CN109587627B - Indoor positioning method for improving terminal heterogeneity problem based on RSSI - Google Patents

Indoor positioning method for improving terminal heterogeneity problem based on RSSI Download PDF

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CN109587627B
CN109587627B CN201811515688.1A CN201811515688A CN109587627B CN 109587627 B CN109587627 B CN 109587627B CN 201811515688 A CN201811515688 A CN 201811515688A CN 109587627 B CN109587627 B CN 109587627B
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CN109587627A (en
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王结太
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Jiaxing University
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
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Abstract

The invention discloses an indoor positioning method for improving the problem of terminal heterogeneity based on RSSI, which comprises the following steps. Step (1): and determining each test point in the test space, and calculating the distance from each test point to each transmitting end. Step (2): the distance data obtained from each test point are combined identically, and the data in the number of combinations must be greater than or equal to 3. According to the indoor positioning method for improving the terminal heterogeneity problem based on the RSSI, disclosed by the invention, the values of A and n are not required to be known any more when the data is subjected to standardized processing, and the influence caused by equipment difference and environment difference is eliminated theoretically; the fingerprint data is obtained by processing the distance from the fingerprint coordinate point to each transmitting end, manual acquisition is not needed, and manual loss is reduced; the positioning speed is reduced through standardization processing, and the real-time positioning is guaranteed to a certain extent through advanced preparation of fingerprint data.

Description

Indoor positioning method for improving terminal heterogeneity problem based on RSSI
Technical Field
The invention belongs to the technical field of wireless signal positioning, and particularly relates to an indoor positioning method for improving the problem of terminal heterogeneity based on RSSI.
Background
Conventional RSSI-based indoor positioning algorithms such as fingerprint algorithms, maximum likelihood estimation algorithms, trilateration algorithms, minimum maximum algorithms, and the like. The RSSI ranging principle is used in these algorithms, and the relationship between the received signal strength of a wireless signal and the signal transmission distance is represented by equation (1), where RSSI is the received signal strength, d is the distance between the transmitting end and the receiving end, n is the signal propagation factor, and a is the absolute value of the signal strength received by the receiving end when the transmitting end and the receiving end are 1 meter apart.
Figure GDA0002894495130000011
As can be seen from equation (1), the values of constants a and n determine the relationship between the received signal strength and the signal transmission distance.
The maximum likelihood estimation algorithm, the trilateral location algorithm and the minimum maximum algorithm are based on the formula (1), under the condition that the signal transmission distance is determined, the corresponding algorithm is used for location, namely, under different environments and different terminal conditions, the value of A and n needs to be continuously debugged to achieve better location. The fingerprint algorithm is different from other three algorithms, and the matching between the RSSI is used, and the method comprises the following steps:
1. through a receiving terminal, each test point in a test space receives RSSI of each transmitting end in the space as fingerprint data;
2. receiving the RSSI of each sending end as test data at a certain position in a test space through a receiving terminal needing to be positioned;
3. and matching the test data with the fingerprint data by a k-nearest neighbor (knn) algorithm, wherein the test point which is matched to be the best is the positioning position.
The algorithm does not completely depend on the formula (1), is not influenced by complex environment, has high accuracy, but consumes manpower compared with other algorithms, and the fingerprint data depends on a receiving terminal, so that once the model of the receiving terminal is changed, the corresponding fingerprint data are different; or the environment when the fingerprint is acquired is changed, corresponding fingerprint data can be different. The invention improves data processing on the basis of fingerprint algorithm, and is not influenced by environment and different receiving terminals on the premise of not consuming manpower.
Disclosure of Invention
Aiming at the defects of the background art, the invention overcomes the defects and provides the indoor positioning method for improving the terminal heterogeneity problem based on the RSSI, the knn algorithm matching is carried out after the data are processed, the influence caused by A and n is effectively eliminated, and the positioning accuracy is improved.
The invention adopts the following technical scheme that the indoor positioning method based on the RSSI improved terminal heterogeneity problem comprises the following steps:
step (1): determining each test point in the test space, and calculating the distance from each test point to each transmitting end;
step (2): the same combination is carried out on a plurality of distance data obtained by each test point, and the data in the combination number is required to be more than or equal to 3;
and (3): selecting the same combination from each test point, and under the combination, firstly carrying out logarithmic processing on the distance data of each test point, then carrying out standardized processing on the distance data to obtain data serving as fingerprint data under the combination, wherein the processed data of each test point under each combination is used as the fingerprint data;
and (4): collecting signal intensity values of more than three sending ends at a position point needing positioning through a receiving end, and standardizing absolute values of the collected signal intensity values to be used as test data;
and (5): and comparing the sending end serial numbers corresponding to the test data, selecting the fingerprint data of the corresponding combination, and matching the test data and the fingerprint data by using a k-nearest neighbor algorithm, wherein the matched optimal position point is the solved positioning point.
