerprint can obtain from ready-made Wireless Communication Equipment (such as the equipment of WiFi or ZigBee compatibility) easily.
Traditional wireless fingerprint location technology adopts two stage pattern.First stage is the training stage, or be called signal acquisition stage, namely adopt artificial method in advance the wireless signal strength (Wi-Fi signal strength or Zig-Bee signal strength signal intensity as different radio route) on each position indoor to be carried out repeatedly record, and store after record result treatment in the entry of corresponding physical location in a database.Due to the uncertainty of radio signal propagation and indoor situations, the collection of signal strength data needs a large amount of repetition repeatedly.Meanwhile, the levels of precision of physical location also has influence on the accuracy of final positioning result, needs a large amount of manpower of consumption and time to carry out preparation and site inspection (SiteSurvey), sets up wireless signal map (RadioMap) in advance.Through the training of first stage, after fingerprint database establishes, system enters second stage, namely actual service stage.User can obtain the wireless fingerprint information of oneself in the region of existing wireless signal distribution plots, and this information is sent to positioning service module as the foundation of inquiring about.By location algorithm, the wireless fingerprint in the wireless fingerprint information and date storehouse send user is compared, and returns the immediate positional information of similarity to user.
Because wireless fingerprint location technology utilizes the existing network facilities to position, do not increase the overhead of system, therefore Chinese scholars has carried out a large amount of deep research to wireless fingerprint localization method.But as mentioned above, also there is following defect in existing wireless fingerprint location technology:
(1) need to carry out high cost, inefficient manual site exploration
On-site land survey needs carry out the sampling of wireless signal fingerprint to each position of locating area and manually mark, time and effort consuming, and is difficult to all positions covering locating area.
(2) poor to the tolerance of environmental change and wireless signal fluctuation
One large feature of indoor environment is that environment is changeable, and radio signal propagation characteristic is complicated, and signal fluctuation is large.Based on the method for on-site land survey, be difficult to the dynamic change conformed; Traditional method directly utilizes Euclidean distance between wireless signal fingerprint as feature, is difficult to the fluctuation adapting to wireless signal.
(3) the logic location of room level cannot be realized
Traditional method is intended to the location realizing absolute coordinate mostly, cannot realize distinguishing different rooms.And in fact, room information has larger using value in practice.
Current, smart mobile phone has powerful calculating and communication capacity, the transducer of built-in various feature richness, and almost binds together with user whenever and wherever possible.Therefore, mobile phone can be regarded as the interface of the more and more important information of between user and environment one.By smart mobile phone and built-in transducer thereof, not only can the abundant environmental data of perception, can also catch the movable information of user, the indoor positioning of this non-at-scene exploration type provides possibility.
Summary of the invention
The present invention one of is intended to solve the problems of the technologies described above at least to a certain extent or at least provides a kind of useful business to select.For this reason, the object of the invention is to propose one and there is data acquisition flexibly and easily, the wireless indoor location method that positioning result logicality is good.
According to a kind of wireless indoor location method of the embodiment of the present invention, comprise the following steps: S1. utilizes smart mobile phone automatically to gather wireless fingerprint data and user's Mobile data, formed fingerprint collection F and Distance matrix D ', and carry out preliminary treatment; S2. according to pretreated described fingerprint collection F and Distance matrix D ', build fingerprint space; S3. unstressed plane graph is generated, carry out key feature extraction, and carry out space coordinate conversion, wherein, described key feature extracts and comprises corridor recognition, room identification and reference point coupling, and described space coordinate conversion comprises the conversion of floor-level and the conversion of room-level.
