CN110650436B - WiFi data-based position fingerprint database establishing and fitting method - Google Patents
WiFi data-based position fingerprint database establishing and fitting method Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04W4/02—Services making use of location information
- H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
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- H—ELECTRICITY
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- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/391—Modelling the propagation channel
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
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- H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
Abstract
The method is suitable for the technical field of WiFi data positioning. The invention discloses a WiFi data-based position fingerprint database establishing and fitting method, which comprises a position fingerprint database establishing step and a fingerprint database fitting step, wherein the position fingerprint database establishing step comprises the steps of grid fingerprint database division, grid area division is carried out on latitudes of fingerprint database areas to be divided, two points are set as diagonal lines of a rectangular area, and blank grid fingerprints are obtained; and (4) fingerprint database filling, namely filling the WiFi signal intensity data uploaded by the user in an accumulated manner and the measured WiFi signal intensity data into grids at corresponding positions according to the signal intensity data positions. Due to the adoption of the plane grid fingerprint database, the problem of positioning accuracy reduction caused by uneven distribution of fingerprint information can be effectively avoided. Meanwhile, gaussian filtering is carried out on the original collected data, so that errors brought by data collection are reduced, and a solid foundation is laid for further improving indoor positioning accuracy subsequently.
Description
Technical Field
The invention relates to the technical field of mobile communication and Internet of things, in particular to a WiFi data-based position fingerprint database establishing and fitting method.
Background
In recent years, attention is paid to a WiFi indoor positioning scheme which relies on indoor hotspots for positioning, and the positioning mode has the advantages of wide indoor coverage range and low deployment cost. The invention provides an indoor position fingerprint database establishing and fitting scheme based on WiFi data, which is a part of an indoor positioning system of a WiFi fingerprint database and mainly explains the establishing and fitting process of the fingerprint database.
In an indoor environment, the WiFi signal strength value received at each location is closely related to the distance from an AP (Access Point) and the surrounding environment, where the location Point is generally referred to as a fingerprint Point, and the fingerprint Point and the WiFi signal strength value received at the Point form a fingerprint. A series of fingerprint sets collected in a region to be estimated are called a fingerprint library, and during real-time positioning, the signal intensity of a point to be measured is compared with the data of the fingerprint library so as to obtain the position of the point to be estimated. The key to the above process is how to establish a location fingerprint database, which is also a hotspot of various WiFi indoor positioning studies. At present, two common fingerprint database establishing methods are mainly used, namely a direct statistical method and a propagation model estimation method.
1. Direct statistical method for position fingerprint database
The direct statistical method obtains abundant longitude and latitude position points and WiFi signal RSSI values at all positions by carrying out a large amount of manual field measurement on a position fingerprint database, and finally establishes the position points and the WiFi signal RSSI of the points as the position fingerprint database. The principle of construction of the location-specific fingerprint library is shown in fig. 1. Fig. 1 reflects that a single terminal can usually receive signals transmitted from multiple APs at a certain location, the location point (x) i ，y j ) Its fingerprint information may be expressed as RSSI ij1 ,RSSI ij2 ，...，RSSI ijp Indicating the terminal is located at (x) i ，y j ) Signal strength of p APs received on the grid.
Although the position fingerprint database direct statistical method can establish a fingerprint database, the method is limited by the high cost of manual data acquisition, and sufficient fingerprint data cannot be obtained, so that the method cannot be popularized and applied in a large quantity. Meanwhile, the position fingerprint database is established by using direct sample data acquisition, and the data of the position fingerprint points is difficult to avoid the phenomenon of uneven data distribution due to the limitation of manual data acquisition. During the sample collection process, the distribution of the fingerprint points of the sample positions in a certain area is uneven, as shown in fig. 2.
2. Method for inferring propagation model of location fingerprint library
In order to expand the number of location fingerprints under limited data conditions, there are proposals for signal strength prediction methods based on signal propagation models. The signal propagation models are various, and the propagation model of formula (1) is frequently used.
