CN104635206B - A kind of method and device of wireless location - Google Patents
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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
- G01S5/0278—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 involving statistical or probabilistic considerations
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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
- G01S5/14—Determining absolute distances from a plurality of spaced points of known location
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- H—ELECTRICITY
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Abstract
The present invention provides a kind of method and device of wireless location, and this method includes：Acquire the received signal strength of multiple known reference points respectively on tested point；The valued space of tested point position is estimated according to the received signal strength；The tested point position valued space that estimation obtains is divided into multiple equal-sized grids；Corresponding Pearson product-moment correlation coefficient is solved, and determines minimum Pearson product-moment correlation coefficient ρL, by the ρLLocational space of the grid as tested point where corresponding position.The wireless location of degree of precision can be realized with lower computation complexity through the invention.
Description
Technical Field
The present invention relates to the field of communications, and in particular, to a method and an apparatus for wireless positioning.
Background
Because of the availability and low cost of Received Signal Strength (RSS) information, RSS-based wireless positioning is gaining widespread interest and widespread use. RSS based positioning can be generally classified into two broad categories: position fingerprint identification and triangle positioning. The former needs to establish a database in advance and update the database along with environmental changes, and the cost of establishing and maintaining the database is high, so that the database is mostly used in laboratories, buildings and the like at present and is not popularized in a large range. The distance between the point to be measured and a known reference point is calculated through a path loss model, and then triangular positioning is carried out according to the position of the known reference point and the estimated distance. The scheme based on the triangular positioning is simple and easy to implement, and has been widely applied to the fields of commerce, scientific research and the like. However, since wireless signals are highly susceptible to environmental changes, estimation of unknown path loss models is difficult, thus affecting the use of triangular positioning schemes.
In order to solve the RSS-based triangle positioning problem under the unknown path loss model, the following solutions are proposed in the existing research: [1] modeling a joint estimation problem of path loss index and position into a nonlinear optimization problem, and solving the problem based on a Levenberg-Marquardt (Levenberg-Marquardt method) algorithm; [2] defining distance compatibility based on a root axis, dynamically estimating a path loss index by maximizing the distance compatibility, and further performing triangle algorithm positioning by using the path loss index; [3] performing joint estimation modeling on the path loss index and the position to form a nonlinear optimization problem, and solving by adopting a Gaussian-Seidel (Gauss-Seidel) algorithm; [4] simplifying the complexity of the Lavenberg-Marquardt implementation in [1] by reducing the dimension of the jacobian matrix; [5] the original joint estimation problem of the path loss index and the position containing 3 variables is converted into a univariate optimization problem to be solved through linear path loss model processing, and the obtained value is used as an initial value to be substituted into a scheme [4] to further improve the positioning accuracy.
Although the five schemes can solve the positioning problem under the unknown path loss model, the five schemes also have respective defects. [1] The computation is complex and the result is limited by the choice of initial values; [2] the distance compatibility in (1) is easy to generate errors under a noise channel, so that wrong path loss indexes and position estimation are caused; [3] the nonlinear Gaussian-Seidel algorithm in the method does not ensure that the global optimal solution is output in the non-convex optimization problem, and the result also depends on the selection of a proper initial value; [4] although the application of [1] is simplified, the complexity is still not low, and the problem that the [1] result is limited to the initial value is inherited; [5] the nonlinear path loss model is subjected to linearization processing, details are lost, errors are introduced, and complexity is low.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a wireless positioning method and device, which can realize high-precision wireless positioning with low calculation complexity.
In order to solve the above technical problem, the present invention provides a wireless positioning method, including:
respectively acquiring the received signal strength of a plurality of known reference points on a point to be measured;
estimating a value space of a position to be measured according to the received signal strength;
dividing the position value space of the point to be measured obtained by estimation into a plurality of grids with equal size;
solving corresponding Pearson product-moment correlation coefficient and determining the minimum Pearson product-moment correlation coefficient rhoLWill be the rhoLAnd taking the grid where the corresponding position is as the position space of the point to be measured.
Further, the method also has the following characteristics: the step of dividing the estimated value space of the positions of the points to be measured into a plurality of grids with equal size comprises the following steps:
dividing the position value space of the point to be measured obtained by estimation into m equal-size areas with the area of s2m2Square grid of (2), with the central position of each gridThe selection of s as possible values of all positions of the point to be measured is determined by the expected iteration times and the positioning accuracy requirement.