In one embodiment of the invention, described S1 comprises further: S11. gathers the signal of wireless network and the reading of acceleration transducer and direction sensor by smart mobile phone, suppose there be m wireless network access point in region, then described smart mobile phone RSS fingerprint obtained of certain position in region is designated as the vector f=(s of a m dimension
1, s
2..., s
m), wherein s
irepresent the RSS value of i-th wireless network access point, define d ' again
ijfor f
iand f
jbetween distance, be the step number that user walks between the two positions, after the fingerprint-collection stage terminates, obtain fingerprint collection F={f
i, i=1...n}, wherein n is the number of fingerprint, and Distance matrix D '=[d '
ij]; S12. preliminary treatment is carried out, to two fingerprint f to described fingerprint collection F
i=(s
1, s
2..., s
m) and f
j=(t
1, t
2..., t
m), definition f
iand f
jdiversity factor is
if δ
ijbe less than predetermined threshold ∈, then f
iand f
jin the generative process of fingerprint space, be taken as identical point, otherwise be taken as different points; S13. to described Distance matrix D ' carry out preliminary treatment, the beeline between often pair of fingerprint is calculated according to shortest path first, if namely at f
iand f
jcertain intermediate node of existence f
k, meet d '
ij> d '
ik+ d '
kj, then d '
ijbe updated to d '
ik+ d '
kj.
In one embodiment of the invention, described S2 comprises: according to pretreated described fingerprint collection F and Distance matrix D ', the Euclidean space all fingerprint map tieed up to a d by MDS algorithm.
In one embodiment of the invention, described corridor recognition comprises: utilize the fingerprint on degree acquisition corridor, centre, wherein, according to the distance between fingerprint, set up minimum spanning tree T, calculate the middle degree of each point on described minimum spanning tree, the part that middle degree is high is considered as the fingerprint on corridor, is designated as corridor fingerprint collection F
c, wherein said middle degree B (v) is defined as: the figure G=(V, E) be made up of vertex set V and limit collection E for,
wherein, σ
stfor the shortest path number from s to t, σ
stv () is from s to t and through the shortest path number of v.
In one embodiment of the invention, the identification of described room comprises: utilize cluster to obtain room fingerprint collection, wherein, remove described corridor fingerprint collection F in all fingerprint spaces
c, adopt K-means algorithm to remaining fingerprint F-F
ccarry out cluster, obtain individual the trooping of k k and (be designated as
i=1,2 ..., k), think same after cluster
in institute a little from same room with them.
In one embodiment of the invention, described reference point coupling comprises: utilizing the door in room as setting up the critical reference points contacted between unstressed plane graph and fingerprint space, wherein, first defining
with
for inside and outside room door from the point that door is nearest, then define room door fingerprint collection
described F
din all fingerprints can be organized into a chain in minimum spanning tree T, therefore by described F
dbe expressed as vector form F
d=(f
1, f
2..., f
k), and in unstressed plane graph, definition P
d=(p
1, p
2..., p
k) on corridor from each nearest sampled point, described in each, sampled point is at P
dthe order of middle appearance is along corridor from side to opposite side, due to from F
dto P
dbe mapped as corresponding or just reverse order is corresponding, achieve reference point coupling and determine the corresponding of certain room fingerprint collection and certain room in plane graph in unstressed plane graph.
In one embodiment of the invention, the conversion of described floor-level comprises: utilize a transformation matrix to realize the conversion of the floor-level of unstressed plane graph and fingerprint spatial visualization figure, wherein, suppose at F
din the coordinate of a fingerprint be x
i=[x
i 1x
i 2... x
i d]
t, wherein d is the dimension in fingerprint space, the coordinate y of corresponding position
i=[y
i 1y
i 2... y
i d]
t, definition A is the transformation matrix of d × d, B=[b
1b
2... b
d]
t, owing to there being k=|F
d| bar equation y
i=Ax
i+ B, is rewritten as H by described k bar equation
iz=G
i, wherein H
i=[x
i t1], z=[AB]
t, G
i=y
i t, combine described k bar equation, have Hz=G, wherein H
iand G
ifor i-th row of H and G, solved by least square method
try to achieve A and B, thus be x=[x for coordinate
i 1x
i 2... x
i d]
ta fingerprint, can be considered its physical location from the sampled point that Ax+B is nearest.
