CN112135249A - RSSI-based weighted centroid positioning algorithm improvement method - Google Patents

RSSI-based weighted centroid positioning algorithm improvement method Download PDF

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CN112135249A
CN112135249A CN202011147372.9A CN202011147372A CN112135249A CN 112135249 A CN112135249 A CN 112135249A CN 202011147372 A CN202011147372 A CN 202011147372A CN 112135249 A CN112135249 A CN 112135249A
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rssi
algorithm
positioning
node
centroid
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任晓奎
于百川
李岩
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Liaoning Technical 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
    • H04W4/025—Services making use of location information using location based information parameters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/14—Determining absolute distances from a plurality of spaced points of known location
    • 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
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks

Abstract

The invention discloses an RSSI-based weighted centroid positioning algorithm improvement method, which aims at solving the problem of RSSI ranging error in a wireless sensor network, improves unreasonable factors existing in a weighted centroid algorithm, and provides a new positioning (RSSI-DSW) algorithm combining a ranging centroid algorithm based on a signal strength indicator and the weighted centroid positioning algorithm. In the ranging stage, due to the complexity and randomness of an indoor environment, the strength of the acquired signal has a large error, and based on the fact that the received RSSI value is filtered by the improved Gaussian filtering algorithm, the filtered RSSI value is used as a final value to carry out distance conversion, the algorithm reduces ranging errors to a certain extent, and positioning accuracy is improved.

