CN111880140A - RSSI-based wireless sensor network arc triangle positioning method - Google Patents
RSSI-based wireless sensor network arc triangle positioning method Download PDFInfo
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Abstract
The invention provides an RSSI-based wireless sensor network arc triangle positioning method, which comprises the following steps: s10, establishing an indoor positioning layout model based on circular-arc triangular layout, and arranging a plurality of beacon nodes on the indoor positioning layout model; s20, initializing the indoor positioning layout model and the position information of the beacon nodes; s30, continuously receiving a plurality of RSSI signals of the node to be tested, filtering the RSSI signals through a Kalman filter, selecting three RSSI signals with the largest numerical value, and respectively calculating three testing distances corresponding to the three RSSI signals through a path loss model to determine the spatial range of the node to be tested; s40, calculating the prediction coordinates of the node to be measured by using a particle swarm algorithm; the invention can optimize the indoor positioning layout structure and improve the positioning precision, and is suitable for the field of indoor positioning.
Description
Technical Field
The invention relates to the technical field of indoor positioning of wireless sensors, in particular to a wireless sensor network arc triangle positioning method based on RSSI (received signal strength indicator).
Background
Rssi (received Signal Strength indication) is a positioning technique that measures the distance between a Signal point and a receiving point according to the Strength of the received Signal, and then performs positioning calculation according to corresponding data.
The existing outdoor positioning algorithm based on RSSI has many methods, and the outdoor positioning technology has the defects of poor stability, reliability, penetration and the like for indoor positioning, so that the complicated positioning effect on an indoor environment cannot be realized.
As a novel short-distance and low-speed indoor positioning technology, ZigBee has the advantages of low cost and low power consumption, and is more and more popular with people, however, because of being influenced by relevant factors such as indoor environment, the accuracy of indoor positioning is not high.
Disclosure of Invention
Aiming at the defects in the related technology, the technical problem to be solved by the invention is as follows: the RSSI-based wireless sensor network arc triangle positioning method can optimize an indoor positioning layout structure and improve positioning accuracy.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
the RSSI-based wireless sensor network arc triangle positioning method comprises the following steps:
s10, establishing an indoor positioning layout model based on circular-arc triangular layout, and arranging a plurality of beacon nodes on the indoor positioning layout model;
s20, initializing the indoor positioning layout model and the position information of the beacon nodes;
s30, continuously receiving a plurality of RSSI signals of the node to be tested, filtering the RSSI signals through a Kalman filter, selecting three RSSI signals with the largest numerical value, and respectively calculating three testing distances corresponding to the three RSSI signals through a path loss model to determine the spatial range of the node to be tested;
and S40, calculating the predicted coordinates of the node to be measured by using a particle swarm algorithm.
Preferably, in S10, an indoor positioning layout model based on circular-arc triangular layout is established, and a plurality of beacon nodes are arranged on the indoor positioning layout model, which specifically includes:
s101, dividing a region to be positioned into a plurality of grids;
s102, a plurality of circles with the same radius are arranged in each grid, and the radius of each circle is on the other circle;
s103, setting the area of the enclosed city of the three 1/6 arc sections as a positioning partition; wherein: the area surrounded by the three circular arcs is a Luo-Ke triangle;
s104, arranging beacon nodes on fixed points of the Luo-Kes triangle.
Preferably, in step S101, the area to be located is divided into a plurality of grids: the grid is as follows: 8m by 8m square regions; step S102, a plurality of circles with the same radius are arranged in each grid, the radius of each circle is in another circle, and the radius of each circle is 4 m.
Preferably, in step S40, the calculating the predicted coordinates of the node to be measured by using a particle swarm algorithm specifically includes:
s401, initializing the number of particle swarms and the number of iterations, randomly endowing each particle with an initial position and a speed, and setting the initial particle fitness as an adaptive value corresponding to the global optimal position, wherein the fitness is set as 100;
s402, calculating the particle swarm fitness;
s403, for each particle, comparing the adaptive value of the current position with the adaptive value corresponding to the global optimal position of the particle, and if the adaptive value of the current position is higher, updating the global optimal position by using the current position;
s404, updating the speed and the position of each particle;
s405, if the ending condition is not met, returning to the step S402, if the ending condition is met, ending the algorithm, and determining the global optimal position as a global optimal solution;
and S405, taking the global optimal solution as a prediction coordinate of the node to be detected and outputting the prediction coordinate.
