CN116782374A - WiFi fingerprint library updating method and device and electronic equipment - Google Patents
WiFi fingerprint library updating method and device and electronic equipment Download PDFInfo
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- G01S5/0252—Radio frequency fingerprinting
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Abstract
The application discloses a WiFi fingerprint library updating method, and belongs to the technical field of mobile communication and the Internet of things. The WiFi fingerprint library updating method comprises the following steps: acquiring WiFi signal intensity values of N acquisition positions in M positions in a WiFi fingerprint library, wherein M is an integer greater than 1, and N is a positive integer smaller than M; creating a signal intensity distribution matrix based on the WiFi signal intensity values of the N acquisition positions; generating signal distribution data based on the signal strength distribution matrix; and updating the WiFi signal intensity values of the M positions in the WiFi fingerprint library based on the signal distribution data and the WiFi signal intensity values of the N acquisition positions. According to the application, the WiFi signal intensity value of the non-collected position can be fitted through the WiFi signal intensity value of the collected position and the signal distribution data, so that the WiFi fingerprint library is updated, and the updated positioning precision in the WiFi fingerprint library is further improved.
Description
Technical Field
The application relates to the technical field of mobile communication and the internet of things, in particular to a WIFI fingerprint library updating method, a WIFI fingerprint library updating device and electronic equipment.
Background
Nowadays, with the high-speed development of the internet of things technology, the indoor positioning technology becomes a hotspot with great attention, and the fingerprint positioning algorithm based on WiFi is more widely paid attention to. The WiFi signal intensity value received at each location in the indoor environment is related to an Access point (Access point) distance of the AP, each location may be referred to as a fingerprint point, where the fingerprint point and the WiFi signal intensity value received by the fingerprint point form a fingerprint, and the fingerprint library represents a series of fingerprint sets in the indoor area to be detected. When the setting position of some APs wireless Access points (Access points) changes, the WiFi fingerprint database also needs to be updated.
In the prior art, in order to realize automatic update of a WiFi fingerprint library, signal intensity of each point location can be directly counted by manually collecting field measurement data, on the other hand, signal intensity of each point location can be obtained through collecting a small number of known points and through a constructed signal attenuation model, but most of the prior art is based on ideal conditions, and assumed that a collection area of signals presents finished Gaussian-like distribution, but in practice, wiFi signals in many indoor scenes only cover part of the collection area, and for distribution curves of certain WiFi signals, only one side or one section of the Gaussian-like curve can be collected, if the distribution curves are directly simulated by Gaussian functions, great difference exists, even fitting cannot be achieved, so that finally obtained result errors are large.
Disclosure of Invention
The disclosure provides a WiFi fingerprint library updating method, a WiFi fingerprint library updating device, electronic equipment and a storage medium, so as to solve the problem that the updating positioning accuracy in the WiFi fingerprint library is poor.
According to an aspect of the present disclosure, there is provided a WiFi fingerprint library updating method, including:
acquiring WiFi signal intensity values of N acquisition positions in M positions in a WiFi fingerprint library, wherein M is an integer greater than 1, and N is a positive integer smaller than M;
creating a signal intensity distribution matrix based on the WiFi signal intensity values of the N acquisition positions;
generating signal distribution data based on the signal strength distribution matrix;
and updating the WiFi signal intensity values of the M positions in the WiFi fingerprint library based on the signal distribution data and the WiFi signal intensity values of the N acquisition positions.
According to another aspect of the present disclosure, there is provided a WiFi fingerprint library updating apparatus, including:
the acquisition module is used for acquiring WiFi signal intensity values of N acquisition positions in M positions in the WiFi fingerprint library, M is an integer greater than 1, and N is a positive integer smaller than M;
the creating module is used for creating a signal intensity distribution matrix based on the WiFi signal intensity values of the N acquisition positions;
The generation module is used for generating signal distribution data based on the signal intensity distribution matrix;
and the updating module is used for updating the WiFi signal intensity values of the M positions in the WiFi fingerprint library based on the signal distribution data and the WiFi signal intensity values of the N acquisition positions.
According to another aspect of the present disclosure, there is provided an electronic device including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the WiFi fingerprint library updating method provided by the present disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the WiFi fingerprint library updating method provided by the present disclosure.
In the method, wiFi signal intensity values of a part of acquisition positions in the acquisition positions are acquired firstly, a signal intensity distribution matrix is constructed according to the collected WiFi signal intensity values, signal distribution data can be obtained from the signal intensity distribution matrix, wiFi signal intensity values of non-acquisition positions in the acquisition positions can be obtained according to the signal distribution data and the acquired WiFi signal intensity values, updating operation of a WiFi fingerprint library is completed by the method, and updated positioning accuracy in the WiFi fingerprint library is improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
fig. 1 is a flowchart of a WiFi fingerprint library updating method provided by the present disclosure;
fig. 2 is a block diagram of a WiFi fingerprint library updating apparatus provided by the present disclosure;
fig. 3 is another block diagram of a WiFi fingerprint library updating apparatus provided by the present disclosure;
fig. 4 is another block diagram of a WiFi fingerprint library updating apparatus provided by the present disclosure;
fig. 5 is another block diagram of a WiFi fingerprint library updating apparatus provided by the present disclosure;
fig. 6 is a block diagram of an electronic device used to implement a WiFi fingerprint library update method of an embodiment of the disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Referring to fig. 1, fig. 1 is a flowchart of a WiFi fingerprint library updating method provided by the present disclosure, as shown in fig. 1, including the following steps:
step S101, acquiring WiFi signal intensity values of N acquisition positions in M positions in a WiFi fingerprint library, wherein M is an integer greater than 1, and N is a positive integer smaller than M.
