CN107801168B - Outdoor self-adaptive passive target positioning method - Google Patents

Outdoor self-adaptive passive target positioning method Download PDF

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CN107801168B
CN107801168B CN201710704510.0A CN201710704510A CN107801168B CN 107801168 B CN107801168 B CN 107801168B CN 201710704510 A CN201710704510 A CN 201710704510A CN 107801168 B CN107801168 B CN 107801168B
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CN107801168A (en
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童文灿
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Longyan University
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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
    • H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination

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Abstract

The invention relates to an outdoor self-adaptive passive target positioning method, which adopts a clustering idea to realize target clustering, then utilizes an RSSI ranging model and a weighted polygonal centroid algorithm to calculate the final position coordinates of a passive target, reduces the error of passive multi-target positioning, the average error of a simulation result is 1.18, and the positioning result track of the multi-target is basically matched with the trend of an actual position track. The method can be applied to large-scale random-distribution field application scenes, each sensing node does not need to be accurately positioned, and the beacon node is used as a reference point to realize the positioning of the target organism. The relative position coordinates of each node in the wireless sensor network can be adaptively established by each sensing node in the initialization stage, so that the actual application requirements can be met, the required hardware cost is low, the communication overhead in the positioning process is low, and the power consumption is low.

Description

Outdoor self-adaptive passive target positioning method
Technical Field
The invention belongs to the field of wireless communication, and particularly relates to an outdoor self-adaptive passive target positioning method.
Background
The wireless sensor network is used as a core technology in the era of the internet of things, and is widely applied to various application scenes of short-distance wireless networking such as military affairs, security protection, environment monitoring, search and rescue, target tracking and the like. Based on the interaction scenario of each sensing node and the environment, the nodes are generally randomly scattered in a certain monitoring area, so that the positioning of the nodes is the basis and the premise in most application scenarios.
Currently, the target positioning technology is mainly divided into two types, namely active target positioning and passive target positioning, and the essential difference is whether a target carries equipment to participate in receiving or sending wireless signals. Active positioning technology generally requires that a positioning target carries a signal transceiver, and positioning is realized through attenuation or phase change of signals; passive positioning does not require any data transceiver carried by the target, which brings great difficulty to passive positioning feasibility research and positioning accuracy improvement.
From the positioning algorithm, the method can be divided into two categories of ranging-based and non-ranging-based. Ranging-based positioning calculates node location by measuring point-to-point distance or angle information between nodes and using trilateration, triangulation, or maximum likelihood estimation. Common ranging techniques are RSSI, TOA, TDOA, and AOA. In the ranging technology based on the RSSI model, a wireless sensor network obtains received data packet information through a received signal strength value RSSI, and calculates the distance between nodes, so that the position of each target node is determined. In the positioning algorithm without distance measurement, distance and angle information is not needed, and the algorithm realizes the positioning of the nodes according to the information such as network connectivity and the like. In the above-mentioned positioning techniques and positioning algorithms, passive positioning for multiple targets has not been a mature algorithm for a while.
Disclosure of Invention
The invention aims to provide an outdoor self-adaptive passive target positioning method, which realizes the positioning of a passive random target through node self-positioning, condensed type hierarchical target clustering and trilateral and multilateral centroid algorithm based on an RSSI ranging model.
