CN109375163B - High-precision indoor positioning method and terminal - Google Patents
High-precision indoor positioning method and terminal Download PDFInfo
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- CN109375163B CN109375163B CN201811008067.4A CN201811008067A CN109375163B CN 109375163 B CN109375163 B CN 109375163B CN 201811008067 A CN201811008067 A CN 201811008067A CN 109375163 B CN109375163 B CN 109375163B
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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
- G01S5/06—Position of source determined by co-ordinating a plurality of position lines defined by path-difference measurements
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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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- 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
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
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Abstract
The invention discloses a high-precision indoor positioning method and a terminal, wherein an indoor area is divided into more than two sub-areas, a judgment domain of the sub-areas is calculated, and a transmission model of the sub-areas is a logarithmic distance loss model; detecting a wireless signal intensity value of an information source on a point to be measured to obtain a sub-region to be measured where the point to be measured is located, determining the position of the point to be measured according to a logarithmic distance loss model of the sub-region to be measured, wherein the wireless signal intensity value falls into a judgment domain of the sub-region to be measured; according to the invention, a plurality of wireless signal transmission models are introduced, the region where the point to be measured is located is finally determined in a step-by-step approximation mode in the positioning stage, and model parameters corresponding to the region are selected for accurate positioning calculation. The difference of wireless signal transmission paths from each point to the information source in the indoor environment is fully considered, so that the loss calculation of the wireless signals at each indoor point is more in line with objective practice, and the determined positions of the points to be measured are more accurate.
Description
Technical Field
The invention relates to the technical field of indoor positioning, in particular to a high-precision indoor positioning method and a high-precision indoor positioning terminal.
Background
With the progress of science and technology and the development of society, Location Based Service (LBS) has become a basic Service requirement necessary for people's daily work and life. Positioning technologies can be generally classified into two types, an outdoor positioning technology and an indoor positioning technology. In an outdoor environment, Global Navigation Satellite terminals (GNSS) such as a Global Positioning System (GPS) and a BeiDou Navigation Satellite System (BDS) provide a position service accurate to a meter level for a user, and the problem of accurate Positioning in an outdoor space is basically solved. However, in an indoor environment where a human being stays 80% of the time in daily life, due to the shielding effect of a building on a wireless signal, the GNSS signal intensity is sharply reduced, so that the positioning accuracy is greatly reduced, and the indoor location service requirement cannot be met. The problem is particularly prominent in large and complex indoor environments such as superstores, comprehensive transportation hubs and underground mines. Therefore, how to improve the accuracy of the indoor positioning technology in a large and complicated indoor environment is a key research point in the field of the current positioning technology.
An indoor positioning technology based on Received Signal Strength (RSS) is a mainstream indoor positioning technology at present because it has the characteristics of directly using existing widely deployed WLAN equipment, low hardware cost, convenient deployment, and the like.
Indoor positioning technologies based on RSS are mainly classified into positioning technologies based on ranging and positioning technologies based on location fingerprints. The positioning method based on the position fingerprint specifically comprises the following steps: after wireless signal characteristics of a specific position are detected and a position fingerprint database is established according to the wireless signal characteristics, the wireless signal characteristics are detected in a positioning stage, and the position fingerprint database is subjected to matching calculation by using a specific matching algorithm, so that the position information of a target node is estimated. The positioning method based on the position fingerprint does not need specific information of the reference point, and has unique advantages in specific occasions where the reference point cannot be calibrated or is inconvenient to calibrate. However, the positioning method based on the position fingerprint establishes a complicated and huge fingerprint database in advance. The workload of establishing the fingerprint database is very huge, and the workload of maintenance is also extremely complicated.
The ranging-based positioning method is to determine the distances between the target node and a plurality of reference points with known positions by utilizing the attenuation characteristics of wireless signals in the transmission process, and further calculate to obtain the position information of the target node. The positioning method based on distance measurement is simple in deployment, low in manufacturing cost, small in maintenance workload and easy to popularize. However, due to the complexity and diversity of the propagation paths of the wireless signals in the indoor environment, a single wireless signal transmission model cannot well describe the propagation characteristics of the wireless signals at different indoor points, which results in large errors and low positioning accuracy.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: provided are a high-precision indoor positioning method and a terminal.
In order to solve the technical problems, the invention adopts the technical scheme that:
a high-precision indoor positioning method comprises the following steps:
s1, dividing an indoor area into more than two sub-areas, and calculating a judgment domain of the sub-areas, wherein a transmission model of the sub-areas is a logarithmic distance loss model;
s2, detecting a wireless signal intensity value of an information source on a point to be measured to obtain a sub-region to be measured where the point to be measured is located, determining the position of the point to be measured according to a logarithmic distance loss model of the sub-region to be measured, wherein the wireless signal intensity value falls into a judgment domain of the sub-region to be measured.