In one embodiment of the invention, the conversion of described room-level comprises: utilize MDS algorithm the fingerprint from same room with them to be transformed to the space of a d dimension further, the sampled point in corresponding room is made to form the unstressed plane graph of a d dimension, by as a reference point with point farthest recently from room door, the transformation relation between fingerprint room and physical room can be determined, by repeating aforesaid operations one by one to all rooms, achieve the conversion of the unstressed plane graph in each room and the room-level of fingerprint spatial visualization figure.
Wireless indoor location method of the present invention is without the need to carrying out artificial on-site land survey to locating area, finger print information can be provided easily by the multiple user collaboratives in system, its positioning result precision is high, logicality is good, and the redundant information produced when inquiring about and moving at ordinary times all can be used as upgrade information.
Additional aspect of the present invention and advantage will part provide in the following description, and part will become obvious from the following description, or be recognized by practice of the present invention.
Accompanying drawing explanation
Above-mentioned and/or additional aspect of the present invention and advantage will become obvious and easy understand from accompanying drawing below combining to the description of embodiment, wherein:
Fig. 1 is the flow chart of the indoor space locating method of the embodiment of the present invention.
Fig. 2 is the 2D fingerprint space schematic diagram that example of the present invention provides.
Fig. 3 is the 3D fingerprint space schematic diagram that example of the present invention provides.
Fig. 4 is the result schematic diagram of carrying out sampling on indoor plane figure that example of the present invention provides.
Fig. 5 is the unstressed plane graph of 2D of the plane graph sampled point generation that example of the present invention provides.
Fig. 6 is the unstressed plane graph of 3D of the plane graph sampled point generation that example of the present invention provides.
Fig. 7 is the minimum spanning tree schematic diagram in the 3D fingerprint space that example of the present invention provides.
Fig. 8 is the cluster result schematic diagram of the fingerprint space removing corridor section that example of the present invention provides.
Embodiment
Be described below in detail embodiments of the invention, the example of described embodiment is shown in the drawings, and wherein same or similar label represents same or similar element or has element that is identical or similar functions from start to finish.Be exemplary below by the embodiment be described with reference to the drawings, be intended to for explaining the present invention, and can not limitation of the present invention be interpreted as.
In describing the invention, it will be appreciated that, term " " center ", " longitudinal direction ", " transverse direction ", " length ", " width ", " thickness ", " on ", D score, " front ", " afterwards ", " left side ", " right side ", " vertically ", " level ", " top ", " end " " interior ", " outward ", " clockwise ", orientation or the position relationship of the instruction such as " counterclockwise " are based on orientation shown in the drawings or position relationship, only the present invention for convenience of description and simplified characterization, instead of indicate or imply that the device of indication or element must have specific orientation, with specific azimuth configuration and operation, therefore limitation of the present invention can not be interpreted as.
In addition, term " first ", " second " only for describing object, and can not be interpreted as instruction or hint relative importance or imply the quantity indicating indicated technical characteristic.Thus, be limited with " first ", the feature of " second " can express or impliedly comprise one or more these features.In describing the invention, the implication of " multiple " is two or more, unless otherwise expressly limited specifically.
In the present invention, unless otherwise clearly defined and limited, the term such as term " installation ", " being connected ", " connection ", " fixing " should be interpreted broadly, and such as, can be fixedly connected with, also can be removably connect, or connect integratedly; Can be mechanical connection, also can be electrical connection; Can be directly be connected, also indirectly can be connected by intermediary, can be the connection of two element internals.For the ordinary skill in the art, above-mentioned term concrete meaning in the present invention can be understood as the case may be.
In the present invention, unless otherwise clearly defined and limited, fisrt feature second feature it " on " or D score can comprise the first and second features and directly contact, also can comprise the first and second features and not be directly contact but by the other characterisation contact between them.And, fisrt feature second feature " on ", " top " and " above " comprise fisrt feature directly over second feature and oblique upper, or only represent that fisrt feature level height is higher than second feature.Fisrt feature second feature " under ", " below " and " below " comprise fisrt feature immediately below second feature and tiltedly below, or only represent that fisrt feature level height is less than second feature.
