CN104869541A - Indoor positioning tracking method - Google Patents
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Abstract
The application discloses an indoor positioning tracking method. The method comprises the following steps: acquiring WIFI fingerprint at the current positioning moment; computing the first position estimation position of a user at the current positioning moment; collecting acceleration information and direction information in the current target movement time slot; computing the second position estimation information of the user at the current positioning moment; using the first position estimation information and the second position estimation information to multiply respective weight coefficient, and then adding to obtain position of the user at the current positioning moment. Therefore, the position information of the user is obtained through the adding of the first position estimation information and the second position estimation information after different weight coefficients are respectively exerted on the first position estimation information obtained through the WIFI fingerprint and the second position estimation information obtained through the acceleration information and the direction information, the first position estimation information obtained through the WIFI fingerprint is calibrated through the adoption of the acceleration information and the direction information, thereby improving the precision of the finally obtained user position information.
Description
Technical field
The present invention relates to indoor positioning technologies field, particularly a kind of indoor positioning method for tracing.
Background technology
At present, along with the development of WLAN (wireless local area network), many building interiors have all deployed one or more WIFI accessing points.On this basis, the indoor positioning technologies based on WIFI have also been obtained certain development.
But, for same indoor location, the signal strength signal intensity of the same WIFI of the change along with time accessing points is not changeless, but there is certain fluctuation, makes thus in the process of location tracking, to there is larger error based on the indoor positioning technologies of WIFI at present.
Can find out in sum, the accuracy how improving indoor positioning tracing process is current problem demanding prompt solution.
Summary of the invention
In view of this, the object of the present invention is to provide a kind of indoor positioning method for tracing, improve the accuracy of indoor positioning tracing process.Its concrete scheme is as follows:
A kind of indoor positioning method for tracing, be applied to intelligent mobile terminal, described intelligent mobile terminal comprises WIFI signal receiver module, acceleration transducer and magnetometer; Described method comprises:
Utilize described WIFI signal receiver module, gather the signal strength signal intensity when all WIFI signal that can receive indoor under the prelocalization moment, obtain described when the WIFI fingerprint under the prelocalization moment;
Utilize the described WIFI fingerprint database of working as the WIFI fingerprint under the prelocalization moment and building in advance, calculate the described primary importance estimated information as user under the prelocalization moment;
Utilize described acceleration transducer and described magnetometer respectively, correspondingly gather the acceleration information in current goal traveling time section and directional information, described current goal traveling time section be described when the prelocalization moment and on one locate time period between the moment;
The described second place estimated information as user under the prelocalization moment is calculated based on described acceleration information and described directional information, specifically comprise, analyzing and processing is carried out to described acceleration information and described directional information, correspondingly obtain the mobile vector information of user, described mobile vector information and described upper one is located the second place estimated information of user under the moment to carry out being added and process, obtain the described second place estimated information as user under the prelocalization moment; Wherein, the second place estimated information inscribing user during initial alignment is the information consistent with the primary importance estimated information inscribing user during described initial alignment;
By described when the primary importance estimated information under the prelocalization moment with to be describedly added after the second place estimated information under the prelocalization moment is multiplied by respective weight coefficient respectively, obtain the described positional information as user under the prelocalization moment.
Preferably, describedly carry out analyzing and processing to described acceleration information and described directional information, the process correspondingly obtaining the mobile vector information of user comprises:
Analyze described acceleration information and described directional information, the user obtaining carrying moving direction information moves step number; Described user is moved step number to carry out being multiplied process with the user's step information preset, correspondingly obtain described mobile vector information.
Preferably, the building process of described WIFI fingerprint database comprises:
Gridding process is carried out to the interior space, obtains indoor grid collection;
Concentrate the position at each grid vertex place as collection point described indoor grid successively, the signal strength signal intensity of WIFI signal that can receive all on this collection point is gathered, obtains the WIFI fingerprint corresponding with each grid vertex; And using the numbering of the numbering of the WIFI accessing points corresponding to the WIFI signal in each WIFI fingerprint with strongest signal strength as this WIFI fingerprint;
The numbering of the coordinate of each grid vertex, corresponding WIFI fingerprint and this WFI fingerprint is stored in database, obtains described WIFI fingerprint database.