Description

RSSI-based weighted centroid positioning algorithm improvement method
Technical Field
The invention belongs to the technical field of wireless sensor positioning, and particularly relates to an improved method of a weighted centroid positioning algorithm based on RSSI (received signal strength indicator).
Background
In recent years, with the rapid development of scientific technologies such as the internet of things and artificial intelligence, wireless sensor networks have become a current hot research field. Over the past few years, many micro wireless devices and embedded devices have been in the wake, and the rapid development of wireless sensors is changing our living environment. Wireless Sensor Networks (WSNs) are composed of a large number of sensors that are small in size, consume little energy, are low in price, and have functions of computing, communicating, storing, and even moving, and these sensors can autonomously sense the environment, acquire and process data, and finally transmit the data to an observer. This dynamic sensing, processing, and messaging capabilities of WSNs enable WSNs to be applied in a variety of human activities, including military, medical, daily, commercial, and the like. Today, with the rapid development of the internet of things technology, a wireless sensor network has become one of the indispensable means for acquiring and acquiring a large amount of physical data in the future.
In the process of providing services for people by the WSN, a plurality of scenes need to provide position information of the sensor. If the specific location information of the target point is not known, then the various data metrics collected are meaningless. For example, in the case of a forest fire, even if smoke signals emitted by the peripheral sensors are received, the specific position distribution of the sensors cannot be determined, and the information does not contain position information, so that fire extinguishment cannot be rapidly implemented, and the collected information does not have any significance, so that the collected information contains position information, which promotes the development of various positioning technologies to a certain extent. At present, a positioning system generally applied by people is mainly a Global Positioning System (GPS) and has the characteristics of high positioning accuracy and the like, but the application of the traditional GPS positioning in a wireless sensor network has many limitations, the GPS positioning technology has poor indoor positioning effect, cannot be applied to indoor scenes, has high cost, and can cause high cost when used in areas with densely distributed nodes. Therefore, designing a low-cost and efficient positioning algorithm has become a research hotspot of the wireless sensor network.
In the prior art (Liu Yun Jie, King Ming, Zhiyi, RSSI-based wireless sensor network correction weighted centroid positioning algorithm [ J ] sensing technical report 2010, 23(05):717 and 721.), unreasonable factors existing in the weighted centroid algorithm are improved, the sum of reciprocal testing distances is adopted to replace the reciprocal of the sum of distances as weights, the concept of correction coefficients is provided, and the condition that three circles are not intersected is ignored.
In addition, in the prior art (dynasty, zhangqi, zhangfeng, improved weighted centroid location algorithm [ J ] electrical measurement and instrument, 2014, 51(21):63-66.) based on RSSI ranging, an index is added to the selection of weight values to correct on the basis of the technology, but the index division is too rough, under the condition that circles do not intersect, a group of anchor nodes are directly taken to carry out position compensation, a larger RSSI value is abandoned, and the precision error is increased.
In addition, in the prior art (Caltabiano D, Muscoto G, Russo F. localization and Self-Calibra-tion of a Robot for the wlan amplification [ C ]// Proceedings of the 2004IEEE ICRA. New Oreans: IEEE Robotics and Automation Society, 2004.) through the use of Kalman filtering to perform the loop filtering processing on the RSSI, experiments show that the algorithm significantly reduces the ranging error, but brings a lot of communication overhead and calculation data, and the loop randomness increases the uncertainty of the algorithm.
In the conventional method, a group of anchor nodes are directly taken down to perform bit complementing under the condition that circles do not intersect, a large RSSI value is abandoned, the precision error is increased, and the acquired signal strength has a large error due to the complexity and randomness of an indoor environment.
Disclosure of Invention
Based on the defects of the prior art, the technical problem solved by the invention is to provide an improved method of the RSSI-based weighted centroid positioning algorithm.
In the ranging stage, due to the complexity and randomness of an indoor environment, the strength of the acquired signal has a large error, and based on the fact that the received RSSI value is filtered by the improved Gaussian filtering algorithm, the filtered RSSI value is used as a final value to carry out distance conversion, the algorithm reduces ranging errors to a certain extent, and positioning accuracy is improved.
In the positioning stage, the distance proportion model is selected, and the problem of no solution caused by non-intersection of anchor node circles is solved. And the RSSI-DSW positioning algorithm adopts the sum of distance inverses to replace the reciprocal of the distance sum as the weight, so that the anchor nodes close to the unknown node occupy larger weight, and the power value n is increased to prevent excessive correction, and the correction degree is adjusted by adjusting the value of n, thereby improving and reducing the positioning error to a certain extent and improving the positioning precision.
In order to solve the technical problem, the invention provides an improved method of a weighted centroid location algorithm based on RSSI, which comprises the following steps:
step S1: the anchor node periodically sends self information to the periphery, wherein the self information comprises a node ID and self position information;
step S2: after the unknown node receives certain coordinate information, new information is not received any more, and received RSSI value data is preprocessed;
step S3: arranging the anchor nodes from large to small according to the obtained RSSI value, and selecting the first 4 anchor nodes with the strongest signals to participate in positioning;
step S4: processing each RSSI value by utilizing an improved Gaussian filtering algorithm to obtain a final RSSI value;
step S5: according to the improved RSSI ranging model, the RSSI value is converted into the distance d through the following formula, wherein A is d0The signal strength, d is the distance (m) from the transmitting node to the receiving node, and n is the loss factor;
RSSI＝A-10nlg(d)；
step S6: using a distance proportion model to arrange and combine three random anchor nodes in four anchor nodes to obtain corresponding triangle centroid O1、O2、O3And O4Coordinates;
step S7: calculating an unknown node coordinate O (x, y) according to the obtained centroid coordinate according to an RSSI-DSW positioning algorithm;
step S8: calculating the error (absolute positioning error) of the unknown node as shown in the following formula, wherein (X, Y) is the estimated coordinate of the unknown node, and (X, Y) is the real coordinate of the unknown node:
Figure BDA0002740086780000041
therefore, in the positioning stage, the distance proportion model is selected, the problem of no solution caused by non-intersection of anchor node circles is solved, the RSSI-DSW positioning algorithm is provided, the algorithm adopts the sum of distance inverses to replace the inverse of the sum of the distance as the weight, so that the anchor nodes close to unknown nodes occupy larger weight, the power value n is increased to prevent excessive correction, the correction degree is adjusted by adjusting the value of n, the positioning error is reduced to a certain extent, and the positioning precision is improved. The positioning algorithm of the invention has great advantages in positioning accuracy. The experimental result shows that compared with the traditional algorithm, the optimization degree of the positioning precision is improved by 56.06% compared with the traditional centroid algorithm, the optimization degree of the positioning precision is improved by 23.16% compared with the weighted centroid algorithm, the positioning precision is improved, and the method has certain practical value.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical means of the present invention more clearly understood, the present invention may be implemented in accordance with the content of the description, and in order to make the above and other objects, features, and advantages of the present invention more clearly understood, the following detailed description is given in conjunction with the preferred embodiments, together with the accompanying drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings of the embodiments will be briefly described below.
FIG. 1 is a flow chart of an improved method of the RSSI-based weighted centroid location algorithm of the present invention;
FIG. 2 is a distance scale model.
Detailed Description
Other aspects, features and advantages of the present invention will become apparent from the following detailed description, taken in conjunction with the accompanying drawings, which form a part of this specification, and which illustrate, by way of example, the principles of the invention. In the referenced drawings, the same or similar components in different drawings are denoted by the same reference numerals.
As shown in FIG. 1, the invention introduces an improved Gaussian filter model, a distance scale model and an RSSI-DSW positioning algorithm, and FIG. 2 shows the distance scale model.
The improved method of the RSSI-based weighted centroid positioning algorithm is characterized by comprising the following steps of:
step S1: the anchor node periodically sends self information to the periphery, wherein the self information comprises a node ID and self position information;
step S2: after the unknown node receives certain coordinate information, new information is not received any more, and received RSSI value data is preprocessed;
step S3: arranging the anchor nodes from large to small according to the obtained RSSI value, and selecting the first 4 anchor nodes with the strongest signals to participate in positioning;
step S4: processing each RSSI value by utilizing an improved Gaussian filtering algorithm to obtain a final RSSI value;
step S5: according to the improved RSSI ranging model, the RSSI value is converted into the distance d through the following formula, wherein A is d0The signal strength, d is the distance (m) from the transmitting node to the receiving node, and n is the loss factor;
RSSI＝A-10nlg(d)；
step S6: using a distance proportion model to arrange and combine three random anchor nodes in four anchor nodes to obtain corresponding triangle centroid O1、O2、O3And O4Coordinates;
step S7: calculating an unknown node coordinate O (x, y) according to the obtained centroid coordinate according to an RSSI-DSW positioning algorithm;
step S8: calculating the error (absolute positioning error) of the unknown node as shown in the following formula, wherein (X, Y) is the estimated coordinate of the unknown node, and (X, Y) is the real coordinate of the unknown node:
Figure BDA0002740086780000061
when the circles do not intersect, as shown in fig. 2, the centers of the three circles are connected, for example, point F, when the radius of the circle is larger, the distance from the unknown node is farther, the error is larger, so point F should be close to point a with a smaller radius, so that circle a plays a larger role, and the position of point F should be the same as point a
Figure BDA0002740086780000062
F (x) can be obtained according to the following formulaF,yF)。
Figure BDA0002740086780000063
In the same way, E (x) can be obtainedE,yE) And D (x)D,yD) The coordinates of (a). And (3) forming a new triangular area through the obtained D point, the E point and the F point, and obtaining a centroid of the triangle formed by the D point, the E point and the F point to obtain a centroid O.
Obtaining D, E and F coordinate points through a distance proportion model based on an RSSI-DSW centroid positioning algorithm, forming a triangle by taking the three points as vertexes, and solving the centroid O of the triangle1(xo1,yo1) Wherein
Figure BDA0002740086780000064
Center of mass O1Has a weight value of
Figure BDA0002740086780000065
The same principle can be used to obtain other three permutation combinations, their mass center and weight are O respectively2(xo2,yo2)、O3(xo3,yo3)、O4(xo4,yo4)，
Figure BDA0002740086780000071
The coordinates of the unknown node are O (x, y).
Considering the influence of the distance between an unknown node and an anchor node on positioning, the distance between the anchor node and the unknown node plays a main role, so that the distance reciprocal sum is adopted, a power value n is added to prevent excessive correction, the correction degree is adjusted by adjusting the value of n, and the algorithm formula after correction is as follows:
Figure BDA0002740086780000072
the improved Gaussian filter model processing process is as follows:
step1, firstly, carrying out Gaussian filtering on a group of received RSSI values to obtain
Figure BDA0002740086780000073
Step2, selecting the RSSI value of the high probability occurrence area through a Gaussian model, and removing some abnormal values to obtain the RSSI value which is respectively compared with the RSSI value obtained by the removal of the abnormal values
Figure BDA0002740086780000074
Calculating the difference, and squaring the difference to obtain
Figure BDA0002740086780000075
Step3. calculating the weighting coefficient w by the formula in step2i，
Figure BDA0002740086780000076
Step4. finally, the final RSSI value after the filtering of the group is obtained:
Figure BDA0002740086780000077
according to the RSSI-based weighted centroid positioning algorithm improvement method, the Gaussian filter model, the distance proportion model and the RSSI-DSW positioning algorithm are improved, and the positioning accuracy is greatly improved.
While the foregoing is directed to the preferred embodiment of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.