Further, the method also has the following characteristics: solving corresponding Pearson product moment correlation coefficient rhol(l =1,2, …, m) is achieved by:
wherein, kifor the number of received signal strength measurements from the ith reference point, ri,jFor the jth received signal strength received from the ith reference point, j =1,2, …, ki；
(xi,yi) I =1,2, … N, N ≧ 3, for the location of the known reference point.
Further, the method also has the following characteristics: the grid corresponding positionObtained by the following formula:
in order to solve the above problem, the present invention further provides a wireless positioning apparatus, including:
the acquisition module is used for respectively acquiring the received signal strength of a plurality of known reference points on the point to be measured;
the estimation module is used for estimating the value space of the position to be measured according to the received signal strength;
the dividing module is used for dividing the position value space of the point to be measured, which is obtained by estimation, into a plurality of grids with equal size;
the processing module is used for solving the corresponding Pearson product moment correlation coefficient and determining the minimum Pearson product moment correlation coefficient rhoLWill be the rhoLAnd taking the grid where the corresponding position is as the position space of the point to be measured.
Further, the device also has the following characteristics:
the dividing module is specifically used for estimating the obtained point to be measuredThe position value space is divided into m equal-size areas of s2m2Square grid of (2), with the central position of each gridThe selection of s as possible values of all positions of the point to be measured is determined by the expected iteration times and the positioning accuracy requirement.
Further, the device also has the following characteristics:
the solving module is used for solving the corresponding Pearson product moment correlation coefficient rho through the following formulal(l=1,2,…,m)，
Wherein,
kifor the number of received signal strength measurements from the ith reference point, ri,jFor the jth received signal strength received from the ith reference point, j =1,2, …, ki；(xi,yi) I =1,2, … N, N ≧ 3, for the location of the known reference point.
Further, the device also has the following characteristics: the rhoLCorresponding positionObtained by the following formula:
the invention provides a wireless positioning method and device, which can realize high-precision wireless positioning with low calculation complexity.
Drawings
Fig. 1 is a flowchart of a method for wireless positioning according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a wireless positioning apparatus according to an embodiment of the present invention;
FIG. 3 is a graph showing the results of the example of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.
In the embodiment of the invention, RSS information from N (N is more than or equal to 3) known reference points is measured on the point to be measured, and the position of the point to be measured is estimated by utilizing the RSS information. Assuming that the target node is located at (x, y); the known position of the reference point is (x)i,yi) (i =1,2, … N); the number of RSS measurements from the i-th reference point is kiThe jth RSS received from the ith reference point is ri,j(j=1,2,…,ki). From a path loss model of the radio propagation, ri,jCan be given by:
where α is the power received at a distance of 1m from the transmitting antenna, n is the path loss exponent, and the value range of this value is usually within the limits of the existing studiesComprises the following steps: n is more than or equal to 2 and less than or equal to 5; omegajRepresenting shadow fading, it is generally assumed in wireless transmission model studies that it follows a gaussian distribution, such as ωj～N(0,σ2)。
Is provided withR is abovei,jThe expression can be simplified as: r isi,j=α-10nβi+ωj. Accordingly, r can be easily foundi,jand betaiThe correlation is linear. Further, n is not less than 2 and not more than 5, ri,jand betaiAnd is linearly inversely correlated.
The degree of correlation between two variables can be described in general by the pearson product-moment correlation coefficient p. The value range of the Pearson product moment correlation coefficient is-1 ≤ rho ≤ 1, and rho >0 represents positive correlation of two variables, i.e. one variable increases with the increase of the other variable; ρ <0 denotes that the two variables are inversely related, i.e., one variable decreases as the other increases; ρ =0 indicates that the two variables are not linearly related; ρ = ± 1 means that the two variables can be described by a good linear equation, i.e. the values of both variables fall on the same straight line.
Because the accurate position estimation of the point to be measured meets the expression ri,j=α-10nβi+ωjI.e. the accurate position estimate should be such that ri,jand betaiThe linear negative correlation relation is satisfied, so the estimation problem of the position of the point to be measured can be converted into the minimization problem of solving the correlation coefficient of the Pearson product moment as follows:
namely:wherein,
on the basis of establishing the model, the embodiment of the invention provides a simple and feasible algorithm to solve the minimization problem. The algorithm adopts the idea of hierarchical processing to iteratively solve the position of a point to be measured: firstly, discretizing a positioning space into a plurality of positioning areas, and reducing the positioning space to a certain area through Pearson product moment correlation coefficient calculation; and then repeating the previous step in the selected small area until the iteration termination condition is met.