Preferably, the summit spacing between described indoor grid often pair of adjacent mesh summit of concentrating is all equal.
Preferably, described summit spacing is the spacing being more than or equal to noise threshold spacing; Described noise threshold spacing, for when indoor exist noise signal, can distinguish the minimum spacing of two collection points by the signal strength signal intensity differentiating WIFI signal.
Preferably, describedly concentrate the position at each grid vertex place as collection point described indoor grid successively, gather the signal strength signal intensity of WIFI signal that can receive all on this collection point, the process obtaining the WIFI fingerprint corresponding with each grid vertex comprises:
Concentrate the position at each grid vertex place as collection point described indoor grid successively, get average after all repeated acquisition is carried out repeatedly to the signal strength signal intensity of each WIFI signal in WIFI signal that can receive all on this collection point, obtain the WIFI fingerprint corresponding with each grid vertex.
Preferably, when the WIFI fingerprint under the prelocalization moment and the WIFI fingerprint database built in advance described in described utilization, when the process of the primary importance estimated information of user under the prelocalization moment comprises described in calculating:
Determine the described numbering when the WIFI fingerprint under the prelocalization moment;
From described WIFI fingerprint database, take out and the described all WIFI fingerprints consistent when the numbering of the WIFI fingerprint under the prelocalization moment, form WIFI sample fingerprint space;
Utilize location algorithm to process described WIFI sample fingerprint space, obtain the described primary importance estimated information as user under the prelocalization moment.
Preferably, described location algorithm is K nearest neighbour classification algorithm.
Preferably, when the WIFI fingerprint under the prelocalization moment and the WIFI fingerprint database built in advance described in described utilization, when the process of the primary importance estimated information of user under the prelocalization moment comprises described in calculating:
Determine the described numbering when the WIFI fingerprint under the prelocalization moment;
From described WIFI fingerprint database, take out and the described all WIFI fingerprints consistent when the numbering of the WIFI fingerprint under the prelocalization moment, form WIFI sample fingerprint space;
Utilize location algorithm to process described WIFI sample fingerprint space, obtain described treating correction position information as user under the prelocalization moment;
Utilize and described treat that correction position information carries out correcting process to described as user under the prelocalization moment when the Kalman filter under the prelocalization moment, obtain the described primary importance estimated information as user under the prelocalization moment;
Wherein, the primary importance estimated information inscribing user during described initial alignment is and the information that correction position information is consistent for the treatment of of inscribing user during described initial alignment; The described Kalman filter obtained after the Kalman filter under the prelocalization moment is utilize displacement of targets vector to reset the Kalman filter under the described upper location moment; Described displacement of targets vector is described displacement vector between the primary importance estimated information of user under the correction position information for the treatment of and described upper one of user under the prelocalization moment locates the moment.
Preferably, described location algorithm is K nearest neighbour classification algorithm.
In the present invention, by utilizing the WIFI fingerprint of working as under the prelocalization moment and the WIFI fingerprint database built in advance, calculate the primary importance estimated information as user under the prelocalization moment; The second place estimated information as user under the prelocalization moment is calculated based on acceleration information and directional information; Finally be added after the primary importance estimated information under the prelocalization moment and second place estimated information are multiplied by respective weight coefficient respectively, obtain the positional information as user under the prelocalization moment.Visible, the present invention is when carrying out indoor positioning and following the trail of, by applying different weight coefficients respectively to the primary importance estimated information obtained based on WIFI fingerprint and based on the second place estimated information that acceleration information and directional information obtain, the positional information of user is obtained after addition, be equivalent to like this utilize acceleration information and directional information to carry out correction process to the primary importance estimated information obtained based on WIFI fingerprint, thus improve the accuracy of the customer position information finally obtained, also namely, invention increases the accuracy of indoor positioning tracing process.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is only embodiments of the invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to the accompanying drawing provided.
