CN115150744A - Indoor signal interference source positioning method for large conference venue - Google Patents
Indoor signal interference source positioning method for large conference venue Download PDFInfo
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- H—ELECTRICITY
- 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
- H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0252—Radio frequency fingerprinting
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- 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 a method for positioning an indoor signal interference source of a large conference venue, which comprises the following steps: acquiring a reference point off-line fingerprint database and a test point on-line fingerprint database; carrying out averaging processing, dimension reduction and decorrelation processing on the reference point offline fingerprint database and the test point online fingerprint database to obtain a corresponding reference point principal component database and a corresponding test point principal component database; obtaining the data similarity of the reference point principal component database and the test point principal component database based on the Pearson correlation coefficient, sequencing the data similarity, and selecting the coordinate of the reference point with the highest data similarity; and weighting the coordinates of the target test point based on the coordinates of the reference point with the highest data similarity to obtain the positioning coordinates of the target test point. The positioning result obtained by the positioning method of the invention is more practical and the positioning precision is also improved.
Description
Technical Field
The invention belongs to the field of indoor positioning, and particularly relates to a method for positioning an indoor signal interference source of a large conference venue.
Background
In recent years, with the deep application of communication technology, location positioning services have attracted much attention, and PL services have been applied to many important fields and occasions, such as positioning, disaster relief, security, navigation, and tracking. Meanwhile, the demand for indoor location services in the commercial and military fields has spawned many location technologies and systems for indoor applications. The well-known satellite navigation systems such as GPS and beidou can not provide indoor full-coverage location services, and the technologies on which indoor positioning can depend include wireless local area network, bluetooth, ultra-wideband, radio frequency identification technology, infrared rays, ultrasonic waves and the like. Each positioning technique may be applicable only to one or a few specific scenarios. In a large conference venue, because the WLAN has the advantages of wide deployment range, flexible installation, easy expansion, and the like, positioning of signal interference sources based on the WLAN has become a research hotspot in the field of location awareness.
Currently, there are many measurement quantities as the positioning characteristic parameters, such as Time of Arrival (Time of Arrival, toA), frequency difference of Arrival (FDoA), received Signal Strength (RSS), angle of Arrival (AoA), and combinations thereof. Each characteristic parameter has its advantages and disadvantages. ToA-based positioning algorithms rely heavily on signal propagation time synchronization between a wireless transmitter and an Access Point (AP). TDoA-based techniques may be used without synchronization between the wireless transmitter and the AP, but require timely synchronization of the reference nodes. Both TOA and TDoA based methods are only applicable to Line-of-Sight (LoS) positioning scenarios. In contrast, the AoA-based algorithm acquires the phase difference of the arriving signals through the array antenna, and converts the phase difference into the arriving angle, so that time synchronization is not required, and parameters such as transmitter power are not required. However, the positioning accuracy of AoA-based positioning techniques is susceptible to non-line-of-Sight (NLoS) positioning scenarios and multipath effects, and requires the use of expensive array antennas.
Currently, the RSS fingerprint positioning technology based on WLAN is usually adopted to solve the problem of positioning signal interference sources. However, due to various abnormal situations, such as equipment failure and malicious attacks, the received RSS data is not accurate or even wrong, and the positioning accuracy is seriously affected. Moreover, even if RSS data are accurately acquired, when the models of the acquisition terminal used for acquiring the RSS signal value in the online stage are inconsistent with those in the offline stage, the RSS signals acquired by the acquisition terminal and the RSS signal value in the offline stage are obviously different, so that a positioning result and an actual position generate a large deviation. That is, conventional RSS-based fingerprinting techniques fail to address heterogeneity. Although the existing researchers adopt a method of adjusting the RSS signal difference of two devices on line in real time, the calculation amount is large during operation, and the positioning time is long; researchers put forward that the RSS signal change characteristics among different devices have a linear relation, and the RSS difference of the heterogeneous devices can be corrected through a linear regression model, but a large amount of manpower and material resources are consumed for building the linear relation model for the devices of different models. Researchers also digitize the RSS values, so that the size difference of RSS signals among different devices can be reduced to a certain extent, but the heterogeneity cannot be fundamentally solved, and the positioning accuracy is not high.