The invention discloses an outdoor self-adaptive passive target positioning method, which comprises the following steps:
step 1, calculating the distance between beacon nodes based on RSSI
The distance between the beacon nodes is calculated by combining a free space propagation model and a logarithm-normal distribution model, and the free space wireless propagation loss model is as follows:
Figure 513180DEST_PATH_IMAGE001
(
Figure 961479DEST_PATH_IMAGE002
)
wherein the content of the first and second substances,das a distance from the source，fIs a radio frequency (rf) band, or a radio frequency band,kis a path attenuation factor;
the log-normal distribution wireless propagation loss model is:
Figure 147741DEST_PATH_IMAGE003
(2)
wherein the content of the first and second substances,
Figure 808529DEST_PATH_IMAGE004
is a passing distancedThe loss of the latter path is reduced by the loss,
Figure 527961DEST_PATH_IMAGE005
is a Gaussian random distribution variable, take d0(ii) =1m, and the result is obtained by substituting equation (1)lossIs that
Figure 881582DEST_PATH_IMAGE006
The RSSI value of each unknown node and the beacon node can be obtained according to the formula (2) as follows:
Figure 555140DEST_PATH_IMAGE007
（3）
according to formulas (1) to (3), the distance between the node and the beacon node can be obtained according to the RSSI of any node received by the beacon node;
step 2, self-positioning of reference nodes
All nodes formed by a wireless module and a sensing module in the wireless sensing network are collectively called as reference nodes, the reference nodes are divided into a center node and monitoring nodes, and the distance between the nodes is calculated through received RSSI (received signal strength indicator); self-adaptively establishing position information of each node in the wireless sensor network by self-positioning of the reference node, wherein the position information is represented by a plane coordinate;
2.1 each reference node establishes distance mapping between itself and other reference nodes according to the RSSI values of other reference nodes received by the reference node, and establishes the following two sets:
reference node set:
Figure 285199DEST_PATH_IMAGE008
wherein, in the step (A),
Figure 360602DEST_PATH_IMAGE009
the central node is identified and,
Figure 885124DEST_PATH_IMAGE010
identification monitoring node
Figure 45978DEST_PATH_IMAGE011
；
Set of distances between reference nodes:
Figure 48569DEST_PATH_IMAGE012
wherein, in the step (A),
Figure 103113DEST_PATH_IMAGE013
representing the distance between a reference node i and a reference node j, and n represents the number of the reference nodes in the wireless sensor network;
2.2 after the two sets are established, each reference node starts a self-positioning process, and respective position coordinates are established:
central node
Figure 438017DEST_PATH_IMAGE009
Is initialized to (0, 0) as the origin of the plane coordinate system, the center node
Figure 945222DEST_PATH_IMAGE009
According to the distance set between the reference nodes, two monitoring nodes closest to the reference nodes are selected
Figure 626870DEST_PATH_IMAGE014
,
Figure 801500DEST_PATH_IMAGE015
To do so by
Figure 543191DEST_PATH_IMAGE009
And
Figure 537691DEST_PATH_IMAGE014
the connecting line of the X axis and the X axis is used for establishing a plane coordinate system,
Figure 147664DEST_PATH_IMAGE014
the coordinates are
Figure 786587DEST_PATH_IMAGE016
,
Figure 89393DEST_PATH_IMAGE017
The coordinates are
Figure 945091DEST_PATH_IMAGE018
,
Figure 358755DEST_PATH_IMAGE019
Is a straight line
Figure 976818DEST_PATH_IMAGE020
And a straight line
Figure 325891DEST_PATH_IMAGE021
The included angle between the two parts is smaller than the included angle,
Figure 294984DEST_PATH_IMAGE022
；
obtaining any one monitoring node
Figure 856546DEST_PATH_IMAGE023
Has the coordinates of
Figure 860274DEST_PATH_IMAGE024
Wherein
Figure 380248DEST_PATH_IMAGE025
Is a straight line
Figure 571058DEST_PATH_IMAGE026
And a straight line
Figure 326525DEST_PATH_IMAGE021
Inter-angle, monitoring node
Figure 293081DEST_PATH_IMAGE027
Axial coordinate with sign passing
Figure 108591DEST_PATH_IMAGE028
Is determined if
Figure 662063DEST_PATH_IMAGE029
Then, then
Figure 221220DEST_PATH_IMAGE023
Has the coordinates of
Figure 809328DEST_PATH_IMAGE030
Otherwise, monitoring the node
Figure 264580DEST_PATH_IMAGE023
Has the coordinates of
Figure 695561DEST_PATH_IMAGE031
；
Step 3, target positioning
The target positioning refers to a process of monitoring a random organism through a wireless sensor network and further providing position information of the random organism relative to a reference node, clustering targets by using a clustering algorithm, and determining final position coordinates of the targets in different categories through a weighted centroid method.