RSSI＝Pt-K-10αlog10d (1)
Where α is called the path loss exponent, pt is the transmit power, and K is a constant that depends on the environment and frequency. d denotes a signal collection terminal position point (x) i ，y j ) The distance from the AP location. When the parameters of the formula (1) are determined, the RSSI value of each position point can be calculated.
The propagation model-based inference method has very poor generalization to environments, and different environments need to update a set of model parameters or even change a set of propagation models. Therefore, if a single propagation model is used to predict the RSSI values of fingerprint points, once the model parameters are wrong or the environment changes, the model parameters may cause error amplification in the fingerprint database, which may ultimately affect the accuracy of indoor positioning.
The existing WiFi data indoor position fingerprint database establishing method has the following defects:
1. position fingerprint point information distribution is uneven
Because the position fingerprint database is built by using direct sample data acquisition, and the limitation of indoor manual data acquisition, the data of the position fingerprint points is difficult to avoid presenting the phenomenon of uneven data distribution. In the sample collection process, the non-uniform distribution of the position points can cause defects in two aspects, on one hand, the redundancy of fingerprint data occurs in partial areas, and the storage and calculation cost is increased. On the other hand, the position fingerprint information of partial areas is sparse, so that the final positioning accuracy is reduced.
2. The fitting mode of the fingerprint database has low precision
Fitting a WiFi fingerprint library is required to save data acquisition costs. For example, patent document CN104853317A discloses a method for constructing and updating a fingerprint database in WiFi indoor positioning, specifically, in an early stage of fitting, a method of tracing points on WiFi data and directly constructing the fingerprint database based on an anchor node is needed. The accuracy of the fingerprint database established by the method greatly depends on the number of the tracing nodes, and the tracing nodes are required to be densely acquired before regression is carried out. However, the acquisition of the tracing nodes requires a lot of manpower, and the method is not suitable for large-scale indoor positioning scenes.
Patent document CN105338498A discloses a method for constructing a fingerprint database in a WiFi indoor positioning system, which is specifically a fingerprint database filling mode, and is to select reference points in a grid manner in a positioning area, collect signal strength information on some reference points on site, calculate signal strength information of other reference points which are not collected by using the collected reference point strength information to obtain a propagation process, and realize signal strength filling in an unknown area. However, in practical applications, a wireless channel in an indoor environment is very complex, different propagation environments have different propagation models, and a single propagation model lacks effective generalization. Therefore, the propagation model estimation method described in patent document CN105338498A is not easy to be generalized, nor is it an optimal fingerprint library fitting method.
Disclosure of Invention
The technical problem mainly solved by the invention is to provide a WiFi data-based position fingerprint database establishing and fitting method, which not only overcomes the defects of uneven distribution of fingerprint points, namely a planar grid fingerprint database, but also overcomes the defects brought by a fixed model, and improves the adaptability and positioning accuracy of a propagation model.
In order to solve the above problems, the present invention provides a method for establishing and fitting a WiFi data-based location fingerprint database, the method for establishing and fitting an indoor location fingerprint database based on WiFi data comprises,
the method comprises the steps of establishing a position fingerprint database and fitting the fingerprint database, wherein the establishing step of the position fingerprint database comprises the steps of dividing a grid fingerprint database, dividing a grid area into latitudes of fingerprint database areas to be divided, setting two points as diagonal lines of a rectangular area, and obtaining blank grid fingerprints;
and filling a fingerprint database, namely filling the WiFi signal intensity data uploaded by the user in an accumulated way and the measured WiFi signal intensity data into grids at corresponding positions according to the signal intensity data positions, wherein the step of filling the fingerprint database also comprises the step of filtering the WiFi signal intensity data except for singular AP, the filtering process adopts a Gaussian weight function, and the inversion of the Gaussian weight function adopts a pseudo-inverse to replace the pseudo-inverse.
Further, the fingerprint database is a planar rectangular grid fingerprint database.