In order to solve the technical problem, the invention adopts another technical scheme as follows:
a high accuracy indoor positioning terminal comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
s1, dividing an indoor area into more than two sub-areas, and calculating a judgment domain of the sub-areas, wherein a transmission model of the sub-areas is a logarithmic distance loss model;
s2, detecting a wireless signal intensity value of an information source on a point to be measured to obtain a sub-region to be measured where the point to be measured is located, determining the position of the point to be measured according to a logarithmic distance loss model of the sub-region to be measured, wherein the wireless signal intensity value falls into a judgment domain of the sub-region to be measured.
The invention has the beneficial effects that: dividing an indoor area into a plurality of sub-areas, calculating judgment domains of the sub-areas, wherein a transmission model of each sub-area is an independent logarithmic distance loss model, detecting a wireless signal intensity value of an information source on a point to be measured according to the logarithmic distance loss model, and calculating according to the judgment domain in which the wireless signal intensity value falls so as to determine the position of the point to be measured; therefore, the invention introduces a plurality of wireless signal transmission models, finally defines the area of the point to be measured in a positioning stage by means of successive approximation, and selects the model parameters corresponding to the area to perform accurate positioning calculation. The difference of wireless signal transmission paths from each point to the information source in the indoor environment is fully considered, so that the loss calculation of the wireless signals at each indoor point is more in line with objective practice, and the determined positions of the points to be measured are more accurate.
Drawings
Fig. 1 is a schematic flow chart of a high-precision indoor positioning method according to an embodiment of the present invention;
FIG. 2 is a schematic position diagram of a trilateration algorithm according to an embodiment of the present invention;
FIG. 3 is a schematic plan view of an indoor area according to an embodiment of the present invention;
fig. 4 is a schematic plan view of an indoor area after being partitioned based on a source a according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a high-precision indoor positioning terminal according to an embodiment of the present invention.
Description of reference numerals:
1. a high-precision indoor positioning terminal; 2. a memory; 3. a processor.
Detailed Description
In order to explain technical contents, achieved objects, and effects of the present invention in detail, the following description is made with reference to the accompanying drawings in combination with the embodiments.
The most key concept of the invention is as follows: an indoor area is divided into a plurality of sub-areas, so that a plurality of wireless signal transmission models are introduced.
Before this, in order to facilitate understanding of the technical solution of the present invention, the english abbreviations, devices and the like referred to in the present invention are described as follows:
(1) logarithmic distance loss model: before describing the logarithmic distance loss model, it is necessary to describe a wireless signal transmission model, which is a mathematical model of propagation of a wireless signal through a medium in a certain medium environment. The degree of signal attenuation generated in the process of transmitting the wireless signals in the medium space and the distance of transmission can be obtained through a wireless signal transmission modelThe quantitative relation between the indoor positioning parameters and the indoor positioning parameters is crucial to obtaining satisfactory indoor positioning performance by reasonably selecting the signal transmission model and the transmission model parameters which are in fit with the current environment. The propagation model of the wireless signal in free space provides the received signal strength PrCan be represented by the following formula (1):
in the above formula (1), PtFor transmitting end signal power, λ is wireless signal wavelength, GtFor transmitting end antenna gain, GrFor the receiving end antenna gain, d is the spacing distance of the transmitting and receiving ends, and L is the system loss coefficient. It is troublesome to measure and position by directly using a free space propagation model, so that a common logarithmic distance loss model for indoor positioning is expressed by the following formula (2):
in the above equation (2), p (d) is the received signal strength, i.e. RSS value, from the receiving end with distance d. P (d)0) Is a distance d from the source0The strength of the signal received by the receiving end. n is a path loss exponent, which is generally obtained by actual measurement, and in general, the more obstacles on a propagation path, the larger the value of n, and thus the larger the amplitude of the drop of a wireless signal within a unit propagation distance. d0The distance is taken as a reference distance according to the actual situation of the site, and the value is usually 1 for the convenience of calculation and measurement. X is a Gaussian random variable with dBm as a unit, the mean value of 0 and the variance range of 4-10.
For engineering applications, the following equation (3) is commonly used as a simplified form of the log-distance loss model:
R＝A-10nlg(d)
in the above formula (3), d is the distance between the source and the receiver, R is the signal strength received by the receiver, a is the signal strength received at a distance of 1m from the source, and n is the path loss. When the method is applied, a plurality of sample points with different positions are selected in advance, A, n parameters of the model are determined through regression analysis, and therefore the quantitative relation between the RSS detected by the receiving end and the d is established. In the positioning stage, the distance d between the point to be measured and the information source can be determined by detecting the RSS value received by the point to be measured.
