CN110677813B - Visible light indoor positioning method of DV-hop based on fruit fly correction - Google Patents

Visible light indoor positioning method of DV-hop based on fruit fly correction Download PDF

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CN110677813B
CN110677813B CN201810706504.3A CN201810706504A CN110677813B CN 110677813 B CN110677813 B CN 110677813B CN 201810706504 A CN201810706504 A CN 201810706504A CN 110677813 B CN110677813 B CN 110677813B
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hop
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indoor positioning
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张月霞
殷生旺
吴嘉敏
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Beijing Information Science and Technology University
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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
    • H04W4/029—Location-based management or tracking services
    • 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
    • G01S5/16—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using electromagnetic waves other than radio waves
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B10/00—Transmission systems employing electromagnetic waves other than radio-waves, e.g. infrared, visible or ultraviolet light, or employing corpuscular radiation, e.g. quantum communication
    • H04B10/11—Arrangements specific to free-space transmission, i.e. transmission through air or vacuum
    • H04B10/114—Indoor or close-range type systems
    • H04B10/116—Visible light communication

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Abstract

The invention provides a visible light indoor positioning method of DV-hop based on drosophila correction. Wherein the method comprises the following steps: firstly, a mathematical model of visible light indoor positioning is established, and an unknown node of a visible light system is positioned by using DV-hop-based protocol. The optimal anchor node in the DV-hop visible light positioning method is selected by introducing a drosophila optimization search algorithm, so that the average hop distance is closer to the real distance between the anchor node and an unknown node, and the positioning precision is improved.