Further, after filtering the WiFi signal strength data, the filtering method further includes calculating a filtered signal strength mean value of each raster data.
Further, the filtering process includes gaussian filtering.
Further, the latitude and longitude area to be divided is set to be (30.00, 120.00); (30.50,120.50).
Further, the blank grid fingerprint obtaining step includes setting a segmentation distance, setting segmentation lengths in longitude and latitude directions, and obtaining two basic lists;
and row-column splicing, namely splicing rows and columns after row splicing, or splicing rows and columns after column splicing, and outputting blank grid fingerprints.
Further, each grid point of the fingerprint library corresponds to one signal strength data.
Further, the probability density function of the gaussian filtering is,
wherein the content of the first and second substances,
n is the total number of samples of RSSI data, RSSI t For the measured signal strength of one of the total samples, data in the range of (μ -2S, μ + 2S) is retained for all RSSIs, μ being the mean of the sample set and S being the standard deviation.
Further, the mean value of the signal strength data set is calculated by the formula,
wherein P is the number of APs received by the fingerprint point.
Further, the step of fitting the fingerprint library comprises
Establishing a polynomial fitting model to learn the existing data to obtain the fitting model, and setting a fitting grid signal intensity representation function f (x, y) on a local subdomain of the fitting region to be represented as
Where i denotes the corresponding parameter number of the subregion to be fitted, a (x, y) = [ a = 1 (x，y)，a 2 (x，y)，...，a m (x，y)] T For the coefficient matrix to be solved, as a function of coordinates (x, y), where x represents a longitude variable, y represents a latitude variable, and the linear basis p (x, y) = [1, x, y ] of the longitude and latitude coordinates]Second radical p 2 (x，y)＝[1，x，y，x 2 ，xy，y 2 ]。
Further, the linear basis p of the latitude and longitude coordinates(x，y)＝[1，x，y]Second radical p 2 (x，y)＝[1，x，y，x 2 ，xy，y 2 ]The weight function constructs a loss function:
where n is the number of nodes in the region of influence, f (x, y) is the fitting function, z 1 Is x + x 1 Node x of I Weight function w (x-x) I )
Further, the weight function is a gaussian weight function.
Further, the coefficients a (x, y) are obtained,
a(x，y)＝A -1 (x，y)B(x，y)z，
wherein the content of the first and second substances,
further, instead of the pseudo-inverse,
B＝[w(x-x 1 )p(x 1 )，w(x-x 2 )p(x 2 )，w(x-x n )p(x n )]
z T ＝[z 1 ,z 2 ,...，z n ]
z T for the RSSI value data set of the sample, equation a (x, y) = A -1 (x, y) B (x, y) z, into formula
A final fit function is obtained that is,
wherein
Indicating a locationSignal strength of the fingerprint lattice at i under the influence of area node n, z i Are the corresponding signal parameters.
The position fingerprint database establishing and fitting method based on WiFi data comprises a position fingerprint database establishing step and a fingerprint database fitting step, wherein the position fingerprint database establishing step comprises grid fingerprint database division, grid areas are divided according to the latitude of a fingerprint database area to be divided, two points are set as the diagonal line of a rectangular area, and a blank grid fingerprint is obtained; and (4) fingerprint database filling, namely filling the WiFi signal intensity data uploaded by the user in an accumulated manner and the measured WiFi signal intensity data into grids at corresponding positions according to the signal intensity data positions. Due to the adoption of the plane grid fingerprint library, the problem of positioning accuracy reduction caused by uneven distribution of fingerprint information can be effectively avoided. Meanwhile, gaussian filtering is carried out on the original collected data, so that errors brought by data collection are reduced, and a solid foundation is laid for further improving indoor positioning accuracy subsequently.
Drawings
In order to illustrate the embodiments of the invention or the technical solutions in the prior art more clearly, the drawings that are needed in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the description only show some embodiments of the invention and therefore should not be considered as limiting the scope, and for a person skilled in the art, other related drawings can also be obtained from these drawings without inventive effort.