According to the above technical solution, the distance in step (1) is d _ ij, where i is the ith location point, j is the jth sender, and d _ ij represents the distance from the ith location point to the jth sender.
According to the technical scheme, the concrete situation of combining the data in the step (2) is as follows: assuming that there are 4 receiving terminals in a certain test space, there are 4 distances d from each position point to each receiving terminal respectivelyi1，di2，di3，di4Wherein i is the ith position point, 1,2,3, 4 are the serial number of the transmitting terminal, and the following 5 combinations are obtained: (d)i1，di2，di3)，(di1，di2，di4)，(di1，di3，di4)，(di2，di3，di4)，(di1，di2，di3，di4)。
According to the technical scheme, the principle steps of standardizing the data in the step (3) are as follows:
step a, obtaining a distance formula of signal intensity:
Figure GDA0002894495130000031
and the value of a, n is fixed but unknown, then the right part of the equation is converted to a linear function:
Figure GDA0002894495130000032
wherein the content of the first and second substances,
Figure GDA0002894495130000033
are all constant values;
b, knowing the nature of mathematical expectation and variance, and a standardized algorithm formula;
mathematically desirable properties: if there is a random variable X, for any constant a, b, there are:
E(aX+b)＝aE(X)+b，
the nature of the variance: if there is a random variable X, for any constant a, b, there are:
Var(aX+b)＝a2Var(X)，
the corresponding standard deviations are:
Figure GDA0002894495130000034
a normalized algorithm formula:
Figure GDA0002894495130000041
wherein x isiAs raw data, ziIn order to be able to normalize the new data,
Figure GDA0002894495130000042
the average value of the original data is obtained, and s is the standard deviation of the original data;
step c, selecting the RSSI of 3 transmitting terminals obtained at a certain position as a group of data (RSSI)1，rssi2，rssi3) And connecting the linear function of the step a and the property of the step b and a standardized formula to obtain:
Figure GDA0002894495130000043
wherein m and s are the mean value and standard deviation of the set of RSSI data, i is 1,2, and 3;
and d, corresponding to the RSSI selected in the step c, obtaining:
Figure GDA0002894495130000044
Figure GDA0002894495130000045
according to the technical scheme, the fingerprint data source steps in the step (3) are as follows:
in a first step, a combination (d) is selected from 5 combinationsi1，di2，di3)；
Second, obtained after logarithmic treatment (log)10 di1，log10 di2，log10 di3)；
Thirdly, carrying out standardization processing on the data after logarithmic processing to obtain:
Figure GDA0002894495130000046
wherein dm and ds are (log)10 di1，log10 di2，log10 di3) Average and standard deviation of
Figure GDA0002894495130000047
As fingerprint data of the ith position point under the combination;
fourthly, repeating the second step and the third step for each position point in the test space to obtain the fingerprint data of each position point, and taking the fingerprint data as the fingerprint data of the whole test space under the combination;
and fifthly, repeating the fourth step for each combination to obtain the fingerprint data under each combination as final fingerprint data.
According to the technical scheme, the data matching step in the step (5) is as follows:
obtaining all fingerprint data;
(ii) receiving the RSSI of each sending end at a position point needing positioning, and carrying out standardization processing on the absolute values of the RSSI to obtain a new data set as test data;
finding fingerprint data under corresponding combination in all fingerprint data as offline data aiming at sending end serial numbers of received RSSI (received signal strength indicator) in (ii)
(iv) calculating the distance between the new data group and the test data under each coordinate point in the offline data;
(v) sorting according to the distance increasing order, and selecting k coordinate points with the minimum distance;
and (vi) selecting k as 1 under the method, and taking the coordinate point with the minimum distance between the offline data and the test data as the coordinate point obtained by positioning.
The indoor positioning method for improving the terminal heterogeneity problem based on the RSSI has the advantages that firstly, the values of A and n are not needed to be known any more by carrying out standardized processing on data, and the influence caused by equipment difference and environment difference is eliminated theoretically; secondly, the fingerprint data is obtained by processing the distance from the fingerprint coordinate point to each transmitting end, manual acquisition is not needed, and manual loss is reduced; in addition, the standardization process can reduce the positioning speed, and the fingerprint data is prepared in advance, so that the positioning real-time performance is guaranteed to a certain extent.