(2) And trilateral positioning algorithm: the positioning method which is widely applied at present is a trilateral positioning algorithm. As shown in fig. 2, on a two-dimensional plane, if the distances between the point to be measured and the three reference points with known positions can be determined, the three reference points are taken as the center of a circle in sequence, the distance between the point to be measured and the reference points is taken as the radius to make a circle, the intersection point of the three circles is the position of the point to be measured, and if the three-dimensional plane is in a three-dimensional space, the four reference points are required to be spherically intersected to make positioning calculation. In the invention, the reference point is the information source.
(3) The Maximum Likelihood method is also called Maximum Likelihood estimation method, which is called Maximum Likelihood estimation method, and is also called Maximum Likelihood estimation or Maximum Likelihood estimation.
Referring to fig. 1 to 4, a high-precision indoor positioning method includes the steps of:
s1, dividing an indoor area into more than two sub-areas, and calculating a judgment domain of the sub-areas, wherein a transmission model of the sub-areas is a logarithmic distance loss model;
s2, detecting a wireless signal intensity value of an information source on a point to be measured to obtain a sub-region to be measured where the point to be measured is located, determining the position of the point to be measured according to a logarithmic distance loss model of the sub-region to be measured, wherein the wireless signal intensity value falls into a judgment domain of the sub-region to be measured.
From the above description, the beneficial effects of the present invention are: the invention introduces a plurality of wireless signal transmission models, finally defines the area of the point to be measured in a positioning stage by a gradual approximation mode, and selects the model parameters corresponding to the area to carry out accurate positioning calculation. The difference of wireless signal transmission paths from each point to the information source in the indoor environment is fully considered, so that the loss calculation of the wireless signals at each indoor point is more in line with objective practice, and the determined positions of the points to be measured are more accurate.
Further, the step S1 is specifically:
constructing planes corresponding to the information sources one by one;
dividing the plane into sub-regions corresponding to the information source according to the blocking degree of the shielding object of the information source on the wireless signal transmission path, and calculating the judgment domains of all the sub-regions, wherein the transmission model of the sub-regions is a logarithmic distance loss model.
From the above description, the main reasons that the conventional RSS indoor positioning method based on trilateral positioning has low accuracy are that the indoor environment is complex, the shielding situations of wireless signals from the signal source to indoor points on propagation paths are different, the attenuation rates of the wireless signal strength of each propagation path are greatly different, and the single set of propagation model parameters adopted by the method ignore the differences and naturally introduce large errors; therefore, the areas are divided according to the blocking degree of the shielding object of the information source on the wireless signal transmission path, and the difference caused by the shielding object condition can be eliminated as much as possible, so that the positioning accuracy is further improved.
Further, the step S2 is specifically:
s2.1, sequentially detecting wireless signal intensity values of first to Nth signal sources on a point to be measured to obtain all sub-regions to be measured where the point to be measured is located, and generating a sub-region equation set, wherein the wireless signal intensity values fall into a judgment region of the sub-regions to be measured, N is the number of the signal sources and is more than or equal to 3;
s2.2, obtaining the spacing distance between the point to be measured and the information source according to the logarithmic distance loss model of the sub region to be measured, and obtaining a position coordinate equation set related to the point to be measured according to a sub region equation set;
s2.3, solving the position equation set to obtain the position of the point to be measured.
From the above description, the wireless signal strength values of different information sources on the point to be measured are different, and the sub-regions are determined according to the wireless signal strength values, so that which sub-region is used for solving is determined, when N is greater than or equal to 3, at least three equations are provided, and the position of the point to be measured can be obtained by using a trilateration algorithm.
Further, before the step S2 of solving the position equation set, the method further includes the steps of:
and converting the position equation system by using a maximum likelihood method.
From the above description, when the number of the sources is greater than or equal to 3, the form of the position coordinate equation system remains unchanged. Due to the problems of hardware limitation and measurement accuracy, if a position coordinate equation set is directly used for solving the position of a point to be measured in engineering application, a situation of no solution can occur, so that the position equation set is converted by using a maximum likelihood method to avoid the situation of no solution.
Further, after generating the sub-region equation set in step S2.1, the following steps are also performed:
judging whether each wireless signal strength value in the sub-region equation set only falls into the range of one judgment domain, if so, executing the step S2.2, and if any one wireless signal strength value in the sub-region equation set falls into the ranges of more than two judgment domains, splitting the sub-region equation set into sub-region sub-equation sets, wherein each wireless signal strength value in the sub-region sub-equation sets only falls into the range of one judgment domain;
and calculating the region confidence degrees of all the sub-region sub-equation sets to obtain the sub-region sub-equation set with the maximum region confidence degree, and executing the step S2.2.
From the above description, it can be known that when any one wireless signal intensity value in the sub-region equation set falls within the range of more than two decision domains, the final solution is not unique, and the position of the point to be measured is one and only one at a certain moment, so that the solution of one and only one equation set in the sub-region equation set is the most objective and actual, the incompatibility degree of the sub-region equation set is taken as the measure of the degree of the solution of the equation set approaching the objective and actual, the equation set with the lowest incompatibility degree is the best choice in the sub-region equation set, and the solution is the best solution, so that the position of the point to be measured which is the most objective and actual is most is obtained.