In addition, the method comprises the following steps of firstly clustering the targets by using a clustering algorithm, and then determining the final position coordinates of the targets in different classes by using a weighted centroid method:
3.1 object clustering
Classifying the targets by adopting an agglomeration type hierarchical clustering algorithm, and adopting a maximum distance measurement method as an inter-cluster distance measurement method, namely the maximum distance between every two clusters
Figure 402617DEST_PATH_IMAGE032
，
Figure 501023DEST_PATH_IMAGE033
And
Figure 766657DEST_PATH_IMAGE034
are respectively a cluster
Figure 419355DEST_PATH_IMAGE035
Of the object of (1), the
Figure 195681DEST_PATH_IMAGE036
The upper limit is set to
Figure 617435DEST_PATH_IMAGE037
，
Figure 680069DEST_PATH_IMAGE038
For the monitoring radius of the monitoring node in the wireless sensor network, is provided with
Figure 695430DEST_PATH_IMAGE039
The method comprises the following steps that (1) each monitoring node monitors a target, and the specific target clustering process is as follows:
Figure 400081DEST_PATH_IMAGE002
will turn over
Figure 551707DEST_PATH_IMAGE039
Each monitoring node is independently regarded as a cluster, and the maximum distance between every two clusters is calculated;
Figure 50822DEST_PATH_IMAGE040
let all maximum distances be less than
Figure 52014DEST_PATH_IMAGE037
The two clusters of (a) are merged into a new cluster;
Figure 29197DEST_PATH_IMAGE041
recalculating the distances between the new cluster and all clusters;
Figure 425543DEST_PATH_IMAGE042
repeating the above 2 and 3 until there is no inter-cluster distance smaller than
Figure 705346DEST_PATH_IMAGE043
The case (1); in the step, the number of the targets needing to be positioned at the current moment is represented by the number of clusters formed by clustering, which means that all nodes in each cluster monitor the targets, and different clusters monitor different targets;
3.2 object location calculation
Target positioning is the process of calculating the final position coordinates of each type of targets after the targets are clustered, and weighted trilateral and multilateral qualities are usedThe centroid of each type of target is calculated by a heart method, so that the final position coordinate of the target is determined; calculating the distances from the target to all monitoring nodes in the corresponding target cluster by using an infrared ranging model, and taking the distances as weight values during positioning;
Figure 819933DEST_PATH_IMAGE044
the monitoring nodes are clustered to form a plurality of clusters, the number of the monitoring nodes in each cluster is represented by n, n is larger than or equal to 1:
if n =1, the coordinates of the monitoring node in the cluster are used
Figure 476173DEST_PATH_IMAGE045
As a coordinate position of the target, i.e.
Figure 727026DEST_PATH_IMAGE046
；
If n =2, the coordinate of the target is the average of the coordinates of the two monitoring nodes in the cluster, i.e. the target is the target of the two monitoring nodes in the cluster
Figure 567943DEST_PATH_IMAGE047
；
If it is
Figure 45192DEST_PATH_IMAGE048
Then, then
Figure 364177DEST_PATH_IMAGE049
A node can form
Figure 109017DEST_PATH_IMAGE050
The distance between the target and the vertex of the triangle can be obtained according to the infrared distance measurement model
Figure 120836DEST_PATH_IMAGE051
、
Figure 678856DEST_PATH_IMAGE052
And
Figure 942478DEST_PATH_IMAGE053
using the weighted trilateral centroid firstBy the method
Figure 167923DEST_PATH_IMAGE050
The centroid of each triangle and the trilateral centroid formula are
Figure 960430DEST_PATH_IMAGE054
Wherein
Figure 536905DEST_PATH_IMAGE055
、
Figure 338639DEST_PATH_IMAGE056
、
Figure 153011DEST_PATH_IMAGE057
Representing a positioning factor, wherein the influence of the coordinate of the monitoring node which is closer to the target is larger; then use
Figure 772211DEST_PATH_IMAGE050
Calculating the center of mass of each triangle by a polygonal center of mass method to obtain the final coordinates of the target
Figure 678725DEST_PATH_IMAGE058
。
The method is applied to a large-scale randomly-scattered field application scene, each sensing node does not need to be accurately positioned, and the beacon node is used as a reference point to realize the positioning of the target organism. The relative position coordinates of each node in the wireless sensor network can be adaptively established by each sensing node in the initialization stage, so that the actual application requirements can be met, the required hardware cost is low, the communication overhead in the positioning process is low, and the power consumption is low.
Detailed Description
The invention discloses an outdoor self-adaptive passive target positioning method, which comprises the following steps:
step 1, wireless transmission loss model
The accuracy of the RSSI positioning algorithm depends largely on the radio propagation path loss. Common wireless propagation path loss models include a free space propagation model (free space propagation model), a log-distance path loss model (log-distance path loss model), a Hata model (Hata model), a log-normal distribution model (log-distance distribution), and the like. In the actual environment in the field, radio through-thickness loss is changed compared with a theoretical value of propagation in free space due to the influence of factors such as multipath, diffraction, obstacles and the like.
