CN109587627A - The indoor positioning algorithms of terminal heterogeneity are improved based on RSSI - Google Patents

The indoor positioning algorithms of terminal heterogeneity are improved based on RSSI Download PDF

Info

Publication number
CN109587627A
CN109587627A CN201811515688.1A CN201811515688A CN109587627A CN 109587627 A CN109587627 A CN 109587627A CN 201811515688 A CN201811515688 A CN 201811515688A CN 109587627 A CN109587627 A CN 109587627A
Authority
CN
China
Prior art keywords
data
rssi
test
combination
distance
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201811515688.1A
Other languages
Chinese (zh)
Other versions
CN109587627B (en
Inventor
王结太
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jiaxing University
Original Assignee
Jiaxing University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Jiaxing University filed Critical Jiaxing University
Priority to CN201811515688.1A priority Critical patent/CN109587627B/en
Publication of CN109587627A publication Critical patent/CN109587627A/en
Application granted granted Critical
Publication of CN109587627B publication Critical patent/CN109587627B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • 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
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/318—Received signal strength

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Collating Specific Patterns (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The invention discloses a kind of indoor positioning algorithms that terminal heterogeneity is improved based on RSSI, include the following steps.Step (1): determining each test point in the test space, calculates each test point to each distance for sending terminal.Step (2): doing identical combination to multiple range data that each test point obtains, and the data in number of combinations must be greater than being equal to 3.The indoor positioning algorithms disclosed by the invention that terminal heterogeneity is improved based on RSSI are made standardized processing to data and are no longer needed for knowing A, and the value of n theoretically eliminates the need for influencing brought by equipment difference and environmental difference；Finger print data is dealt with by the distance to fingerprint coordinate points to each transmitting terminal, is not needed manually to acquire, is reduced artificial loss；Standardization can drag down the speed of positioning, and finger print data ensure that the real-time of positioning by preparation in advance to a certain extent.

Description

Indoor positioning algorithm 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 algorithm 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.
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 an indoor positioning algorithm for improving the terminal heterogeneity problem based on RSSI, and the influence caused by A and n is effectively eliminated and the positioning accuracy is improved by processing data and then matching knn algorithm.
The invention adopts the following technical scheme that the indoor positioning algorithm 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 sending terminal;
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): acquiring signal intensity values of three or more transmitting ends at a position point to be positioned through a receiving end, and standardizing absolute values of the acquired 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:and the value of a, n is fixed but unknown, then the right part of the equation is converted to a linear function:
wherein,are all usualA numerical value;
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:
a normalized algorithm formula:
wherein x isiAs raw data, ziIn order to be able to normalize the new data,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 4 transmitting terminals obtained at a certain position as a group of data (RSSI)1，rssi2，rssi3，rssi4) And connecting the linear function of the step a and the property of the step b and a standardized formula to obtain:
wherein m and s are the mean and standard deviation of the set of RSSI data, respectively, and i is 1,2,3, 4;
and d, corresponding to the RSSI selected in the step c, obtaining:
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 combinations (for the case of claim 3)i1，di2，di3)；
Second, obtained after logarithmic treatment (log)10di1，log10di2，log10di3)；
Thirdly, carrying out standardization processing on the data after logarithmic processing to obtain:
wherein dm and ds are (log)10di1，log10di2，log10di3) Average and standard deviation ofAs 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 according to claim 5;
(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 algorithm 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.
Drawings
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 algorithm 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 algorithm for improving the terminal heterogeneity problem based on RSSI, fig. 2 shows offline fingerprint data of the indoor positioning algorithm for improving the terminal heterogeneity problem based on RSSI, fig. 3 shows average positioning errors of a algorithms of the indoor positioning algorithm for improving the terminal heterogeneity problem based on RSSI in different situations, fig. 4 shows average positioning errors of n algorithms of the indoor positioning algorithm for improving the terminal heterogeneity problem based on RSSI in different situations, and fig. 5 shows main steps of the indoor positioning algorithm for improving the terminal heterogeneity problem based on RSSI.
Preferably, according to the above preferred embodiment, the indoor positioning algorithm for improving the problem of terminal heterogeneity based on RSSI comprises the following steps:
step (1): determining each test point in the test space, and calculating the distance from each test point to each sending terminal;
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): acquiring signal intensity values of three or more transmitting ends at a position point to be positioned through a receiving end, and standardizing absolute values of the acquired 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, the distance in 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:and the value of a, n is fixed but unknown, then the right part of the equation is converted to a linear function:
wherein,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:
a normalized algorithm formula:
wherein x isiAs raw data, ziIn order to be able to normalize the new data,the average value of the original data is obtained, and s is the standard deviation of the original data;
c, selecting a certainOne location obtains the RSSI of 4 transmitters as a set of data (RSSI)1，rssi2，rssi3，rssi4) And connecting the linear function of the step a and the property of the step b and a standardized formula to obtain:
wherein m and s are the mean and standard deviation of the set of RSSI data, respectively, and i is 1,2,3, 4;
and d, corresponding to the RSSI selected in the step c, obtaining:
at the same time, it can also be seen from step c that the data set is subjected toNormalization 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 combinations (for the case of claim 3)i1，di2，di3)；
Second, obtained after logarithmic treatment (log)10di1，log10di2，log10di3)；
Thirdly, carrying out standardization processing on the data after logarithmic processing to obtain:
wherein dm and ds are (log)10di1，log10di2，log10di3) Average and standard deviation ofAs 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 according to claim 5;
(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 algorithm 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.
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 algorithm 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 (6)