As shown in fig. 5, a high-precision indoor positioning terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the following steps when executing the computer program:
s1, dividing an indoor area into more than two sub-areas, and calculating a judgment domain of the sub-areas, wherein a transmission model of the sub-areas is a logarithmic distance loss model;
s2, detecting a wireless signal intensity value of an information source on a point to be measured to obtain a sub-region to be measured where the point to be measured is located, determining the position of the point to be measured according to a logarithmic distance loss model of the sub-region to be measured, wherein the wireless signal intensity value falls into a judgment domain of the sub-region to be measured.
From the above description, the beneficial effects of the present invention are: the invention introduces a plurality of wireless signal transmission models, finally defines the area of the point to be measured in a positioning stage by a gradual approximation mode, and selects the model parameters corresponding to the area to carry out accurate positioning calculation. The difference of wireless signal transmission paths from each point to the information source in the indoor environment is fully considered, so that the loss calculation of the wireless signals at each indoor point is more in line with objective practice, and the determined positions of the points to be measured are more accurate.
Further, the step S1 is specifically:
constructing planes corresponding to the information sources one by one;
dividing the plane into sub-regions corresponding to the information source according to the blocking degree of the shielding object of the information source on the wireless signal transmission path, and calculating the judgment domains of all the sub-regions, wherein the transmission model of the sub-regions is a logarithmic distance loss model.
From the above description, the main reasons that the conventional RSS indoor positioning method based on trilateral positioning has low accuracy are that the indoor environment is complex, the shielding situations of wireless signals from the signal source to indoor points on propagation paths are different, the attenuation rates of the wireless signal strength of each propagation path are greatly different, and the single set of propagation model parameters adopted by the method ignore the differences and naturally introduce large errors; therefore, the areas are divided according to the blocking degree of the shielding object of the information source on the wireless signal transmission path, and the difference caused by the shielding object condition can be eliminated as much as possible, so that the positioning accuracy is further improved.
Further, the step S2 is specifically:
s2.1, sequentially detecting wireless signal intensity values of first to Nth signal sources on a point to be measured to obtain all sub-regions to be measured where the point to be measured is located, and generating a sub-region equation set, wherein the wireless signal intensity values fall into a judgment region of the sub-regions to be measured, N is the number of the signal sources and is more than or equal to 3;
s2.2, obtaining the spacing distance between the point to be measured and the information source according to the logarithmic distance loss model of the sub region to be measured, and obtaining a position coordinate equation set related to the point to be measured according to a sub region equation set;
s2.3, solving the position equation set to obtain the position of the point to be measured.
From the above description, the wireless signal strength values of different information sources on the point to be measured are different, and the sub-regions are determined according to the wireless signal strength values, so that which sub-region is used for solving is determined, when N is greater than or equal to 3, at least three equations are provided, and the position of the point to be measured can be obtained by using a trilateration algorithm.
Further, before solving the position equation set in step S2, the processor executes the computer program to further implement the following steps:
and converting the position equation system by using a maximum likelihood method.
From the above description, when the number of the sources is greater than or equal to 3, the form of the position coordinate equation system remains unchanged. Due to the problems of hardware limitation and measurement accuracy, if a position coordinate equation set is directly used for solving the position of a point to be measured in engineering application, a situation of no solution can occur, so that the position equation set is converted by using a maximum likelihood method to avoid the situation of no solution.
Further, after generating the sub-region equation set in step S2.1, the processor executes the computer program to further implement the following steps:
judging whether each wireless signal strength value in the sub-region equation set only falls into the range of one judgment domain, if so, executing the step S2.2, and if any one wireless signal strength value in the sub-region equation set falls into the ranges of more than two judgment domains, splitting the sub-region equation set into sub-region sub-equation sets, wherein each wireless signal strength value in the sub-region sub-equation sets only falls into the range of one judgment domain;
and calculating the region confidence degrees of all the sub-region sub-equation sets to obtain the sub-region sub-equation set with the maximum region confidence degree, and executing the step S2.2.
From the above description, it can be known that when any one wireless signal intensity value in the sub-region equation set falls within the range of more than two decision domains, the final solution is not unique, and the position of the point to be measured is one and only one at a certain moment, so that the solution of one and only one equation set in the sub-region equation set is the most objective and actual, the incompatibility degree of the sub-region equation set is taken as the measure of the degree of the solution of the equation set approaching the objective and actual, the equation set with the lowest incompatibility degree is the best choice in the sub-region equation set, and the solution is the best solution, so that the position of the point to be measured which is the most objective and actual is most is obtained.