The invention adopts a mode of combining a free space propagation model and a logarithm-normal distribution model, and the free space wireless propagation loss model comprises the following steps:
Figure 408783DEST_PATH_IMAGE001
(
Figure 218608DEST_PATH_IMAGE002
)
wherein the content of the first and second substances,dis the distance from the source inkm ；fIs radio frequency in MHZ;kis a path attenuation factor;
the log-normal distribution wireless propagation loss model is:
Figure 743130DEST_PATH_IMAGE003
(2)
wherein the content of the first and second substances,
Figure 28618DEST_PATH_IMAGE004
is a passing distancedThe latter path loss, in dB;
Figure 172154DEST_PATH_IMAGE005
is a Gaussian random distribution variable with a standard deviation range of 410;kthe range of the path attenuation factor is 2-5; getd(ii) =1m, and the result is obtained by substituting equation (1)lossIs that
Figure 961119DEST_PATH_IMAGE006
The signal strength of each unknown node and the beacon node is obtained according to the formula (2):
Figure 797488DEST_PATH_IMAGE007
（3）
according to the formulas (1) to (3), the beacon node receives the RSSI of any node, and then the distance between the node and the beacon node can be obtained;
in a wireless sensor network, theoretically, the position of a target node is measured by the euclidean distance between the target node and a plurality of beacon nodes, and a trilateration method is commonly used. In the field application scene of the invention, the target node refers to a random target which can be sensed by the pyroelectric infrared sensor, and the target node does not have a wireless signal, so that the position information of the target node can be obtained only by referring to the position information of the node and the detection range of the sensor. According to the method, target clustering and calculation of the final position coordinates of the target are realized through clustering and weighted trilateral and multilateral centroid algorithms;
step 2, self-positioning of reference nodes
All nodes formed by a wireless module and a sensing module in the wireless sensing network are collectively called as reference nodes, the reference nodes are divided into a center node and monitoring nodes, and the distance between the nodes is calculated through received RSSI (received signal strength indicator);
the self-positioning of the reference node aims to adaptively establish the position information of each node in the wireless sensor network, and the position information is expressed by plane coordinates.
Each reference node establishes distance mapping between itself and other reference nodes according to the RSSI values of other reference nodes received by the reference node, and establishes the following two sets:
reference node set:
Figure 39113DEST_PATH_IMAGE008
wherein, in the step (A),
Figure 110974DEST_PATH_IMAGE009
the central node is identified and,
Figure 659505DEST_PATH_IMAGE010
identification monitoring node
Figure 260251DEST_PATH_IMAGE011
；
Set of distances between reference nodes:
Figure 395697DEST_PATH_IMAGE059
wherein, in the step (A),
Figure 740091DEST_PATH_IMAGE013
representing the distance between a reference node i and a reference node j, and n represents the number of the reference nodes in the wireless sensor network;
after the two sets are established, the reference nodes start a self-positioning process and establish respective position coordinates;
central node
Figure 769226DEST_PATH_IMAGE009
Is initialized to (0, 0) as the origin of the plane coordinate system, the center node
Figure 416240DEST_PATH_IMAGE009
According to the distance set between the reference nodes, two monitoring nodes closest to the reference nodes are selected
Figure 163616DEST_PATH_IMAGE014
,
Figure 452646DEST_PATH_IMAGE015
To do so by
Figure 70709DEST_PATH_IMAGE009
And
Figure 544416DEST_PATH_IMAGE014
the connecting line of the X axis and the X axis is used for establishing a plane coordinate system,
Figure 621831DEST_PATH_IMAGE014
the coordinates are
Figure 573606DEST_PATH_IMAGE016
,
Figure 311755DEST_PATH_IMAGE017
The coordinates are
Figure 566150DEST_PATH_IMAGE018
,
Figure 22539DEST_PATH_IMAGE019
Is a straight line
Figure 387793DEST_PATH_IMAGE020
And a straight line
Figure 246027DEST_PATH_IMAGE021
The included angle between the two parts is smaller than the included angle,
Figure 795957DEST_PATH_IMAGE022
；