Preferably, in step S404, calculating the particle swarm fitness specifically includes:
adaptation function of particle
Calculating an adaptive value of the current position of the particle through an adaptive function of the particle;
wherein: (x, y) is the position coordinate of the current particle, (xi, yi) is the position coordinate of the beacon i, and di is the actual distance from the current particle to the beacon i.
Preferably, in step S404, updating the speed and the position of each particle specifically includes:
the d-dimension velocity of particle i is more recent:
the d-dimensional position of particle i is updated:
updating the speed and the position of each particle through updating the d-dimension speed of the particle i and the d-dimension position of the particle i; wherein:
formula x for particle positioni＝(xi1,xi2,...xiD) Represents;
formula v for particle velocityi＝(vi1,vi2...viD) Represents;
location of best experience of individual particles is expressed in pbesti＝(pi1,pi2,...piD) Represents;
the best location experienced by the population is given by the formula gbest ═ g1,g2...gD) Represents;
the learning factor c1 takes a value of 2 and the learning factor c2 takes a value of 2.
Preferably, in step S401, the number of particle groups and the number of iterations are initialized, an initial position and a speed are randomly assigned to each particle, and the initial particle fitness is set as an adaptive value corresponding to the global optimal position, where the fitness is set to be 100:
the number of the particle groups is set to 10, and the number of iterations is set to 1000.
Preferably, the path loss model is:
wherein: pr(d) Is the received signal strength received by the receiving terminal.
The invention has the beneficial technical effects that:
1. the invention establishes an indoor positioning layout model based on circular-arc triangular layout, and adopts the particle swarm algorithm to calculate the predicted coordinates of the nodes to be measured, compared with the traditional method, the indoor positioning layout structure is optimized, the beacon nodes are easy to arrange, and are far away from the wall surface, the wall column and the object close to the wall, so that the influence of the wall surface and the indoor object is small, and the positioning precision is improved.
2. Compared with the traditional square layout, the traditional triangular layout and the equal-arc trilateral layout, the indoor positioning layout model based on the circular-arc triangular layout reduces the number of arranged beacon nodes, reduces RSSI (received signal strength indicator) acquisition, reduces the workload of processing received signals and the like, and improves the working efficiency.
Drawings
Fig. 1 is a schematic flowchart of a RSSI-based arc triangle positioning method for a wireless sensor network according to an embodiment of the present invention;
FIG. 2 is a graph illustrating a relationship between a distance and an RSSI value according to an embodiment of the present invention;
FIG. 3 is a diagram of a path loss model according to an embodiment of the present invention;
FIG. 4 is a model diagram of an indoor positioning layout of the arc-triangle layout according to the present invention;
FIG. 5 is a deployment diagram of the indoor positioning layout model based on the circular triangle layout and the conventional indoor positioning layout model of the present invention;
FIG. 6 is a comparison graph of positioning errors between the indoor positioning layout model based on the circular-arc triangular layout and the conventional indoor positioning layout model according to the present invention;
Detailed Description
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 embodiments, but not all embodiments, of the present 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.
Example one
Fig. 1 is a schematic flowchart of a RSSI-based wireless sensor network arc triangle positioning method according to an embodiment of the present invention, and as shown in fig. 1, the RSSI-based wireless sensor network arc triangle positioning method includes the following steps:
s10, establishing an indoor positioning layout model based on circular-arc triangular layout, and arranging a plurality of beacon nodes on the indoor positioning layout model;
s20, initializing the indoor positioning layout model and the position information of the beacon nodes;
s30, continuously receiving a plurality of RSSI signals of the node to be tested, filtering the RSSI signals through a Kalman filter, selecting three RSSI signals with the largest numerical value, and respectively calculating three testing distances corresponding to the three RSSI signals through a path loss model to determine the spatial range of the node to be tested;
and S40, calculating the predicted coordinates of the node to be measured by using a particle swarm algorithm.
