CN109444812B - RSSI indoor positioning method introducing dynamic threshold - Google Patents

RSSI indoor positioning method introducing dynamic threshold Download PDF

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CN109444812B
CN109444812B CN201811142949.XA CN201811142949A CN109444812B CN 109444812 B CN109444812 B CN 109444812B CN 201811142949 A CN201811142949 A CN 201811142949A CN 109444812 B CN109444812 B CN 109444812B
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CN109444812A (en
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蒋毅
张若南
朱慧霞
李彬
翟道森
潘松
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Northwestern Polytechnical University
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    • 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
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Abstract

The invention discloses an RSSI (received signal strength indicator) indoor positioning method introducing a dynamic threshold, which comprises the steps of establishing a unitary linear regression equation to calculate and obtain a signal strength value of a virtual reference label of a newly-added boundary area, and adding a virtual reference label with the same density distribution as that of a central area; and then, taking the positioning boundary line as a mirror surface, mapping the reference label in the positioning area to the other side of the boundary line to form a mirror image reference label and a mirror image virtual label, finding out an absolute value of the difference between the RSSI value of the reference label which is the largest difference with the RSSI value of the label to be detected, modifying a unified threshold value used when constructing an adjacent map into a dynamic threshold value which is changed according to the difference of the labels to be detected, constructing the adjacent map according to the dynamic threshold value, screening out the reference label suitable for the label to be detected, solving the position of the label to be detected according to the weight value and the coordinate of the reference label, and completing the positioning of the target in. The invention improves the positioning precision at the boundary by the newly added virtual reference label.

Description

RSSI indoor positioning method introducing dynamic threshold
Technical Field
The invention belongs to the technical field of Radio Frequency Identification (RFID) communication, and particularly relates to an RSSI (received signal strength indicator) indoor positioning method introducing a dynamic threshold.
Background
Although the mature gps (global Positioning system) is widely used in outdoor environment, its Positioning effect in indoor environment is not satisfactory, because the wireless signals transmitted by satellites cannot be effectively utilized in indoor environment. Radio Frequency Identification (RFID) technology enters the field of indoor positioning due to its advantages of non-contact, non-line-of-sight, low cost, large storage capacity, high Identification speed, strong anti-interference capability, good safety and the like.
Among various indoor positioning methods based on RFID, it is found through comparison that a system for performing indoor positioning using Received Signal Strength Indication (RSSI) has the characteristics of low cost and easy implementation, and can achieve a certain degree of positioning accuracy, and thus, the system becomes a practical indoor positioning method at present. VIRE (Active RFID-based Localization Using Virtual Reference elevation) is an RSSI-based indoor Localization algorithm, and the Localization effect of the LANDMAC (inductor Localization Sensing Using RFID) system is greatly improved by methods of linearly inserting Virtual Reference tags into a Localization area, constructing a proximity map to exclude small probability positions of tags to be detected and the like. Nonetheless, VIRE still has shortcomings. The newly added virtual reference label only covers the central part of the positioning area, so that the positioning effect of the point to be measured positioned at the boundary is not as good as that at the center; in addition, there is still room for further improvement in the overall positioning accuracy of the VIRE. Therefore, the present invention focuses on the problem of how to improve the positioning accuracy of the VIRE algorithm.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide an RSSI indoor positioning method with dynamic threshold introduced, which uses four boundary lines of the positioning area as mirror surfaces, and through the mapping function, symmetrically maps various reference labels in the range of approximately half of the central part of the positioning area to the outside of the boundary of the positioning area, and improves the positioning accuracy at the boundary by these newly added virtual reference labels.
The invention adopts the following technical scheme:
a RSSI indoor positioning method introducing dynamic threshold values is characterized in that a VIRE algorithm is utilized to establish a unitary linear regression equation of signal intensity values and coordinate values of an actual reference label and a virtual reference label, the signal intensity value of the virtual reference label in a newly added boundary area is obtained through calculation, and the virtual reference labels with the same density distribution as that of a central area are added in a positioning boundary area; then, taking the positioning boundary line as a mirror surface, mapping the reference label in the positioning area to the other side of the boundary line to form a mirror image reference label and a mirror image virtual label, and indirectly forming the boundary area into a central area surrounded by the virtual label at four circles; according to the RSSI value of the tag to be detected and the RSSI value of the reference tag read by the reader, finding out the RSSI value of the reference tag which has the maximum difference with the RSSI value of the tag to be detected to obtain the absolute value of the difference between the RSSI value and the RSSI value of the reference tag, determining the threshold value used when the tag to be detected constructs the adjacent map, and modifying the uniform threshold value used when the adjacent map is constructed into a dynamic threshold value which is changed according to the difference of the tag to be detected; and then constructing an adjacent map according to the dynamic threshold value to screen out a reference label suitable for the label to be detected, solving the position of the label to be detected according to the weight value and the coordinate of the reference label, and completing the positioning of the target in the whole area to be detected.