Positioning techniques mostly rely on complex and diverse radio signals transmitted by radio transmitters. Accurate detection of an Unknown Radio Transmitter (URT) is important for preventing illegal occupation of radio signal resources in a large conference venue and protecting a communication system from harmful signal interference. At present, the following technical problems also exist for a fixed receiver and a positioning transmitter: the fingerprint database established by using a signal source with certain fixed emission intensity and frequency cannot perform online matching positioning of transmitters with different intensities and frequencies. Therefore, there is an urgent need to develop a positioning method to realize accurate detection of the radio transmitter.
Disclosure of Invention
The invention aims to provide a method for positioning an indoor signal interference source of a large conference venue, so as to solve the problems in the prior art.
In order to achieve the above object, the present invention provides a method for positioning an indoor signal interference source of a large conference venue, comprising the following steps:
acquiring a reference point off-line fingerprint database and a test point on-line fingerprint database;
carrying out averaging removal processing, dimension reduction and decorrelation processing on the reference point offline fingerprint database and the test point online fingerprint database to obtain corresponding reference point principal component databases and test point principal component databases;
obtaining the data similarity of the reference point principal component database and the test point principal component database based on the Pearson correlation coefficient, sequencing the data similarity, and selecting the coordinate of the reference point with the highest data similarity;
and weighting the coordinates of the target test point based on the coordinates of the reference point with the highest data similarity to obtain the positioning coordinates of the target test point.
Preferably, the process of obtaining the reference point offline fingerprint database includes:
dividing a positioning area and selecting a reference point to obtain the received signal strength of different receiving ends of the reference point;
performing pairwise difference processing on the received signal strengths of different receiving ends of the reference point to obtain the received signal strength difference of the reference point;
and constructing a reference point offline fingerprint database based on the received signal strength difference of the reference point.
Preferably, the process of acquiring the received signal strengths of the different receiving ends of the reference point includes:
based on a lognormal shadow model and a free space propagation model, the path loss index, the Gaussian random variable, the transmitting power of the radio transmitter, the antenna gain of a transmitting end, the antenna gains of different receiving ends, the wavelength of a carrier wave of the transmitter, the system loss factors of different receiving ends and the reference distance are processed to obtain the received signal strength of different receiving ends of a reference point.
Preferably, the process of obtaining the online fingerprint database of the test point includes:
dividing a test area and selecting test points to obtain the received signal strength of different receiving ends of the test points;
performing pairwise difference processing on the received signal strengths of different receiving ends of the test point to obtain the received signal strength difference of the test point;
and constructing a test point online fingerprint database based on the received signal strength difference of the test points.
Preferably, the process of obtaining the received signal strengths of different receiving ends of the test point includes:
based on a lognormal shadow model and a free space propagation model, the path loss index, the Gaussian random variable, the transmitting power of the radio transmitter, the antenna gain of a transmitting end, the antenna gains of different receiving ends, the wavelength of a carrier wave of the transmitter, the system loss factors of the different receiving ends and the test distance are processed to obtain the received signal strength of the different receiving ends of the test point.
Preferably, the process of performing the averaging processing, the dimension reduction and the decorrelation processing on the reference point offline fingerprint database and the test point online fingerprint database comprises the following steps:
based on a principal component analysis method, after carrying out mean value removal processing on the reference point off-line fingerprint database and the test point on-line fingerprint database, constructing corresponding covariance matrixes;
acquiring an initial eigenvalue of the covariance matrix;
arranging the initial characteristic values from large to small, acquiring corresponding characteristic vectors and storing the corresponding characteristic vectors into a first target matrix;
extracting a plurality of columns of the first target matrix and storing the columns of the first target matrix to a projection matrix;
performing orthogonalization processing on the projection matrix to obtain a second target matrix;
and projecting the reference point offline fingerprint database and the test point online fingerprint database to the second target matrix to obtain a corresponding reference point principal component database and a corresponding test point principal component database.
Preferably, the process of obtaining the data similarity of the reference point principal component database and the test point principal component database based on the pearson correlation coefficient includes:
acquiring Pearson correlation coefficients of each test point and all reference points based on corresponding mean values and standard deviations in the reference point principal component database and the test point principal component database; and in a preset value range, the larger the absolute value of the Pearson correlation coefficient is, the higher the data similarity is.
Preferably, the process of weighting the coordinates of the target test point includes:
and obtaining a received signal strength distance based on the received signal strength of the reference point and the received signal strength of the target test point, weighting the received signal strength distance as a weight to the coordinate of the target test point, and obtaining the positioning coordinate of the target test point. .