Fig. 1 is a kind of indoor positioning method for tracing flow chart disclosed in the embodiment of the present invention;
Fig. 2 is a kind of flow chart building WIFI fingerprint database disclosed in the embodiment of the present invention;
Fig. 3 is a kind of flow chart calculating primary importance estimated information disclosed in the embodiment of the present invention;
Fig. 4 is the disclosed another kind of flow chart calculating primary importance estimated information of the embodiment of the present invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, be clearly and completely described the technical scheme in the embodiment of the present invention, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.
The embodiment of the invention discloses a kind of indoor positioning method for tracing, be applied to intelligent mobile terminal, intelligent mobile terminal comprises WIFI signal receiver module, acceleration transducer and magnetometer; Fig. 1 is the flow chart of the indoor positioning method for tracing in the present embodiment, it should be noted that, not life period sequencing between step S101 and step S102.Above-mentioned indoor positioning method for tracing comprises:
Step S101: utilize WIFI signal receiver module, gathers the signal strength signal intensity when all WIFI signal that can receive indoor under the prelocalization moment, obtains when the WIFI fingerprint under the prelocalization moment; Utilize the WIFI fingerprint of working as under the prelocalization moment and the WIFI fingerprint database built in advance, calculate the primary importance estimated information as user under the prelocalization moment.
Step S102: utilize acceleration transducer and magnetometer respectively, correspondingly gathers the acceleration information in current goal traveling time section and directional information, current goal traveling time section be when the prelocalization moment and on one locate time period between the moment; The second place estimated information as user under the prelocalization moment is calculated based on acceleration information and directional information, specifically comprise, analyzing and processing is carried out to acceleration information and directional information, correspondingly obtain the mobile vector information of user, mobile vector information and upper one is located the second place estimated information of user under the moment to carry out being added and process, obtain the second place estimated information as user under the prelocalization moment; Wherein, the second place estimated information inscribing user during initial alignment is the information consistent with the primary importance estimated information inscribing user during initial alignment.
Wherein, above-mentioned analyzing and processing is carried out to acceleration information and directional information, the process correspondingly obtaining the mobile vector information of user specifically can comprise: analyze acceleration information and directional information, and the user obtaining carrying moving direction information moves step number; User is moved step number to carry out being multiplied process with the user's step information preset, correspondingly obtain mobile vector information.The detailed process analyzed acceleration information and directional information can be utilize a pedometer model built based on acceleration and directional information to analyze acceleration information and directional information, and the user being carried moving direction information accordingly moves step number.
Step S103: be added when the primary importance estimated information under the prelocalization moment with after the second place estimated information under the prelocalization moment is multiplied by respective weight coefficient respectively, obtains the positional information as user under the prelocalization moment.
Such as, S is set to by when the primary importance estimated information under the prelocalization moment
rt (), its weight coefficient is set to η, is set to S by when the second place estimated information under the prelocalization moment
st (), its weight coefficient is set to 1-η, also namely means, is η to the degree of belief of primary importance estimated information, is 1-η to the degree of belief of second place estimated information, then when the positional information of user under the prelocalization moment is S (t)=η S
r(t)+(1-η) S
s(t).
In the embodiment of the present invention, by utilizing the WIFI fingerprint of working as under the prelocalization moment and the WIFI fingerprint database built in advance, calculate the primary importance estimated information as user under the prelocalization moment; The second place estimated information as user under the prelocalization moment is calculated based on acceleration information and directional information; Finally be added after the primary importance estimated information under the prelocalization moment and second place estimated information are multiplied by respective weight coefficient respectively, obtain the positional information as user under the prelocalization moment.Visible, the present invention is when carrying out indoor positioning and following the trail of, by applying different weight coefficients respectively to the primary importance estimated information obtained based on WIFI fingerprint and based on the second place estimated information that acceleration information and directional information obtain, the positional information of user is obtained after addition, be equivalent to like this utilize acceleration information and directional information to carry out correction process to the primary importance estimated information obtained based on WIFI fingerprint, thus improve the accuracy of the customer position information finally obtained, also namely, invention increases the accuracy of indoor positioning tracing process.