In the first embodiment, an RSSI ranging technology is adopted to perform ranging on a node to be measured, the RSSI ranging technology is well known for low energy consumption, low cost and easy implementation, and according to the principle that radio waves or sound waves are transmitted in a medium, the signal power is attenuated along with the propagation distance; according to the transmitting power of the known signal of the beacon node and the signal power received by the node, the distance between the nodes can be calculated through an attenuation model between the signal and the distance; due to the influence of distance and obstacles in the signal propagation process. The power strength of the signal is attenuated, which indirectly affects the accuracy.
The relationship between the transmission power and the reception power of the wireless signal can be represented by equation (1).
PR＝PT/rn(1)
Wherein P isRIs the received power, P, of the radio signalTIs the transmitting power of the wireless signal, r is the distance between the transceiver units, n is the propagation factor, the magnitude of the value depends on the environment of the wireless signal propagation; due to the influence of factors such as multipath effect, logarithm is taken to (1) in the transmission process of the wireless signal to obtain a Shadow model, and the model is shown by an equation (2):
in the formula (2), d is the actual distance between the known node and the unknown node; d0For experimental reference distances, generally take d0＝1m；Pr(d) The received signal strength received by the receiving terminal; pr (d)0) A received signal strength is 1m for a reference distance; n is a signal path loss factor, the detailed value of which is changed by environmental influences; x is Gaussian random noise with the mean value of 0 and follows Gaussian distribution; in the experimental environment, X is ignored, Pr (d)0) Denoted by a, a simplified Shadow model is obtained, represented by formula (3):
Pr(d)＝A-10nlg(d) (3)
in the formula (3), A is the power of the received signal when the signal is transmitted for 1m, and the relation between the strength of the received signal and the signal transmission distance is determined by the numerical values of constants A and n; if the signal propagation factor n is a fixed value and the value of A is changed, the relationship between RSSI and propagation distance under different transmission signal powers can be obtained by the formula (3), the signal attenuation is serious in the process of near distance propagation, and the signal attenuation is slow in the process of long distance propagation. If the value A is not changed, the relationship between the RSSI and the signal propagation distance can be obtained; when the value of n is smaller, the attenuation of the signal in the propagation process is smaller, and the signal propagation distance is long. In addition, increasing the transmitted signal power may also increase the signal propagation distance. The propagation factor mainly depends on the interference of the wireless signal in the air such as attenuation, reflection, multipath effect and the like, and if the interference is small, the smaller the value of the propagation factor n, the farther the signal propagation distance is, and the more accurate the RSSI-based ranging is.
The common chip CC2530 of the wireless sensor node provides a method for detecting the received strength of measurement, and the node can complete RSSI measurement while receiving data without configuring extra hardware. Although the RSSI positioning accuracy is not high, the RSSI is sufficient for most positioning applications, so its positioning applications are widespread.
The invention receives 100 groups of data of indoor Received Signal Strength Indication (RSSI) at different distances respectively, after Kalman filter filtering, the relation between the distance and the RSSI value is obtained through linear fitting, and FIG. 2 is a relation graph of the distance and the RSSI value in the first embodiment of the invention, as shown in FIG. 2: linear fitting results in a-39.80 dB, path loss factor n 1.85, and the Shadow model pr (d) -39.80-18.5lg (d).
Therefore, the RSSI-distance relationship is expressed by equation (4):
FIG. 3 is a diagram of a path loss model according to an embodiment of the present invention; as can be seen from the path loss model of fig. 3; within 0-12 m, the RSSI decreases with increasing distance, but the decrease trend is smaller and smaller.
The invention takes 4m as a beacon node as the optimal communication distance, and the attenuation formed by the RSSI value to the distance is obvious in the optimal communication distance. After the distance exceeds 4m, the change range of the RSSI value is small, but the distance change is large, the experimental error is large, and the positioning precision is inaccurate. However, due to factors such as environment, the RSSI of the received signal fluctuates up and down, so that the positioning error caused by the fluctuation of energy increases as the distance increases.
FIG. 4 is a model diagram of an indoor positioning layout of the arc-triangle layout according to the present invention; as shown in fig. 4, in the present invention, the step S10 is to establish an indoor positioning layout model based on circular-arc triangular layout, and arrange a plurality of beacon nodes on the indoor positioning layout model, which specifically includes:
s101, dividing a region to be positioned into a plurality of grids;
s102, a plurality of circles with the same radius are arranged in each grid, and the radius of each circle is on the other circle;
s103, setting the area of the enclosed city of the three 1/6 arc sections as a positioning partition; wherein: the area surrounded by the three circular arcs is a Luo-Ke triangle;
s104, arranging beacon nodes on fixed points of the Luo-Kes triangle.