Fig. 1 is a schematic diagram of a conventional WiFi data location fingerprint database establishment process.
FIG. 2 is a diagram of a prior art sample location fingerprint point distribution.
FIG. 3 is a schematic flow chart of a WiFi data-based location fingerprint database establishment and fitting method of the present invention.
Fig. 4 is a schematic diagram of the region to be divided.
Fig. 5 is a schematic diagram of grid cutting.
FIG. 6 is a schematic diagram of a blank fingerprint library.
FIG. 7 is a schematic diagram of a WiFi data grid fingerprint database establishment process of the present invention.
Fig. 8 schematic diagram of WiFi signal strength filling result (db).
FIG. 9 is a three-dimensional schematic of the fitting results.
Fig. 10 illustrates an inscribed sample selection process.
FIG. 11 is a schematic diagram of the results of a library of location fingerprints.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
To more clearly illustrate the technical solution of the present invention, the following claims are further described in detail with reference to specific embodiments and the drawings, it is to be understood that the following drawings only show some embodiments of the present invention, and therefore should not be considered as limiting the scope, and any limited number of modifications made by anyone within the scope of the claims still fall within the scope of the claims.
It should be understood that in the description of the present invention, it should be noted that the terms "upper", "lower", and the like refer to orientations or positional relationships based on orientations or positional relationships shown in the drawings or orientations or positional relationships conventionally used in the product of the present invention, which are merely for convenience of description and simplification of description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed in a specific orientation, and operate, and therefore, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like are used merely to distinguish one description from another, and are not to be construed as indicating or implying relative importance.
As shown in fig. 1, the present invention provides an embodiment of a method for establishing and fitting a location fingerprint database based on WiFi data.
The WiFi data-based location fingerprint database establishing and fitting method comprises the following steps,
a step of establishing a position fingerprint database and a step of fitting the fingerprint database, wherein the step of establishing the position fingerprint database comprises,
dividing a grid fingerprint database, namely dividing the latitude of a fingerprint database area to be divided into grid areas, and setting two points as the diagonal lines of a rectangular area to obtain blank grid fingerprints;
and (4) fingerprint database filling, namely filling the WiFi signal intensity data uploaded by the user in an accumulated manner and the measured WiFi signal intensity data into grids at corresponding positions according to the positions of the signal intensity data, namely matching the RSSI data into a well-divided blank grid fingerprint database according to the latitude and longitude areas to which the RSSI data belongs.
Specifically, the grid fingerprint database is established for two purposes, the first purpose is to reduce the redundancy of sample points, and the second purpose is to improve the matching rate of points to be positioned and the sample points in the positioning process. The specific division is described in detail in the following embodiments of formula (1) to formula (3), and formula (4) to formula (6). The WiFi data-based location fingerprint database establishment comprises location fingerprint database establishment, and the location fingerprint database establishment comprises a fingerprint database dividing step.
In order to overcome the defect of nonuniform distribution of the position points of the fingerprint database, the fingerprint database adopts a planar rectangular grid fingerprint database. Therefore, before the planar rectangular grid fingerprint database is filled with data, the grid area needs to be divided according to the longitude and latitude. Specifically, according to the theory that a rectangular range can be determined from two points, the latitude and longitude area to be divided can be set as: '30.00, 120.00;30.50 120.50', the two points are the diagonal lines of the rectangular area, and the determined area is as shown in fig. 4, that is, two points can determine a plane area. The divided areas can comprise indoor areas and outdoor areas, and the invention mainly solves the positioning state of the indoor areas.