The values of s and m vary in each iteration as described above.
The embodiment of the invention models the RSS-based wireless positioning problem under an unknown path loss model into an optimization problem of the correlation coefficient of the minimum Pearson product moment, provides a simple and feasible iterative solution algorithm, and can directly realize accurate positioning with lower complexity under the condition of not estimating a path loss index compared with the prior art. Although the algorithm provided by the embodiment of the invention does not jointly estimate the path loss model parameters, the path loss model parameters can be easily and directly obtained by linear regression calculation after the position estimation is obtained.
The embodiment of the invention provides a wireless positioning scheme based on RSS under the condition of unknown path loss model. According to the scheme, firstly, RSS is utilized to model a wireless positioning problem under an unknown path loss model into a problem of minimizing Pearson product moment correlation coefficient, and then a simple and feasible solving algorithm is provided. The specific implementation process is shown in fig. 1, and comprises the following steps:
101. on the point to be measured, respectively collecting RSS from N (N is more than or equal to 3) known reference points, wherein the number of collected samples of the RSS of each reference point is ki(i =1,2, … N), the position of the known reference point being known asFrom the ithThe jth RSS of the reference point is ri,j(j=1,2,…,ki)。
Wherein, the reference point position can be obtained by different ways such as GPS (global positioning system), manual estimation, maps, CAD software (computer aided design) and the like); the RSS samples can be collected by a notebook computer, PDA (personal digital assistant), smart phone, etc. equipped with a wireless network card.
Taking the collection of Wi-Fi RSS signals as an example, after wireless network monitoring software is installed on a notebook computer running a Window operating system, the software can be run to collect RSS of surrounding Wi-Fi access points.
According to the definition of the Pearson product moment correlation coefficient and a path loss model, modeling the positioning estimation problem of the point to be measured into a problem of minimizing the Pearson product moment correlation coefficient, namely:
102. and estimating the value space of the position of the point to be measured.
Roughly estimating the value space X ∈ [ X ] of the position to be measured by using the known reference point position, RSS received by the point to be measured, transmission distance of wireless signals and other information (such as buildings, regions and the like where the point to be measured is located) related to the position to be measured1,X2],y∈[Y1,Y2]。
Such as: if the point to be measured is known to be in a certain floor, the coordinate space of the floor can be used as the value space of the position of the point to be measured; if no reference information related to the position of the point to be measured exists, the position of the point to be measured can be estimated according to a centroid algorithmAnd taking the point as a center to be a square with the side length of 2D, and taking the square as a value space of the position of the point to be measured. Wherein D is the corresponding wireless transmission distance of the wireless communication technology, such as 802.11g roomThe inner transmission distance is about 38m and the outdoor transmission distance is about 140 m.
103. Discretizing the position value space of the point to be measured, and dividing the estimated position value space of the point to be measured into a plurality of grids with equal size.
Dividing the position value space of the point to be measured estimated in the step 102 into m equal-size areas with the area of s2m2Square grid of (2), with the central position of each gridAs possible values for all positions of the point to be measured. The selection of s is determined by the expected iteration number and the positioning accuracy requirement, and the smaller the expected iteration number is, the higher the positioning accuracy requirement is, and the smaller the value of s is.
104. Solving corresponding Pearson product-moment correlation coefficient and determining the minimum Pearson product-moment correlation coefficient rholCorresponding position space: all possible positions of the point to be measuredSubstituting the following equation:
solving the corresponding Pearson product-moment correlation coefficient rhol(l =1,2, …, m), and finds the smallest pearson product-moment correlation coefficient ρLWill be the rhoLCorresponding toThe grid is used as the position space of the point to be measured.
And repeating the steps 103-104 until the iteration termination condition is met. The minimum Pearson product moment correlation coefficient rho obtained at this timeLCorresponding positionI.e. the position estimate to be solved for
The iteration termination condition can be determined according to the actual system requirements of the positioning precision and the operation time, and if the positioning precision is required to be 1m magnitude, the iteration can be set to be terminated when s is less than or equal to 1 m.
Thus, positioning problem solving based on RSS under the condition of unknown path loss model is completed.
Fig. 2 is a schematic diagram of a wireless positioning apparatus according to an embodiment of the present invention, and as shown in fig. 2, the apparatus of the embodiment includes:
the acquisition module is used for respectively acquiring the received signal strength of a plurality of known reference points on the point to be measured;
the estimation module is used for estimating the value space of the position to be measured according to the received signal strength;
the dividing module is used for dividing the position value space of the point to be measured, which is obtained by estimation, into a plurality of grids with equal size;
the processing module is used for solving the corresponding Pearson product moment correlation coefficient and determining the minimum Pearson product moment correlation coefficient rhoLWill be the rhoLAnd taking the grid where the corresponding position is as the position space of the point to be measured.