The invention has the technical effects that:
(1) The invention introduces the database constructed by the strength difference of the received signals without being influenced by the strength and the frequency of the radio transmitter, can adapt to the positioning of different unknown radio transmitters by only establishing an off-line database, solves the problem of the heterogeneity of the existing indoor positioning technology, and can accurately complete the positioning of unknown signal sources.
(2) The method uses the principal component analysis method to extract the principal component of the received signal intensity difference fingerprint database, and solves the problem of characteristic redundancy after increasing the data dimension of the received signal intensity difference fingerprint database; the invention also provides a method for judging the similarity of the offline and online fingerprint databases after PCA processing by using the Pearson correlation coefficient, which does not need to consider the data dimension and has higher positioning precision.
(3) In order to realize more accurate positioning on an unknown signal source, the main factors influencing the precision of the fingerprint positioning technology in practical application are considered, the invention selects the distances between the received signal strengths of a plurality of reference points and test points as weights on the basis of the Pearson correlation coefficient matching method, weights the coordinates of the selected reference points according to the distances, the positioning result after weighting is more practical, and the positioning precision is also improved.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
FIG. 1 is a system block diagram in an embodiment of the invention;
fig. 2 is a flowchart of a positioning method in an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
Example one
As shown in fig. 1-2, the present embodiment provides a method for positioning an indoor signal interference source of a large conference venue, including:
the positioning target studied in this embodiment is an unknown signal source, that is, information parameters such as the transmission intensity and frequency of a wireless transmitter are unknown, and the process of finding the transmitter by using a signal receiver is used. In this embodiment, the Received Signal Strength RSS Received Signal Strength information of two different receiving end AP receiving ends is subtracted to obtain a Received Signal Strength Difference (Received Signal Strength Difference), which is used as a characteristic parameter to establish a fingerprint database, so that the Difference of the Received Signal strengths RSS of heterogeneous devices can be solved, and the detection of an unknown radio transmitter URT can be realized.
Compared with a received signal strength RSS fingerprint database, the dimension of the received signal strength difference RSSD fingerprint database constructed by the embodiment is obviously increased, and correlation redundancy exists. The Principal Component Analysis (PCA) can compress and denoise data only by decomposing characteristic values, and is the most basic and widely applied data dimension reduction method. The mathematical nature of dimensionality reduction is to map a high-dimensional feature space to a low-dimensional feature space, and PCA aims to reduce the number of data set variables while preserving as much feature information as possible. PCA maps high-dimensional features (n-dimensional) onto a low-dimensional space (k-dimensional), new low-dimensional features are called principal components, are reconstructed by linear combination on the basis of the original high-dimensional features, have mutually orthogonal characteristics, and can eliminate mutual influence among variables of original data. The data required by the PCA is close to normal distribution, the RSSD fingerprint database constructed by the embodiment meets the application conditions of the PCA, the dimensionality reduction of the data features can be realized through the PCA, and the main components are extracted from the redundant features.
Conventional absolute distance measurement methods, such as euclidean distance, are related to the basic properties of the samples, i.e., the euclidean distance between two samples is related to the unit of measurement of the raw data. The samples after PCA processing have eliminated the dimensional influence between the features and are no longer suitable for the absolute distance measurement method. Common methods for measuring relative distance include chi-square distance, cosine similarity, pearson correlation coefficient, and the like. Chi-square distance is derived from chi-square test in statistics and is complex to apply specifically. Compared with cosine similarity, the Pearson correlation coefficient modifies component values by using the sample average value, reduces the influence of different sample offsets, and is an improvement of the cosine similarity. The embodiment provides that the similarity of the principal components of the two RSSD samples with the difference in the received signal strength is effectively measured by adopting the Pearson correlation coefficient, the measurement can better reflect the actual significance of a matching link, and a reference point is reasonably selected to obtain the positioning coordinate.
Due to the complexity of the signal propagation environment, the problem that the pearson similarity is high in some cases but the difference between the actual received signal strengths is large exists, and in order to solve the problem and further improve the positioning accuracy, the embodiment introduces weights, and the weighted coordinates are used as the final positioning result.
The positioning method proposed in this embodiment is denoted as "PCA-PW-KNN", and first obtains a relationship between a received signal strength difference RSSD and different radio transmitters, and the specific process includes:
the lognormal shadow model can accurately model the change of the received power in the indoor wireless propagation environment, as shown in formula (1):
wherein: p (d) represents the received signal strength of any point at a distance d from the AP of the receiving end; p (d) 0 ) Indicates the distance d from the AP of the receiving end 0 A received signal strength of a reference point of interest; alpha is a path loss index, and is 1.6-1.8 when a sight distance exists indoors, and is 4-6 when a shelter exists indoors; χ is a gaussian random variable obeying a mean value of 0 and a standard deviation of σ; from the free space propagation model, P (d) can be calculated using equation (2):
wherein: p is t Is the transmit power of the radio transmitter; g t Gain for the transmitting end antenna; g Receiving end AP λ is the wavelength of the transmitter carrier, L, for the antenna gain of the receiving end AP Receiving end AP Is the system loss factor.