Determining a region needing grid segmentation, and needing a position segmentation algorithm, wherein the position refers to a rectangular range determined by two diagonal points, and can be understood as a grid for segmenting a large-range region into small regions, so as to form a blank grid fingerprint database. The specific segmentation idea is as follows:
by setting the division step size to 10, the longitude direction to be m parts and the latitude direction to be n parts, the region can be divided into m x n parts
m＝(max(longitude)-min(longitude))/10，m∈N (1)
n＝(max(latitude)-min(latitude))/10，n∈N (2)
After longitude division and latitude division, two basic lists list (list) and list (late) are obtained, and row and column splicing cannot be carried out simultaneously in the grid fingerprint cutting algorithm. Taking the above flow as an example, the column splicing process may be affected by the row splicing. When the a-th row splicing operation is completed, the ordinal number of the column splicing is i
i＝long(list(longitude))+(long(list(longitude))*a-1) (3)
After the region splicing is completed, it means that the grid fingerprint can be output, and the final grid fingerprint output of the scheme is shown in fig. 6. It should be noted that, the grid is still in a blank state so far, and the fingerprint information needs to be filled to form a preliminary fingerprint library.
Filling step of fingerprint database
The original data in the invention are WiFi data and partial measurement data uploaded by users in an accumulated way, and based on the planar grid segmentation result, the scheme fills RSSI (Received Signal Strength Indication) data of WiFi to a corresponding grid. Prior to the direct grid fingerprint library filling, the RSSI data is gaussian filtered and averaged herein.
It has been explained above that the process of the terminal collecting WiFi data to directly build the location fingerprint database is as shown in fig. 1 above. Unlike fig. 1, the position point of the present scheme is determined by the grid point, and the position where the sample is acquired will also be one-to-one matched with the position of the grid. The grid fingerprint library construction process is shown in fig. 7.
In the scheme, in order to improve the subsequent position positioning efficiency, each grid point of the final fingerprint database can only correspond to one RSSI value. Due to the complexity of signal propagation, the variability between WiFi devices, and the instability of the propagation environment, such phenomena may cause data errors in data at both the transmitting end and the receiving end. Therefore, the scheme provides a noise point filtering and averaging mode, which can reduce the error on one hand and calculate the RSSI value of each grid of the final fingerprint database on the other hand.
Let all AP sample sets collected by the terminal be { RSSI 1 ,RSSI 2 ，...，RSSI n }. For data acquisitionThere is the error in the collection process, and sampling error is alleviated to this scheme provides the mode of gaussian filtering. Let all RSSI data samples obey (μ, s) 2 ) Is a Gaussian distribution of which the probability density function is
Wherein
N is the total number of samples of RSSI data, RSSI i The signal strength measured for a certain one of the total samples. For all RSSIs, the data in (μ -2s, μ +2 s) is retained and the data outside the above range is deleted. The RSSI data is subjected to the Gaussian filtering operation, so that errors caused in the data acquisition process can be avoided to a certain extent.
Furthermore, the scheme adopts a method of RSSI data set mean value to calculate the RSSI value of the fingerprint database of each sample point, and the specific operation mode is
Where P is the number of APs received by the fingerprint point. The averaging method can overcome errors caused by differences between different WiFi equipment and acquisition equipment to a certain extent.
Table 1 location fingerprint database data correspondence:
taking RSSI (Received Signal Strength Indication) value of the WiFi Signal as an example, the filling effect is shown in fig. 8.
4.2 fingerprint library fitting
It can be analyzed from fig. 8 that, when the currently acquired data is used for filling, a large amount of data is missing, which is caused by the difficulty of completely acquiring each grid. The amount of the sample data and the labor cost are in a direct proportion relation, and if a large amount of samples are manually collected, the labor cost is increased. The fingerprint library fitting scheme provided by the invention can save human resources on one hand and can realize data filling of all fingerprint grid library points on the other hand.