Specifically, the method comprises the following steps:
s1, configuring 4 readers at four corners of the positioning area, uniformly distributing virtual reference labels with an N-N structure in a square formed by taking every four actual reference labels as vertex angles, and establishing a unitary linear regression equation according to the RSSI of the actual reference labels read by the readers and the RSSI of the N-N distributed virtual reference labels calculated by a linear interpolation method mentioned in VIRE;
s2, calculating the signal intensity value of each newly added boundary virtual reference label on the boundary according to the unary linear regression equation established in the step S1;
s3, taking four boundary lines as mirror surfaces, symmetrically mapping labels in approximately half of a region to be detected on the inner side of the boundary lines to the outer sides of the boundary lines, respectively mapping an actual reference label and a virtual reference label in the region to be detected to a mirror image reference label and a mirror image virtual label in a mirror image region, and calculating the RSSI value of the mirror image label read by a reader in the system;
s4, according to the RSSI value of the to-be-detected label and the RSSI value of the reference label read by the reader, finding out the RSSI value of the reference label which is the largest difference with the RSSI value of the to-be-detected label, calculating the absolute value of the difference between the RSSI value and the RSSI value of the reference label, and determining the threshold value used when the to-be-detected label constructs the adjacent map;
s5, constructing an adjacent map according to a threshold value, screening out reference labels suitable for the labels to be tested, solving the weights of the reference labels, and then solving the estimated positions (x ', y') of the labels to be tested by using the weight values and the coordinates thereof;
s6, if the first estimated position (x ', y') of the to-be-detected label is located in the boundary area, multiplying the absolute value in the step S4 by 0.35 to serve as a secondary threshold used when the to-be-detected label constructs the adjacent map, and then using the new threshold to construct the adjacent map, and solving the weighting factor and the estimated position of the to-be-detected label; if the first estimated position (x ', y') of the tag to be detected is located in the central area, the original threshold value and the positioning result are not changed.
Further, in step S1, the unary linear equation is as follows:
Figure BDA0001816208400000031
wherein,
Figure BDA0001816208400000032
Figure BDA0001816208400000033
x is the abscissa of the position of the tag, S is the RSSI value read by the reader to the tag, the number of the readers in the system is set as n, and the RSSI values of the four actual reference tags and the virtual reference tags on the reader i are respectively set as Si1，Si2，Si3，Si4And si1，si2，si3，si4，si5，si6I is more than or equal to 1 and less than or equal to n, and the abscissa of the position in the system is X1，X2，X3，X4And x1，x2，x3，x4，x5，x6RSSI value and abscissa data set are (X)1,Si1)，(X2,Si2)，(X3,Si3)，(X4,Si4) And (x)1,si1)，(x2,si2)，(x3,si3)，(x4,si4)，(x5,si5)，(x6,si6)。
Further, in step S2, the RSSI value of the newly added virtual reference tag is obtained by using linear interpolation at the portion between the four boundary lines and the outermost periphery of the central area.
Further, the linear interpolation formula in the horizontal direction is as follows:
Figure BDA0001816208400000041
the linear interpolation formula in the vertical direction is as follows:
Figure BDA0001816208400000042
wherein S isk(Ti,j) Represents the RSSI value of the virtual reference tag at (i, j) read by the kth reader,
Figure BDA0001816208400000043
0≤p＝i％n≤n-1，0≤q＝j％n≤n-1。
further, in step S3, the mirror image labels outside the boundary are added to the system by using the four boundary lines, i.e. the upper, lower, left and right boundary lines as mirror planes, and the mapping function is as follows:
counting the actual reference labels in the positioning area from left to right and from bottom to top in sequence when the x axis is taken as a mirror surface until the actual reference labels reach half of the total number of the actual reference labels in the system, and taking a straight line represented by a vertical coordinate of the corresponding actual reference label at the moment as an upper boundary of the area to be mirrored; respectively taking the x axis, the y axis and the right boundary of the positioning area as the lower boundary, the left boundary and the right boundary of the area to be mirrored; mapping the actual reference label and the virtual reference label in the area to be mirrored to a corresponding mirror area below an x axis according to a mirror imaging principle to form a mirror reference label and a mirror virtual label; the position coordinates of the label in the mirror image area and the label in the area to be mirrored before mapping are symmetrical about an x axis and are an image and an object respectively;
counting the actual reference labels in the positioning area from bottom to top in the sequence from left to right when the y axis is taken as the mirror surface until the actual reference labels reach half of the total number of the actual reference labels in the system, and taking the straight line represented by the abscissa of the corresponding actual reference label at the moment as the right boundary of the area to be mirrored; respectively taking the upper boundaries of the x axis, the y axis and the positioning area as the lower boundary, the left boundary and the upper boundary of the area to be mirrored; mapping the actual reference label and the virtual reference label in the region to be mirrored to a y axis according to a mirror imaging principle, and enabling the mirror region corresponding to the left to be a mirror image reference label and a mirror image virtual label; the position coordinates of the label in the mirror image area and the label in the area to be mirrored before mapping are symmetrical about the y axis and are an image and an object respectively;
when the straight line of the right boundary of the positioning area is taken as the mirror surface, counting the actual reference labels in the positioning area from bottom to top and from right to left until the number of the actual reference labels in the system is half of the total number, and taking the straight line represented by the abscissa of the corresponding actual reference label at the moment as the left boundary of the area to be mirrored; respectively taking the x axis, the upper boundary and the right boundary of the positioning area as the lower boundary, the upper boundary and the right boundary of the area to be mirrored; mapping the actual reference label and the virtual reference label in the area to be mirrored to the right boundary of the positioning area according to the mirror imaging principle, and enabling the mirror area corresponding to the right to be the mirror image reference label and the mirror image virtual label; the position coordinates of the label in the mirror image area and the label in the area to be mirrored before mapping are symmetrical relative to the right boundary of the positioning area and are an image and an object respectively;
when the straight line where the upper boundary of the positioning area is located is taken as the mirror surface, counting the actual reference labels in the positioning area from left to right and from top to bottom in sequence until the number of the actual reference labels in the system is half of the total number, and taking the straight line represented by the vertical coordinate of the corresponding actual reference label at the moment as the lower boundary of the area to be mirrored; and respectively taking the y axis, the upper boundary and the right boundary of the positioning area as the left boundary, the upper boundary and the right boundary of the area to be mirrored. Mapping the actual reference label and the virtual reference label in the area to be mirrored to a corresponding mirror area above the upper boundary of the positioning area according to a mirror imaging principle to form a mirror image reference label and a mirror image virtual label; the position coordinates of the label in the mirror image area and the label in the area to be mirror imaged before mapping are symmetrical about the upper boundary of the positioning area and are an image and an object respectively.