The M positions may be any position where signal intensity collection is to be performed indoors, and the N acquisition positions may be partial positions covered by indoor WiFi signals.
And acquiring WiFi signal intensity values of the N acquisition positions, wherein the acquired data can be in a WiFi fingerprint library, and the WiFi signal intensity values of the N acquisition positions can be calculated by the distance from an AP wireless Access point (Access Piont) and environmental factors.
In addition, the N acquisition positions may be selected at even intervals among the M positions, and the signal acquisition may be performed.
And step S102, creating a signal intensity distribution matrix based on the WiFi signal intensity values of the N acquisition positions.
The above-mentioned construction of the signal intensity distribution matrix may be obtained by:
assuming that the WiFi signal intensity values of the M positions need to be acquired in the WiFi fingerprint library, where m=m×n, and the N acquisition positions are selected as the positions for signal acquisition at uniform intervals among the M positions, where n=m/2*n/2, the N acquisition positions represent 1/4 of the M positions, and an interval between M and N is 1.
And if fingerprints with the same identification in the acquired data are stored together, the WiFi signal strength value of the acquired position of the kth identification can be expressed by the formula:
wherein m and n represent the abscissa and ordinate, rsti, respectively, of the acquisition location within the acquisition region i,j WiFi signal strength values representing acquisition locations of coordinates (i, j).
Note that if no WiFi signal is acquired at the acquisition position, rsi=0 at the acquisition position.
In addition, the WiFi signal intensity values of the N acquisition positions can be clearly shown through the signal intensity distribution matrix, so that the data can be intuitively embodied, and meanwhile, the calculation of the WiFi signal intensity values can be conveniently carried out.
It should be understood that the number of the N collection positions may be set according to an indoor collection environment or a calculation requirement, and the number of the N collection positions may be 1/2, 1/3, or 1/4 of the number of the M positions, which is not limited in the embodiment of the present invention.
And step S103, generating signal distribution data based on the signal intensity distribution matrix.
The signal distribution data represents a specific portion of the gaussian function corresponding to an element in the intensity distribution matrix.
The signal distribution data may be obtained by analyzing the signal intensity distribution matrix row by row or column by column, for example: and grouping the signal intensity distribution matrix according to rows or columns, analyzing the intensity of the WiFi signal intensity value of each row group or each column group, finding out the maximum WiFi signal intensity value in each row group or column group and the minimum WiFi signal intensity value of each row group or column group at the head-tail position, obtaining WiFi signal intensity values of all acquisition positions meeting the conditions, comparing the collected data with preset conditions, and finally obtaining the signal distribution data.
The minimum WiFi signal strength value of each row group or column group at the end-to-end position may be determined within a preset unit range, for example: searching the minimum WiFi signal intensity value in the first x elements of each row group or searching the minimum WiFi signal intensity value in the first x elements of each column group; the minimum WiFi signal strength value is found in the last x elements of each row group or the minimum WiFi signal strength value is found in the last x elements of each column group.
In addition, the signal distribution data may be a specific part of the signal distribution including the row group or the column group similar to a gaussian function, for example: the signal distribution of the row or column resembles a monotonically decreasing portion of a gaussian function; the signal distribution of the row or column resembles the monotonically increasing part of a gaussian function; the signal distribution of the row or the column is in a Gaussian-like part which tends to be zero; the signals of the row or the column are distributed on both sides of the central axis of the gaussian-like function.
It should be noted that, the signal distribution may be selected by comparing the difference between the WiFi signal strength values to select a portion of the corresponding function, for example: the difference value between the maximum WiFi signal intensity value in each row group or each column group and the minimum WiFi signal intensity value in the head position in each row group or each column group is not more than a preset threshold value, and the signal distribution of the row group or the column group is considered to be similar to the monotonically decreasing part of the Gaussian function; the difference value between the maximum WiFi signal intensity value in each row group or each column group and the minimum WiFi signal intensity value in the tail position in each row group or each column group is not more than a preset threshold value, and the signal distribution of the row group or the column group is considered to be similar to the monotonically increasing part of the Gaussian function; the difference value between the maximum WiFi signal intensity value in each row group or each column group, the minimum WiFi signal intensity value in the head position in each row group or each column group and the minimum WiFi signal intensity value in the tail position in each row group or each column group does not exceed a preset threshold value, and then the signal distribution of the row group or the column group is similar to the part of the Gaussian function tending to zero; the difference between the maximum WiFi signal intensity value in each row group or column group, the minimum WiFi signal intensity value in the head position in each row group or column group, and the minimum WiFi signal intensity value in the tail position in each row group or column group exceeds a preset threshold, and the signal distribution of the row group or column group is similar to the two sides of the gaussian function central axis.