From equation (2), it can be seen that the received signal strength RSS has a relationship with the hardware configuration of the receiving end AP, that is, the received signal strength RSS is affected by the device heterogeneity.
According to the lognormal shadow model, the received signal strength RSS of the p-th receiving end AP and the q-th receiving end AP can be expressed as:
according to the free space propagation model, the received signal strength RSS of the p-th receiving end AP and the q-th receiving end AP can be written as:
the received signal strength RSS of the p-th receiving end AP and the q-th receiving end AP is subtracted to obtain a received signal strength difference RSSD:
as can be seen from equation (7), the RSSD is not affected by the diversity of the radio transmitters.
Assuming that there are n receiving end APs in the positioning area, where n is usually greater than 3, for the same positioning target, the RSS (received signal strength) of every two receiving end APs are subtracted to form a group of RSSD (received signal strength difference), and each point in the offline fingerprint database corresponds to each other
This is done by comparing the RSSD data with the received signal strength, which significantly increases the original database dimension and provides redundancy of correlation. Therefore, it is necessary to extract the main component.
The positioning process of the embodiment includes:
an off-line stage: uniformly selecting reference points RP in a target positioning area, and collecting received signal strength RSS sample vectors of each RP position and each access point receiving end AP. The position coordinates of each reference point and the corresponding received signal strength RSS sample data form a position fingerprint. Then, all fingerprint information is stored in the database, as shown in table 1:
TABLE 1
Reference point coordinate mean value of received signal strength RSS samples acquired by reference point for M receiving end APs
Wherein the first subscript L in the received signal strength RSS represents the reference point serial number and the second subscript M represents the receiving end AP serial number.
The RSS data of the received signal strength of any two receiving end APs are subtracted to obtain the RSSD offline fingerprint database with the received signal strength difference as shown in table 2:
TABLE 2
The unprocessed RSSD off-line fingerprint database can be regarded as
Type matrix, corresponding to each RP
The present embodiment uses a PCA method to solve the problem that linear correlation between features is easy to occur in high-dimensional features, that is, redundant features exist, and the principle is as follows:
(1) The average value for each row in table 2 is calculated as shown in equation (8):
each row of data in table 2 was subjected to a de-equalization process:
wherein at least
For example, the following steps are carried out:
(2) Calculating RSSD * Covariance matrix C of (a):
(3) Solving the eigenvalue lambda and the eigenvector u of the covariance matrix C, arranging the lambda from large to small, and storing the corresponding eigenvector to a matrix A, wherein A is
A type matrix.
(4) Extracting the first a columns of A and storing the extracted columns in a projection matrix, an
I.e. retaining the eigenvectors corresponding to the first a largest eigenvalues, orthogonalizing the projection matrix and then writing the orthogonalized projection matrix as matrix B, where B is
A type matrix.
(5) RSSD * Projecting the off-line sample to a selected feature vector space B to realize dimension reduction of the off-line sample and obtain principal component features:
Z 1 ＝RSSD * ·B (12)
Z 1 for an L × a matrix, the principal component fingerprint database for obtaining the reference point RP is:
to be provided with
For example, the following steps are carried out:
therefore, the dimension reduction and decorrelation processing of the RSSD off-line fingerprint database is realized.
(II) an online stage: when a Test Point (TP) enters a Test area, obtaining a received signal strength RSS real-time measurement result of the unknown signal source relative to all receiving end APs, obtaining an unprocessed received signal strength difference RSSD online fingerprint database through subtraction, and performing de-equalization processing on the database, namely subtracting
Record as
And (3) performing dimension reduction on the online sample:
assuming that there are J test targets, the principal component fingerprint database for the test point TP is obtained as follows:
the data features after PCA dimension reduction are used as projection of the original features in the subspace, the change between the projection and the original features is large, the association degree of the off-line and on-line principal component fingerprint databases after dimension reduction cannot be reflected by using absolute distances (such as Euclidean distances), and the data features should be matched better by using relative distances. The present embodiment uses the pearson correlation coefficient to describe the similarity between two databases, and selects an appropriate reference point for calculating the TP coordinate.