Description

Visible light indoor positioning method of DV-hop based on fruit fly correction
Technical Field
The invention relates to the technical field of visible light indoor positioning optimization, in particular to a visible light indoor positioning method based on Drosophila correction DV-hop.
Background
The demand for indoor positioning technology is increasing and receiving more and more attention. Positioning algorithms can be broadly divided into ranging and non-ranging. The DV-Hop positioning algorithm is a non-ranging-based positioning algorithm, and is favored because of no need of increasing hardware cost and high positioning precision. The visible light communication technology is a communication system using a white light LED as an information carrier, has the advantages of rich spectrum resources, energy conservation, environmental protection, low power consumption, high positioning precision and the like, and becomes a research hotspot. Therefore, the research on the DV-hop-based visible light positioning algorithm has very important significance.
At present, the existing research on the visible light positioning algorithm is concentrated on a distance measurement method, and the method is easily influenced by multipath effect and LED receiving angle, so that the positioning accuracy is reduced. In the DV-hop localization algorithm, the method of replacing the actual distance of the anchor node with the estimated average hop distance of the unknown node brings inevitable error to the localization.
Disclosure of Invention
The invention provides a fruit fly correction-based DV-hop visible light indoor positioning method, which is characterized in that an unknown node of a visible light system is positioned by using a non-ranging DV-hop protocol, and a fruit fly optimization search algorithm is introduced to select an optimal anchor node in the DV-hop visible light positioning algorithm, so that the average hop distance is closer to the real distance between the anchor node and the unknown node, and the positioning accuracy is improved.
The visible light indoor positioning method based on Drosophila modified DV-hop comprises the following steps:
1) establishing a mathematical model of visible light indoor positioning;
2) the anchor node broadcasts grouping information containing self position and minimum hop count;
3) establishing a mathematical model of average distance per hop;
4) method for solving optimal average distance per hop 'by utilizing fruit fly optimization algorithm based on adaptive step length'i；
5) Calculating the distance from the unknown node to the anchor node;
6) and estimating the self coordinate position by using a maximum likelihood estimation method.
The method for establishing the mathematical model of visible light indoor positioning in the step 1 comprises the following steps:
and a room with a positioning space of 5m multiplied by 3m in the visible light indoor positioning model. Several white light LED light sources (S)1，S2…SN) Arranged on the ceiling in a room, and the coordinates of the ceiling are known as (x)1，y1，z1)，(x2，y2，z2)…(xN，yN，zN). And the PN is a node to be positioned, is positioned at any position in a room, has unknown coordinates and is set as (x, y, z).
In the step 2, the anchor node broadcasts the grouping information including its own position and the minimum hop count.
The anchor node broadcasts a packet (id) containing a node identification number, a node position and a hop count value to the neighbor nodesi，xi，yiHop), the neighbor nodes record the identification numbers, coordinate values and smaller hop values of the nodes. And forwarding the packet with the hop value added by 1.
In step 3, a mathematical model of the average per-hop distance is established.
Calculating the actual distance d between the anchor nodes i and j by using the formula (1) according to the recorded node position informationij：
Figure GSB0000193464990000021
Calculating average hop distance hopsize of each anchor node by using formula (2)i：
Figure GSB0000193464990000022
Wherein hossijExpressed as the number of hops between anchor nodes i to j (i ≠ j), ε ═ dij-deijL is deij＝hopsij×hopsizeiThe resulting error. Rational hopsizeiε should be minimized so the calculation becomes a minimization problem, the mathematical model is shown in equation (3).
Figure GSB0000193464990000023
In the step 4, the optimal average hop distance hopsize 'is obtained by utilizing a fruit fly optimization algorithm'i。
In solution space [0, max (d)ij)]Internally randomly generating an initial population (solution set) t;
evaluating all individuals in the population by using the formula (3), and selecting the optimal individual for storage;
optimizing the rest individuals by adopting a self-adaptive step length algorithm to obtain a new population;
repeating the steps for the obtained new population until the iteration number reaches the set maximum value;
output optimal solution to variable hopsize'i。
In the step 5, the distance from the unknown node to the anchor node is calculated:
deij＝hopsij×hopsize′i (4)
in the step 6, the self coordinate position is estimated by using a maximum likelihood estimation method.
The unknown node estimates the coordinate position of the unknown node by using 3 or more anchor node distances.
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In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings, in which:
FIG. 1 is a schematic flow chart of the operation of the present invention.
FIG. 2 shows exemplary simulation results of average positioning errors (R20 m) of DV-hop and Drosophila DV-hop at different beacon node numbers.
FIG. 3 shows exemplary simulation results of the average positioning error (R30 m) of DV-hop and Drosophila DV-hop at different beacon node numbers.
Detailed Description
The following describes in further detail embodiments of the present invention with reference to the accompanying drawings.
FIG. 1 is a schematic flow chart of the operation of the present invention. First, the anchor node broadcasts packet information including its own location and the minimum number of hops. Then, the average distance per hop of the optimal anchor node is solved by a drosophila optimization algorithm. And (3) carrying out weighting processing on the unknown nodes to average the distance of each hop, calculating the distance between the unknown nodes and the anchor nodes, and finally estimating the coordinate positions of the unknown nodes by utilizing a maximum likelihood estimation algorithm.
FIG. 1 shows a schematic flow chart of the operation of the method of the present invention, wherein the specific steps are as follows:
1. randomly distributing 200 nodes in an area of 100m by 100m, wherein the communication radius between the nodes is 20m and 30 m;
2. calculating the average hop distance of each anchor node;
Figure GSB0000193464990000031
3. optimizing the average hop distance by using a drosophila optimization algorithm;
calculating the actual distance between the anchor nodes i and j by using the formula (2) according to the recorded node position information
Figure GSB0000193464990000032
The estimated distance between the anchor nodes i and j is equal to the minimum hop count obtained in the first stage, and is obtained by multiplying the average hop distance obtained by the formula (1), namely deij＝hopsij×hopsizei
The following formula was optimized using a drosophila optimization algorithm:
Figure GSB0000193464990000033
get the best anchor node average distance per hop of hopsize'i。
4. Estimating the position of an unknown node;
deij＝hopsij×hopsize′i (4)
5. calculating the positioning precision;
Figure GSB0000193464990000041
wherein (x)i，yi，zi) Is the true coordinate of the unknown node, (x'i，y′i，z′i) Are the located coordinates.
And finally, verifying the performance of the algorithm through experimental simulation. The algorithm adopts the positioning precision as the evaluation standard of the simulation result of the algorithm. Fig. 2 and 3 show simulation results of conventional DV-hop and drosophila-modified DV-hop algorithms at communication radii of 20m and 30m, and the results show that the positioning accuracy of both algorithms increases with the number of beacons, and becomes stable when the number of beacons exceeds 30. The average error of the Drosophila modified DV-hop algorithm is obviously lower than that of the DV-hop positioning algorithm, the average positioning error of the Drosophila modified DV-hop algorithm is smaller than that of the DV-hop positioning algorithm along with the increase of the number of beacon nodes, and the positioning accuracy change curve is more stable.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and it is obvious that those skilled in the art can make various changes and modifications of the present invention without departing from the spirit and scope of the present invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (3)