Further, in step S4, multiplying the absolute value of the difference between the two values by a constant coefficient 0.55 as a threshold value when constructing a proximity map corresponding to the reader i, where each reader corresponds to a proximity map, including the whole positioning range and the newly added mirror image range, dividing the positioning range and the newly added mirror image range into a plurality of small regions centered on the reference tag, taking the RSSI value of the small region as the RSSI value corresponding to the reference tag at the center of the region, comparing the RSSI value with the RSSI value of each region after the reader reads the RSSI value of the tag to be detected, if the difference between the two values is within the solved threshold range, the reader marks the region as "1", taking the intersection of the proximity maps corresponding to all readers in the system, excluding the small probability position of the tag to be detected, and selecting the reference tag closest to the tag to be detected.
Further, in step S5, the final weighting factor ω is calculatediCalculating to obtain the first estimated position (x ', y') of the to-be-detected label as follows:
Figure BDA0001816208400000061
wherein (x)i,yi) Position coordinates, omega, of reference labels reserved for excluding small probability positions via a proximity mapi＝ω1i×ω2i，ω1iRepresenting the difference in signal strength values, ω, between the virtual reference tag remaining after excluding a small probability location using a proximity map and the tag under test2iThe density of the selected virtual reference label is shown, and the magnitude of the value is in positive correlation with the density.
Further, the difference ω of the signal intensity values between the virtual reference tag and the tag under test, which is retained after excluding the small probability location using the proximity map1iThe calculation is as follows:
Figure BDA0001816208400000062
density omega of selected virtual reference tags2iThe calculation is as follows:
Figure BDA0001816208400000063
wherein K represents the total number of readers in the system; sk(Ti) Representing the corresponding ith virtual on the kth readerThe RSSI value of the reference tag; sk(R) represents the RSSI value of the to-be-detected label collected by the kth reader, naThe total number of the reference labels selected by the small probability position elimination method of the adjacent map; p is a radical ofiIs the ratio of the number of the areas directly connected with the area represented by the selected reference label i to the total number of the areas to be detected; n isciIs the number of regions linked to the region represented by the selected reference label i.
Further, in step S6, the threshold value modification process includes: and calculating the absolute value of the difference between the RSSI value of the tag to be detected and the RSSI value of the reference tag which has the largest difference with the RSSI value, and multiplying the absolute value by a new constant coefficient 0.35 to serve as a new threshold value.
Compared with the prior art, the invention has at least the following beneficial effects:
the invention relates to an RSSI indoor positioning method introducing a dynamic threshold, which is based on VIRE and combines the plane mirror imaging principle. The method comprises the steps of firstly adding virtual reference labels which are distributed in the same density as the central area in a positioning boundary area, then mapping various reference labels in nearly half range in the positioning area to the other side of the boundary area by taking the positioning boundary area as a mirror surface to form a mirror image reference label and a mirror image virtual label, and thus enabling the boundary area to be indirectly a central area surrounded by the virtual labels at four sides. The mirror algorithm with the dynamic threshold can improve the positioning effect of the VIRE algorithm to a greater extent, and can achieve a more excellent positioning result than BVIRE.
Furthermore, a unitary linear regression equation is established according to the RSSI of the actual reference tag read by the reader and the RSSI of the virtual reference tag with N-N distribution calculated by using the linear interpolation method mentioned in the VIRE, the equation is totally consistent with the idea of obtaining the RSSI value by using the linear interpolation method used in the VIRE, the signal intensity value is still assumed to follow linear change, and in addition, the unitary linear regression equation has the advantages of simple calculation and small error.
Furthermore, the calculation method of the RSSI value of the virtual reference tag inserted in the boundary area is consistent with the linear interpolation method, so that the validity of the RSSI data in the system is consistent, and the reference data with the validity consistent with that in the VIRE can be obtained based on simpler thought and calculation method.
Furthermore, the research on the VIRE result shows that the label to be detected positioned in the central area can achieve a better positioning effect compared with the boundary area, the rule is simulated, and on the premise of reasonable assumption, the mirror image label is added outside the boundary in the system, so that the boundary is positioned in the center of a new area, and the positioning precision of the boundary is expected to be improved; by adopting the method of introducing the mirror image label, the boundary area can be uniformly covered by more virtual reference labels, more effective data information is provided for the positioning of the label to be detected at the boundary, and the positioning precision at the boundary is hopefully improved.