Calculating the Pearson correlation coefficient of the TP and RP principal component fingerprint database according to equation (17):
wherein
Is a mean value, σ α 、σ β Is the standard deviation.
The Pearson correlation coefficient of each TP and all RPs can be obtained by calculation, the value range is from-1 to +1, 0 represents no correlation, negative values represent negative correlation, positive values represent positive correlation, and the larger the absolute value is, the stronger the correlation is. And selecting the coordinates of the K RPs with the maximum Pearson correlation coefficients for position estimation of the subsequent TP.
In order to solve the problem that the pearson similarity is high in some cases but the difference between the actual received signal strengths is large due to the complexity of the signal propagation environment, and further improve the positioning accuracy, the distance D between the received signal strength RSS of the selected K RP fingerprint coordinates and the received signal strength RSS of TP reception is calculated and used as the weight:
and weighting the RP coordinates to obtain weighted coordinates as a final positioning result, wherein the weighted coordinates are shown as the following formula:
existing indoor positioning techniques based on received signal strength RSS are unable to address the heterogeneity and the fingerprint database based on received signal strength RSS is dependent on the power and frequency of the radio transmitter. In the off-line database establishment stage, the databases are different due to the diversity of the radio transmitters, the off-line stage has a large workload, and the positioning of the unknown signal source cannot be accurately completed. None of the existing techniques for improving the received signal strength RSS fundamentally solves the above problems, and the present embodiment fundamentally solves the above drawbacks by introducing the received signal strength difference RSSD. The database based on the received signal strength difference RSSD is not influenced by the strength and frequency of the radio transmitter, and only an off-line database needs to be established to adapt to the positioning of different unknown radio transmitters.
In the research process, the problem of feature redundancy caused by the fact that data dimension is increased in the RSSD fingerprint database with the received signal strength difference is found, and for the embodiment, the PCA is used for achieving dimension reduction decorrelation and principal component extraction. The existing positioning matching algorithm is not suitable for similarity determination of an offline fingerprint database and an online fingerprint database after PCA dimension reduction, and the embodiment provides that a Pearson correlation coefficient is used for solving the problem. The Pearson correlation coefficient is optimized for the Euclidean distance, does not need to consider the data dimension, and has higher positioning precision.
In order to realize more accurate positioning for an unknown signal source, the embodiment takes the RSS (received signal strength) distances of the selected K RPs and the TP as weights on the basis of the pearson correlation coefficient matching method, and considers the contribution of the K most similar fingerprint data in estimating the positioning result, where the larger the distance is theoretically, the farther the fingerprint point is from the positioning point, the smaller the contribution of the fingerprint point to the positioning result is, and the positioning result obtained by directly taking the average value of the K fingerprint data is more practical according to the positioning result after weighting the distance, and the positioning accuracy is also improved.
The above description is only for the preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A method for positioning an indoor signal interference source of a large conference venue is characterized by comprising the following steps:
acquiring a reference point off-line fingerprint database and a test point on-line fingerprint database;
carrying out averaging processing, dimension reduction and decorrelation processing on the reference point off-line fingerprint database and the test point on-line fingerprint database to obtain a corresponding reference point principal component database and a corresponding test point principal component database;
obtaining the data similarity of the reference point principal component database and the test point principal component database based on the Pearson correlation coefficient, sequencing the data similarity, and selecting the coordinate of the reference point with the highest data similarity;
and weighting the coordinates of the target test point based on the coordinates of the reference point with the highest data similarity to obtain the positioning coordinates of the target test point.
2. The method of claim 1, wherein the positioning method of signal interference sources in the large conference venue is characterized in that,
the process of obtaining the reference point offline fingerprint database comprises the following steps:
dividing a positioning area and selecting a reference point to obtain the received signal strength of different receiving ends of the reference point;
performing pairwise difference processing on the received signal strengths of different receiving ends of the reference point to obtain the received signal strength difference of the reference point;
and constructing a reference point offline fingerprint database based on the received signal strength difference of the reference point.
3. The method of claim 2, wherein the positioning method of signal interference sources in the large conference venue is characterized in that,
the process of acquiring the received signal strength of different receiving ends of the reference point comprises the following steps:
based on a lognormal shadow model and a free space propagation model, the path loss index, the Gaussian random variable, the transmitting power of the radio transmitter, the antenna gain of a transmitting end, the antenna gains of different receiving ends, the wavelength of a carrier wave of the transmitter, the system loss factors of different receiving ends and the reference distance are processed to obtain the received signal strength of different receiving ends of a reference point.