4.2.1 polynomial function fitting
According to the scheme, a polynomial fitting model is established to learn existing data to obtain the fitting model. Specifically, on the local subdomains of the fitting region, the fitting grid signal intensity representation function f (x, y) is expressed as
Where i denotes the corresponding parameter number of the subregion to be fitted, a (x, y) = [ a = [ (-) ] 1 (x，y)，a 2 (x，y)，...，a m (x，y)] T The coefficient matrix to be solved is a function of coordinates (x, y), wherein x represents a longitude variable and y represents a latitude variable. p (x, y) = [ p ] 1 (x，y)，p 2 (x，y)，...，p m (x，y)] T It is a perfect polynomial of k-th order, called polynomial basis function, and m is the number of terms of the basis function. For example, the problem addressed by the present invention is an approximately planar problem, the linear basis p (x, y) = [1, x, y ]]The secondary radical p 2 (x，y)＝[1，x，y，x 2 ，xy，y 2 ]Introducing a weight function to construct a loss function:
where n is the number of nodes in the affected area, f (x)Y) is a fitting function, z 1 Is x + x 1 Node x of I Weight function w (x-x) I ) The weight function is a Gaussian weight function. To determine the coefficient a (x, y), equation (9) should take a minimum value, and equation (9) is derived for a:
a(x，y)＝A -1 (x，y)B(x，y)z， (11)
wherein
The process of a (x, y) inversion is very complicated in calculation amount, and the inversion is generally performed by adopting elementary transformation, but the phenomenon that the inversion cannot be performed occurs in many scenes. In order to avoid that A (x, y) can not be inverted, the invention adopts pseudo-inversion instead, and the situation that the metamorphosis matrix can not be inverted can be avoided.
B＝[w(x-x 1 )p(x 1 )，w(x-x 2 )p(x 2 )，w(x-x n )p(x n )] (13)
z T ＝[z 1 ,z 2 ，...，z n ] (14)
z T For the sample RSSI value data set, the final fitting function can be obtained by substituting equation (11) into equation (8):
wherein
Representing the signal strength of the fingerprint lattice at location i under the influence of the area node n, Z i Are the corresponding signal parameters.
Taking an indoor area as an example, the final polynomial fitting effect is shown in fig. 9. Where the vertical axis represents WiFi signal strength (RSSI) data for each grid, where each grid is populated with WiFi signal strength RSSI data after the data is completely fitted.
Careful inspection of the upper graph reveals that the fitting result has abrupt changes at the edges due to the existence of the drift of the data itself, which is frequent in practical applications. A mutation in the fitting value may cause part of the fitting result to be out of the range of (μ -2s, μ +2 s) described above, so that the fitting result deviates from the actual value. Analyzing the above situation means that the fitting of the fingerprint library cannot rely entirely on polynomial functions, and separate processing is required for the mutation outliers. So far, sample data are greatly enriched through polynomial function fitting, and the problem of insufficient sample points can not occur when an interpolation method is used for grid fitting. Therefore, in order to solve the problem of the mutated outliers, the invention proposes an inverse distance weighting algorithm to fit the outlier regions.
4.2.2 inverse distance weighted fitting
The inverse distance weighting algorithm is an interpolation algorithm based on the distance between a point to be fitted and a known point, and can better solve the problem of sudden change of the fitting result of the polynomial function. And (3) removing fitting data points out of the range of (mu-2 s and mu +2 s) aiming at the fitting result of 4.2.1, and calculating the RSSI value of the point to be fitted by using inverse distance weighting, wherein the specific operation method comprises the following steps:
a) All known grid points are known as known data, and the RSSI value is recorded as Z (X) i ，Y j ，X i 、Y j Representing the horizontal and vertical coordinates in the grid fingerprint;
b) Sequentially selecting grid position information to be fitted as (X) 0 ，Y 0 )；
c) Calculating the distance of the grid to be calibrated from all inscribed sample grids
For the concept of inscribed samples, the invention is defined as all known points contained in the circular area with the point to be fitted as the center point and r as the radius, the process is shown in fig. 10. Within this region, the total number of sample points is denoted as N.
d) Calculate the weight for each calibration grid: the weight is a function of the reciprocal of the distance between the point to be fitted and each sample point
e) Calculating the signal intensity difference of the grid to be calibrated:
and repeating the steps a-e until all the grid fingerprint points to be filled are calculated.