Furthermore, in order to make the reference position data selected by each label to be detected more targeted, the unified threshold value of all labels to be detected in the VIRE is changed into the dynamic threshold value which is different according to the different labels to be detected, so that the reference label most suitable for the current label to be detected can be obtained, the positioning error can be effectively reduced, and a new idea of future development is provided for the application of using the RFID technology to realize indoor positioning.
Furthermore, the setting method of the final weight factor is consistent with the VIRE, and is divided into the product of two sub-factors, so that the probability relation represented by the adjacent map can be more reasonably utilized, the corresponding setting method of the weight factor is changed along with the introduction of the adjacent map in the VIRE, and the effectiveness of the final positioning result can be improved.
Furthermore, because the proportion of the first threshold selection is set to be 0.55, more labels to be detected at the center need to be properly adjusted according to the first estimated position of the labels to be detected, the adjusting method here is to first judge whether the labels to be detected are located in the boundary area, so as to determine whether the proportion of 0.35 appropriate for the boundary area is used as auxiliary data when the secondary threshold is set, and errors caused by assuming that all labels to be detected are located in the center area during the first positioning can be corrected in time, so that a better positioning result as a whole is achieved.
In summary, compared with the VIRE, the invention increases the cardinality of the data volume by adding the mirror image label outside the boundary to provide more label signal strength value information as the reference data; the adopted dynamic threshold can be used for screening effective data from a plurality of reference data in a more reasonable mode and using the effective data; the number of each virtual reference label introduced on the basis of VIRE is basically consistent with that of BVIRE, and the method achieves better overall positioning effect through the use of dynamic threshold values.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
FIG. 1 is a system layout diagram of a mirroring algorithm;
FIG. 2 is a boundary virtual reference tag construction diagram;
FIG. 3 is a mapping process of border area reference labels;
FIG. 4 is a small probability elimination method for a neighborhood map;
FIG. 5 is a diagram of central region point distribution;
FIG. 6 shows the positioning effect of different algorithms on the central region to-be-measured point;
FIG. 7 is a diagram of a distribution of points to be measured in a boundary region;
FIG. 8 shows the positioning effect of different algorithms on the boundary region points to be measured;
FIG. 9 shows the positioning effect of different algorithms on the overall area to-be-measured point.
Detailed Description
The invention provides an RSSI indoor positioning method introducing a dynamic threshold, which is based on VIRE and combines the plane mirror imaging principle. The method comprises the steps of firstly adding virtual reference labels which are distributed in the same density as the central area in a positioning boundary area, then mapping various reference labels in nearly half range in the positioning area to the other side of the boundary area by taking the positioning boundary area as a mirror surface to form a mirror image reference label and a mirror image virtual label, and thus enabling the boundary area to be indirectly a central area surrounded by the virtual labels at four sides. In order to preferably select the reference label most suitable for each label to be detected to a greater extent, the algorithm is modified in terms of threshold value, and the uniform threshold value used when the adjacent map is constructed is modified into a dynamic threshold value which is changed according to the difference of the labels to be detected. Simulation results show that the mirror image algorithm with the dynamic threshold can improve the positioning effect of the VIRE algorithm to a great extent, and the overall positioning effect is superior to the BVIRE.
The invention relates to an RSSI indoor positioning method introducing a dynamic threshold, which comprises the following steps:
s1, on the basis of the VIRE algorithm, establishing a proper unary linear regression equation according to the signal intensity values and coordinate values of the actual reference label and the virtual reference label in the central area;
s2, calculating the signal intensity value of each newly added boundary virtual reference label on the boundary (the virtual reference label is different according to the distribution density of the virtual reference label at the center part of the VIRE, and sometimes needs to be added into the system, and sometimes does not need to be added) according to the unary linear regression equation established in the step S1;
s3, obtaining the signal intensity value of the virtual reference label added into the part between the four boundary lines and the outermost periphery of the central area by using a linear interpolation method;
and S4, after the previous step, the region to be detected is full of various reference labels (including the label in the initial VIRE and the newly added virtual reference label) with the same density distribution. Then, taking four boundary lines as mirror surfaces, symmetrically mapping labels in approximately half of a region to be detected on the inner side of the boundary lines (wherein the inner side refers to the direction of the region to be detected, and the outer side refers to the direction opposite to the region to be detected) to the outer side of the boundary lines, and respectively mapping an actual reference label and a virtual reference label in the region to be detected into a mirror image reference label and a mirror image virtual label in the mirror image region;
s5, calculating signal intensity values of mirror image tags read by readers in the system, wherein RSSI values of tags read by a part of readers can be directly obtained according to 'symmetrical equality', and the other RSSI values need to be obtained through a regression equation method;
s6, according to the RSSI value of the label to be detected read by the reader and the RSSI values of the various reference labels, finding out the RSSI value of the reference label which has the maximum difference with the RSSI value of the label to be detected, and calculating the absolute value of the difference between the RSSI value and the RSSI value. Multiplying the value by a constant coefficient 0.55 to serve as a threshold value used when the to-be-detected label constructs an adjacent map;
s7, constructing an adjacent map according to a threshold value, screening out reference labels suitable for the labels to be tested, solving the weights of the reference labels, and then solving the positions of the labels to be tested by using the weight values and the coordinates thereof;
s8, judging whether the estimated position of the to-be-detected label is located in the boundary area, if so, modifying the first threshold value, wherein the modifying process is as follows: and calculating the absolute value of the difference between the RSSI value of the tag to be detected and the RSSI value of the reference tag which has the largest difference with the RSSI value, and multiplying the absolute value by a new constant coefficient 0.35 to serve as a new threshold value. And then, using the new threshold value to construct an adjacent map, solving a weight factor, estimating the position of the label to be detected and the like.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
1. As shown in fig. 1, the typical via algorithm layout has 16 actual reference tags distributed in a 4 × 4 structure at the center of an 8 × 8 positioning region, and 4 readers are arranged at four corners of the positioning region, i.e., at four positions with coordinates (0,0), (0,8), (8,0), and (8, 8). Virtual reference labels with an N × N (in this case, N is 5) structure are uniformly distributed in a square formed by taking every four actual reference labels as vertex angles, as shown in the right part indicated by arrows in the figure. And establishing a proper unary linear regression equation on the basis, and solving the RSSI value of the boundary virtual reference label read by each reader.