The invention belongs to wireless indoor positioning field, relate to a kind of implementation method of wireless indoor location technology of non-at-scene exploration type.The present invention utilizes smart mobile phone for carrier, coordinate multiple embedded type sensor, by the fingerprinting localization algorithm of non-at-scene exploration type, the physical distance between wireless fingerprint is derived based on user's mobile route, and utilize MDS algorithm to generate fingerprint space system based on this physical distance, be converted into physical coordinates again, realize invention indoor user being positioned to service.
As shown in Figure 1, the indoor space locating method of the embodiment of the present invention comprises the steps.
S1. utilize smart mobile phone automatically to gather wireless fingerprint data and user's Mobile data, formed fingerprint collection F and Distance matrix D ', and carry out preliminary treatment.Particularly, comprise further:
S11. image data
At collection phase, user does not need to carry out special training, only needs to walk as usual and carries out daily routines under construction.The mobile phone that user carries can collect the RSS feature of WiFi by each point on walking path.Meanwhile, the walking step number between each sampled point of user extrapolated by the acceleration transducer utilizing mobile phone integrated, determines the physical distance between each point with this.Particularly, suppose in the A of region, have m wireless network access point.For each position in the A of region, RSS fingerprint can be defined as the vector f=(s of a m dimension
1, s
2..., s
m), wherein, s
irepresent the RSS value of i-th wireless network access point.In addition, we arrange, if this position cannot monitor i-th wireless network access point, and s
ivalue be set to 0.Definition d '
ijfor f
iand f
jbetween distance, computational process is: d '
ijbe defaulted as just infinite; When user is at a certain position record f
i, he walks to another location record f
j, d '
ijbe the step number that user walks between the two positions.After the fingerprint-collection stage terminates, we obtain a fingerprint collection F={f
i, i=1...n}(n is the number of fingerprint) and Distance matrix D '=[d '
ij].
S12. preliminary treatment is carried out to fingerprint collection
In order to build fingerprint space, need to carry out preliminary treatment to data.User action is normally arbitrary and random, and walking path may be intersect.Therefore, fingerprint may be overlapping.Preprocessing process merges similar fingerprint, and wherein " similar " means them probably from identical (or closely) position.We represent the difference between fingerprint with δ, to two fingerprint f
i=(s
1, s
2..., s
m) and f
j=(t
1, t
2..., t
m), the diversity factor defining them is
To f
iand f
jif, their diversity factor δ
ijbe less than predefined threshold value ∈, they are taken as identical point in the generative process of fingerprint space.Otherwise, f
iand f
jbe taken as different points.
S13. matrix of adjusting the distance carries out preliminary treatment
We need to carry out preliminary treatment to calculate walking distance to the original data obtained from acceleration transducer.In theory, distance can be obtained by carrying out integration to the quadratic power of acceleration to the time.But due to the existence of noise, error can promptly accumulate.In order to avoid the accumulation of error, we adopt the method being calculated step number by the numerical value of acceleration transducer, using the index of step number as walking distance.For fingerprint f
iand f
jif neither one user passes by one and connects their path, and the distance between them is by unavailable.But, f
iand f
jcan be communicated with to get up by many user paths.Therefore, can with connecting f
iand f
jthe length of shortest path estimate d '
ij.In addition, the d ' recorded
ijalso may be updated.Such as, we adopt classical Floyd-Warshall algorithm (shortest path first) to calculate minimum range between often pair of fingerprint.This algorithm complex is O (n
3), n is the number of fingerprint.If made certain intermediate node k, d '
ij> d '
ik+ d '
kj, then d '
ijbe updated to d '
ik+ d '
kj.Do like this and can eliminate user and to detour or pace back and forth the error caused.For convenience's sake, we still represent the distance matrix processed with D '.