Shown in Figure 2, the building process of the WIFI fingerprint database in the step S101 of a upper embodiment comprises:
Step S201: carry out gridding process to the interior space, obtains indoor grid collection.
Wherein, the above-mentioned interior space can refer to the two dimensional surface on indoor certain level height, and certainly, in order to improve the accuracy of data further, the above-mentioned interior space also can refer to indoor three-dimensional space.
Preferably, the summit spacing between indoor grid often pair of adjacent mesh summit of concentrating is all equal.What above-mentioned indoor grid was concentrated often organize between adjacent grid vertex forms polygonized structure, and can be triangular structure, also can be quadrangle, pentagon or hexagonal structure etc.Certainly, in the application of reality, in order to reduce amount of calculation, can carry out arranged evenly to grid vertex according to the mode forming quadrangle.
Preferably, summit spacing is the spacing being more than or equal to noise threshold spacing; Noise threshold spacing, for when indoor exist noise signal, can distinguish the minimum spacing of two collection points by the signal strength signal intensity differentiating WIFI signal.Such as, summit spacing is set to 2m.
Step S202: concentrate the position at each grid vertex place as collection point indoor grid successively, gathers the signal strength signal intensity of WIFI signal that can receive all on this collection point, obtains the WIFI fingerprint corresponding with each grid vertex; And using the numbering of the numbering of the WIFI accessing points corresponding to the WIFI signal in each WIFI fingerprint with strongest signal strength as this WIFI fingerprint.
Such as, suppose certain collection point to collect 5 WIFI signal, the signal strength signal intensity of each WIFI signal is followed successively by-45 ,-40 ,-65 ,-110 and-110, then the WIFI fingerprint on this collection point is [-45 ,-40,-65,-110 ,-110], obviously, in this WIFI fingerprint, strongest signal strength is-40, is the numbering of this WIFI fingerprint with the numbering of WIFI accessing points corresponding to the WIFI signal producing this strongest signal strength.
In addition, in order to improve accuracy and the reference value of WIFI fingerprint database, thus the accuracy of customer position information improving primary importance estimated information and finally obtain, preferably, above-mentionedly concentrate the position at each grid vertex place as collection point indoor grid successively, the signal strength signal intensity of WIFI signal that can receive all on this collection point is gathered, the process obtaining the WIFI fingerprint corresponding with each grid vertex specifically can comprise: concentrate the position at each grid vertex place as collection point indoor grid successively, average is got after all repeated acquisition is carried out repeatedly to the signal strength signal intensity of each WIFI signal in WIFI signal that can receive all on this collection point, obtain the WIFI fingerprint corresponding with each grid vertex.Such as, each WIFI signal that each collection point can receive all is carried out to the collection of the signal strength signal intensity of at least 30 times, then the data of repeated sampling are averaging processing, correspondingly obtain the signal strength signal intensity of each WIFI signal that each collection point can receive.
Step S203: the numbering of the coordinate of each grid vertex, corresponding WIFI fingerprint and this WFI fingerprint is stored in database, obtains WIFI fingerprint database.
Shown in Figure 3, in the step S101 of previous embodiment, utilize when the WIFI fingerprint under the prelocalization moment and the WIFI fingerprint database built in advance, the process calculated when the primary importance estimated information of user under the prelocalization moment can specifically comprise:
Step S301: determine the numbering when the WIFI fingerprint under the prelocalization moment.
Wherein, determine that the process of the numbering of WIFI fingerprint can content disclosed in refer step S202, do not repeat them here.
Step S302: from WIFI fingerprint database, takes out all WIFI fingerprints consistent with the numbering when the WIFI fingerprint under the prelocalization moment, forms WIFI sample fingerprint space.