Referring to fig. 1 to 4, a first embodiment of the present invention is:
a high-precision indoor positioning method comprises the following steps:
s1, constructing planes corresponding to the information sources one by one, dividing the planes into sub-regions corresponding to the information sources according to the blocking degree of the shielding objects of the information sources on the wireless signal transmission path, and calculating judgment domains of all the sub-regions, wherein the transmission models of the sub-regions are logarithmic distance loss models;
wherein, inIn this embodiment, as shown in fig. 3, there are three signal sources in the indoor area, A, B, C are deployed, there are three walls in the room, which are ej, fm, and go, respectively, and in order to simplify the analysis and highlight the problem elements, the thickness of the three walls is assumed to be 0. First, with the signal source a as a reference, the transmission path of the wireless signal transmitted from the signal source a is shown by a dotted line in fig. 3, and on this basis, the indoor space can be divided into regions according to the signal source a according to the blocking degree of the blocking object on the transmission path of the wireless signal. As shown in fig. 4, the parameter values of the logarithmic distance loss models corresponding to the sub-regions za1, za2, za3, and za4 are different from each other, and the parameter values are the values of a and n in the formula (3), and are determined by actual conditions. These sub-regions constitute an indoor space divided into regions based on the source A, and Z is expressedaThe formula (4) is the following, which is the union of them:
Za＝za1∪za2∪za3∪za4＝{za1，za2，za3，za4}。
it is obvious from the foregoing that Za corresponds to and completely coincides with the actual size of the indoor space, which is hereinafter referred to as the a plane. Similarly, a B plane and a C plane, namely Z, can be constructed by respectively taking the information source B and the information source C as referencesb、Zc. As can be seen from fig. 4, za1 is a polygon formed by a plurality of line segments. In general, sub-region za1 may be represented by formula (5):
za1＝{(x，y)|fi(x，y)＜0，i＝1，2，...，r}
fi (x, y) is an analytic expression of r straight lines constituting the polygonal outline, and r is 5 for the sub-region za 1.
Remember | p1-P2I is point p1And p2The point p (x, y) is an arbitrary point on the subregion za1, and the point a (xa, ya) is the position of the source a, then the RSS minimum value R based on the source a, which can be obtained from all points on the subregion za1, can be obtained from the equations (3) and (5)a1I.e. formula (6):
minRa1＝A-10nlg(||p(x，y)-a(xa，ya)||)s.t.fi(x，y)＜0，i＝1，2，...，r
it is clear that equation (6) is a linear programming problem, and R can be solved by the simplex methoda1Minimum value of (1), denoted as Ra1_min. In the same way, R can be obtaineda1Maximum value R ofa1_max. Let the RSS value based on the source a corresponding to an arbitrary point p (x, y) on the sub-region za1 be Rp, and considering the connectivity of the sub-region za1 and the monotonicity of the equation (3), the following equation (7) is obvious:
thus, sub-region za1 is aligned with Ra1_min、Ra1_maxThe set of values establishes a correspondence. Defining the range determined by the set of values corresponding to the sub-region as the decision domain of the region, where the decision domain of the sub-region za1 is [ R ]al_min，Ral_max]. Similarly, decision domains for the remaining sub-regions za2, za3, za4 of the a-plane can be established.
Referring to the processing procedure of the plane a, the same processing can be performed on the plane B, C, and the boundary range of each region in the plane B, C and the transmission model parameters and the decision domain corresponding to each region are constructed. This completes the localization process of the indoor space.
S2.1, sequentially detecting wireless signal intensity values of the first to the Nth signal sources on the to-be-detected point to obtain all to-be-detected sub-regions where the to-be-detected point is located, generating a sub-region equation set, wherein the wireless signal intensity values fall into a judgment region of the to-be-detected sub-regions, N is the number of the signal sources and is more than or equal to 3;
in the embodiment, RSS values of A, B, C detected at the point p (x, y) to be measured with unknown position are R respectivelya、Rb、RcWhile noting that the region where p (x, y) is located is zp. Consider first the simplest case, namely Ra、Rb、RcAt A, B, C three planes (Z)a、Zb、Zc) All fall within a corresponding decision domain, i.e. the following equation (8):
wherein ja1, jb1 and jc1 are decision domains corresponding to the sub-regions za1, zb1 and zc1 respectively, and the equation set of the sub-regions is as follows (9):
s2.2, obtaining the spacing distance between the point to be measured and the information source according to the logarithmic distance loss model of the sub region to be measured, and obtaining a position coordinate equation set related to the point to be measured according to the sub region equation set;
in this embodiment, from equation (3), the following equation (10) can be obtained by combining known conditions:
this is a system of equations for x, y, where x, y are the position coordinates of the point p (x, y) to be measured and (x)1，y1)、(x2，y2)、(x3，y3) The position coordinates of source a, source B, source C, respectively, are known constants. d1、d2、d3The distances between the point p (x, y) to be measured and the information source A, B and C are calculated by the formula (3), and the model parameters corresponding to each region in the formula (3) are established during the indoor space regionalization. For the present embodiment, there are only 3 sources deployed in the indoor environment, so the value of n in equation (10) is 3.