S2. according to pretreated fingerprint collection F and Distance matrix D ', build fingerprint space
Particularly, using D ' as input, the Euclidean space that all fingerprint map are tieed up to a d by MDS algorithm.Fingerprint space when d gets 2 and 3 respectively as shown in Figure 1 and Figure 2.
S3. the mating of fingerprint space and physical location
S31. unstressed plane graph generates
In architecture and architectural engineering, plane graph is room in an explanation building, the vertical view of relation between space and other physical features.In plan view, distance between the walls often can be drawn the length that room-size and wall are described.Due to wall and the impact of other barriers, two distances of position on plane graph are not necessarily equal with the walking distance between them.In order to address this problem, we introduce unstressed plane graph.As shown in Figure 3, on plane graph, we are by interested region stress and strain model, and be averaged sampling.According to the feature of the localization method based on fingerprint, the spacing of grid can get 1-3 rice usually.Spacing gets the accuracy that too conference reduces location, and it is very little to get the too little lifting brought accuracy.By calculating the distance between each sampled point, we obtain a Distance matrix D=[d
ij], wherein d
ijrepresent on plane graph, two sampled point p
iand p
jbetween walking distance.Using D as input, all sampled points are mapped to the Euclidean space of a d dimension by MDS algorithm.In unstressed plane graph, their walking distances in actual plan view between corresponding position of Euclidean distance between 2 reflection.Conveniently observe, getting d is 2 and 3, and the unstressed plane graph generated respectively as shown in Figure 4, Figure 5.
S32. key feature extracts
Be divided into three steps: corridor recognition, room identification, reference point coupling
(1) corridor recognition
In a building, corridor is connected to each room.User moves towards another from a room must pass corridor usually.Corridor this characteristic in realistic space has also been reacted to unstressed plane graph and fingerprint spatially.The fingerprint collected in corridor is in key position in fingerprint space.Consider the centrality (centrality) in graph theory, these fingerprints have a larger centrality value.In graph theory, the centrality of point can get the number of degrees, intermediateness, tightness etc.Example employing intermediateness of the present invention obtains the fingerprint on corridor.Conceptually, if the probability that point appears on any shortest path is comparatively large, so it has higher intermediateness.For the figure G=(V, E) that is made up of vertex set V and limit collection E, centrad is defined as
Wherein, σ
stfor the shortest path number from s to t, σ
stv () is from s to t and through the shortest path number of v.
According to the distance between fingerprint, we set up a minimum spanning tree T, as shown in Figure 6.Utilize intermediateness that all fingerprints are divided into two parts, the part that intermediateness is high is considered as the fingerprint on corridor, and this part fingerprint collection is denoted as F
c.
(2) room identification
F is removed in all fingerprints
c, we observe remaining fingerprint can form several trooping, difference troop between fingerprint spatially separate.Consider computational efficiency, we adopt K-means algorithm to carry out cluster to remaining fingerprint.The value of getting K is the room number on plane graph, F-F
cbe polymerized to individual the trooping of k (to be designated as
i=1,2 ..., k), as shown in Figure 7.After cluster, we think same
in institute a little from same room with them, next also need to utilize reference point to mate to determine which of this room corresponding flat figure.
(3) reference point coupling
Example of the present invention utilizes the door in room as setting up the critical reference points contacted between unstressed plane graph and fingerprint space.We are by such as giving a definition
Like this,
the inside and outside point nearest from door of door can be regarded as.Definition
f
din all fingerprints can be organized into a chain in minimum spanning tree T, like this, we can by F
dbe expressed as vector form F
d=(f
1, f
2..., f
k).In unstressed plane graph, definition P
d=(p
1, p
2..., p
k) on corridor from each nearest sampled point, put at P
dthe order of middle appearance be along corridor from side to opposite side.Like this, from F
dto P
dmapping only have two kinds of situations: corresponding or just contrary.Definition l=(l
1, l
2..., l
k-1), l
i=|| p
i+1-p
i||, l '=(l '
1, l '
2..., l '
k-1), l
i=|| p '
i+1-p '
i||.The similarity of definition l and l ' is s
1, computational methods are
Use the same method and calculate the similarity of opposite vector of l and l ', be designated as s
2.If s
1>=s
2, then F is thought
dto P
deach point is corresponding; Otherwise, think F
dto P
deach point is just contrary.