In above-mentioned steps, form WIFI sample fingerprint space and mean the position of user has been narrowed down in the region belonging to this WIFI sample fingerprint space, thus decrease the amount of calculation in next step computational process.
Step S303: utilize location algorithm to process WIFI sample fingerprint space, obtains the primary importance estimated information as user under the prelocalization moment.
Wherein, preferred location algorithm is KNN algorithm, i.e. K nearest neighbour classification algorithm, certainly, does not get rid of here and other location algorithm can be utilized to process WIFI sample fingerprint space.
In order to improve the accuracy of the primary importance estimated information as user under the prelocalization moment, Kalman filter can be utilized to carry out correcting process to it.Concrete, shown in Figure 4, in the step S101 of previous embodiment, utilize when the WIFI fingerprint under the prelocalization moment and the WIFI fingerprint database built in advance, the process calculated when the primary importance estimated information of user under the prelocalization moment can also specifically comprise:
Step S401: determine the numbering when the WIFI fingerprint under the prelocalization moment.
Wherein, determine that the process of the numbering of WIFI fingerprint can content disclosed in refer step S202, do not repeat them here.
Step S402: from WIFI fingerprint database, takes out all WIFI fingerprints consistent with the numbering when the WIFI fingerprint under the prelocalization moment, forms WIFI sample fingerprint space.
In above-mentioned steps, form WIFI sample fingerprint space and mean the position of user has been narrowed down in the region belonging to this WIFI sample fingerprint space, thus decrease the amount of calculation in next step computational process.
Step S403: utilize location algorithm to process WIFI sample fingerprint space, obtain when under the prelocalization moment user treat correction position information.
Wherein, preferred location algorithm is K nearest neighbour classification algorithm.
Step S404: utilizing when as user under the prelocalization moment, the Kalman filter under the prelocalization moment is to treating that correction position information carries out correcting process, obtaining when the primary importance estimated information of user under the prelocalization moment.
Wherein, the primary importance estimated information inscribing user during initial alignment is and the information that correction position information is consistent for the treatment of of inscribing user during initial alignment; The Kalman filter obtained after the Kalman filter under the prelocalization moment is utilize displacement of targets vector to reset the Kalman filter under the upper location moment; Displacement of targets vector is the displacement vector between the primary importance estimated information of user under the correction position information for the treatment of and upper one of user under the prelocalization moment locates the moment.
From upper, the primary importance estimated information inscribing user during initial alignment is and the information that correction position information is consistent for the treatment of of inscribing user during initial alignment, in other words, without the need to inscribing treating of obtaining when initial alignment, correction position information carries out correcting process.Only have when user excessively locates moment to next from the initial alignment moment, the primary importance estimated information inscribing user during Kalman filter initial alignment just need be utilized to carry out correcting process, the primary importance estimated information of user is inscribed when obtaining next location, Kalman filter used in said process utilizes the parameter preset to the Kalman filter obtained after carrying out initialization, such as, the user velocity parameter v that default is utilized
0initialization Kalman filter is carried out with parameter θ.Wherein, the user velocity parameter preset can be the normal leg speed of user, and as 1.5m/s, and parameter θ can be set to 0.5.Kalman filter in the present embodiment presets in the user velocity parameter of starting stage, and in the process of user's movement, the user velocity parameter in corresponding Kalman filter then correspondingly can change along with the change of displacement of targets vector; It should be noted that, the parameter θ in Kalman filter can remain unchanged.