obtaining any one monitoring node
Figure 615009DEST_PATH_IMAGE023
Has the coordinates of
Figure 908587DEST_PATH_IMAGE024
Wherein
Figure 729650DEST_PATH_IMAGE025
Is a straight line
Figure 716061DEST_PATH_IMAGE026
And a straight line
Figure 22408DEST_PATH_IMAGE021
Inter-angle, monitoring node
Figure 854098DEST_PATH_IMAGE027
Axial coordinate with sign passing
Figure 421345DEST_PATH_IMAGE028
Is determined if
Figure 188444DEST_PATH_IMAGE029
Then, then
Figure 106722DEST_PATH_IMAGE023
Has the coordinates of
Figure 617469DEST_PATH_IMAGE030
Otherwise, monitoring the node
Figure 39223DEST_PATH_IMAGE023
Has the coordinates of
Figure 367436DEST_PATH_IMAGE031
；
Step 3, target positioning
The target positioning refers to a process of monitoring a random living being through a wireless sensor network and then providing position information of the random living being relative to a reference node. In an actual application environment, the position accuracy of the target is related to the monitoring radius of the monitoring nodes in the wireless sensor network, the RSSI value with a random component and the random distribution of the wireless sensor network, and the positioning accuracy of the target can be improved by using a centroid method or a maximum likelihood estimation method. However, in practical application scenarios, multiple targets may appear simultaneously, and therefore, the accuracy of positioning cannot be guaranteed simply by using the centroid method or the maximum likelihood estimation method. The method firstly utilizes a clustering algorithm to cluster the targets, and then determines the final position coordinates of the targets of different categories by a weighted centroid method.
3.1 object clustering
There are a number of clustering algorithms currently available. The choice of clustering algorithm for a particular application depends on the type of data, the purpose of the clustering. The main clustering algorithms can be divided into the following categories: partitioning methods, hierarchical methods, density-based methods, mesh-based methods, and model-based methods. The invention adopts an agglomeration type hierarchical clustering algorithm to classify the targets, the inter-cluster distance measurement method adopts a maximum distance measurement method,
Figure 615752DEST_PATH_IMAGE060
wherein, in the step (A),
Figure 320403DEST_PATH_IMAGE033
and
Figure 596664DEST_PATH_IMAGE034
are respectively a cluster
Figure 971144DEST_PATH_IMAGE035
Object in (1), maximum distance between clusters
Figure 598435DEST_PATH_IMAGE036
The upper limit is set to
Figure 185405DEST_PATH_IMAGE037
，
Figure 847331DEST_PATH_IMAGE038
For the monitoring radius of the monitoring node in the wireless sensor network, is provided with
Figure 392712DEST_PATH_IMAGE039
The method comprises the following steps that (1) each monitoring node monitors a target, and the specific target clustering process is as follows:
Figure 241720DEST_PATH_IMAGE002
will turn over
Figure 22594DEST_PATH_IMAGE039
Each monitoring node is independently regarded as a cluster, and the maximum distance between every two clusters is calculated;
Figure 647348DEST_PATH_IMAGE040
let all maximum distances be less than
Figure 488265DEST_PATH_IMAGE037
The two clusters of (a) are merged into a new cluster;
Figure 965514DEST_PATH_IMAGE041
restart of the operationRespectively calculating the distances between the new cluster and all clusters;
Figure 284500DEST_PATH_IMAGE042
repeating the above 2 and 3 until there is no inter-cluster distance smaller than
Figure 655438DEST_PATH_IMAGE043
The case (1); in the step, the number of the targets needing to be positioned at the current moment is represented by the number of clusters formed by clustering, which means that all nodes in each cluster monitor the targets, and different clusters monitor different targets;
3.2 object location calculation
Target localization is the process of calculating the final position coordinates of each type of target after the above-mentioned clustering of targets. The centroid of each class of object is calculated using a weighted three-and multi-sided centroid method to determine the final position coordinates of the object. And calculating the distances from the target to all monitoring nodes in the corresponding target cluster by using an infrared ranging model, and taking the distances as weight values during positioning.