Claims (1)

1. An improved method of a weighted centroid locating algorithm based on RSSI is characterized by comprising the following steps:
step S1: the anchor node periodically sends self information to the periphery, wherein the self information comprises a node ID and self position information;
step S2: after the unknown node receives certain coordinate information, new information is not received any more, and received RSSI value data is preprocessed;
step S3: arranging the anchor nodes from large to small according to the obtained RSSI value, and selecting the first 4 anchor nodes with the strongest signals to participate in positioning;
step S4: processing each RSSI value by utilizing an improved Gaussian filtering algorithm to obtain a final RSSI value;
step S5: according to the improved RSSI ranging model, the RSSI value is converted into the distance d through the following formula, wherein A is d0The signal strength, d is the distance from the transmitting node to the receiving node, and n is a loss factor;
RSSI＝A-10nlg(d)；
step S6: using a distance proportion model to arrange and combine three random anchor nodes in four anchor nodes to obtain corresponding triangle centroid O1、O2、O3And O4Coordinates;
step S7: calculating an unknown node coordinate O (x, y) according to the obtained centroid coordinate according to an RSSI-DSW positioning algorithm;
step S8: calculating the error of the unknown node as shown in the following formula, wherein (X, Y) is the estimated coordinate of the unknown node, and (X, Y) is the real coordinate of the unknown node:
Figure FDA0002740086770000011
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CN113473363A (en) * 2021-07-01 2021-10-01 江苏希塔信息科技有限公司 Indoor positioning method of intersected circles based on zooming
CN113640740A (en) * 2021-08-04 2021-11-12 成都诚骏科技有限公司 Indoor high-precision positioning method for intelligent warehousing management system
CN114745661A (en) * 2022-04-07 2022-07-12 中山大学 Data processing method for positioning wireless sensor network node

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