Concrete, will treat that correction position information is set to S as user under the prelocalization moment
r(t) ', the primary importance estimated information of user under the upper location moment is set to S
r(t-1), then above-mentioned displacement of targets vector is d=S
r(t) '-S
r(t-1), utilize the estimating speed v '=d/ Δ t of user in this displacement of targets Vector operation current goal traveling time section, wherein, Δ t is current goal traveling time section, namely the current location moment and on one locate time period between the moment; And then obtain the erection rate of user in current goal traveling time section:
v(t)＝θv(t-1)+(1-θ)v′；
Wherein v (t-1) is the erection rate in a upper target traveling time section; Then obtain when the Kalman filter under the prelocalization moment after utilizing the erection rate of user in current goal traveling time section to reset the Kalman filter under the upper location moment; Finally utilize when under the prelocalization moment Kalman filter to when under the prelocalization moment user treat correction position information S
r(t) ' carry out correcting process, obtain the primary importance estimated information as user under the prelocalization moment, namely primary importance estimated information is:
S
r(t)＝θv(t)Δt+(1-θ)d+S
r(t-1)。
Visible, in the present embodiment, by building Kalman filter, and utilize Kalman filter to treat correction position information to have carried out correcting process, thus improve the accuracy of the primary importance estimated information as user under the prelocalization moment, and then improve the accuracy of the customer position information finally obtained.
Finally, also it should be noted that, in this article, the such as relational terms of first and second grades and so on is only used for an entity or operation to separate with another entity or operating space, and not necessarily requires or imply the relation that there is any this reality between these entities or operation or sequentially.And, term " comprises ", " comprising " or its any other variant are intended to contain comprising of nonexcludability, thus make to comprise the process of a series of key element, method, article or equipment and not only comprise those key elements, but also comprise other key elements clearly do not listed, or also comprise by the intrinsic key element of this process, method, article or equipment.When not more restrictions, the key element limited by statement " comprising ... ", and be not precluded within process, method, article or the equipment comprising described key element and also there is other identical element.
Above a kind of indoor positioning method for tracing provided by the present invention is described in detail, apply specific case herein to set forth principle of the present invention and execution mode, the explanation of above embodiment just understands method of the present invention and core concept thereof for helping; Meanwhile, for one of ordinary skill in the art, according to thought of the present invention, all will change in specific embodiments and applications, in sum, this description should not be construed as limitation of the present invention.
Claims (10)
1. an indoor positioning method for tracing, is characterized in that, is applied to intelligent mobile terminal, and described intelligent mobile terminal comprises WIFI signal receiver module, acceleration transducer and magnetometer; Described method comprises:
Utilize described WIFI signal receiver module, gather the signal strength signal intensity when all WIFI signal that can receive indoor under the prelocalization moment, obtain described when the WIFI fingerprint under the prelocalization moment;
Utilize the described WIFI fingerprint database of working as the WIFI fingerprint under the prelocalization moment and building in advance, calculate the described primary importance estimated information as user under the prelocalization moment;
Utilize described acceleration transducer and described magnetometer respectively, correspondingly gather the acceleration information in current goal traveling time section and directional information, described current goal traveling time section be described when the prelocalization moment and on one locate time period between the moment;
The described second place estimated information as user under the prelocalization moment is calculated based on described acceleration information and described directional information, specifically comprise, analyzing and processing is carried out to described acceleration information and described directional information, correspondingly obtain the mobile vector information of user, described mobile vector information and described upper one is located the second place estimated information of user under the moment to carry out being added and process, obtain the described second place estimated information as user under the prelocalization moment; Wherein, the second place estimated information inscribing user during initial alignment is the information consistent with the primary importance estimated information inscribing user during described initial alignment;
By described when the primary importance estimated information under the prelocalization moment with to be describedly added after the second place estimated information under the prelocalization moment is multiplied by respective weight coefficient respectively, obtain the described positional information as user under the prelocalization moment.
2. indoor positioning method for tracing according to claim 1, is characterized in that, describedly carries out analyzing and processing to described acceleration information and described directional information, and the process correspondingly obtaining the mobile vector information of user comprises:
Analyze described acceleration information and described directional information, the user obtaining carrying moving direction information moves step number; Described user is moved step number to carry out being multiplied process with the user's step information preset, correspondingly obtain described mobile vector information.