S33. space coordinate conversion
Be divided into two steps: the conversion of floor-level, the conversion of room-level
(1) conversion of floor-level
Can see from the visual figure in unstressed plane graph and fingerprint space, their structure is closely similar, but the change having some small, as translation, rotation and reflection.We utilize a transformation matrix to realize the coupling of floor-level.
Suppose at F
din the coordinate of a fingerprint be x
i=[x
i 1x
i 2... x
i d]
t, d is the dimension in fingerprint space.The coordinate y of corresponding position
i=[y
i 1y
i 2... y
i d]
t.Definition A is the transformation matrix of d × d, B=[b
1b
2... b
d]
t.We have k=|F
d| bar equation
y
i=Ax
i+B
This k bar equation is rewritten as by we
H
iz=G
i
Wherein H
i=[x
i t1], z=[AB]
t, G
i=y
i t.Combine this k bar equation, have
Hz=G
H
iand G
ifor i-th row of H and G.
Can be obtained by least square method
Like this, we can obtain A and B.Be x=[x for coordinate
i 1x
i 2x
i d]
ta fingerprint, can be considered its physical location from the sampled point that Ax+B is nearest.
(2) conversion of room-level
Mention above, fingerprint has been assigned in different fingerprint rooms by cluster by example of the present invention, and by reference to Point matching, obtains the one-to-one relationship between physical space on fingerprint space and plane graph.Again utilize MDS, the fingerprint from same room with them is transformed to the space of a d dimension.Similarly, the sampled point in corresponding room is made to form the unstressed plane graph of a d dimension.By as a reference point with point farthest recently from room door, the transformation relation between fingerprint room and physical room can be determined.By repeating this step to all rooms, we achieve the conversion of room-level.
In sum, wireless indoor location method of the present invention is without the need to carrying out artificial on-site land survey to locating area, finger print information can be provided easily by the multiple user collaboratives in system, its positioning result precision is high, logicality is good, and the redundant information produced when inquiring about and moving at ordinary times all can be used as upgrade information.
It should be noted that, describe and can be understood in flow chart or in this any process otherwise described or method, represent and comprise one or more for realizing the module of the code of the executable instruction of the step of specific logical function or process, fragment or part, and the scope of the preferred embodiment of the present invention comprises other realization, wherein can not according to order that is shown or that discuss, comprise according to involved function by the mode while of basic or by contrary order, carry out n-back test, this should understand by embodiments of the invention person of ordinary skill in the field.
In the description of this specification, specific features, structure, material or feature that the description of reference term " embodiment ", " some embodiments ", " example ", " concrete example " or " some examples " etc. means to describe in conjunction with this embodiment or example are contained at least one embodiment of the present invention or example.In this manual, identical embodiment or example are not necessarily referred to the schematic representation of above-mentioned term.And the specific features of description, structure, material or feature can combine in an appropriate manner in any one or more embodiment or example.
Although illustrate and describe embodiments of the invention above, be understandable that, above-described embodiment is exemplary, can not be interpreted as limitation of the present invention, those of ordinary skill in the art can change above-described embodiment within the scope of the invention when not departing from principle of the present invention and aim, revising, replacing and modification.