On the premise of knowing the RSSI of the actual reference tag read by the reader and the RSSI of the virtual reference tag with N × N distribution calculated by the linear interpolation method mentioned in the VIRE, the method for establishing the unary linear regression equation is as follows:
the basic expression of a unary linear equation is
Figure BDA0001816208400000111
Wherein, x is the abscissa of the position of the tag, and s is the RSSI value read by the reader to the tag;
Figure BDA0001816208400000112
Figure BDA0001816208400000113
please refer to fig. 2, which shows four actual reference tags T1，T2，T3，T4And a plurality of virtual reference tags such as t1, t2, t3, t4, t5, and t6, for example, if the number of readers in the system is n, the RSSI values of the four actual reference tags and the plurality of virtual reference tags on the reader i are respectively Si1，Si2，Si3，Si4And si1，si2，si3，si4，si5，si6Wherein 1. ltoreq. i.ltoreq.n, and the abscissa of the position of these in the system is X1，X2，X3，X4And x1，x2，x3，x4，x5，x6. Then, in connection with equation (1-1), the associated RSSI values and abscissa data set (X) may be used1,Si1)，(X2,Si2)，(X3,Si3)，(X4,Si4) And (x)1,si1)，(x2,si2)，(x3,si3)，(x4,si4)，(x5,si5)，(x6,si6) The equations (1-2) and (1-3) are substituted, respectively, to find a set corresponding to the reader i
Figure BDA0001816208400000114
And
Figure BDA0001816208400000115
the value is obtained.
Then according to the formula (1-1) and the boundary virtual reference label T11And T12Can get their RSSI values on the reader i.
By the method, the RSSI values read by the readers of the virtual reference tags on the left and right boundaries of the positioning area can be obtained. The RSSI values of the virtual reference tags on the upper and lower boundaries of the positioning area can be obtained by a method similar to the above method, and only x in the above three formulas is replaced by y.
2. The RSSI value of the newly added virtual reference tag is then obtained using linear interpolation at the portions between the four border lines and the outermost periphery of the central region.
Wherein the linear interpolation formula is as follows:
in the horizontal direction:
Figure BDA0001816208400000121
in the vertical direction:
Figure BDA0001816208400000122
in the above two formulae, Sk(Ti,j) Represents the RSSI value of the virtual reference tag at (i, j) read by the kth reader, wherein,
Figure BDA0001816208400000123
0≤p＝i％n≤n-1，0≤q＝j％n≤n-1。
after linear interpolation is used in the boundary area, the virtual reference labels with the same density are distributed in the boundary part and the central part of the positioning area. A specific illustration can be seen in fig. 1 with the first three rows of labels (partially) indicated by the lower arrow, wherein the third row is the border virtual label on the lower border.
3. For the classical layout in fig. 1, the system has a total of four readers, and all are distributed on the boundary line of the positioning area. The upper, lower, left and right boundary lines are respectively used as mirror surfaces, mirror image labels outside the boundary lines are added into the system through the mapping action,
the specific addition method is as follows:
①, taking an x axis as a mirror surface, mapping an actual reference label and a virtual reference label in an area enclosed by the x axis, the y-3 and the x-8 into an area bounded by the x axis, the y-3 and the x-8 according to a mirror imaging principle to form a mirror image reference label and a mirror image virtual label;
②, taking the y axis as a mirror surface, mapping the actual reference label and the virtual reference label in the area enclosed by the x axis, the y axis, the x-3 and the y-8 into the area bounded by the x axis, the y axis, the x-3 and the y-8 according to the mirror imaging principle to form a mirror image reference label and a mirror image virtual label;
③, taking the straight line of x-8 as a mirror surface, mapping the real reference label and the virtual reference label in the area enclosed by the x-axis, x-5, x-8 and y-8 into the area bounded by the x-axis, x-8, x-11 and y-8 according to the mirror imaging principle to form a mirror image reference label and a mirror image virtual label;
④, a straight line of y-8 is used as a mirror surface, and an actual reference label and a virtual reference label in an area enclosed by y-axis, x-8, y-5 and y-8 are mapped to an area with y-axis, x-8, y-8 and y-11 as boundaries according to the mirror imaging principle to form a mirror image reference label and a mirror image virtual label.
The overall layout of the system is shown in fig. 3 (only part of the system is shown due to the excessive number of labels) by the mapping effect of the upper four boundary lines.