And step S104, updating WiFi signal intensity values of the M positions in the WiFi fingerprint library based on the signal distribution data and the WiFi signal intensity values of the N acquisition positions.
The signal distribution data may represent a function portion corresponding to the signal distribution of each row group or column group in the N acquisition positions, a corresponding signal attenuation model may be generated according to the corresponding function portion, and WiFi signal intensity values of required positions in the N acquisition positions are substituted into the signal attenuation model, so as to obtain WiFi signal intensity values of non-acquisition positions.
Wherein, partial curves in the matching corresponding functions may be used to fit the WiFi signal profile, for example: partial curves of the Gaussian function, the cauchy function and the multimodal Gaussian function are adopted to divide the curves with the WiFi signals, so that the WiFi signal intensity values of the non-acquired positions are estimated.
In the embodiment, the number of fingerprint point positions to be acquired is firstly set, then partial positions are selected to acquire WiFi signal intensity values, the acquired WiFi signal intensity values are constructed into a signal intensity distribution matrix, signal distribution data are acquired from the signal intensity distribution matrix, the signal distribution data represent the signal distribution of the WiFi signal intensity values in the signal intensity distribution matrix and the specific parts of corresponding Gaussian-like functions respectively, a corresponding signal attenuation model is constructed according to the signal distribution data, the WiFi signal intensity values of acquired positions are substituted into the constructed signal attenuation model, wiFi signal intensity values of non-acquired positions are obtained, the updating of a WiFi fingerprint library is completed, and the positioning precision of the updating of the WiFi fingerprint library can be improved under the limited acquisition positions by the method.
As an optional implementation manner, before the generating signal distribution data based on the signal intensity distribution matrix, the method further includes: a target acquisition position with a WiFi signal intensity value of 0 is arranged in the signal intensity distribution matrix, and the average value of the signal intensities of the left and right adjacent acquisition positions is used as the WiFi signal intensity value of the target acquisition position when at least one of the signal intensities of the left and right adjacent acquisition positions in the signal intensity distribution matrix is not 0; and under the condition that at least one of the two adjacent acquisition positions in the signal intensity distribution matrix is not 0, taking the average value of the signal intensities of the two adjacent acquisition positions as the WiFi signal intensity value of the target acquisition position.
In this embodiment, the optimization processing is performed on the 0 element in the signal intensity distribution matrix, first, the position of the 0 element in the signal intensity distribution matrix is found, and the non-0 property of the adjacent element around the 0 element is determined, if the position is not 0, the WiFi signal intensity value of the 0 element is replaced by the average value of the WiFi signal intensity values of the adjacent elements around, if at least one of the two elements around the 0 element is 0, the non-0 property of the adjacent elements around the 0 element is determined, and if the adjacent elements around the 0 element are not 0, the WiFi signal intensity value of the 0 element is replaced by the average value of the WiFi signal intensity values of the adjacent elements around. The whole operation is to pre-process the signal intensity distribution matrix, so that errors generated by subsequent calculation are reduced, and the updated positioning accuracy in the WiFi fingerprint library is improved.
As an optional implementation manner, the generating signal distribution data based on the signal intensity distribution matrix includes: dividing the WiFi signal intensity values of the fingerprint acquisition positions into a plurality of groups according to a first rule according to the signal intensity distribution matrix, wherein the first rule comprises one of the following: if the number of lines in the signal intensity distribution matrix is greater than or equal to the number of columns, the WiFi signal intensity values of the fingerprint acquisition positions are grouped according to the columns; if the number of lines in the signal intensity distribution matrix is less than or equal to the number of columns, grouping the signal intensities of the fingerprint acquisition positions according to the lines; acquiring first numerical information of each independent group in the plurality of groups, wherein the first numerical information comprises a maximum WiFi signal intensity value in each independent group, a minimum WiFi signal intensity value in the first H acquisition positions in each independent group and a minimum WiFi signal intensity value in the last K acquisition positions in each independent group, and H and K are integers larger than 1; generating said signal profile data for each of said independent groups according to said first numerical information, said signal profile data comprising at least one of: the signal distribution is a Gaussian-like function monotonically decreasing part, the signal distribution is a Gaussian-like function monotonically increasing part, the signal distribution is a Gaussian-like function zero-tending part and the signal distribution is a Gaussian-like function part on two sides of the central axis.
The first rule may be set according to a distribution situation of WiFi signal intensity values in the signal intensity distribution matrix, and specifically, if a number of lines in the signal intensity distribution matrix is equal to or greater than a number of columns, the WiFi signal intensity values of the plurality of fingerprint acquisition positions are grouped according to the columns; if the number of lines in the signal intensity distribution matrix is less than or equal to the number of columns, the signal intensities of the fingerprint acquisition positions are grouped according to the lines, and the method enables the number of elements in each group to meet the calculation requirement, so that the calculation accuracy is improved.
In this embodiment, by grouping the signal intensity distribution matrix, each grouping can be matched with a curve in a gaussian-like function, so that the signal curve of each grouping can be highly matched with a function with higher similarity, and then a signal attenuation model or other calculation modes with higher matching degree can be fitted.