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FIG. 1 is a basic logic flow diagram of the present invention.
FIG. 2 is a schematic diagram of the offline fingerprint data of the present invention.
Fig. 3 is a schematic diagram of the average positioning error of different cases a under various algorithms of the present invention.
FIG. 4 is a graph illustrating the average positioning error for different cases n under various algorithms of the present invention.
Fig. 5 is a schematic diagram of the main steps of the present invention.
Detailed Description
The invention discloses an indoor positioning method for improving the problem of terminal heterogeneity based on RSSI, and the specific implementation of the invention is further described below by combining with the preferred embodiment.
Referring to fig. 1 to 5 of the drawings, fig. 1 shows a basic processing logic of the indoor positioning method for improving the terminal heterogeneity problem based on RSSI, fig. 2 shows offline fingerprint data of the indoor positioning method for improving the terminal heterogeneity problem based on RSSI, fig. 3 shows average positioning errors of algorithms a in different situations of the indoor positioning method for improving the terminal heterogeneity problem based on RSSI, fig. 4 shows average positioning errors of algorithms n in different situations of the indoor positioning method for improving the terminal heterogeneity problem based on RSSI, and fig. 5 shows main steps of the indoor positioning method for improving the terminal heterogeneity problem based on RSSI.
Preferably, according to the above preferred embodiment, the indoor positioning method for improving the problem of terminal heterogeneity based on RSSI includes the following steps:
step (1): determining each test point in the test space, and calculating the distance from each test point to each transmitting end;
step (2): the same combination is carried out on a plurality of distance data obtained by each test point, and the data in the combination number is required to be more than or equal to 3;
and (3): selecting the same combination from each test point, and under the combination, firstly carrying out logarithmic processing on the distance data of each test point, then carrying out standardized processing on the distance data to obtain data serving as fingerprint data under the combination, wherein the processed data of each test point under each combination is used as the fingerprint data;
and (4): collecting signal intensity values of more than three sending ends at a position point needing positioning through a receiving end, and standardizing absolute values of the collected signal intensity values to be used as test data;
and (5): and comparing the sending end serial numbers corresponding to the test data, selecting the fingerprint data of the corresponding combination, and matching the test data and the fingerprint data by using a k nearest neighbor (knn) algorithm, wherein the matched optimal position point is the solved positioning point.
Further onThe distance in the step (1) is dijWhere i is the ith location point, j is the jth sender, dijIndicating the distance from the ith location point to the jth sender.
Further, the specific case of combining the data in the step (2) is as follows: assuming that there are 4 receiving terminals in a certain test space, there are 4 distances d from each position point to each receiving terminal respectivelyi1，di2，di3，di4Wherein i is the ith position point, 1,2,3, 4 are the serial number of the transmitting terminal, and the following 5 combinations are obtained: (d)i1，di2，di3)，(di1，di2，di4)，(di1，di3，di4)，(di2，di3，di4)，(di1，di2，di3，di4)。
Further, the principle steps of normalizing the data in the step (3) are as follows:
step a, obtaining a distance formula of signal intensity:
Figure GDA0002894495130000071
and the value of a, n is fixed but unknown, then the right part of the equation is converted to a linear function:
Figure GDA0002894495130000072
wherein the content of the first and second substances,
Figure GDA0002894495130000073
are all constant values;
b, knowing the nature of mathematical expectation and variance, and a standardized algorithm formula;
mathematically desirable properties: if there is a random variable X, for any constant a, b, there are:
E(aX+b)＝aE(X)+b，
the nature of the variance: if there is a random variable X, for any constant a, b, there are:
Var(aX+b)＝a2Var(X)，
the corresponding standard deviations are:
Figure GDA0002894495130000074
a normalized algorithm formula:
Figure GDA0002894495130000075
wherein x isiAs raw data, ziIn order to be able to normalize the new data,
Figure GDA0002894495130000081
the average value of the original data is obtained, and s is the standard deviation of the original data;
step c, selecting the RSSI of 3 transmitting terminals obtained at a certain position as a group of data (RSSI)1，rssi2，rssi3) And connecting the linear function of the step a and the property of the step b and a standardized formula to obtain:
Figure GDA0002894495130000082
wherein m and s are the mean value and standard deviation of the set of RSSI data, i is 1,2, and 3;
and d, corresponding to the RSSI selected in the step c, obtaining:
Figure GDA0002894495130000083
Figure GDA0002894495130000084
at the same time, it can also be seen from step c that the data set is subjected to
Figure GDA0002894495130000085
Normalization and data set | rssiiThe new data from the normalization process is equal, again according to the four equations above. That is, for a data set (log)10d1，log10d2，log10d3，log10d4) Normalization and data set (| rssi)1|，|rssi2|，|rssi3|，|rssi4|) the new data from the normalization process is the same.