The RSSI value of the mirror tag in the mirror area is described here only by taking the left boundary as an example:
for the left boundary of the area, due to the symmetric distribution of the tags on both sides, the tags and the various angle and distance information between the tags and the boundary line are all symmetrically the same, so the RSSI values of the mirror reference tag and the mirror virtual tag read by the reader No. 0 and the reader No. 1 on the boundary line, which are about the left side of the boundary, should be equal to the RSSI values of the actual reference tag and the virtual reference tag located on the right side of the boundary.
For the reader No. 2 and the reader No. 3 in the figure, the position and angle information of the object and the image on the left boundary from the object and the image no longer have a symmetrical relationship, so the RSSI values of the mirror image reference tag and the mirror image virtual tag on the left side of the left boundary read by the readers cannot be directly obtained according to the symmetry equality as described above, and other methods are needed.
For the readers No. 2 and No. 3, the RSSI values of the left-border left-side mirror image reference tag and the mirror image virtual tag on the corresponding readers can be calculated by using the regression equation method described above through the known RSSI values of the left-border right-side actual reference tag and the virtual reference tag.
4. So far, the mirror image algorithm has obtained the RSSI values of various tags in the system on each reader, and then according to the RSSI value of the tag to be detected read by the reader i, the RSSI value with the maximum difference between the signal strengths of the tag to be detected and the corresponding reader is found out, and the absolute value of the difference between the two is calculated, and then the value is multiplied by a constant coefficient 0.55 to be used as a threshold value when constructing the adjacent map corresponding to the reader i.
The process of building a proximity map is shown in fig. 4: each reader corresponds to a proximity map which comprises the whole positioning range and divides the positioning area into a plurality of small areas with reference tags (comprising actual reference tags and virtual reference tags) as the centers, and the RSSI value of each small area is taken as the RSSI value corresponding to the reference tag in the center of the area. When the reader reads the RSSI of the tag to be detected, the RSSI is compared with the RSSI of each area, and if the difference between the RSSI and the RSSI is within the solved threshold range, the reader marks the area as 1 (as shown by a black square in the figure). And finally, taking intersection from adjacent maps corresponding to all readers in the system, and eliminating the small probability position of the tag to be detected, thereby selecting the reference tag closest to the tag to be detected.
5. After the reference label closest to the label to be detected is obtained, the weight values of the reference label and the label to be detected need to be solved, and the product of two sub-factors in the VIRE is still selected as the weight factor. The specific formula is as follows:
Figure BDA0001816208400000141
ω1iexpressing the difference of signal intensity values between the virtual reference label and the label to be detected which are reserved after the small probability position is eliminated by using the adjacent map, wherein in the formula (1-6), K represents the total number of readers in the system; sk(Ti) The RSSI value of the corresponding ith virtual reference label on the kth reader is represented; skAnd (R) represents the RSSI value of the to-be-detected label collected by the kth reader.
Figure BDA0001816208400000142
ω2iRepresenting the density of the selected virtual reference label, the magnitude of which is positively correlated with the density, in equations (1-7), naThe total number of the reference labels selected by the small probability position elimination method of the adjacent map; p is a radical ofiIs the ratio of the number of the areas directly connected with the area represented by the selected reference label i to the total number of the areas to be detected; n isciIs the number of regions linked to the region represented by the selected reference label i.
The final weighting factor is formulated as follows:
ωi＝ω1i×ω2i(1-8)
the estimated position of the to-be-detected label can be calculated according to the weight values in the formulas (1-8)
Figure BDA0001816208400000151
Wherein (x)i,yi) References retained for excluding small probability locations across a neighboring mapThe location coordinates of the tag.
6. And (4) according to the estimated position of the label to be detected calculated in the previous step, judging whether the label to be detected is positioned in a boundary area, namely the part between the four boundary lines and the outermost periphery of the central area, if the label to be detected is positioned in the area, readjusting the threshold value to perform secondary positioning, and correcting the primary positioning result.
The new threshold setting method at this time is as follows: and modifying the constant coefficient in the fourth step to be 0.35, and then performing the subsequent steps of constructing a proximity map, determining the weight factor of the reference label, performing secondary positioning and the like.
Simulation and analysis
Simulation environment: selecting the region to be tested as shown in FIG. 1, and taking 1000 random points (as shown in FIG. 5) located in the central region as the points to be tested for simulation. And selecting values of simulation parameters N, N, th, k and the like when the positioning effect of algorithms such as LANDMARC, VIRE, BVIRE and the like is good.
Fig. 6 shows the error accumulation distribution diagram after the 1000 random points to be measured are positioned by using four different algorithms, and the simulation diagram shows that the mirror image algorithm has much improved positioning effect on the VIRE at the center, and can achieve better positioning effect than the BVIRE.
Then 1000 random points (as shown in fig. 7) located in the boundary area are taken as points to be measured for simulation. Fig. 8 shows an error accumulation distribution diagram after positioning the points to be measured by using four different algorithms, and a simulation diagram shows that the mirror image algorithm also improves the positioning effect of the VIRE at the boundary, and can achieve the same level of positioning effect as the BVIRE.