1. An indoor positioning algorithm 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 sending terminal;
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): acquiring signal intensity values of three or more transmitting ends at a position point to be positioned through a receiving end, and standardizing absolute values of the acquired 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.
2. The indoor positioning algorithm for improving terminal heterogeneity problem based on RSSI of claim 1, wherein the distance in step (1) is d _ ij, where i is the ith position point, j is the jth sender, and d _ ij represents the distance from the ith position point to the jth sender.
3. The indoor positioning algorithm for improving terminal heterogeneity problem based on RSSI as claimed in claim 2, wherein the step (2) combines the data 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)。
4. The indoor positioning algorithm for improving terminal heterogeneity problem based on RSSI of claim 3, wherein the principle steps of normalizing the data in step (3) are as follows:
step a, obtaining a distance formula of signal intensity:and the value of a, n is fixed but unknown, then the right part of the equation is converted to a linear function:
wherein,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:
a normalized algorithm formula:
wherein x isiAs raw data, ziIn order to be able to normalize the new data,the average value of the original data is obtained, and s is the standard deviation of the original data;
c, selecting RSSI of 4 transmitting terminals obtained at a certain position as a group of numbersAccording to (rssi)1，rssi2，rssi3，rssi4) And connecting the linear function of the step a and the property of the step b and a standardized formula to obtain:
wherein m and s are the mean and standard deviation of the set of RSSI data, respectively, and i is 1,2,3, 4;
and d, corresponding to the RSSI selected in the step c, obtaining:
5. the indoor positioning algorithm for improving terminal heterogeneity problem based on RSSI of claim 3, wherein the fingerprint data source step in step (3) is as follows:
in a first step, a combination (d) is selected from 5 combinations (for the case of claim 3)i1，di2，di3)；
Second, obtained after logarithmic treatment (log)10di1，log10di2，log10di3)；
Thirdly, carrying out standardization processing on the data after logarithmic processing to obtain:
wherein dm and ds are (log)10di1，log10di2，log10di3) Average and standard deviation ofAs 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.
6. The indoor positioning algorithm for improving terminal heterogeneity problem based on RSSI of claim 3, wherein the data matching step in step (5) is as follows:
obtaining all fingerprint data according to claim 5;
(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.
CN201811515688.1A 2018-12-12 2018-12-12 Indoor positioning method for improving terminal heterogeneity problem based on RSSI Expired - Fee Related CN109587627B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201811515688.1A CN109587627B (en) 2018-12-12 2018-12-12 Indoor positioning method for improving terminal heterogeneity problem based on RSSI

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811515688.1A CN109587627B (en) 2018-12-12 2018-12-12 Indoor positioning method for improving terminal heterogeneity problem based on RSSI

Publications (2)

Publication Number Publication Date
CN109587627A true CN109587627A (en) 2019-04-05
CN109587627B CN109587627B (en) 2021-04-06

Family

ID=65929152

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811515688.1A Expired - Fee Related CN109587627B (en) 2018-12-12 2018-12-12 Indoor positioning method for improving terminal heterogeneity problem based on RSSI

Country Status (1)

Country Link
CN (1) CN109587627B (en)

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011019125A1 (en) * 2009-08-12 2011-02-17 한국과학기술원 Participatory place recognition method and method for operating a wireless terminal using a wireless lan received signal strength indicator
CN102625447A (en) * 2012-03-14 2012-08-01 东南大学 Heuristic node localization method in wireless sensor network
CN103533650A (en) * 2013-10-28 2014-01-22 哈尔滨工业大学 Cosine-similarity-based indoor positioning method capable of improving positioning precision
CN103582119A (en) * 2013-10-12 2014-02-12 上海交通大学 Fingerprint database construction method of WiFi indoor positioning system
CN103945332A (en) * 2014-04-28 2014-07-23 清华大学 Received signal strength and multi-path information combined neural network indoor positioning method
CN103997717A (en) * 2014-06-12 2014-08-20 福建师范大学 Real-time indoor positioning system and method
CN104703128A (en) * 2014-11-10 2015-06-10 浙江大学城市学院 Indoor positioning system and method based on WLAN wireless signal strength
CN106658704A (en) * 2016-11-23 2017-05-10 上海斐讯数据通信技术有限公司 Positioning method and system of starting point of indoor positioning
CN106804059A (en) * 2017-01-18 2017-06-06 南京邮电大学 Terminal difference removing method in indoor positioning technologies based on signal intensity
CN107948930A (en) * 2017-12-31 2018-04-20 电子科技大学 Indoor positioning optimization method based on location fingerprint algorithm
EP3396400A1 (en) * 2017-04-27 2018-10-31 Deutsche Telekom AG A system and method for clustering wi-fi fingerprints for indoor-outdoor detection