1. The visible light indoor positioning method of DV-hop based on drosophila correction is characterized by comprising the following steps:
1) establishing a mathematical model of visible light indoor positioning;
2) the anchor node broadcasts grouping information containing self position and minimum hop count;
3) establishing a mathematical model of average distance per hop;
4) method for solving optimal average distance per hop 'by utilizing fruit fly optimization algorithm based on adaptive step length'i；
5) Calculating the distance from the unknown node to the anchor node;
6) estimating the coordinate position of the self by using a maximum likelihood estimation method;
in step 3, a mathematical model of the average distance per hop is established:
the visible light indoor positioning model is provided with a plurality of white light LED light sources (S)1，S2…SN) Arranged on the ceiling in a room, and the coordinates of the ceiling are known as (x)1，y1，z1)，(x2，y2，z2)…(xN，yN，zN) PN is a node to be positioned, is positioned at any position in a room, has unknown coordinates and is set as (x, y, z);
the anchor node broadcasts a packet (id) containing a node identification number, a node position and a hop count value to the neighbor nodesi，xi，yiHop), the neighbor nodes record the identification number, coordinate value and smaller hop value of each node, and forward the packet after the hop value is added with 1;
calculating the actual distance d between the anchor nodes i and j by using the formula (1) according to the recorded node position informationij：
Figure FSB0000195055140000011
Calculating average hop distance hopsize of each anchor node by using formula (2)i：
Figure FSB0000195055140000012
Wherein hossijExpressed as the number of hops between anchor nodes i to j (i ≠ j), ε ═ dij-deijL is deij＝hopsij×hopsizeiInduced errors, rational hopsizeiε should be minimized so the calculation becomes a minimum problem, and the mathematical model is shown in equation (3):
Figure FSB0000195055140000013
wherein in the step 4, the optimal average hop distance hopsize 'is obtained by utilizing a fruit fly optimization algorithm'i：
In solution space [0, max (d)ij)]Internally randomly generating an initial population (solution set) t;
evaluating all individuals in the population by using the formula (3), and selecting the optimal individual for storage;
optimizing the rest individuals by adopting a self-adaptive step length algorithm to obtain a new population;
repeating the steps for the obtained new population until the iteration number reaches the set maximum value;
output optimal solution to variable hopsize'i。
2. The drosophila-modified DV-hop-based visible light indoor positioning method as claimed in claim 1, wherein in said step 5, the distance d from the unknown node to the anchor node is calculatedeij：
deij＝hopsij×hopsize′i (4) 。
3. The drosophila-modified DV-hop-based visible light indoor positioning method as claimed in claim 1, wherein in said step 6, the self-coordinate position is estimated by using maximum likelihood estimation, and the self-coordinate position is estimated by using 3 or more anchor node distances for the unknown node.
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CN105682026A (en) * 2016-01-08 2016-06-15 南昌大学 Improved DV-Hop localization method based on hop count threshold optimal average hop distance
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Denomination of invention: DV hop indoor visible light localization method based on drosophila correction

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