And finally, integrating the central area and the boundary area, and analyzing the positioning effect of different algorithms on the whole area to be detected. Considering that the main moving range of the indoor object is still the central area, a total of 1000 random numbers are selected as the points to be measured when selecting the points to be measured, wherein 900 are located in the central area, and the other 100 are located in the boundary area. The final simulation results are shown in fig. 9. As can be seen from the figure, the mirror image algorithm obviously improves the positioning effect of VIRE, and the overall positioning effect is better than that of BVIRE.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims (6)

1. An RSSI indoor positioning method introducing dynamic threshold is characterized in that a VIRE algorithm is utilized to establish a unary linear regression equation of signal intensity values and coordinate values of an actual reference label and a virtual reference label, the signal intensity value of the virtual reference label in a newly added boundary area is obtained through calculation, and the virtual reference label with the same density distribution as that of a central area is added in a positioning boundary area; then, taking the positioning boundary line as a mirror surface, mapping the reference label in the positioning area to the other side of the boundary line to form a mirror image reference label and a mirror image virtual label, and indirectly forming the boundary area into a central area surrounded by the virtual label at four circles; according to the RSSI value of the tag to be detected and the RSSI value of the reference tag read by the reader, finding out the RSSI value of the reference tag which has the maximum difference with the RSSI value of the tag to be detected to obtain the absolute value of the difference between the RSSI value and the RSSI value of the reference tag, determining the threshold value used when the tag to be detected constructs the adjacent map, and modifying the uniform threshold value used when the adjacent map is constructed into a dynamic threshold value which is changed according to the difference of the tag to be detected; then, constructing an adjacent map according to the dynamic threshold value to screen out a reference label suitable for the label to be detected, solving the position of the label to be detected according to the weight value and the coordinate of the reference label, and completing the positioning of the target in the whole area to be detected, wherein the method comprises the following steps:
s1, configuring 4 readers at four corners of the positioning area, uniformly distributing virtual reference labels with an N-N structure in a square formed by taking every four actual reference labels as vertex angles, and establishing a unitary linear regression equation according to the RSSI of the actual reference labels read by the readers and the RSSI of the N-N distributed virtual reference labels calculated by a linear interpolation method mentioned in VIRE;
s2, calculating the signal strength value of each newly added boundary virtual reference label on the boundary according to the unitary linear regression equation established in step S1, obtaining the RSSI value of the newly added virtual reference label by using a linear interpolation method at the portion between the four boundary lines and the outermost periphery of the central region, wherein the linear interpolation formula in the horizontal direction is as follows:
Figure FDA0002391890850000011
the linear interpolation formula in the vertical direction is as follows:
Figure FDA0002391890850000012
wherein S isk(Ti,j) Represents the RSSI value of the virtual reference tag at (i, j) read by the kth reader,
Figure FDA0002391890850000021
0≤p＝i％n≤n-1，0≤q＝j％n≤n-1；
s3, taking four boundary lines as mirror surfaces, symmetrically mapping labels in approximately half of a region to be detected on the inner side of the boundary lines to the outer sides of the boundary lines, respectively mapping an actual reference label and a virtual reference label in the region to be detected to a mirror image reference label and a mirror image virtual label in a mirror image region, and calculating the RSSI value of the mirror image label read by a reader in the system;
s4, according to the RSSI value of the to-be-detected label read by the reader and the RSSI value of the reference label, finding the RSSI value of the reference label which has the maximum difference with the RSSI value of the to-be-detected label, calculating the absolute value of the difference between the RSSI value and the RSSI value of the reference label, determining the threshold value used when the to-be-detected label constructs the adjacent map, multiplying the absolute value of the difference between the RSSI value and the threshold value by a constant coefficient 0.55 to be used as the threshold value when the adjacent map corresponding to the reader i is constructed, wherein each reader corresponds to one adjacent map and comprises the whole positioning range and a newly-added mirror range, dividing the positioning range and the newly-added mirror range into a plurality of small areas taking the reference label as the center, taking the RSSI value of the small area as the RSSI value corresponding to the reference label at the center of the area, comparing the RSSI value with the RSSI value of each area after the reader reads the RSSI value of the to-detected label, if, taking intersection of adjacent maps corresponding to all readers in the system, excluding the small probability position of the tag to be detected, and selecting a reference tag close to the tag to be detected;
s5, constructing an adjacent map according to a threshold value, screening out reference labels suitable for the labels to be tested, solving the weights of the reference labels, and then solving the estimated positions (x ', y') of the labels to be tested by using the weight values and the coordinates thereof;
s6, if the first estimated position (x ', y') of the to-be-detected label is located in the boundary area, multiplying the absolute value in the step S4 by 0.35 to serve as a secondary threshold used when the to-be-detected label constructs the adjacent map, and then using the new threshold to construct the adjacent map, and solving the weighting factor and the estimated position of the to-be-detected label; if the first estimated position (x ', y') of the tag to be detected is located in the central area, the original threshold value and the positioning result are not changed.