The specific values of H and K may be set according to the size of the signal intensity distribution matrix and the actual indoor environment, and on the other hand, H and K may be expressed as values having the same size, and then the signal intensity distribution matrix may be subjected to data analysis, for example: and acquiring the maximum WiFi signal intensity value in each independent group, the minimum WiFi signal intensity value in the first 3 acquisition positions and the minimum WiFi signal intensity value in the last 3 acquisition positions.
As an alternative embodiment, said generating said signal distribution data for each of said independent groups according to said first numerical information comprises: if the difference between the maximum WiFi signal intensity value in each independent group and the minimum WiFi signal intensity value in the first H acquisition positions does not exceed a preset threshold value, the signal distribution of the independent groups is a Gaussian-like function monotonically decreasing part; if the difference between the maximum WiFi signal intensity value in each independent group and the minimum WiFi signal intensity value in the last K acquisition positions is not more than a preset threshold value, the signal distribution of the independent group is a Gaussian-like function monotonically increasing part; if the difference among the maximum WiFi signal intensity value in each independent group, the minimum WiFi signal intensity value in the first H acquisition positions and the minimum WiFi signal intensity value in the last K acquisition positions does not exceed a preset threshold value, the signals of the independent groups are distributed as a Gaussian-like function zero-approaching part; if the difference between the maximum WiFi signal intensity value in each independent group, the minimum WiFi signal intensity value in the first H acquisition positions and the minimum WiFi signal intensity value in the last K acquisition positions exceeds a preset threshold, the signals of the independent groups are distributed as parts on two sides of the axis of the Gaussian-like function.
The setting of the preset threshold may be set according to a signal acquisition environment or a signal strength of an AP wireless Access point (Access point), which is not limited in the embodiment of the present invention.
In addition, in general, the setting of the above-mentioned preset threshold value may be selected to be around 5dB, for example: if the difference between the maximum WiFi signal intensity value in each independent group and the minimum WiFi signal intensity value in the first H acquisition positions is not more than +/-5 dB, the signal distribution of the independent groups is a Gaussian-like function monotonically decreasing part; if the difference between the maximum WiFi signal intensity value in each independent group and the minimum WiFi signal intensity value in the last K acquisition positions is not more than +/-5 dB, the signal distribution of the independent group is a Gaussian-like function monotonically increasing part; if the difference among the maximum WiFi signal intensity value in each independent group, the minimum WiFi signal intensity value in the first H acquisition positions and the minimum WiFi signal intensity value in the last K acquisition positions is not more than +/-5 dB, the signals of the independent groups are distributed as a Gaussian-like function zero-approaching part; if the difference between the maximum WiFi signal intensity value in each independent group, the minimum WiFi signal intensity value in the first H acquisition positions and the minimum WiFi signal intensity value in the last K acquisition positions is more than +/-5 dB, the signals of the independent groups are distributed as parts on two sides of the central axis of the Gaussian-like function.
In this embodiment, the magnitude relation between the maximum WiFi signal intensity value in each independent group, the minimum WiFi signal intensity value in the first H acquisition positions, and the minimum WiFi signal intensity value in the last K acquisition positions is determined, so that the signal curves of the independent groups match the corresponding gaussian function-like curve portions, and a signal attenuation model or other calculation modes with higher matching degree are fitted, so that the calculation accuracy of the WiFi signal intensity values of the non-acquisition positions is improved.
As an optional implementation manner, the determining the WiFi signal intensity value of the set location based on the signal distribution data and the WiFi signal intensity values of the plurality of fingerprint acquisition locations includes: determining a signal attenuation model of the signal intensity distribution matrix according to the signal distribution data, wherein the signal attenuation model comprises at least one of the following components: the system comprises a first attenuation model, a second attenuation model, a third attenuation model and a fourth attenuation model, wherein the first signal attenuation model is generated based on WiFi signal intensity values of fingerprint acquisition positions with signal distribution being a Gaussian-like function monotonically decreasing part; generating WiFi signal intensity values of fingerprint acquisition positions of which the signal distribution corresponding to the second signal attenuation model is a Gaussian-like function monotonically increasing part; generating WiFi signal intensity values of fingerprint acquisition positions of which the signal distribution corresponding to the third signal attenuation model is similar to the Gaussian function and tends to be zero; the corresponding signal distribution of the fourth signal attenuation model is generated as WiFi signal intensity values of fingerprint acquisition positions of two sides of the central axis of the Gaussian-like function; and taking the WiFi signal intensity values of the fingerprint acquisition positions as input, and obtaining the WiFi signal intensity values of the set positions through corresponding attenuation models.
The Gaussian-like function in the signal distribution data may be a cauchy function, and the cauchy function is used as a construction basis of the signal attenuation model, and the cauchy function may be set as follows:
in addition, the first attenuation model, the second attenuation model, the third attenuation model and the fourth attenuation model are all generated by WiFi signal intensity values of the cauchy function curve part corresponding to the fingerprint acquisition positions.