Further, the fingerprint data in step (3) is derived as follows:
in a first step, a combination (d) is selected from 5 combinationsi1，di2，di3)；
Second, obtained after logarithmic treatment (log)10 di1，log10 di2，log10 di3)；
Thirdly, carrying out standardization processing on the data after logarithmic processing to obtain:
Figure GDA0002894495130000086
wherein dm and ds are (log)10 di1，log10 di2，log10 di3) Average and standard deviation of
Figure GDA0002894495130000087
As fingerprint data of the ith position point under the combination;
fourthly, repeating the second step and the third step for each position point in the test space to obtain the fingerprint data of each position point, and taking the fingerprint data as the fingerprint data of the whole test space under the combination;
and fifthly, repeating the fourth step for each combination to obtain the fingerprint data under each combination as final fingerprint data.
Further, the data matching step in step (5) is as follows:
obtaining all fingerprint data;
(ii) receiving the RSSI of each sending end at a position point needing positioning, and carrying out standardization processing on the absolute values of the RSSI to obtain a new data set as test data;
finding fingerprint data under corresponding combination in all fingerprint data as offline data aiming at sending end serial numbers of received RSSI (received signal strength indicator) in (ii)
(iv) calculating the distance between the new data group and the test data under each coordinate point in the offline data;
(v) sorting according to the distance increasing order, and selecting k coordinate points with the minimum distance;
and (vi) selecting k as 1 under the method, and taking the coordinate point with the minimum distance between the offline data and the test data as the coordinate point obtained by positioning.
It should be noted that, according to a variation of the above preferred embodiment, the indoor positioning method for improving the terminal heterogeneity problem based on RSSI may further include the following steps:
step 1: interacting with the ap, acquiring ap and RSSI data corresponding to the terminal, and carrying out preprocessing operations including but not limited to RSSI filtering, grouping and the like;
step 2: and selecting the coordinates of the area where the ap is located according to the acquired ap information, and constructing the offline fingerprint data by using the method mentioned herein. The specific construction method is as follows:
(2.1) acquiring a test space area of the fingerprint to be constructed according to the ap coordinate, and gridding the test space area;
(2.2) for each grid, adopting the formula mentioned above, and carrying out a standardization process by using the distance from the central point of the grid to the AP to obtain offline fingerprint data;
and step 3: carrying out standardization processing on the RSSI data absolute value corresponding to the ap, then bringing the RSSI data absolute value into fingerprint information, carrying out KNN calculation, and obtaining a coordinate point with the minimum data distance;
and 4, step 4: and performing secondary processing on the obtained coordinates, including but not limited to path optimization, point location filtering and other operations, and outputting final coordinate values.
According to a variant of the above preferred embodiment, one of the algorithms described above is described as follows.
input for obtaining ap mac and RSSI value of received terminal signal
output coordinates of terminal
Figure GDA0002894495130000101
Figure GDA0002894495130000111
It should be noted that, in the above description of the steps, for convenience of description, the offline fingerprint construction operation is performed after the signal strength data is acquired, and since the offline fingerprint construction itself consumes resources, most of the offline fingerprint data that may be combined is often constructed in advance in an actual production environment, and a small part of the fingerprint data that cannot be cached is cached after being generated once. Thereby greatly improving the running speed of the algorithm. The final operating speed is comparable to that of the conventional positioning algorithm.
Meanwhile, since the average error of the maximum likelihood estimation method and the trilateration algorithm under different conditions of a and n is very large, the two algorithms are not shown and compared in fig. 3 and 4. It can be seen from fig. 3 and 4 that the indoor positioning method for improving the problem of terminal heterogeneity based on RSSI has little change in the overall average positioning error under different conditions a and n, and effectively reduces errors caused by different environments and different devices.
It will be apparent to those skilled in the art that modifications and equivalents may be made in the embodiments and/or portions thereof without departing from the spirit and scope of the present invention.