Infrared ranging can be used for targets with temperature above absolute zero, and electromagnetic radiation energy is detected
The important parameters of the target distance, which depend on the target surface temperature T and the wavelength. According to Planck's law, the wavelength lambda, the temperature T and the emissivity can be known
Figure 277044DEST_PATH_IMAGE061
Degree of sum radiation
Figure 100643DEST_PATH_IMAGE062
The relationship between them.
Figure 364265DEST_PATH_IMAGE063
The principle of distance measurement is, however, generally to pass through a lower frequency infrared and then measure the phase difference between the echo and the transmitted wave
Figure 324131DEST_PATH_IMAGE064
Calculating the echo time according to the phase difference
Figure 506851DEST_PATH_IMAGE065
Namely:
Figure 191648DEST_PATH_IMAGE066
and finally, solving the target distance.
Figure 118016DEST_PATH_IMAGE067
Is the infrared signal period.
Suppose that
Figure 932388DEST_PATH_IMAGE044
The monitoring nodes are clustered to form a plurality of clusters, and the number of the monitoring nodes in the clusters is used
Figure 426954DEST_PATH_IMAGE068
Represents:
if n =1, the coordinates of the monitoring node in the cluster are used
Figure 225146DEST_PATH_IMAGE045
As a coordinate position of the target, i.e.
Figure 299412DEST_PATH_IMAGE069
；
If n =2, the coordinate of the target is the coordinate mean of two monitoring nodes in the cluster, i.e. the target is a node in the cluster
Figure 499449DEST_PATH_IMAGE070
；
If it is
Figure 164917DEST_PATH_IMAGE048
Then, then
Figure 919246DEST_PATH_IMAGE049
A monitoring node can be formed
Figure 452996DEST_PATH_IMAGE050
The distance between the target and the vertex of the triangle is obtained according to the infrared distance measurement model
Figure 904879DEST_PATH_IMAGE051
、
Figure 600302DEST_PATH_IMAGE052
And
Figure 982873DEST_PATH_IMAGE053
firstly, the weighted trilateral centroid method is used to obtain
Figure 54734DEST_PATH_IMAGE050
The centroid of each triangle, the trilateral centroid formula is:
Figure 839151DEST_PATH_IMAGE054
wherein
Figure 971055DEST_PATH_IMAGE055
、
Figure 840922DEST_PATH_IMAGE056
、
Figure 450895DEST_PATH_IMAGE057
Representing a positioning factor, wherein the influence of coordinates of the monitoring node is larger when the monitoring node is closer to a target; then is reused
Figure 480030DEST_PATH_IMAGE050
Calculating the final position coordinates of the target by a polygonal centroid method
Figure 891158DEST_PATH_IMAGE071
。
The invention discloses a passive multi-target positioning method, which is a difficult problem in the positioning technology in the conventional wireless sensor network, and is based on an outdoor adaptive positioning algorithm of clustering and RSSI (received signal strength indicator), the clustering of targets is realized by adopting a clustering idea, then the final position coordinates of the passive targets are calculated by utilizing an RSSI (received signal strength indicator) ranging model and a weighted polygonal centroid algorithm, the error of the passive multi-target positioning is reduced, the average error of a simulation result is 1.18, and the positioning result track of the multiple targets is basically matched with the trend of an actual position track. The error of the positioning method is within 0.8m under the probability of being close to 85 percent, which is superior to the traditional RSSI and infrared positioning methods, while when the cumulative probability distribution tends to 1, the error of the positioning method is obviously greater than that of the traditional two methods, and the larger errors occur at the boundary of a monitoring area because the crossing density of the monitoring range of the boundary nodes of the wireless sensor network is low, which increases the average error of the method, but the method can be solved by expanding the monitoring range of the sensor network and improving the crossing density of the monitoring range of the nodes, namely, the monitoring range of the wireless sensor network is greater than the actual monitoring boundary.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the technical scope of the present invention, so that any minor modifications, equivalent changes and modifications made to the above embodiment according to the technical spirit of the present invention are within the technical scope of the present invention.