Further, in step S101, the area to be located is divided into a plurality of grids: the grid is as follows: 8m by 8m square regions; step S102, a plurality of circles with the same radius are arranged in each grid, the radius of each circle is in another circle, and the radius of each circle is 4 m.
Example two
On the basis of the first embodiment, in the step S40, the calculating the predicted coordinates of the node to be measured using the particle swarm algorithm includes:
s401, initializing the number of particle swarms and the number of iterations, randomly endowing each particle with an initial position and a speed, and setting the initial particle fitness as an adaptive value corresponding to the global optimal position, wherein the fitness is set as 100;
s402, calculating the particle swarm fitness;
s403, for each particle, comparing the adaptive value of the current position with the adaptive value corresponding to the global optimal position of the particle, and if the adaptive value of the current position is higher, updating the global optimal position by using the current position;
s404, updating the speed and the position of each particle;
s405, if the ending condition is not met, returning to the step S402, if the ending condition is met, ending the algorithm, and determining the global optimal position as a global optimal solution;
s405, taking the global optimal solution as a prediction coordinate of the node to be detected and outputting the prediction coordinate;
wherein: the end conditions are the number of iterations and the minimum error
In the second embodiment, the nodes to be measured are located and calculated by the particle swarm algorithm, which is withdrawn from 1995 and is derived from the research on the predation behavior of the bird swarm. The basic core is to use the sharing of information by the individual birds to obtain the optimal solution of the problem. The particle swarm updates its speed and position by the following formula.
Particle position:
xi＝(xi1,xi2,...xiD) (5)
particle velocity:
vi＝(vi1,vi2...viD) (6)
individual particles experience the best position:
pbesti＝(pi1,pi2,...piD) (7)
best positions experienced by the population:
gbest＝(g1,g2...gD) (8)
the d-dimension velocity of particle i is more recent:
the d-dimensional position of particle i is updated:
the distances from the unknown nodes (x, y) to the beacon nodes A1(x1, y1), A2(x2, y2), A3(x3, y3), …, An (xn, yn) are d1, d2, d3, …, dn respectively, and the ranging errors are sigma 1, sigma 2, sigma 3, …, sigma n respectively, are obtained through a Shadow model through the received RSSI signals. So that they respectively satisfy:
·
·
the sum of the errors is f (x, y), and the smaller the sum of the errors is, the more accurate the position is estimated, and the smaller the error is; thus, the particle fitness location is the location where f (x, y) is the minimum, and the problem can be translated into solving the value of the minimum nonlinear system of equations, i.e., solving the estimated coordinates (x, y) or f (x, y) that minimize the value. And evaluating the quality of the particle position by utilizing a fitness function, and guiding the searching direction of the motion algorithm of the particle. The fitness function of the particle is:
where f (x, y) is the adaptive value of the particle position (x, y), (xi, yi) is the position coordinate of the beacon i, and di is the actual distance from the unknown node to the beacon i.
In the invention, the particle swarm algorithm starts from random solution, and finds the optimal solution through iteration, and evaluates whether the solution is the optimal solution through fitness. The method searches for an optimal solution in a certain area through the fitness, randomly initializes the number and the position of particles, and calculates the fitness. And if the current fitness is better than the optimal position, replacing the optimal position until iteration is carried out for corresponding times, wherein the final optimal solution is the position of the prediction node.
Specifically, in step S404, an adaptive value of the current position of the particle is calculated by formula (12); in the step S404, the speed and the position of each particle are updated through the formula (9) and the formula (10), in this embodiment, the learning factor c1 takes a value of 2, and the learning factor c2 takes a value of 2; w in the formula (9) is the inertia weight of the PSO, and the value thereof is in the interval of [0,1], and generally, an adaptive value method is adopted in application, that is, w is made to be 0.9 at the beginning, so that the PSO global optimization capability is stronger, and the parameter w is decreased progressively along with the depth of iteration, so that the PSO has stronger local optimization capability, and when the iteration is finished, w is made to be 0.1; r1 and r2 are random probability values between [0,1 ].