The generation of the first attenuation model may be represented by the following steps:
1. normalizing the acquired maximum WiFi signal intensity value in each independent group, the minimum WiFi signal intensity value in the first H acquisition positions and the minimum WiFi signal intensity value in the last K acquisition positions:
R(max)＝rssi max /|rssi min(2) |+1
R(min(1))＝rssi min(1) /|rssi min(2) |+1
R(min(2))＝rssi min(2) /|rssi min(2) |+1
wherein, rssi max Representing the maximum WiFi signal strength value, rsi, in each independent group min(1) Representing the smallest WiFi signal strength value, rssi, of the first H acquisition positions min(2) Representing the smallest WiFi signal strength value among the last K acquisition locations.
2. Substituting (0, R (max)) into the above cauchy function results in γ=1/pi R (max), and taking the x value of f (x; 0, 1) =0.01 as the right boundary of the attenuation model, the following formula is therefore taken as the attenuation model in this case:
Wherein,,
the generation of the second attenuation model may be represented by the following steps:
1. normalizing the acquired maximum WiFi signal intensity value in each independent group, the minimum WiFi signal intensity value in the first H acquisition positions and the minimum WiFi signal intensity value in the last K acquisition positions:
R(max)＝rssi max /|rssi min(2) |+1
R(min(1))＝rssi min(1) /|rssi min(2) |+1
R(min(2))＝rssi min(2) /|rssi min(2) |+1
wherein, rssi max Representing the maximum WiFi signal strength value, rsi, in each independent group min(1) Representing the smallest WiFi signal strength value, rssi, of the first H acquisition positions min(2) Representing the smallest WiFi signal strength value among the last K acquisition locations.
2. The attenuation model in this case is derived from the inverse of the first attenuation model described above:
the generation of the third attenuation model may be represented by the following steps:
normalizing the acquired maximum WiFi signal intensity value in each independent group, the minimum WiFi signal intensity value in the first H acquisition positions and the minimum WiFi signal intensity value in the last K acquisition positions:
R(max)＝rssi max /|rssi min(2) |+1
R(min(1))＝rssi min(1) /|rssi min(2) |+1
R(min(2))＝rssi min(2) /|rssi min(2) |+1
wherein, rssi max Representing the maximum WiFi signal strength value, rsi, in each independent group min(1) Representing the smallest WiFi signal strength value, rssi, of the first H acquisition positions min(2) Representing the smallest WiFi signal strength value among the last K acquisition locations.
2. Since the attenuation model is determined to be in a straight line form, an attenuation model in this case is obtained:
The generation of the fourth attenuation model may be represented by the following steps:
1. normalizing the acquired maximum WiFi signal intensity value in each independent group, the minimum WiFi signal intensity value in the first H acquisition positions and the minimum WiFi signal intensity value in the last K acquisition positions:
R(max)＝rssi max /|min(rssi min(1) ,rssi min(2) )|+1
R(min(1))＝rssi min(1) /|min(rssi min(1) ,rssi min(2) )|+1
R(min(2))＝rssi min(2) /|min(rssi min(1) ,rssi min(2) )|+1
wherein, rssi max Representing the maximum WiFi signal strength value, rsi, in each independent group min(1) Representing the smallest WiFi signal strength value, rssi, of the first H acquisition positions min(2) Represents the minimum WiFi signal strength value, min (rssi) min(1) ,rssi min(2) ) Representing rssi min(1) And rssi min(2) The smaller of (3).
2. Substituting R (max) for f (x; 0, γ) yields the following formula:
3. then rssi is added again min(1) And rssi min(2) Of the larger of (a, max (rsi) min(1) ,rssi min(2) ) Substitution into the above formula:
i.e. finally the fourth attenuation model described above can be represented by:
in the embodiment, through classifying different signal curves and generating corresponding signal attenuation models by using different signal curves and WiFi signal intensities, the prediction and calculation of the WiFi signal intensity values can be completed under various conditions, and the calculation accuracy of the WiFi signal intensity values at the non-collected positions is improved.
It should be noted that each independent group corresponds to a signal attenuation model, i.e. a piecewise curve of the cauchy distribution function.
As an optional implementation manner, the obtaining, by using the signal attenuation model, the WiFi signal intensity values of the plurality of fingerprint acquisition positions as input, the WiFi signal intensity values of the set positions includes: determining a value interval and the number of unknown acquisition positions in the signal attenuation model, wherein the number of the unknown acquisition positions is the number of the set positions minus the number of the plurality of fingerprint acquisition positions; determining an input value of the signal attenuation model according to the value interval and the number of the unknown acquisition positions; substituting the input value of the signal attenuation model into the signal attenuation model to obtain a WiFi signal intensity value of an unknown acquisition position; and combining the WiFi signal intensity values of the unknown acquisition positions and the WiFi signal intensity values of the fingerprint acquisition positions to obtain the WiFi signal intensity values of the set positions.
The WiFi signal intensity values of the non-collected positions may be obtained by uniformly making a difference between the collected positions through the signal attenuation model, and the calculation of the WiFi signal intensity values of the non-collected positions is performed by taking the fourth attenuation model as an example.
The formula of the fourth attenuation model is as follows:
1. Dividing the value interval of the independent variable x, namely, when x=0, the independent variable x corresponds to the maximum value of a certain row in the signal intensity distribution matrix and corresponds to the minimum value of the head and tail of the row respectively.