The dividing module may be specifically configured to divide the estimated value space of the position of the point to be measured into m equal-sized values with an area of s2m2Square grid of (2), with the central position of each gridThe selection of s as possible values of all positions of the point to be measured is determined by the expected iteration times and the positioning accuracy requirement.
Wherein the solving module is availableSolving corresponding Pearson product moment correlation coefficient rho by the following formulal(l=1,2,…,m)，
Wherein,
kifor the number of received signal strength measurements from the ith reference point, ri,jFor the jth received signal strength received from the ith reference point, j =1,2, …, ki；(xi,yi) I =1,2, … N, N ≧ 3, for the location of the known reference point.
The rhoLCorresponding positionObtained by the following formula:
a specific wireless location embodiment is given below. This example takes as an example the experimental data used in the Wi-Fi positioning system named COMPASS, university of manhaim, germany. The experimental scene is an office floor with the width of about 15m and the length of about 36 m. There are 14 Wi-Fi access points with known locations within the office area. The laboratory database contained RSS from these 14 access points collected at 612 known location test points, with 110 RSS samples collected at each test point. In order to examine the positioning performance of the present invention under different reference point numbers (i.e., different N), the access point a with a true position (7.125, 6.269) is used as a point to be measured, and N points are randomly selected from 612 known position test points as reference points, so as to perform a detailed positioning process description.
According to the above description of the implementation process, if the total number of iterations is two, s of the first iteration is 3, and s of the second iteration is 1, the positioning of the test points can be specifically described as follows:
201. and randomly selecting N test points from the 612 known position test points as reference points, and simultaneously selecting corresponding RSSs.
And determining the position value space of the test point by the known office building space, wherein x belongs to [0,36], y belongs to [0,15], namely the position space is L =36 in length and W =15 in width.
202. Dividing the space of the point to be measured into 60 3 multiplied by 3m2I.e. m =60, s = 3. Taking the central point of each grid as a possible value of the position to be measured, the following are:
203. solving rho according to Pearson product moment correlation coefficient formulalAnd determining the correlation coefficient rho of the minimum Pearson product momentLA corresponding location space.
204. Repeating 202-203 steps, where s =1 and m =9, at a minimum ρLCorresponding positionAs position estimates for the test points.
If Root Mean Square Error (RMSE) is used as the performance index, the positioning result of the embodiment of the present invention obtained by 10000 monte carlo simulations can be given by fig. 2, and it can be seen from the figure that the positioning performance of the solution improves as the number of reference points increases. Taking the number of reference points N =10 as an example, at this time, the positioning RMSE of the embodiment of the present invention is 3.066m, which is significantly improved by about 61% and 92.6% compared to the background 7.852m of the conventional centroid positioning algorithm and 41.027m of the technical scheme [1], respectively (the reason that the error of the technical scheme [1] is large is that multiple iterations in 10000 monte carlo simulations do not converge on the global optimum value). If the median error value is used as the performance index, when the number of reference points is N =10, the positioning error of the embodiment of the present invention is 2.0125m, which is significantly improved by about 71.3% and 35.6% compared to 7.0028m of the centroid algorithm and 3.1413m of the background scheme [1], respectively. In addition, although the algorithm of the embodiment of the invention is more complex than the simplest centroid algorithm, the complexity is still lower, when N =10, only 144s is needed for 10000 times of positioning with the core i5, and compared with 988s in the background technical scheme [1], the time is saved by nearly 85.4%. In addition, the positioning accuracy can be further improved by increasing the reference points, increasing the iteration times, reducing the size of the grids in the iteration and the like.
It will be understood by those skilled in the art that all or part of the steps of the above methods may be implemented by instructing the relevant hardware through a program, and the program may be stored in a computer readable storage medium, such as a read-only memory, a magnetic or optical disk, and the like. Alternatively, all or part of the steps of the above embodiments may be implemented using one or more integrated circuits. Accordingly, each module/unit in the above embodiments may be implemented in the form of hardware, and may also be implemented in the form of a software functional module. The present invention is not limited to any specific form of combination of hardware and software.