3. indoor positioning method for tracing according to claim 2, is characterized in that, the building process of described WIFI fingerprint database comprises:
Gridding process is carried out to the interior space, obtains indoor grid collection;
Concentrate the position at each grid vertex place as collection point described indoor grid successively, the signal strength signal intensity of WIFI signal that can receive all on this collection point is gathered, obtains the WIFI fingerprint corresponding with each grid vertex; And using the numbering of the numbering of the WIFI accessing points corresponding to the WIFI signal in each WIFI fingerprint with strongest signal strength as this WIFI fingerprint;
The numbering of the coordinate of each grid vertex, corresponding WIFI fingerprint and this WFI fingerprint is stored in database, obtains described WIFI fingerprint database.
4. indoor positioning method for tracing according to claim 3, is characterized in that, the summit spacing between often pair of adjacent mesh summit that described indoor grid is concentrated is all equal.
5. indoor positioning method for tracing according to claim 4, is characterized in that, described summit spacing is the spacing being more than or equal to noise threshold spacing; Described noise threshold spacing, for when indoor exist noise signal, can distinguish the minimum spacing of two collection points by the signal strength signal intensity differentiating WIFI signal.
6. indoor positioning method for tracing according to claim 5, it is characterized in that, describedly concentrate the position at each grid vertex place as collection point described indoor grid successively, gather the signal strength signal intensity of WIFI signal that can receive all on this collection point, the process obtaining the WIFI fingerprint corresponding with each grid vertex comprises:
Concentrate the position at each grid vertex place as collection point described indoor grid successively, get average after all repeated acquisition is carried out repeatedly to the signal strength signal intensity of each WIFI signal in WIFI signal that can receive all on this collection point, obtain the WIFI fingerprint corresponding with each grid vertex.
7. the indoor positioning method for tracing according to any one of claim 1 to 6, it is characterized in that, when the WIFI fingerprint under the prelocalization moment and the WIFI fingerprint database built in advance described in described utilization, when the process of the primary importance estimated information of user under the prelocalization moment comprises described in calculating:
Determine the described numbering when the WIFI fingerprint under the prelocalization moment;
From described WIFI fingerprint database, take out and the described all WIFI fingerprints consistent when the numbering of the WIFI fingerprint under the prelocalization moment, form WIFI sample fingerprint space;
Utilize location algorithm to process described WIFI sample fingerprint space, obtain the described primary importance estimated information as user under the prelocalization moment.
8. indoor positioning method for tracing according to claim 7, is characterized in that, described location algorithm is K nearest neighbour classification algorithm.
9. the indoor positioning method for tracing according to any one of claim 1 to 6, it is characterized in that, when the WIFI fingerprint under the prelocalization moment and the WIFI fingerprint database built in advance described in described utilization, when the process of the primary importance estimated information of user under the prelocalization moment comprises described in calculating:
Determine the described numbering when the WIFI fingerprint under the prelocalization moment;
From described WIFI fingerprint database, take out and the described all WIFI fingerprints consistent when the numbering of the WIFI fingerprint under the prelocalization moment, form WIFI sample fingerprint space;
Utilize location algorithm to process described WIFI sample fingerprint space, obtain described treating correction position information as user under the prelocalization moment;
Utilize and described treat that correction position information carries out correcting process to described as user under the prelocalization moment when the Kalman filter under the prelocalization moment, obtain the described primary importance estimated information as user under the prelocalization moment;
Wherein, the primary importance estimated information inscribing user during described initial alignment is and the information that correction position information is consistent for the treatment of of inscribing user during described initial alignment; The described Kalman filter obtained after the Kalman filter under the prelocalization moment is utilize displacement of targets vector to reset the Kalman filter under the described upper location moment; Described displacement of targets vector is described displacement vector between the primary importance estimated information of user under the correction position information for the treatment of and described upper one of user under the prelocalization moment locates the moment.
10. indoor positioning method for tracing according to claim 9, is characterized in that, described location algorithm is K nearest neighbour classification algorithm.
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