2. And respectively counting i unknown acquisition positions between the position of the maximum WiFi signal intensity value of the row and the position of the minimum WiFi signal intensity value of the row head alpha and j unknown acquisition positions between the position of the minimum WiFi signal intensity value of the row tail sigma.
For example: the WiFi signal intensity value distribution of a certain independent group in the signal intensity distribution matrix is as follows
h＝[-53,-58,-56,-52,-43,-50,-55,-63,-64]
Wherein the maximum value is-43, the minimum value in the first 3 rows and the last 3 rows is-58 and-64 respectively, 2 other WiFi signal intensity values are arranged between-58 and-43 in h, and 3 other WiFi signal intensity values are arranged between-43 and-64. Since these acquired positions are acquired every 1, i.e. half of the acquired positions, there are a total of 4 acquisition positions between-58 and-43 and 6 acquisition positions between-43 and-64.
3. Dividing the interval of the fourth attenuation model by 2 (i+1) and 2 (j+1), wherein the value of the corresponding independent variable x isAnd->
4. Substituting the obtained x value into the fourth attenuation model to obtain a corresponding y value, and performing inverse normalization processing because of the previous normalization processing, so as to obtain a corresponding WiFi signal intensity value, wherein the WiFi signal intensity value is an estimated value corresponding to the forward acquisition position.
In the embodiment, firstly, the generated signal attenuation model is subjected to value interval division, then the number of unknown acquisition positions among known acquisition positions is required to be obtained, the intervals are equally divided according to the number of the unknown acquisition positions, the values of corresponding independent variables are obtained, the independent variables are substituted into the signal attenuation model to obtain the corresponding variable values, finally, the WiFi signal intensity values corresponding to the unknown acquisition positions are obtained through data processing optimization, and the method can improve the updated positioning precision in the WiFi fingerprint library.
Referring to fig. 2, fig. 2 is a WiFi fingerprint library updating apparatus provided in the present disclosure, as shown in fig. 2, a WiFi fingerprint library updating apparatus 200 includes:
an obtaining module 201, configured to obtain WiFi signal strength values of N acquisition positions in M positions in a WiFi fingerprint library, where M is an integer greater than 1, and N is a positive integer less than M;
a creating module 202, configured to create a signal strength distribution matrix based on WiFi signal strength values of the N acquisition positions;
a generating module 203, configured to generate signal distribution data based on the signal intensity distribution matrix;
and the updating module 204 is configured to update the WiFi signal strength values of the M positions in the WiFi fingerprint library based on the signal distribution data and the WiFi signal strength values of the N acquisition positions.
Optionally, as shown in fig. 3, the apparatus 200 further includes:
a first determining module 205, configured to, when there is a target acquisition position with a WiFi signal intensity value of 0 in the signal intensity distribution matrix and at least one of signal intensities of two adjacent left and right acquisition positions in the signal intensity distribution matrix is not 0, take an average value of the signal intensities of the two adjacent left and right acquisition positions as a WiFi signal intensity value of the target acquisition position;
the second determining module 206 is configured to have a target acquisition position with a WiFi signal intensity value of 0 in the signal intensity distribution matrix, where the two adjacent acquisition positions on the left and right in the signal intensity distribution matrix are both 0, and if at least one of the two adjacent acquisition positions on the upper and lower in the signal intensity distribution matrix is not 0, take the average value of the signal intensities of the two adjacent acquisition positions as the WiFi signal intensity value of the target acquisition position.
Optionally, as shown in fig. 4, the generating module 203 includes:
a grouping unit 2031, configured to divide WiFi signal intensity values of the plurality of fingerprint acquisition positions into a plurality of groups according to a first rule according to the signal intensity distribution matrix, where the first rule includes one of: if the number of lines in the signal intensity distribution matrix is greater than or equal to the number of columns, the WiFi signal intensity values of the fingerprint acquisition positions are grouped according to the columns; if the number of lines in the signal intensity distribution matrix is less than or equal to the number of columns, grouping the signal intensities of the fingerprint acquisition positions according to the lines;
An obtaining unit 2032, configured to obtain first numerical information of each independent group in the plurality of groups, where the first numerical information includes a maximum WiFi signal strength value in each independent group, a minimum WiFi signal strength value in the first H acquisition positions in each independent group, and a minimum WiFi signal strength value in the last K acquisition positions in each independent group, and H and K are integers greater than 1;
a first generating unit 2033, configured to generate the signal distribution data of each independent group according to the first numerical information, where the signal distribution data includes at least one of the following: the signal distribution is a Gaussian-like function monotonically decreasing part, the signal distribution is a Gaussian-like function monotonically increasing part, the signal distribution is a Gaussian-like function zero-tending part and the signal distribution is a Gaussian-like function part on two sides of the central axis.