The foregoing is only a preferred embodiment of the present invention, and naturally there are many other embodiments of the present invention, and those skilled in the art can make various corresponding changes and modifications according to the present invention without departing from the spirit and the essence of the present invention, and these corresponding changes and modifications should fall within the scope of the appended claims.
Claims (8)
1. A method of wireless positioning, comprising:
respectively acquiring the received signal strength of a plurality of known reference points on a point to be measured;
estimating a value space of the position of the point to be measured according to the received signal strength, and executing subsequent steps;
step (a): dividing the obtained position value space of the point to be measured into a plurality of grids with equal size;
step (b): solving the Pearson product-moment correlation coefficient of the grid and determining the minimum Pearson product-moment correlation coefficientρLWill be the rhoLCorresponding positionThe grid is used as the position space of the point to be measured;
repeating the steps (a) - (b) until an iteration termination condition is met, and obtaining the minimum Pearson product moment correlation coefficient rho at the momentLCorresponding positionI.e. the position estimate to be solved for.
2. The method of claim 1, wherein: the step of dividing the obtained value space of the positions of the points to be measured into a plurality of grids with equal size comprises the following steps:
dividing the obtained position value space of the point to be measured into m equal-size areas with the area of s2m2Square grids of (2), at the position of the center point of each gridAll position possible values of the point to be measured are taken; where l is 1,2, …, m, s is selected according to the expected number of iterations and the positioning accuracy requirement.
3. The method of claim 2, wherein: solving for Pearson product-moment correlation coefficient ρ of the gridlIs realized by the following formula:
wherein,kifor the number of received signal strength measurements from the ith reference point, ri,jFor the jth receiving from the ith reference pointReceived signal strength, j ═ 1,2, …, ki；
(xi,yi) Is the position of the known reference point, (x, y) is the position of the point to be measured, plAnd (3) the correlation coefficient of the Pearson product moment of the grid, i is 1,2, … N, and N is more than or equal to 3.
4. The method of claim 3, wherein: the rhoLCorresponding positionObtained by the following formula:
5. an apparatus for wireless positioning, comprising:
the acquisition module is used for respectively acquiring the received signal strength of a plurality of known reference points on the point to be measured;
the estimation module is used for estimating the value space of the position to be measured according to the received signal strength;
the dividing module is used for dividing the position value space of the point to be measured obtained by estimation into a plurality of grids with equal size, or dividing the position space of the point to be measured obtained by the processing module into a plurality of grids with equal size;
a processing module for solving the Pearson product-moment correlation coefficient of the grid and determining the minimum Pearson product-moment correlation coefficient rhoLWill be the rhoLCorresponding positionThe grid is used as the position space of the point to be measured; when the iteration termination condition is met, obtaining the minimum Pearson product moment correlation coefficient rho at the momentLCorresponding positionAs the position estimate to be solved for.
6. The apparatus of claim 5, wherein:
the dividing module is specifically configured to divide the estimated position space of the point to be measured or divide the position space of the point to be measured into m equal-sized areas with an area of s2m2Square grids of (2), at the position of the center point of each gridAll position possible values of the point to be measured are taken; where l is 1,2, …, m, s is selected according to the expected number of iterations and the positioning accuracy requirement.
7. The apparatus of claim 6, wherein:
the processing module solves the corresponding Pearson product moment correlation coefficient rho through the following formulal，
Wherein,
kifor the number of received signal strength measurements from the ith reference point, ri,jFor the jth received signal strength received from the ith reference point, j is 1,2, …, ki；(xi,yi) Is the position of the known reference point, (x, y) is the position of the point to be measured, plAnd (3) the correlation coefficient of the Pearson product moment of the grid, i is 1,2, … N, and N is more than or equal to 3.
8. The apparatus of claim 7, wherein: the rhoLCorresponding positionObtained by the following formula:
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| CN108716918B (en) * | 2018-04-24 | 2021-08-24 | 合肥工业大学 | RSSI indoor positioning algorithm based on grid clustering |
| CN110673181B (en) * | 2019-08-19 | 2022-03-22 | 中国电波传播研究所（中国电子科技集团公司第二十二研究所） | GNSS interference source positioning method based on grid energy traversal search |
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| CN103221838A (en) * | 2010-11-17 | 2013-07-24 | Lg电子株式会社 | Positioning method and apparatus in wireless communication system |
| CN102098780A (en) * | 2010-12-14 | 2011-06-15 | 北京邮电大学 | Positioning method and device |
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| JP6300922B2 (en) | 2018-03-28 |
| WO2015070613A1 (en) | 2015-05-21 |
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