1) A certain indoor area is selected as a fitting result to be displayed, and the fitting result of the indoor fingerprint database based on the WiFi data is shown in fig. 11.
2) And establishing one of the key points and the protection points of the indoor position fingerprint database. The position fingerprint database is established mainly by the segmentation of a plane grid and the filling processing of the data of the grid fingerprint database; (step 4.1)
3) The indoor fingerprint library fitting mode is another protection point of the invention. The method designs a polynomial function model, learns the rule of known sample points through the model, solves the polynomial function parameters, and finally calculates unknown raster data by using the polynomial. In order to solve the problem of sudden change in polynomial fitting, the scheme uses an inverse distance weighting algorithm as a supplement of a fitting algorithm. (step 4.2)
The above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: it is to be understood that modifications may be made to the above-described embodiments, or equivalents may be substituted for some of the features of the embodiments, and such modifications or substitutions may be made without departing from the spirit and scope of the embodiments of the present invention.
Claims (9)
1. The WiFi data-based position fingerprint database establishing and fitting method comprises the steps of,
the method comprises the steps of establishing a position fingerprint database and fitting the fingerprint database, wherein the establishing step of the position fingerprint database comprises the steps of dividing a grid fingerprint database, dividing a grid area into latitudes of fingerprint database areas to be divided, setting two points as diagonal lines of a rectangular area, and obtaining blank grid fingerprints;
filling a fingerprint database, namely filling the WiFi signal intensity data uploaded by the user in an accumulated way and the measured WiFi signal intensity data into grids at corresponding positions according to the positions of the signal intensity data; the fingerprint library filling comprises fingerprint library fitting, and pseudo-inverse substitution is adopted when the fitting function of the fingerprint library is subjected to inverse calculation.
2. The WiFi data based location fingerprint database creation and fitting method of claim 1, wherein the fingerprint database is a flat rectangular grid fingerprint database.
3. The WiFi data based location fingerprint database creation and fitting method of claim 1 or 2, wherein before the fingerprint database filling step further comprises filtering WiFi signal strength data but excluding singular APs.
4. The WiFi data based location fingerprint database creation and fitting method of claim 3, further comprising calculating the filtered signal strength mean of each grid data after filtering the WiFi signal strength data.
5. The WiFi data based location fingerprint database creation and fitting method of claim 1, characterized in that the latitude and longitude area to be divided is set to (30.00, 120.00); (30.50,120.50).
6. The WiFi data-based location fingerprint library creation and fitting method of claim 1, wherein the blank grid fingerprint obtaining step comprises,
setting a segmentation distance, and setting segmentation lengths in the longitude and latitude directions to obtain two basic lists;
and row-column splicing, namely splicing rows and columns after row splicing, or splicing rows and columns after column splicing, and outputting blank grid fingerprints.
7. The WiFi data based location fingerprint library creation and fitting method of claim 1, wherein each grid point of the fingerprint library corresponds to one signal strength data.
8. The WiFi data-based location fingerprint library creation and fitting method of claim 3, wherein the filtering process comprises Gaussian filtering with a probability density function of,
wherein
N is the total number of samples of RSSI data, RSSI i For the measured signal strength of one of the total samples, data in the range of (μ -2S, μ + 2S) is retained for all RSSIs, μ being the mean of the sample set and S being the standard deviation.
9. The WiFi data based location fingerprint library creation and fitting method of claim 7, wherein the mean of the set of signal strength data is calculated as,
wherein P is the number of APs received by the fingerprint point.
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| CN110418283B (en) * | 2019-06-25 | 2023-02-24 | 北京航空航天大学 | WiFi data-based location fingerprint database creation method |
| CN111417066B (en) * | 2020-03-05 | 2021-01-05 | 滴图(北京)科技有限公司 | Positioning method, positioning device, electronic equipment and computer readable storage medium |
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