Claims (1)

1. An indoor positioning method for improving the problem of terminal heterogeneity based on RSSI (received signal strength indicator) is characterized by comprising the following steps:
step (1): determining each test point in the test space, and calculating the distance from each test point to each transmitting end;
step (2): the same combination is carried out on a plurality of distance data obtained by each test point, and the data in the combination number is required to be more than or equal to 3;
and (3): selecting the same combination from each test point, and under the combination, firstly carrying out logarithmic processing on the distance data of each test point, then carrying out standardized processing on the distance data to obtain data serving as fingerprint data under the combination, wherein the processed data of each test point under each combination is used as the fingerprint data;
and (4): collecting signal intensity values of more than three sending ends at a position point needing positioning through a receiving end, and standardizing absolute values of the collected signal intensity values to be used as test data;
and (5): comparing the serial numbers of the sending ends corresponding to the test data, selecting the fingerprint data of corresponding combination, and matching the test data and the fingerprint data by using a k-nearest neighbor algorithm, wherein the matched optimal position point is the solved positioning point;
the distance in the step (1) is dijWhere i is the ith location point, j is the jth sender, dijRepresenting the distance from the ith position point to the jth transmitting end;
the concrete situation of combining the data in the step (2) is as follows: assuming that there are 4 receiving terminals in a certain test space, there are 4 distances d from each position point to each receiving terminal respectivelyi1，di2，di3，di4Wherein i is the ith position point, 1,2,3, 4 are the serial number of the transmitting terminal, and the following 5 combinations are obtained: (d)i1，di2，di3)，(di1，di2，di4)，(di1，di3，di4)，(di2，di3，di4)，(di1，di2，di3，di4)；
The data matching step in the step (5) is as follows:
(i) obtaining all fingerprint data;
(ii) receiving the RSSI of each sending end at a position point needing positioning, and carrying out standardization processing on the absolute values of the RSS workers to obtain a new data set as test data;
(iii) finding fingerprint data under corresponding combination in all fingerprint data as offline data aiming at the sending end serial number of the received RSS worker
(iv) Calculating the distance between the new data set and the test data under each coordinate point in the offline data;
(v) sorting according to the distance increasing order, and selecting k coordinate points with the minimum distance;
(vi) selecting a coordinate point with the minimum distance between the off-line data and the test data as a coordinate point obtained by positioning, wherein k is 1;
the principle steps of standardizing the data in the step (3) are as follows:
step a, obtaining a distance formula of signal intensity:
Figure FDA0002905270850000021
and the value of A, n is fixed but unknown, A is the absolute value of the signal strength received by the receiving end when the transmitting end and the receiving end are separated by 1 meter, n is the signal propagation factor, then the right part of the equation is converted into a linear function:
Figure FDA0002905270850000022
wherein the content of the first and second substances,
Figure FDA0002905270850000023
are all constant values;
b, knowing the nature of mathematical expectation and variance, and a standardized algorithm formula;
mathematically desirable properties: if there is a random variable X, for any constant a, b, there are:
E(aX+b)＝aE(X)+b，
the nature of the variance: if there is a random variable X, for any constant a, b, there are:
Var(aX+b)＝a2Var(X)，
the corresponding standard deviations are:
Figure FDA0002905270850000031
a normalized algorithm formula:
Figure FDA0002905270850000032
wherein x isiAs raw data, ziIn order to be able to normalize the new data,
Figure FDA0002905270850000033
the average value of the original data is obtained, and s is the standard deviation of the original data;
step c, selecting the RSSI of 3 transmitting terminals obtained at a certain position as a group of data (RSSI)1，rssi2，rssi3) And connecting the linear function of the step a and the property of the step b and a standardized formula to obtain:
Figure FDA0002905270850000034
wherein m and s are the mean value and standard deviation of the set of RSSI data, i is 1,2, and 3;
the fingerprint data source in the step (3) comprises the following steps:
in a first step, a combination (d) is selected from 5 combinationsi1，di2，di3)；
Second, obtained after logarithmic treatment (log)10di1，log10di2，log10di3)；
Thirdly, carrying out standardization processing on the data after logarithmic processing to obtain:
Figure FDA0002905270850000035
wherein dm and ds are (log)10di1，log10di2，log10di3) Average and standard deviation of
Figure FDA0002905270850000036
As fingerprint data of the ith position point under the combination;
fourthly, repeating the second step and the third step for each position point in the test space to obtain the fingerprint data of each position point, and taking the fingerprint data as the fingerprint data of the whole test space under the combination;
and fifthly, repeating the fourth step for each combination to obtain the fingerprint data under each combination as final fingerprint data.
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