Claims (6)
1. a wireless indoor location method, is characterized in that, comprises the following steps:
S1. utilize smart mobile phone automatically to gather wireless fingerprint data and user's Mobile data, formed fingerprint collection F and Distance matrix D ', and carry out preliminary treatment;
S2. according to pretreated described fingerprint collection F and Distance matrix D ', build fingerprint space, wherein, described S2 comprises: according to pretreated described fingerprint collection F and Distance matrix D ', the Euclidean space all fingerprint map tieed up to a d by MDS algorithm;
S3. the Euclidean space tieed up according to described d generates unstressed plane graph, carries out key feature extraction, and carries out space coordinate conversion,
Wherein, described key feature extracts and comprises corridor recognition, room identification and reference point coupling, described space coordinate conversion comprises the conversion of floor-level and the conversion of room-level, described reference point coupling comprises: utilize the door in room as setting up the critical reference points contacted between unstressed plane graph and fingerprint space, wherein, first define
with
for inside and outside room door from the point that door is nearest, then define room door fingerprint collection
described F
din all fingerprints can be organized into a chain in minimum spanning tree T, therefore by described F
dbe expressed as vector form F
d=(f
1, f
2..., f
k), and in unstressed plane graph, definition P
d=(p
1, p
2..., p
k) on corridor from each nearest sampled point, described in each, sampled point is at P
dthe order of middle appearance is along corridor from side to opposite side, due to from F
dto P
dbe mapped as corresponding or just reverse order is corresponding, achieve reference point coupling and determine the corresponding of certain room fingerprint collection and certain room in plane graph in unstressed plane graph.
2. wireless indoor location method as claimed in claim 1, it is characterized in that, described S1 comprises further:
S11. the signal of wireless network and the reading of acceleration transducer and direction sensor is gathered by smart mobile phone, suppose there be m wireless network access point in region, then described smart mobile phone RSS fingerprint obtained of certain position in region is designated as the vector f=(s of a m dimension
1, s
2..., s
m), wherein s
irepresent the RSS value of i-th wireless network access point, define d ' again
ijfor f
iand f
jbetween distance, be the step number that user walks between the two positions, after the fingerprint-collection stage terminates, obtain fingerprint collection F={f
i, i=1 ... n}, wherein n is the number of fingerprint, and Distance matrix D '=[d '
ij];
S12. preliminary treatment is carried out, to two fingerprint f to described fingerprint collection F
i=(s
1, s
2..., s
m) and f
j=(t
1, t
2..., t
m), definition f
iand f
jdiversity factor is
if δ
ijbe less than predetermined threshold, then f
iand f
jin the generative process of fingerprint space, be taken as identical point, otherwise be taken as different points;
S13. to described Distance matrix D ' carry out preliminary treatment, the beeline between often pair of fingerprint is calculated according to shortest path first, if namely at f
iand f
jcertain intermediate node of existence f
k, meet d '
ij>d '
ik+ d '
kj, then d '
ijbe updated to d '
ik+ d '
kj.
3. indoor orientation method as claimed in claim 1, it is characterized in that, described corridor recognition comprises: utilize the fingerprint on degree acquisition corridor, centre, wherein, according to the distance between fingerprint, set up minimum spanning tree T, calculate the middle degree of each point on described minimum spanning tree, the part that centre degree is high is considered as the fingerprint on corridor, is designated as corridor fingerprint collection F
c, wherein said middle degree B (v) is defined as: the figure G=(V, E) be made up of vertex set V and limit collection E for,
wherein, σ
stfor the shortest path number from s to t, σ
stv () is from s to t and through the shortest path number of v.
4. indoor orientation method as claimed in claim 1, it is characterized in that, the identification of described room comprises: utilize cluster to obtain room fingerprint collection, wherein, remove described corridor fingerprint collection F in all fingerprint spaces
c, adopt K-means algorithm to remaining fingerprint F-F
ccarry out cluster, obtain individual the trooping of k k and (be designated as
i=1,2 ..., k), think same after cluster
in institute a little from same room with them.