Further, in step S401, initializing the number of particle groups, the number of iterations, randomly assigning an initial position and a speed to each particle, and setting the initial particle fitness as an adaptive value corresponding to the global optimal position, where the fitness is set to be 100: the number of the particle groups is set to 10, and the number of iterations is set to 1000.
To better explain the present invention, the indoor positioning layout model based on the circular-arc triangular layout provided in the first embodiment of the present invention is compared with the conventional indoor positioning layout model, and the following description is provided:
fig. 5 is a deployment diagram of an indoor positioning layout model based on circular arc triangular layout and a traditional indoor positioning layout model of the present invention, fig. 6 is a positioning error comparison diagram of the indoor positioning layout model based on circular arc triangular layout and the traditional indoor positioning layout model of the present invention, as shown in fig. 5 and fig. 6, the circular arc triangular layout of the present invention, the square layout, the traditional triangular layout, the improved triangular layout, and the equal arc trilateral layout are respectively arranged in an indoor area of 8m × 8m to carry out an indoor experiment, and RSSI signals and relevant coordinates thereof are respectively subjected to particle swarm algorithm positioning to predict the positions of unknown nodes by collecting RSSI.
In FIG. 5, use
The beacon node is represented by "●", the node to be tested is represented by 8 nodes to be tested, and the coordinates of the nodes to be tested are (1, 1), (2, 2), (3, 3), (5, 5), (6, 6), (7, 7), (0.5, 3), (7.6, 5.2), respectively.
Through an indoor positioning experiment, beacon nodes are arranged in five different layout modes respectively, RSSI signal data of a node to be detected from the beacon nodes are received by a terminal upper computer and stored in a database; and processing the data by a Kalman filter and an average filter to obtain RSSI signal intensity, and finally predicting the node position by using a particle swarm positioning algorithm to obtain an experimental positioning error. The positioning errors and the most significant values of the five layouts are shown in fig. 6 and table 1, respectively.
Table 1 errors in layout
| Layout | Maximum error (m) | Minimum error (m) | Mean error (m) |
| Square layout | 3.81 | 0.81 | 2.16 |
| Traditional triangular layout | 2.79 | 0.72 | 1.35 |
| Improved triangular layout | 1.50 | 0.24 | 0.78 |
| Equal arc trilateral layout | 1.72 | 0.57 | 1.01 |
| Circular arc triangle layout | 1.49 | 0.50 | 0.97 |
As can be seen from fig. 6, the distances between the beacon nodes in the square layout mode are large, which results in the largest positioning error;
compared with square layout, the traditional triangular layout has the advantages that the positioning error is improved, but the coverage rate of the layout area is 65%, the positioning accuracy is improved compared with the square layout, but the positioning accuracy is lower than that of other layouts;
the improved triangle layout has the highest positioning accuracy, but 14 beacon nodes are used, so that the workload of data acquisition and processing is increased;
the coverage rate of the equal-arc trilateral layout reaches 88%, and the average error is small;
circular arc triangle-shaped overall arrangement compares beacon node in equal arc trilateral overall arrangement and arranges more easily, and the node is kept away from wall, wall post and is leaned on the wall object, and positioning accuracy improves to some extent than equal arc trilateral overall arrangement, and circular arc triangle-shaped overall arrangement has positioning accuracy height and advantage such as the overall arrangement is convenient, and positioning accuracy has improved 55.1%, 27.4% and 3.9% respectively than square overall arrangement, traditional triangle-shaped overall arrangement and equal arc trilateral overall arrangement. The conclusion is reached:
1. the positioning average error of the improved triangular layout is 0.75m and 0.24m, but the layout mode is complex and the number of beacon nodes is large;
2. the average error and the maximum error of the square layout and the traditional triangular layout are maximum, and the positioning precision is low;
3. circular arc triangle-shaped overall arrangement compares and reduces a beacon node in equal arc trilateral overall arrangement, and the wall is kept away from to node position most moreover, arranges easily and receives the influence of wall and indoor object less, and positioning accuracy is high.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. The RSSI-based wireless sensor network arc triangle positioning method is characterized by comprising the following steps of: comprises the following steps:
s10, establishing an indoor positioning layout model based on circular-arc triangular layout, and arranging a plurality of beacon nodes on the indoor positioning layout model;
s20, initializing the indoor positioning layout model and the position information of the beacon nodes;
s30, continuously receiving a plurality of RSSI signals of the node to be tested, filtering the RSSI signals through a Kalman filter, selecting three RSSI signals with the largest numerical value, and respectively calculating three testing distances corresponding to the three RSSI signals through a path loss model to determine the spatial range of the node to be tested;
and S40, calculating the predicted coordinates of the node to be measured by using a particle swarm algorithm.
