CN106658643B - RSSI-based effective anchor node selection method - Google Patents

RSSI-based effective anchor node selection method Download PDF

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CN106658643B
CN106658643B CN201611123412.XA CN201611123412A CN106658643B CN 106658643 B CN106658643 B CN 106658643B CN 201611123412 A CN201611123412 A CN 201611123412A CN 106658643 B CN106658643 B CN 106658643B
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anchor node
path loss
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庄毅
夏晓东
顾晶晶
李静
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Nanjing University of Aeronautics and Astronautics
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    • 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
    • H04W64/003—Locating users or terminals or network equipment for network management purposes, e.g. mobility management locating network equipment
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/12—Communication route or path selection, e.g. power-based or shortest path routing based on transmission quality or channel quality
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The invention provides an effective anchor node selection method based on Received Signal Strength Indication (RSSI), belonging to the field of cooperative positioning. The method comprises the following steps: step 1, each anchor node broadcasts its own position and its RSSI signal; step 2, for each anchor node, after receiving signals of other anchor nodes in the communication radius of the anchor node, calculating the area path loss index of the anchor node; step 3, determining constraint conditions and establishing an effective anchor node selection model; step 4, screening the anchor nodes according to the constraint conditions, and selecting an effective anchor node set without barriers around; and 5, selecting 4 effective anchor nodes closest to the target node from the effective anchor node set to carry out four-side positioning. According to the method, the real environment of the sensor node is considered, effective anchor nodes are selected to participate in target positioning, and the influence of factors such as environment and enemy interference on the positioning result is effectively reduced. Meanwhile, the positioning precision and the positioning coverage rate are effectively improved.

Description

RSSI-based effective anchor node selection method
Technical Field
The invention relates to the field of cooperative positioning, in particular to an effective anchor node selection method based on RSSI (received signal strength indicator).
Background
Sensor positioning is an important component of an electronic countermeasure system, and high-difficulty tasks such as resource allocation, task scheduling, target tracking and the like need accurate positioning as a basis. Organizing multiple scout sensors of different categories to perform coordinated scout positioning tasks is a growing trend in today's electronic warfare. The traditional single sensor independent reconnaissance and positioning is limited by the environment, the enemy situation, the performance of the sensor and other aspects, so that the requirement of modern electronic warfare is difficult to meet. The sensor node has wireless communication capability and can easily obtain a signal strength value, so the RSSI is a convenient and cheap ranging technology.
Aiming at different cooperative positioning models and multi-sensor cooperative positioning problems, some related research achievements exist abroad. Jeongsu Lee et al of the university of information and communication designs a military mobile base station positioning system, and the system performs hybrid positioning by using RSSI and AOA signals simultaneously, so that a small-range area where a target is located can be quickly positioned; but are not suitable for accurate positioning. An APIT algorithm is proposed by Tian He and the like of Virginia university, the relative distance between nodes is judged by using the communication information between the nodes and the RSSI value received by the nodes and sent by the neighbor nodes, and compared with the algorithm simply using the communication information, the positioning accuracy can be effectively improved; however, the algorithm requires that each node has good information processing capability and the positioning cost is high.
There have also been some research efforts in multi-sensor co-location in China. For example, Wangshansan et al, the university of defense science and technology, proposes an RSSI-NLP algorithm, calculates the relative relationship of the distance between the communicable nodes according to the RSSI value between the nodes, and combines a linear programming method to carry out positioning, thereby having better positioning effect; however, as the number of unknown nodes increases, the positioning accuracy decreases sharply. The RSSI positioning strategy based on the median principle and the space compensation model is proposed by the kusaku et al of the university of Chongqing, so that the compromise between the algorithm complexity and the positioning performance is realized; but the positioning result depends excessively on the quality of the positioning environment.
Most of the existing positioning algorithms have two defects: on one hand, the complexity of the positioning algorithm is too high, and the computing capability is often insufficient in a network with more nodes; on the other hand, errors are accumulated, and even small errors in the initial positioning environment are accumulated continuously in the positioning process due to the lack of an effective error judgment method, so that the positioning accuracy is reduced finally.
Disclosure of Invention
The invention aims to provide an effective anchor node selection method based on RSSI.
The technical solution for realizing the purpose of the invention is as follows: an effective anchor node selection method based on RSSI specifically comprises the following steps:
step 1, broadcasting the position of each anchor node and an RSSI signal of each anchor node in a positioning scene;
step 2, for each anchor node BiAfter receiving signals of other anchor nodes within the communication radius R, B is calculatediArea path loss exponent n (B)i)；
Step 3, determining constraint conditions and establishing an effective anchor node selection model;
and 4, screening the anchor nodes according to the constraint conditions, and selecting an effective anchor node set S without barriers around.