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011019125A1 (en) * 2009-08-12 2011-02-17 한국과학기술원 Participatory place recognition method and method for operating a wireless terminal using a wireless lan received signal strength indicator
CN102625447A (en) * 2012-03-14 2012-08-01 东南大学 Heuristic node localization method in wireless sensor network
CN103582119A (en) * 2013-10-12 2014-02-12 上海交通大学 Fingerprint database construction method of WiFi indoor positioning system
CN103533650A (en) * 2013-10-28 2014-01-22 哈尔滨工业大学 Cosine-similarity-based indoor positioning method capable of improving positioning precision
CN103945332A (en) * 2014-04-28 2014-07-23 清华大学 Received signal strength and multi-path information combined neural network indoor positioning method
CN103997717A (en) * 2014-06-12 2014-08-20 福建师范大学 Real-time indoor positioning system and method
CN104703128A (en) * 2014-11-10 2015-06-10 浙江大学城市学院 Indoor positioning system and method based on WLAN wireless signal strength
CN106658704A (en) * 2016-11-23 2017-05-10 上海斐讯数据通信技术有限公司 Positioning method and system of starting point of indoor positioning
CN106804059A (en) * 2017-01-18 2017-06-06 南京邮电大学 Terminal difference removing method in indoor positioning technologies based on signal intensity
EP3396400A1 (en) * 2017-04-27 2018-10-31 Deutsche Telekom AG A system and method for clustering wi-fi fingerprints for indoor-outdoor detection
CN107948930A (en) * 2017-12-31 2018-04-20 电子科技大学 Indoor positioning optimization method based on location fingerprint algorithm

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
JIE-TAI WANG: "A Parameter-Irrelevant Range-Based Normalized Wireless Positioning Algorithm", 《2018 11TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID)》 *

Also Published As

Publication number Publication date
CN109587627B (en) 2021-04-06

Similar Documents

Publication Publication Date Title
CN106851573B (en) Log path loss model-based joint weighting K nearest neighbor indoor positioning method
CN107948930B (en) Indoor positioning optimization method based on position fingerprint algorithm
CN106717082B (en) Fingerprint-based indoor positioning to mitigate signal noise
CN107703480B (en) Mixed kernel function indoor positioning method based on machine learning
CN103916820B (en) Wireless indoor location method based on access point stability
CN109951807A (en) Fusion RSS and CSI indoor orientation method based on WiFi signal
CN105491661B (en) Indoor locating system and method based on improved Kalman filter algorithm
CN105657823B (en) WIFI indoor weighted K nearest neighbor positioning algorithm based on kernel function main feature extraction
CN104869641B (en) Wi Fi indoor orientation methods based on AP optimizations
CN105120479B (en) The signal intensity difference modification method of terminal room Wi-Fi signal
CN114330583B (en) Abnormal electricity utilization identification method and abnormal electricity utilization identification system
CN109195110B (en) Indoor positioning method based on hierarchical clustering technology and online extreme learning machine
CN110139359B (en) Interference source positioning processing method and device
CN112748397A (en) UWB positioning method based on self-adaptive BP neural network under non-line-of-sight condition
CN109348416B (en) Fingerprint indoor positioning method based on binary k-means
CN111225334A (en) Positioning method, terminal and computer readable storage medium
CN114757224A (en) Specific radiation source identification method based on continuous learning and combined feature extraction
CN108680897B (en) Indoor positioning method and device, electronic equipment and storage medium
CN109587627B (en) Indoor positioning method for improving terminal heterogeneity problem based on RSSI
CN110751201B (en) SAR equipment task failure cause reasoning method based on textural feature transformation
CN111654808B (en) Method and system for updating fingerprint database and wifi positioning method and system
CN110990383A (en) Similarity calculation method based on industrial big data set
CN116362328A (en) Federal learning heterogeneous model aggregation method based on fairness characteristic representation
CN114459482B (en) Boundary simplification method, path planning method, device, equipment and system
Lei et al. An improved WiFi fingerprint location method for indoor positioning

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20210406

CF01 Termination of patent right due to non-payment of annual fee