R is performed on the formula (10)(k+1)k(ii) (-1) linear transformation, k ═ 1, 2, …, n-1, eliminating quadratic terms and sorting, the result can be represented as following formula (11):
GW＝b
wherein G is formula (12):
w is formula (13):
b is formula (14):
where W is the desired location of the point to be measured. When the number of the sources is greater than 3, the form of the equation (11) remains unchanged, so that the embodiment is the same as the embodiment when the number of the sources is greater than 3.
And S2.3, solving the position equation set to obtain the position of the point to be measured.
In the present embodiment, (x)1，y1)、(x2，y2)、(x3，y3) Then the known position coordinates of the source A, source B, and source C, respectively, d1、d2、d3The distances between the point p (x, y) to be measured and the information source A, B and C are calculated by the formula (3), so that the position of the point p (x, y) to be measured can be solved by the formula (11) to obtain the position of the point to be measured.
Referring to fig. 1 to 4, a second embodiment of the present invention is:
on the basis of the first embodiment, before solving the position equation set in step S2, a high-precision indoor positioning method further includes the steps of:
and converting the position equation set by using a maximum likelihood method.
Note gij、wj、biEach being an element of matrix G, W, b, where i is 1, 2, …, and n, j is 1, 2, then equation (11) may be represented as equation (15) below:
gi1w1+gi2w2＝bi， i＝1，2，...，n
since the RSS values of the respective sources are measured at the point p (x, y) to be measured independently, there is formula (16):
bi＝gi1w1+gi2w2+εi，εi～N(0，σ2)，i＝1，2，...，n
thus, formula (17):
bi～N(gi1w1+gi2w2，σ2)，i＝1，2，...，n
thus biThe combined density of (a) is formula (18):
Lwwhen maximum value is obtained, corresponding w1、w2Namely, the position estimation value of the point to be measured is set as the formula (19):
to LwTaking the logarithm of both sides and considering the sign and constant terms, it is clear that LwTaking the maximum value equivalent to QwA minimum value is obtained. For the convenience of subsequent calculation, Q is addedwThe transformation is in the form of a matrix as shown in (20):
Qw＝(b-GW)T(b-GW)
unfolding and sorting the right term of formula (20) can give formula (21):
Qw＝bTb-2WTGTb+WTGTGW
then, deriving w on both sides of formula (21) to obtain formula (22):
let equation (22) be 0, and the term is shifted to obtain equation (23):
GTGW＝GTb
equation (23) is the result of maximum likelihood conversion of equation (11).
As can be seen from equation (23), r is the matrix G rank, i.e.:
r＝rank(G)
then there must be:
rank(GTG)＝r
meanwhile, the amplification matrix of equation (23) can be expressed as:
(GTG，GTb)＝GT(G，b)
the following reasons:
rank(GT(G，b))≤rank(GT)＝r
furthermore, (G)TG，GTb) Ratio of (G)TG) More, it is obvious to have
rank(GTG，GTb)≥rank(GTG)＝r
Therefore, there must be:
rank(GTG，GTb)＝rank(GTG)＝r
therefore, it is understood that equation (23) does not necessarily have a case of no solution. And (5) solving the formula (23) to obtain the position of the point to be measured.
Referring to fig. 1 to 4, a third embodiment of the present invention is:
on the basis of the first embodiment, after generating the sub-region equation set in step S2.1, the following steps are further performed:
judging whether each wireless signal strength value in the sub-region equation set only falls into the range of one judgment domain, if so, executing the step S2.2, and if any one wireless signal strength value in the sub-region equation set falls into the ranges of more than two judgment domains, splitting the sub-region equation set into sub-region sub-equation sets, wherein each wireless signal strength value in the sub-region sub-equation sets only falls into the range of one judgment domain;
and (4) calculating the region confidence degrees of all the sub-region sub-equation sets to obtain the sub-region sub-equation set with the maximum region confidence degree, and executing the step S2.2.
In this embodiment, if the RSS value of a certain source detected at the position of the point p (x, y) to be measured does not fall into the decision region corresponding to any one of the sub-regions of the source plane through traversal search, this indicates that an error is generated due to an excessive RSS measurement error and needs to be measured again, or it may also be that parameter calibration of the transmission model in the training phase generates an error and needs to be carefully checked and performed again. While the present embodiment discusses the more general case where the RSS values fall within the decision domains corresponding to the multiple sub-regions of the corresponding plane.