4. The method of claim 1, wherein the positioning method of signal interference sources in the large conference venue is characterized in that,
the process of obtaining the online fingerprint database of the test points comprises the following steps:
dividing a test area and selecting test points to obtain the received signal strength of different receiving ends of the test points;
performing pairwise difference processing on the received signal strengths of different receiving ends of the test point to obtain the received signal strength difference of the test point;
and constructing an online fingerprint database of the test points based on the received signal strength difference of the test points.
5. The method of claim 4, wherein the positioning method of signal interference sources in the large conference venue is characterized in that,
the process of obtaining the received signal strength of different receiving ends of the test point comprises the following steps:
based on a lognormal shadow model and a free space propagation model, path loss indexes, gaussian random variables, the transmitting power of a radio transmitter, the antenna gain of a transmitting end, the antenna gains of different receiving ends, the wavelength of a carrier wave of the transmitter, system loss factors of different receiving ends and test distances are processed to obtain the received signal strength of different receiving ends of the test point.
6. The method of claim 1, wherein the positioning method of signal interference sources in the large conference venue is characterized in that,
the process of carrying out the averaging processing, the dimension reduction and the decorrelation processing on the reference point off-line fingerprint database and the test point on-line fingerprint database comprises the following steps:
based on a principal component analysis method, after carrying out mean value removal processing on the reference point off-line fingerprint database and the test point on-line fingerprint database, constructing corresponding covariance matrixes;
acquiring an initial eigenvalue of the covariance matrix;
arranging the initial characteristic values from large to small to obtain corresponding characteristic vectors and storing the corresponding characteristic vectors into a first target matrix;
extracting a plurality of columns of the first target matrix and storing the columns of the first target matrix to a projection matrix;
performing orthogonalization processing on the projection matrix to obtain a second target matrix;
and projecting the reference point offline fingerprint database and the test point online fingerprint database to the second target matrix to obtain a corresponding reference point principal component database and a corresponding test point principal component database.
7. The method of claim 1, wherein the positioning method of signal interference source in large conference venue is,
the process of obtaining the data similarity of the reference point principal component database and the test point principal component database based on the Pearson correlation coefficient comprises the following steps:
acquiring Pearson correlation coefficients of each test point and all reference points based on corresponding mean values and standard deviations in the reference point principal component database and the test point principal component database; and in a preset value range, the larger the absolute value of the Pearson correlation coefficient is, the higher the data similarity is.
8. The method of claim 1, wherein the positioning method of signal interference sources in the large conference venue is characterized in that,
the process of weighting the coordinates of the target test point comprises the following steps:
and obtaining a received signal strength distance based on the received signal strength of the reference point and the received signal strength of the target test point, and weighting the received signal strength distance to the coordinate of the target test point as a weight to obtain the positioning coordinate of the target test point.
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Cited By (3)
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|---|---|---|---|---|
| CN115438035A (en) * | 2022-10-27 | 2022-12-06 | 江西师范大学 | Data exception handling method based on KPCA and mixed similarity |
| CN116094628A (en) * | 2023-02-09 | 2023-05-09 | 恩平市力卡电子有限公司 | Wireless device monitoring system and method based on Internet of things |
| CN116801192A (en) * | 2023-05-30 | 2023-09-22 | 山东建筑大学 | Indoor electromagnetic fingerprint updating method and system by end cloud cooperation |
-
2022
- 2022-08-02 CN CN202210923307.3A patent/CN115150744A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115438035A (en) * | 2022-10-27 | 2022-12-06 | 江西师范大学 | Data exception handling method based on KPCA and mixed similarity |
| CN116094628A (en) * | 2023-02-09 | 2023-05-09 | 恩平市力卡电子有限公司 | Wireless device monitoring system and method based on Internet of things |
| CN116094628B (en) * | 2023-02-09 | 2023-11-21 | 恩平市力卡电子有限公司 | Wireless device monitoring system and method based on Internet of things |
| CN116801192A (en) * | 2023-05-30 | 2023-09-22 | 山东建筑大学 | Indoor electromagnetic fingerprint updating method and system by end cloud cooperation |
| CN116801192B (en) * | 2023-05-30 | 2024-03-12 | 山东建筑大学 | Indoor electromagnetic fingerprint updating method and system by end cloud cooperation |
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