2. The RSSI-based wireless sensor network circular arc triangle positioning method of claim 1, wherein: the S10 is to establish an indoor positioning layout model based on circular-arc triangular layout, and arrange a plurality of beacon nodes on the indoor positioning layout model, including:
s101, dividing a region to be positioned into a plurality of grids;
s102, a plurality of circles with the same radius are arranged in each grid, and the radius of each circle is on the other circle;
s103, setting the area of the enclosed city of the three 1/6 arc sections as a positioning partition; wherein: the area surrounded by the three circular arcs is a Luo-Ke triangle;
s104, arranging beacon nodes on fixed points of the Luo-Kes triangle.
3. The RSSI-based wireless sensor network circular arc triangle positioning method of claim 2, wherein: the step S101 is to divide the area to be located into a plurality of grids: the grid is as follows: 8m by 8m square regions;
step S102, a plurality of circles with the same radius are arranged in each grid, the radius of each circle is in another circle, and the radius of each circle is 4 m.
4. The RSSI-based wireless sensor network circular arc triangle positioning method of claim 1, wherein: in the step S40, the calculating of the predicted coordinates of the node to be measured using the particle swarm algorithm specifically includes:
s401, initializing the number of particle swarms and the number of iterations, randomly endowing each particle with an initial position and a speed, and setting the initial particle fitness as an adaptive value corresponding to the global optimal position, wherein the fitness is set as 100;
s402, calculating the particle swarm fitness;
s403, for each particle, comparing the adaptive value of the current position with the adaptive value corresponding to the global optimal position of the particle, and if the adaptive value of the current position is higher, updating the global optimal position by using the current position;
s404, updating the speed and the position of each particle;
s405, if the ending condition is not met, returning to the step S402, if the ending condition is met, ending the algorithm, and determining the global optimal position as a global optimal solution;
and S405, taking the global optimal solution as a prediction coordinate of the node to be detected and outputting the prediction coordinate.
5. The RSSI-based wireless sensor network circular arc triangle positioning method of claim 4, wherein: the step S404 of calculating the particle swarm fitness specifically includes:
adaptation function of particle
Calculating an adaptive value of the current position of the particle through an adaptive function of the particle;
wherein: (x, y) is the position coordinate of the current particle, (xi, yi) is the position coordinate of the beacon i, and di is the actual distance from the current particle to the beacon i.
6. The RSSI-based wireless sensor network circular arc triangle positioning method of claim 4, wherein: in the step S404, updating the speed and the position of each particle specifically includes:
the d-dimension velocity of particle i is more recent:
the d-dimensional position of particle i is updated:
updating the speed and the position of each particle through updating the d-dimension speed of the particle i and the d-dimension position of the particle i; wherein:
formula x for particle positioni＝(xi1,xi2,...xiD) Represents;
formula v for particle velocityi＝(vi1,vi2...viD) Represents;
location of best experience of individual particles is expressed in pbesti＝(pi1,pi2,...piD) Represents;
the best location experienced by the population is given by the formula gbest ═ g1,g2...gD) Represents;
the learning factor c1 takes a value of 2 and the learning factor c2 takes a value of 2.
7. The RSSI-based wireless sensor network circular arc triangle positioning method of claim 4, wherein: step S401, initializing the number of particle groups and the number of iterations, randomly assigning an initial position and a speed to each particle, and setting the initial particle fitness as an adaptation value corresponding to the global optimal position, where the fitness is set to be 100:
the number of the particle groups is set to 10, and the number of iterations is set to 1000.
8. The RSSI-based wireless sensor network circular arc triangle positioning method of claim 2, wherein: the path loss model is:
wherein: pr(d) Is the received signal strength received by the receiving terminal.
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