Let the RSS values R of A, B, C sources detected by the point p (x, y)a、Rb、RcFalling within the decision domain of a plurality of sub-regions of the corresponding plane, in view of the extreme case, there is equation (24):
from the above formula, m regions of the region zp of the point p (x, y) to be measured satisfy the condition in the plane a, n regions satisfy the condition in the plane B, and k regions satisfy the condition in the plane C. Therefore, the number of the equation sets to be solved is shown as equation (25):
it is obvious that the solutions of W obtained by equation (23) have m × n × k. Simply, the centroid method can be considered to combine all the calculation results, so as to estimate the position of the point p (x, y) to be measured by using equation (26):
for the present embodiment, in an ideal case, equation (11) is necessarily solved, zp is only in Za、Zb、ZcOne of (1)Subregion, i.e. the case where equation (9) must occur. However, zp is now at Za、Zb、ZcIn the case where the equation (24) is present, it is considered reasonable that the possible solution of the equation (11) is deviated by some factor, and the result of the equation (24) appears. Examining the individual parameters of equation (3) can only be the result of errors introduced by the measurement of the R value. As can be known from the common knowledge, the coordinate values of the point p (x, y) to be measured are only one at a time, and therefore, the solution of only one equation set in the m × n × k equation sets determined by the formula (25) is the most objective and practical. And (3) taking the incompatibility degree of the equation set formula (11) as a measure of the degree of the solution of the equation set close to the objective reality, wherein the equation set with the lowest incompatibility degree is the optimal choice in the m x n x k equation sets, and the solution is the optimal solution.
The embodiment is as follows:
the region confidence ρ is defined using equation (27):
c is a non-negative real number corresponding to the m × n × k equations determined by the formula (25), obviously, the value of ρ is between [0, 1], and the value of the parameter c is determined below.
According to the error analysis described above, an error vector m is introducedgSo that the following equation (28) holds:
GW＝b+mε
as can be seen from equation (23), the estimated value for equation (11) is the following equation (29):
substitution of formula (28) and finishing formula (30):
mε＝G(GTG)-1GTb-b
due to the fact that
det(GTG)＝det(GT)det(G)
Subject to the fact that the positions of the sources A, B, C are independent of each other, the set of vectors that make up G are linearly independent, and it is clear that:
det(GT)≠0，det(G)≠0，
det(GTG)≠0
therefore, the right side (G) in the formula (30)TG)-1This term must be present. Let c conform to formula (31):
c＝||mε||
substitution of formula (27) ultimately yields formula (32):
and (3) solving and sequencing rho for the combination of m, n and k determined by the formula (24) according to a formula (32) and a formula (30), wherein the equation system with the maximum rho value is the optimal choice, and then the w calculated according to the formula (23) is the optimal solution of the point p (x, y) to be measured, namely the position of the point to be measured.
Referring to fig. 5, a fourth embodiment of the present invention is:
a high-precision indoor positioning terminal 1 comprises a memory 2, a processor 3 and a computer program stored on the memory 2 and capable of running on the processor 3, wherein the processor 3 realizes step S1, step S2.1, step S2.2 and step S2.3 in the first embodiment when executing the computer program.
Referring to fig. 5, a fifth embodiment of the present invention is:
in addition to the fourth embodiment, the processor 3 implements the steps of the second embodiment when executing the computer program in the high-precision indoor positioning terminal 1.
Referring to fig. 5, a sixth embodiment of the present invention is:
in addition to the fifth embodiment, the processor 3 implements the steps of the third embodiment when executing the computer program in the high-precision indoor positioning terminal 1.
In summary, the high-precision indoor positioning method and terminal provided by the invention introduce a plurality of wireless signal transmission models, finally specify the area where the point to be measured is located in the positioning stage by means of successive approximation, and select the model parameters corresponding to the area to perform precise positioning calculation. The difference of wireless signal transmission paths from each point to the information source in the indoor environment is fully considered, so that the loss calculation of the wireless signals at each indoor point is more in line with objective practice, the region is divided according to the blocking degree of the shielding object of the information source on the wireless signal transmission path, the difference caused by the shielding object condition can be eliminated as much as possible, and the determined position of the point to be measured is more accurate; converting the position equation set by using a maximum likelihood method to avoid the situation of no solution; the incompatibility degree of the sub-region equation set is used as the measurement of the degree of the solution of the equation set close to the objective reality, the equation set with the lowest incompatibility degree is the optimal choice in the sub-region equation set, and the solution is the optimal solution, so that the position of the point to be measured which is closest to the objective reality is obtained, and the condition of multiple solutions is avoided.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent changes made by using the contents of the present specification and the drawings, or applied directly or indirectly to the related technical fields, are included in the scope of the present invention.