Compared with the prior art, the invention has the following remarkable advantages: 1) according to the invention, the effective anchor nodes are dynamically selected for positioning according to the environment of the sensor, so that the influence of factors such as environmental interference on the positioning result is effectively reduced. 2) The method is improved by adopting an empirical value mode aiming at the classical path loss index, and the path loss index between the nodes is dynamically determined according to the environment where the sensor is located. 3) According to the invention, the RSSI-based effective anchor node selection model is applied to the field of wireless sensor cooperative positioning, so that the cooperative positioning precision and the cooperative positioning coverage rate can be obviously improved, and error accumulation is prevented.
Drawings
Fig. 1 is a flowchart of an effective anchor node selection model based on RSSI.
Fig. 2 is an exemplary experimental graph.
Fig. 3 is a node location coverage map of error threshold α.
Fig. 4 is a node placement error map of error threshold α.
Fig. 5 is a positioning error comparison chart.
Detailed Description
The invention deeply analyzes the traditional RSSI positioning method, provides an effective Anchor Node Selection model EAS (effective Anchor-Node Selection) aiming at the problems, and compares the effective Anchor Node Selection model EAS with other models through simulation experiments. Simulation experiment results show that the EAS model can effectively eliminate errors generated by the initial network environment, improve positioning accuracy and effectively prevent error accumulation.
The invention discloses an effective anchor node selection method based on RSSI (received signal strength indicator), which comprises the following steps of:
step 1, broadcasting the position of each anchor node and an RSSI signal of each anchor node in a positioning scene;
step 2, for each anchor node BiOther anchors within the radius of reception of their communication RAfter the node's signal, the anchor node-B is computediArea path loss exponent n (B)i) (ii) a The region path loss exponent n (B)i) The calculation method comprises the following steps:
step 2-1, determining any two anchor node Bi,BjThe formula used is:
Figure BDA0001174765660000021
wherein (x)i,yi,zi),(xj,yj,zj) Are anchor node B respectivelyi,BjThe coordinates of (a);
step 2-2, determining any two anchor node Bsi,BjPath loss exponent n (B) in betweeni,Bj) The formula is as follows:
Figure BDA0001174765660000031
wherein, BjTo the transmitting node, BiIs a receiving node; d is the distance between two nodes; RSSI (B)i,Bj) Is Bi,BjSignal strength between; x0A gaussian random variable defined as mean 0 and variance 5; pTFor transmit power, PL (d)0) Is the received power;
step 2-3, determining Gaussian path loss influence coefficient fi(Bj) The formula is as follows:
wherein (x)i,yi,zi),(xj,yj,zj) Are anchor node B respectivelyi,BjCoordinate of (a), (b), f)i(Bj) Is a gaussian distribution function with a mean value of 0 and a variance of 1;
step 2-4, determining the regional path loss index nR(Bi) Said formulaComprises the following steps:
Figure BDA0001174765660000033
wherein the area path loss exponent nR(Bi) Consisting of N local path loss indices, N being BiThe number of other anchor nodes in the communication radius R, wherein the jth local path loss exponent is determined by the jth (j is more than or equal to 1 and less than or equal to N) anchor node, each local path loss exponent is calculated by the product of the Gaussian path loss influence coefficient and the correlation sum of the path loss exponents, BjIs represented by the path loss exponential correlation sum ofjWith other anchor node Bk(k is more than or equal to 1 and less than or equal to N, and k is not equal to j).
Step 3, determining constraint conditions of the effective anchor nodes, and establishing an effective anchor node selection model; the constraint conditions of the effective anchor nodes are as follows:
wherein n (B)i,Bj) As anchor node BiWith other anchor node-bs within its communication radius Rj(j is not less than 1 and not more than N) path loss index, NR(Bi) As anchor node BiArea path loss exponent of (x)i,yi,zi),(xj,yj,zj) Are anchor node B respectivelyi,Bjα is an error threshold.
And 4, screening the anchor nodes according to the constraint conditions determined in the step 3, selecting an effective anchor node set S without barriers around, and finishing the selection of the effective anchor nodes. The effective anchor node screening principle is as follows:
when anchor node BiWith other anchor node-bs within its communication radius Rj(1. ltoreq. j. ltoreq.N) path loss exponent N (B)i,Bj) And BiRegional path loss exponent nR(Bi) Are all less than the error threshold α, this indicates that the anchor node B isiIs an effectiveThe anchor node of (1); when anchor node BiWhen an obstacle exists around, BiMust not satisfy the effective anchor node constraint condition, and BiIs not a valid anchor node.
In order to make those skilled in the art better understand the technical problems, technical solutions, and technical effects in the present application, the RSSI-based effective anchor node selection model of the present invention is further described in detail below with reference to the accompanying drawings and the detailed description.