5. indoor orientation method as claimed in claim 1, it is characterized in that, the conversion of described floor-level comprises: utilize a transformation matrix to realize the conversion of the floor-level of unstressed plane graph and fingerprint spatial visualization figure, wherein, suppose at F
din the coordinate of a fingerprint be x
i=[x
i 1x
i 2x
i d]
t, wherein d is the dimension in fingerprint space, the coordinate y of corresponding position
i=[y
i 1y
i 2y
i d]
t, definition A is the transformation matrix of d × d, B=[b
1b
2b
d]
t, owing to there being k=|F
d| bar equation y
i=Ax
i+ B, is rewritten as H by described k bar equation
iz=G
i, wherein H
i=[x
i t1], z=[AB]
t, G
i=y
i t, combine described k bar equation, have Hz=G, wherein H
iand G
ifor i-th row of H and G, solved by least square method
try to achieve A and B, thus be x=[x for coordinate
i 1x
i 2x
i d]
ta fingerprint, can be considered its physical location from the sampled point that Ax+B is nearest.
6. indoor orientation method as claimed in claim 1, it is characterized in that, the conversion of described room-level comprises: utilize MDS algorithm the fingerprint from same room with them to be transformed to the space of a d dimension further, the sampled point in corresponding room is made to form the unstressed plane graph of a d dimension, by as a reference point with point farthest recently from room door, the transformation relation between fingerprint room and physical room can be determined, by repeating aforesaid operations one by one to all rooms, achieve the conversion of the unstressed plane graph in each room and the room-level of fingerprint spatial visualization figure.
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| CN103476118B (en) * | 2013-09-29 | 2016-03-23 | 哈尔滨工业大学 | A kind of WLAN indoor location fingerprint positioning method for monitoring in real time |
| CN103905992B (en) * | 2014-03-04 | 2017-04-19 | 华南理工大学 | Indoor positioning method based on wireless sensor networks of fingerprint data |
| CN103916820B (en) * | 2014-03-31 | 2017-06-06 | 浙江大学 | Wireless indoor location method based on access point stability |
| WO2016033800A1 (en) * | 2014-09-05 | 2016-03-10 | Nokia Technologies Oy | Positioning based on radio frequency fingerprints |
| CN104394573B (en) * | 2014-11-26 | 2018-03-09 | 北京邮电大学 | A kind of wireless sensor network Cooperative Localization Method |
| CN105241445B (en) * | 2015-10-20 | 2018-07-31 | 深圳大学 | A kind of indoor navigation data capture method and system based on intelligent mobile terminal |
| CN106961671B (en) * | 2016-01-08 | 2020-03-03 | 高德软件有限公司 | Method and device for collecting indoor positioning data |
| CN105516929B (en) * | 2016-01-25 | 2019-08-27 | 图优化(北京)科技有限公司 | Establish method, indoor orientation method and the corresponding intrument of indoor map data |
| CN105682039B (en) * | 2016-03-15 | 2019-03-22 | 广州杰赛科技股份有限公司 | A kind of RF fingerprint positioning method and system |
| CN107289935B (en) * | 2016-04-01 | 2021-09-14 | 中国航空工业第六一八研究所 | Indoor navigation algorithm suitable for wearable equipment |
| CN106028445B (en) * | 2016-07-04 | 2020-06-26 | 百度在线网络技术（北京）有限公司 | Method and device for determining positioning accuracy |
| CN108235246A (en) * | 2017-12-19 | 2018-06-29 | 清华大学 | A kind of indoor orientation method and system |
| CN110602658B (en) * | 2018-06-13 | 2022-12-30 | 北京智慧图科技有限责任公司 | Continuous positioning method |
| CN109040969A (en) * | 2018-08-10 | 2018-12-18 | 武汉科技大学 | Intelligent Robotic Car optimal acquisition point position selecting method under indoor environment |
| CN110764049B (en) * | 2019-10-30 | 2023-05-23 | 北京小米智能科技有限公司 | Information processing method, device, terminal and storage medium |
| CN111405474A (en) * | 2020-03-11 | 2020-07-10 | 重庆邮电大学 | Indoor fingerprint map self-adaptive updating method based on communication investigation |
| CN111836188B (en) * | 2020-06-17 | 2021-05-18 | 华中科技大学 | Online cooperative positioning and system based on Wi-Fi RSS |
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