Claims (1)

1. An outdoor adaptive passive target positioning method is characterized in that: the target nodes are random targets which can be induced by the pyroelectric infrared sensor, and do not have wireless signals, the position information of the target nodes is obtained through the position information of the reference nodes and the detection range of the sensor, all nodes formed by a wireless module and a sensing module in the wireless sensor network are collectively called as the reference nodes, the reference nodes are divided into center nodes and monitoring nodes, and the distance between all the reference nodes is obtained through received RSSI calculation; the method comprises the steps that self-positioning of reference nodes is carried out, the purpose is to adaptively establish position information of each node in a wireless sensor network, the position information is represented by plane coordinates, when a random target is monitored through the wireless sensor network, a clustering algorithm is firstly utilized to cluster the target, the number of clusters formed by clustering is used for representing the number of target nodes needing to be positioned at the current moment, all the nodes in each cluster monitor the target nodes, and different clusters monitor different target nodes; the method comprises the following steps of calculating the distances from a target node to all monitoring nodes in a corresponding target cluster by using an infrared ranging model, using the distances as weights during positioning, and determining final position coordinates of target nodes of different categories by using a weighted centroid method, wherein the method comprises the following steps:
step 1, calculating the distance between beacon nodes based on RSSI
The distance between the beacon nodes is calculated by combining a free space propagation model and a logarithm-normal distribution model, and the free space wireless propagation loss model is as follows:
loss＝32.44+10klgd+10klgf (1)
wherein d is the distance from the source, f is the radio frequency, and k is the path attenuation factor;
the log-normal distribution wireless propagation loss model is:
PL(d)＝PL(d0)+10klg(d/d0)+Xσ (2)
wherein PL (d) is the path loss after a distance d, X σ is a Gaussian random distribution variable, and d is taken0When equation (1) is substituted for 1m, loss is obtained as PL (d)0) The RSSI value of each unknown node and the beacon node can be obtained according to the formula (2) as follows:
RSSI + antenna gain-path loss pl (d) (3)
According to formulas (1) to (3), the distance between the node and the beacon node can be obtained according to the RSSI of any node received by the beacon node;
step 2, self-positioning of reference nodes
2.1 each reference node establishes distance mapping between itself and other reference nodes according to the RSSI values of other reference nodes received by the reference node, and establishes the following two sets:
reference node set: refer ene ceset＝{a0，a1，…，an-1In which a0The central node is identified and,
Figure FDA0002639760950000022
identifying a monitoring node, wherein i is not equal to 0;
set of distances between reference nodes: distanceset＝{d01，d02…dij，…，dn-2n-1In which d isijRepresenting the distance between a reference node i and a reference node j, and n represents the number of the reference nodes in the wireless sensor network;
2.2 after the two sets are established, each reference node starts a self-positioning process, and respective position coordinates are established:
center node a0Is initialized to (0, 0) as the origin of the plane coordinate system, the center node a0According to the distance set between the reference nodes, two monitoring nodes a closest to the reference nodes are selectedu，avAnd d is0u≤d0vWith a0And auThe connecting line of (a) is an X axis, and a plane coordinate system is established, auThe coordinate is (d)0u，0)，avThe coordinate is (d)ovcosα，dovsin α), α is a straight line a0avAnd a straight line a0auThe included angle between the two parts is smaller than the included angle,
Figure FDA0002639760950000021
obtaining any monitoring node akHas the coordinates of (d)okcosβ，±doksin β), where k ≠ 0, u, v; beta is a straight line a0akAnd a straight line a0auThe included angle between the two, monitor the node akY-axis coordinate sign of (a) by dkvIs determined if
Figure FDA0002639760950000031
Then akHas the coordinates of (d)okcosβ，doksin β), otherwise, node a is monitoredkHas the coordinates of (d)okcosβ，-doksinβ)；
Step 3, target positioning
Firstly, clustering targets by using a clustering algorithm, and then determining the final positions of target nodes of different classes by using a weighted centroid method, wherein the method comprises the following steps:
3.1 object clustering
Classifying the targets by adopting an agglomeration type hierarchical clustering algorithm, and adopting a maximum distance measurement method as an inter-cluster distance measurement method, namely the maximum distance between every two clusters
Figure FDA0002639760950000032