Optionally, the generating unit 2033 includes: if the difference between the maximum WiFi signal intensity value in each independent group and the minimum WiFi signal intensity value in the first H acquisition positions does not exceed a preset threshold value, the signal distribution of the independent groups is a Gaussian-like function monotonically decreasing part; if the difference between the maximum WiFi signal intensity value in each independent group and the minimum WiFi signal intensity value in the last K acquisition positions is not more than a preset threshold value, the signal distribution of the independent group is a Gaussian-like function monotonically increasing part; if the difference among the maximum WiFi signal intensity value in each independent group, the minimum WiFi signal intensity value in the first H acquisition positions and the minimum WiFi signal intensity value in the last K acquisition positions does not exceed a preset threshold value, the signals of the independent groups are distributed as a Gaussian-like function zero-approaching part; if the difference between the maximum WiFi signal intensity value in each independent group, the minimum WiFi signal intensity value in the first H acquisition positions and the minimum WiFi signal intensity value in the last K acquisition positions exceeds a preset threshold, the signals of the independent groups are distributed as parts on two sides of the axis of the Gaussian-like function.
Optionally, as shown in fig. 5, the updating module 204 includes:
a determining unit 2041 for determining a signal attenuation model of the signal intensity distribution matrix according to the signal distribution data, the signal attenuation model comprising at least one of: the system comprises a first attenuation model, a second attenuation model, a third attenuation model and a fourth attenuation model, wherein the first signal attenuation model is generated based on WiFi signal intensity values of fingerprint acquisition positions with signal distribution being a Gaussian-like function monotonically decreasing part; the second signal attenuation model corresponds to fingerprint acquisition positions of which the signal distribution is a part monotonically increasing like a Gaussian function; the corresponding signal distribution of the third signal attenuation model is a fingerprint acquisition position of a Gaussian-like function zero-approaching part; the corresponding signals of the fourth signal attenuation model are distributed to fingerprint acquisition positions of two sides of the central axis of the Gaussian-like function;
the second generating unit 2042 is configured to obtain the WiFi signal intensity values of the set positions through corresponding attenuation models by taking the WiFi signal intensity values of the plurality of fingerprint acquisition positions as input.
Optionally, the second generating unit 2042 includes: determining a value interval and the number of unknown acquisition positions in the signal attenuation model, wherein the number of the unknown acquisition positions is the number of the set positions minus the number of the plurality of fingerprint acquisition positions; determining an input value of the signal attenuation model according to the value interval and the number of the unknown acquisition positions; substituting the input value of the signal attenuation model into the signal attenuation model to obtain a WiFi signal intensity value of an unknown acquisition position; and combining the WiFi signal intensity values of the unknown acquisition positions and the WiFi signal intensity values of the fingerprint acquisition positions to obtain the WiFi signal intensity values of the set positions.
According to an embodiment of the disclosure, the disclosure further provides an electronic device, a readable storage medium.
Fig. 6 illustrates a schematic block diagram of an example electronic device 600 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 6, the apparatus 600 includes a computing unit 601 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 may also be stored. The computing unit 601, ROM 602, and RAM 603 are connected to each other by a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Various components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, mouse, etc.; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as the WiFi fingerprint library update method. For example, in some embodiments, the WiFi fingerprint library updating method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the WiFi fingerprint library updating method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the WiFi fingerprint library update method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (10)
1. A method for updating a WiFi fingerprint library, comprising:
acquiring WiFi signal intensity values of N acquisition positions in M positions in a WiFi fingerprint library, wherein M is an integer greater than 1, and N is a positive integer smaller than M;
creating a signal intensity distribution matrix based on the WiFi signal intensity values of the N acquisition positions;
generating signal distribution data based on the signal strength distribution matrix;
and updating the WiFi signal intensity values of the M positions in the WiFi fingerprint library based on the signal distribution data and the WiFi signal intensity values of the N acquisition positions.
2. The WiFi fingerprint library updating method according to claim 1, wherein prior to said generating signal distribution data based on said signal strength distribution matrix, the method further comprises:
a target acquisition position with a WiFi signal intensity value of 0 is arranged in the signal intensity distribution matrix, and the average value of the signal intensities of the left and right adjacent acquisition positions is used as the WiFi signal intensity value of the target acquisition position when at least one of the signal intensities of the left and right adjacent acquisition positions in the signal intensity distribution matrix is not 0;
and under the condition that at least one of the two adjacent acquisition positions in the signal intensity distribution matrix is not 0, taking the average value of the signal intensities of the two adjacent acquisition positions as the WiFi signal intensity value of the target acquisition position.
3. The WiFi fingerprint library updating method according to claim 1, wherein the generating signal distribution data based on the signal strength distribution matrix comprises:
Dividing the WiFi signal intensity values of the fingerprint acquisition positions into a plurality of groups according to a first rule according to the signal intensity distribution matrix, wherein the first rule comprises one of the following: if the number of lines in the signal intensity distribution matrix is greater than or equal to the number of columns, the WiFi signal intensity values of the fingerprint acquisition positions are grouped according to the columns; if the number of lines in the signal intensity distribution matrix is less than or equal to the number of columns, grouping the signal intensities of the fingerprint acquisition positions according to the lines;
acquiring first numerical information of each independent group in the plurality of groups, wherein the first numerical information comprises a maximum WiFi signal intensity value in each independent group, a minimum WiFi signal intensity value in the first H acquisition positions in each independent group and a minimum WiFi signal intensity value in the last K acquisition positions in each independent group, and H and K are integers larger than 1;
generating said signal profile data for each of said independent groups according to said first numerical information, said signal profile data comprising at least one of: the signal distribution is a Gaussian-like function monotonically decreasing part, the signal distribution is a Gaussian-like function monotonically increasing part, the signal distribution is a Gaussian-like function zero-tending part and the signal distribution is a Gaussian-like function part on two sides of the central axis.