The invention provides an effective anchor node selection model based on RSSI (received signal strength indicator), and the basic flow is shown in figure 1. The method comprises the following specific steps:
step 1: each anchor node broadcasts its own position and its RSSI signal;
step 2: for each anchor node BiAfter receiving signals of other anchor nodes within the communication radius R, B is calculatediArea path loss exponent n (B)i)；
Wherein two anchor node Bs are determinedi,BjThe formula used is:
Figure BDA0001174765660000041
wherein (x)i,yi,zi),(xj,yj,zj) Are anchor node B respectivelyi,BjThe coordinates of (a).
Determining two anchor node Bsi,BjPath loss exponent n (B) in betweeni,Bj) The formula is as follows:
wherein, BjTo the transmitting node, BiIs a receiving node; d is the distance between two nodes; RSSI (B)i,Bj) Is Bi,BjSignal strength between; x0A gaussian random variable defined as mean 0 and variance 5; pTFor transmit power, PL (d)0) Is the received power.
Determining a Gaussian path loss influence coefficient fi(Bj) The formula is as follows:
Figure BDA0001174765660000043
wherein (x)i,yi,zi),(xj,yj,zj) Are anchor node B respectivelyi,BjThe coordinates of (a). f. ofi(Bj) Is a mean of 0 and a variance of 1
Is calculated as a gaussian distribution function. Because the Gaussian distribution has the advantages of high precision, wide coverage and the like, the distance between anchor nodes is used as a random variable of the Gaussian distribution to obtain the path loss influence coefficient f of the Gaussian distributioni(Bj) And quantizing B therebyiOther anchor node pairs n within communication radius RR(Bi) The influence of (c).
Determining a regional path loss exponent nR(Bi) The formula is as follows:
Figure BDA0001174765660000044
wherein the area path loss exponent nR(Bi) Consisting of N local path loss indices, N being BiNumber of other anchor nodes within communication radius R (excluding B)i). Wherein the jth local path loss exponent is determined by the jth (1 ≦ j ≦ N) anchor node. Each local area path loss index is calculated by the product of the Gaussian path loss influence coefficient and the correlation sum of the path loss indexes, and different anchor node pairs B are reflectediThe area path loss exponent. B isjIs represented by the path loss exponential correlation sum ofjWith other anchor node Bk(k is more than or equal to 1 and less than or equal to N, and k is not equal to j). The path loss exponent between every two anchor nodes is calculated once during the calculation process, so it needs to be multiplied by a factor 1/2.
And step 3: determining constraint conditions, and establishing an effective anchor node selection model;
Figure BDA0001174765660000051
wherein n (B)i,Bj) As anchor node BiWith other anchor node-bs within its communication radius Rj(j is not less than 1 and not more than N) path loss index, NR(Bi) As anchor node BiArea path loss exponent of (x)i,yi,zi),(xj,yj,zj) Are anchor node B respectivelyi,Bjα is an error threshold.
And 4, step 4: screening the anchor nodes according to the constraint conditions, and selecting an effective anchor node set S without barriers around;
when anchor node BiWith other anchor node-bs within its communication radius Rj(1. ltoreq. j. ltoreq.N) path loss exponent N (B)i,Bj) And BiRegional path loss exponent nR(Bi) Are all less than the error threshold α, this indicates that the anchor node B isiIs a valid anchor node. When anchor node BiWhen an obstacle exists around, BiMust not satisfy the effective anchor node constraint condition, and BiIs not a valid anchor node.
And 5: and 4 effective anchor nodes closest to the target node T are selected from the effective anchor node set S for four-side positioning, so that the effect of selecting the model by the effective anchor nodes is verified.
According to the invention, the effective anchor nodes are dynamically selected for positioning according to the environment of the sensor, so that the influence of factors such as environmental interference on the positioning result is effectively reduced.
The present invention will be described in further detail with reference to examples.
Examples
200 unknown nodes randomly distributed in a three-dimensional space of 300m × 300m × 300m, as shown in fig. 2; wherein, Δ represents an anchor node, O represents an unknown node, a positioned anchor node positioned by an effective anchor node selection model based on RSSI, and a black area represents a randomly generated barrier area;
the error quantization formula is defined as follows:
Figure BDA0001174765660000052
wherein N represents the total number of unknown nodes, R represents the communication radius of the unknown nodes,representing the actual coordinates of the unknown node m, (x)m,ym,zm) Representing the coordinates of the point m located by the algorithm. For a node that cannot be located or is not located successfully, the location error is taken to be half its communication radius.