p and p' are clusters ci，cjOf dmax(ci，cj) The upper limit is 2r0，r0The method is characterized in that m monitoring nodes are arranged for monitoring targets for monitoring the monitoring radius of the monitoring nodes in the wireless sensor network, and the specific target clustering process is as follows:
1. the m monitoring nodes are independently regarded as a cluster, and the maximum distance between every two clusters is calculated;
2. all maximum distances are less than 2r0The two clusters of (a) are merged into a new cluster;
3. respectively calculating the distances between the new cluster and all clusters again;
4. repeating the steps 2 and 3 until no cluster-to-cluster distance smaller than 2r exists0The case (1); in the step, the number of the target nodes needing to be positioned at the current moment is represented by the number of clusters formed by clustering, which means that all the nodes in each cluster monitor the target nodes, and different clusters monitor different target nodes;
3.2 object location calculation
Target positioning is a process of calculating the final position coordinates of each type of target nodes after the target is clustered, and the centroid of each type of target nodes is calculated by using a weighted trilateral and multilateral centroid method, so that the final position coordinates of the target nodes are determined:
calculating the distances from the target node to all monitoring nodes in the corresponding target cluster by using an infrared ranging model, and taking the distances as weight values during positioning; the m monitoring nodes form a plurality of clusters through clustering, the number of the monitoring nodes in each cluster is represented by n, and n is more than or equal to 1:
if n is 1, the coordinates (x) of the monitoring node in the cluster are usedi，yi) As coordinate positions of target nodes, i.e. positionstarget＝(xi，yi)；
If n is 2, the coordinate of the target node is the average value of the coordinates of the two monitoring nodes in the cluster, namely
Figure FDA0002639760950000041
If n is more than or equal to 3, n target nodes form
Figure FDA0002639760950000042
The distance between the target node and the vertex of the triangle is l according to the infrared distance measurement modeli、ljAnd lkFirstly, the weighted trilateral centroid method is used to obtain
Figure FDA0002639760950000043
The centroid of each triangle and the trilateral centroid formula are as follows:
Figure FDA0002639760950000044
wherein
Figure FDA0002639760950000045
Representing a positioning factor, wherein the influence of the coordinates of the monitoring node which is closer to the target node is larger; then use
Figure FDA0002639760950000046
Calculating the final position coordinates of the target nodes by a polygonal centroid method
Figure FDA0002639760950000051
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103197280A (en) * 2013-04-02 2013-07-10 中国科学院计算技术研究所 Access point (AP) location estimation method based on radio-frequency signal strength
CN103929807A (en) * 2014-04-28 2014-07-16 上海和视环境艺术工程有限公司 Method for precisely positioning device coordinate based on low power consumption
CN104684081A (en) * 2015-02-10 2015-06-03 三峡大学 Wireless sensor network node localization algorithm based on distance clustering selected anchor nodes
CN105430745A (en) * 2015-12-28 2016-03-23 迈普通信技术股份有限公司 Wireless network positioning method based on RSSI (Received Signal Strength Indicator)
CN105635964A (en) * 2015-12-25 2016-06-01 河海大学 Wireless sensor network node localization method based on K-medoids clustering
CN105828435A (en) * 2016-05-30 2016-08-03 天津大学 Distance correction weighted centroid localization method based on reception signal intensity optimization
CN106102161A (en) * 2016-05-30 2016-11-09 天津大学 Based on the data-optimized indoor orientation method of focusing solutions analysis

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8964595B2 (en) * 2013-06-11 2015-02-24 Seven Networks, Inc. Quality of experience enhancement for wireless networks based on received signal strength at a mobile device

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103197280A (en) * 2013-04-02 2013-07-10 中国科学院计算技术研究所 Access point (AP) location estimation method based on radio-frequency signal strength
CN103929807A (en) * 2014-04-28 2014-07-16 上海和视环境艺术工程有限公司 Method for precisely positioning device coordinate based on low power consumption
CN104684081A (en) * 2015-02-10 2015-06-03 三峡大学 Wireless sensor network node localization algorithm based on distance clustering selected anchor nodes
CN105635964A (en) * 2015-12-25 2016-06-01 河海大学 Wireless sensor network node localization method based on K-medoids clustering
CN105430745A (en) * 2015-12-28 2016-03-23 迈普通信技术股份有限公司 Wireless network positioning method based on RSSI (Received Signal Strength Indicator)
CN105828435A (en) * 2016-05-30 2016-08-03 天津大学 Distance correction weighted centroid localization method based on reception signal intensity optimization
CN106102161A (en) * 2016-05-30 2016-11-09 天津大学 Based on the data-optimized indoor orientation method of focusing solutions analysis

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于位置指纹识别的WiFi室内定位算法研究与实现;陈思敏;《万方数据知识服务平台》;20160830;正文第3.2.2节 *

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