4. The method of claim 3, wherein generating the signal distribution data for each individual group based on the first numerical information comprises:
if the difference between the maximum WiFi signal intensity value in each independent group and the minimum WiFi signal intensity value in the first H acquisition positions does not exceed a preset threshold value, the signal distribution of the independent groups is a Gaussian-like function monotonically decreasing part;
if the difference between the maximum WiFi signal intensity value in each independent group and the minimum WiFi signal intensity value in the last K acquisition positions is not more than a preset threshold value, the signal distribution of the independent group is a Gaussian-like function monotonically increasing part;
if the difference among the maximum WiFi signal intensity value in each independent group, the minimum WiFi signal intensity value in the first H acquisition positions and the minimum WiFi signal intensity value in the last K acquisition positions does not exceed a preset threshold value, the signals of the independent groups are distributed as a Gaussian-like function zero-approaching part;
if the difference between the maximum WiFi signal intensity value in each independent group, the minimum WiFi signal intensity value in the first H acquisition positions and the minimum WiFi signal intensity value in the last K acquisition positions exceeds a preset threshold, the signals of the independent groups are distributed as parts on two sides of the axis of the Gaussian-like function.
5. The WiFi fingerprint library updating method according to claim 1, wherein the determining a WiFi signal strength value for a set location based on the signal distribution data and the WiFi signal strength values for the plurality of fingerprint acquisition locations comprises:
determining a signal attenuation model of the signal intensity distribution matrix according to the signal distribution data, wherein the signal attenuation model comprises at least one of the following components: the system comprises a first attenuation model, a second attenuation model, a third attenuation model and a fourth attenuation model, wherein the first signal attenuation model is generated based on WiFi signal intensity values of fingerprint acquisition positions with signal distribution being a Gaussian-like function monotonically decreasing part; generating WiFi signal intensity values of fingerprint acquisition positions of which the signal distribution corresponding to the second signal attenuation model is a Gaussian-like function monotonically increasing part; generating WiFi signal intensity values of fingerprint acquisition positions of which the signal distribution corresponding to the third signal attenuation model is similar to the Gaussian function and tends to be zero; the corresponding signal distribution of the fourth signal attenuation model is generated as WiFi signal intensity values of fingerprint acquisition positions of two sides of the central axis of the Gaussian-like function;
and taking the WiFi signal intensity values of the fingerprint acquisition positions as input, and obtaining the WiFi signal intensity values of the set positions through corresponding attenuation models.
6. The method of updating a WiFi fingerprint library according to claim 5, wherein obtaining the WiFi signal strength values for the set location from the signal attenuation model using the WiFi signal strength values for the plurality of fingerprint acquisition locations as input includes:
determining a value interval and the number of unknown acquisition positions in the signal attenuation model, wherein the number of the unknown acquisition positions is the number of the set positions minus the number of the plurality of fingerprint acquisition positions;
determining an input value of the signal attenuation model according to the value interval and the number of the unknown acquisition positions;
substituting the input value of the signal attenuation model into the signal attenuation model to obtain a WiFi signal intensity value of an unknown acquisition position;
and combining the WiFi signal intensity values of the unknown acquisition positions and the WiFi signal intensity values of the fingerprint acquisition positions to obtain the WiFi signal intensity values of the set positions.
7. A WiFi fingerprint library updating apparatus, comprising:
the acquisition module is used for acquiring WiFi signal intensity values of N acquisition positions in M positions in the WiFi fingerprint library, M is an integer greater than 1, and N is a positive integer smaller than M;
The creating module is used for creating a signal intensity distribution matrix based on the WiFi signal intensity values of the N acquisition positions;
the generation module is used for generating signal distribution data based on the signal intensity distribution matrix;
and the updating module is used for updating the WiFi signal intensity values of the M positions in the WiFi fingerprint library based on the signal distribution data and the WiFi signal intensity values of the N acquisition positions.
8. The WiFi fingerprint library updating apparatus according to claim 7, wherein the apparatus further comprises:
the first determining module is used for determining that a target acquisition position with a WiFi signal intensity value of 0 exists in the signal intensity distribution matrix, and the average value of the signal intensities of the left and right adjacent acquisition positions is used as the WiFi signal intensity value of the target acquisition position when at least one of the signal intensities of the left and right adjacent acquisition positions in the signal intensity distribution matrix is not 0;
the second determining module is configured to determine, in the signal intensity distribution matrix, a target acquisition position having a WiFi signal intensity value of 0, where the two adjacent acquisition positions on the left and right in the signal intensity distribution matrix are both 0, and if at least one of the two adjacent acquisition positions on the upper and lower in the signal intensity distribution matrix is not 0, take a signal intensity average value of the two adjacent acquisition positions as the WiFi signal intensity value of the target acquisition position.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 6.
10. A non-transitory computer readable storage medium storing computer instructions, wherein the computer instructions are for causing the computer to perform the method of any one of claims 1 to 6.
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