The experiment selects 4 different thresholds to test, and fig. 3 is a node location coverage rate graph of an error threshold α, it can be seen from the graph that when α is 0.05, the EAS algorithm is very strict in screening the anchor nodes, few anchor nodes participate in location, and the location coverage rate of unknown nodes is low, when α is 0.1, the screening of the anchor nodes is strict, the anchor nodes participating in location are more than α and 0.05, but the number of the anchor nodes is still less than the number of the unknown nodes, and the location coverage rate of the unknown nodes is not high, when α is 0.15, the screening of the anchor nodes is moderate, the location coverage rate of the unknown nodes is high, and when α is 0.2, the number of the anchor nodes participating in location is increased, but the screening effect of the effective anchor nodes is weak, so that the location coverage rate of the unknown nodes and α are not much different from 0.15.
Fig. 4 is a node positioning error map of the error threshold α, and it can be seen from fig. 4 that as the anchor node density increases, the positioning average error when α is 0.15 is significantly smaller than that when α is 0.2, and from the viewpoint of positioning coverage, the positioning coverage difference between α and α is not large, so α is 0.15, which is a reasonable threshold.
In the case of a determination threshold of 0.15, the accuracy of the fixed path loss exponent model and the EAS model based positioning are compared.
As can be seen from fig. 5, when the number of anchor nodes is small, the advantage of the EAS model is not obvious, and as the number of anchor nodes increases, the selection effect of the EAS model gradually appears, and the positioning effect of the EAS model is obviously better than that of the fixed path loss model.

Claims (2)

1. An effective anchor node selection method based on RSSI is characterized by comprising the following steps:
step 1, broadcasting the position of each anchor node and an RSSI signal of each anchor node in a positioning scene;
step 2, for each anchor node BiCalculating the anchor node B after receiving the signals of other anchor nodes in the communication radius RiArea path loss exponent n (B)i) (ii) a The region path loss exponent n (B)i) The calculation method comprises the following steps:
step 2-1, determining any two anchor node Bi,BjThe formula used is:
Figure FDA0002196525020000011
wherein (x)i,yi,zi),(xj,yj,zj) Are anchor node B respectivelyi,BjThe coordinates of (a);
step 2-2, determining any two anchor node Bsi,BjPath loss exponent n (B) in betweeni,Bj) The formula is as follows:
Figure FDA0002196525020000012
wherein, BjTo the transmitting node, BiIs a receiving node; d is the distance between two nodes; RSSI (B)i,Bj) Is Bi,BjSignal strength between; x0A gaussian random variable defined as mean 0 and variance 5; pTFor transmit power, PL (d)0) Is composed ofReceiving power;
step 2-3, determining Gaussian path loss influence coefficient fi(Bj) The formula is as follows:
Figure FDA0002196525020000013
wherein (x)i,yi,zi),(xj,yj,zj) Are anchor node B respectivelyi,BjCoordinate of (a), (b), f)i(Bj) Is a gaussian distribution function with a mean value of 0 and a variance of 1;
step 2-4, determining the regional path loss index nR(Bi) The formula is as follows:
Figure FDA0002196525020000014
wherein the area path loss exponent nR(Bi) Consisting of N local path loss indices, N being BiThe number of other anchor nodes in the communication radius R, wherein the jth local path loss exponent is determined by the jth (j is more than or equal to 1 and less than or equal to N) anchor node, each local path loss exponent is calculated by the product of the Gaussian path loss influence coefficient and the correlation sum of the path loss exponents, BjIs represented by the path loss exponential correlation sum ofjWith other anchor node Bk(k is more than or equal to 1 and less than or equal to N, and k is not equal to j) is added;
step 3, determining constraint conditions of the effective anchor nodes, and establishing an effective anchor node selection model; the constraint conditions of the effective anchor nodes are as follows:
Figure FDA0002196525020000015
wherein n (B)i,Bj) As anchor node BiWith other anchor node-bs within its communication radius Rj(j is not less than 1 and not more than N) path loss index, NR(Bi) As anchor node BiThe area of (a) path loss exponent,(xi,yi,zi),(xj,yj,zj) Are anchor node B respectivelyi,Bjα is an error threshold;
and 4, screening the anchor nodes according to the constraint conditions determined in the step 3, selecting an effective anchor node set S without barriers around, and finishing the selection of the effective anchor nodes.
2. The RSSI-based method for selecting valid anchor nodes as claimed in claim 1, wherein in step 4, the valid anchor node screening principle is:
when anchor node BiWith other anchor node-bs within its communication radius Rj(1. ltoreq. j. ltoreq.N) path loss exponent N (B)i,Bj) And BiRegional path loss exponent nR(Bi) Are all less than the error threshold α, this indicates that the anchor node B isiIs a valid anchor node; when anchor node BiWhen an obstacle exists around, BiMust not satisfy the effective anchor node constraint condition, and BiIs not a valid anchor node.
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