2. The RSSI indoor positioning method with introduced dynamic threshold as claimed in claim 1, wherein in step S1, the unary linear equation is as follows:
Figure FDA0002391890850000031
wherein,
Figure FDA0002391890850000032
Figure FDA0002391890850000033
x is the abscissa of the position of the tag, S is the RSSI value read by the reader to the tag, the number of the readers in the system is set as n, and the RSSI values of the four actual reference tags and the virtual reference tags on the reader i are respectively set as Si1，Si2，Si3，Si4And si1，si2，si3，si4，si5，si6I is more than or equal to 1 and less than or equal to n, and the abscissa of the position in the system is X1，X2，X3，X4And x1，x2，x3，x4，x5，x6RSSI value and abscissa data set are (X)1,Si1)，(X2,Si2)，(X3,Si3)，(X4,Si4) And (x)1,si1)，(x2,si2)，(x3,si3)，(x4,si4)，(x5,si5)，(x6,si6)。
3. The RSSI indoor positioning method as claimed in claim 1, wherein in step S3, four boundary lines, i.e. upper, lower, left and right, are used as mirror surfaces respectively, and mirror labels outside the boundary are added to the system through mapping, specifically as follows:
counting the actual reference labels in the positioning area from left to right and from bottom to top in sequence when the x axis is taken as a mirror surface until the actual reference labels reach half of the total number of the actual reference labels in the system, and taking a straight line represented by a vertical coordinate of the corresponding actual reference label at the moment as an upper boundary of the area to be mirrored; respectively taking the x axis, the y axis and the right boundary of the positioning area as the lower boundary, the left boundary and the right boundary of the area to be mirrored; mapping the actual reference label and the virtual reference label in the area to be mirrored to a corresponding mirror area below an x axis according to a mirror imaging principle to form a mirror reference label and a mirror virtual label; the position coordinates of the label in the mirror image area and the label in the area to be mirrored before mapping are symmetrical about an x axis and are an image and an object respectively;
counting the actual reference labels in the positioning area from bottom to top in the sequence from left to right when the y axis is taken as the mirror surface until the actual reference labels reach half of the total number of the actual reference labels in the system, and taking the straight line represented by the abscissa of the corresponding actual reference label at the moment as the right boundary of the area to be mirrored; respectively taking the upper boundaries of the x axis, the y axis and the positioning area as the lower boundary, the left boundary and the upper boundary of the area to be mirrored; mapping the actual reference label and the virtual reference label in the region to be mirrored to a y axis according to a mirror imaging principle, and enabling the mirror region corresponding to the left to be a mirror image reference label and a mirror image virtual label; the position coordinates of the label in the mirror image area and the label in the area to be mirrored before mapping are symmetrical about the y axis and are an image and an object respectively;
when the straight line of the right boundary of the positioning area is taken as the mirror surface, counting the actual reference labels in the positioning area from bottom to top and from right to left until the number of the actual reference labels in the system is half of the total number, and taking the straight line represented by the abscissa of the corresponding actual reference label at the moment as the left boundary of the area to be mirrored; respectively taking the x axis, the upper boundary and the right boundary of the positioning area as the lower boundary, the upper boundary and the right boundary of the area to be mirrored; mapping the actual reference label and the virtual reference label in the area to be mirrored to the right boundary of the positioning area according to the mirror imaging principle, and enabling the mirror area corresponding to the right to be the mirror image reference label and the mirror image virtual label; the position coordinates of the label in the mirror image area and the label in the area to be mirrored before mapping are symmetrical relative to the right boundary of the positioning area and are an image and an object respectively;
when the straight line where the upper boundary of the positioning area is located is taken as the mirror surface, counting the actual reference labels in the positioning area from left to right and from top to bottom in sequence until the number of the actual reference labels in the system is half of the total number, and taking the straight line represented by the vertical coordinate of the corresponding actual reference label at the moment as the lower boundary of the area to be mirrored; respectively taking the upper boundary and the right boundary of the y-axis positioning area as the left boundary, the upper boundary and the right boundary of the area to be mirrored; mapping the actual reference label and the virtual reference label in the area to be mirrored to a corresponding mirror area above the upper boundary of the positioning area according to a mirror imaging principle to form a mirror image reference label and a mirror image virtual label; the position coordinates of the label in the mirror image area and the label in the area to be mirror imaged before mapping are symmetrical about the upper boundary of the positioning area and are an image and an object respectively.
4. The RSSI indoor positioning method with the dynamic threshold introduced as claimed in claim 1, wherein in step S5, the final weighting factor ω is determined according toiCalculating to obtain the first estimated position (x ', y') of the to-be-detected label as follows:
Figure FDA0002391890850000041
wherein (x)i,yi) Position coordinates, omega, of reference labels reserved for excluding small probability positions via a proximity mapi＝ω1i×ω2i，ω1iRepresenting the difference in signal strength values, ω, between the virtual reference tag remaining after excluding a small probability location using a proximity map and the tag under test2iThe density of the selected virtual reference label is shown, and the magnitude of the value is in positive correlation with the density.
5. The RSSI indoor positioning method of claim 4, wherein the difference ω in signal strength values between the virtual reference tag and the tag under test that remain after excluding the small probability location using the proximity map is characterized by1iThe calculation is as follows:
Figure FDA0002391890850000051
density omega of selected virtual reference tags2iThe calculation is as follows:
Figure FDA0002391890850000052
wherein K represents the total number of readers in the system; sk(Ti) The RSSI value of the corresponding ith virtual reference label on the kth reader is represented; sk(R) represents the RSSI value of the to-be-detected label collected by the kth reader, naThe total number of the reference labels selected by the small probability position elimination method of the adjacent map; p is a radical ofiIs the ratio of the number of the areas directly connected with the area represented by the selected reference label i to the total number of the areas to be detected; n isciIs the number of regions linked to the region represented by the selected reference label i.
6. The RSSI indoor positioning method with introduced dynamic threshold as claimed in claim 1, wherein in step S6, the threshold is modified by: and calculating the absolute value of the difference between the RSSI value of the tag to be detected and the RSSI value of the reference tag which has the largest difference with the RSSI value, and multiplying the absolute value by a new constant coefficient 0.35 to serve as a new threshold value.
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