Claims (8)
1. A high-precision indoor positioning method is characterized by comprising the following steps:
s1, dividing an indoor area into more than two sub-areas, and calculating a judgment domain of the sub-areas, wherein a transmission model of the sub-areas is a logarithmic distance loss model;
s2, detecting a wireless signal intensity value of an information source on a point to be measured to obtain a sub-region to be measured where the point to be measured is located, determining the position of the point to be measured according to a logarithmic distance loss model of the sub-region to be measured, wherein the wireless signal intensity value falls into a judgment domain of the sub-region to be measured;
the step S1 specifically includes:
constructing planes corresponding to the information sources one by one;
dividing the plane into sub-regions corresponding to the information source according to the blocking degree of the shelters of the information source on the wireless signal transmission path, and calculating the judgment domains of all the sub-regions, wherein the transmission model of the sub-regions is a logarithmic distance loss model, and the judgment domains of the sub-regions are the ranges between the RSS minimum and the RSS maximum of the sub-regions.
2. The high-precision indoor positioning method according to claim 1, wherein the step S2 specifically comprises:
s2.1, sequentially detecting wireless signal intensity values of first to Nth signal sources on a point to be measured to obtain all sub-regions to be measured where the point to be measured is located, and generating a sub-region equation set, wherein the wireless signal intensity values fall into a judgment region of the sub-regions to be measured, N is the number of the signal sources and is more than or equal to 3;
s2.2, obtaining the spacing distance between the point to be measured and the information source according to the logarithmic distance loss model of the sub region to be measured, and obtaining a position coordinate equation set related to the point to be measured according to a sub region equation set;
s2.3, solving the position equation set to obtain the position of the point to be measured.
3. The high-precision indoor positioning method according to claim 2, wherein before solving the position equation set in step S2, the method further comprises the steps of:
and converting the position equation system by using a maximum likelihood method.
4. A high accuracy indoor positioning method according to claim 2, characterized in that, in step S2.1, after generating the sub-region equation set, the following steps are further performed:
judging whether each wireless signal strength value in the sub-region equation set only falls into the range of one judgment domain, if so, executing the step S2.2, and if any one wireless signal strength value in the sub-region equation set falls into the ranges of more than two judgment domains, splitting the sub-region equation set into sub-region sub-equation sets, wherein each wireless signal strength value in the sub-region sub-equation sets only falls into the range of one judgment domain;
and calculating the region confidence degrees of all the sub-region sub-equation sets to obtain the sub-region sub-equation set with the maximum region confidence degree, and executing the step S2.2.
5. A high accuracy indoor positioning terminal comprising a memory, a processor and a computer program stored on said memory and executable on said processor, characterized in that said processor when executing said computer program realizes the steps of:
s1, dividing an indoor area into more than two sub-areas, and calculating a judgment domain of the sub-areas, wherein a transmission model of the sub-areas is a logarithmic distance loss model;
s2, detecting a wireless signal intensity value of an information source on a point to be measured to obtain a sub-region to be measured where the point to be measured is located, determining the position of the point to be measured according to a logarithmic distance loss model of the sub-region to be measured, wherein the wireless signal intensity value falls into a judgment domain of the sub-region to be measured;
the step S1 specifically includes:
constructing planes corresponding to the information sources one by one;
dividing the plane into sub-regions corresponding to the information source according to the blocking degree of the shelters of the information source on the wireless signal transmission path, and calculating the judgment domains of all the sub-regions, wherein the transmission model of the sub-regions is a logarithmic distance loss model, and the judgment domains of the sub-regions are the ranges between the RSS minimum and the RSS maximum of the sub-regions.
6. The high-precision indoor positioning terminal of claim 5, wherein the step S2 is specifically as follows:
s2.1, sequentially detecting wireless signal intensity values of first to Nth signal sources on a point to be measured to obtain all sub-regions to be measured where the point to be measured is located, and generating a sub-region equation set, wherein the wireless signal intensity values fall into a judgment region of the sub-regions to be measured, N is the number of the signal sources and is more than or equal to 3;
s2.2, obtaining the spacing distance between the point to be measured and the information source according to the logarithmic distance loss model of the sub region to be measured, and obtaining a position coordinate equation set related to the point to be measured according to a sub region equation set;
s2.3, solving the position equation set to obtain the position of the point to be measured.
7. The high precision indoor positioning terminal of claim 6, wherein before the solving of the position equation set in step S2, the processor executes the computer program to further implement the following steps:
and converting the position equation system by using a maximum likelihood method.
8. The high accuracy indoor positioning terminal of claim 6, wherein after generating the sub-region equation set in step S2.1, the processor executes the computer program to further implement the following steps:
judging whether each wireless signal strength value in the sub-region equation set only falls into the range of one judgment domain, if so, executing the step S2.2, and if any one wireless signal strength value in the sub-region equation set falls into the ranges of more than two judgment domains, splitting the sub-region equation set into sub-region sub-equation sets, wherein each wireless signal strength value in the sub-region sub-equation sets only falls into the range of one judgment domain;
and calculating the region confidence degrees of all the sub-region sub-equation sets to obtain the sub-region sub-equation set with the maximum region confidence degree, and executing the step S2.2.
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