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CN107801168B - An Outdoor Adaptive Passive Target Location Method - Google Patents

An Outdoor Adaptive Passive Target Location 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

本发明一种室外自适应的被动式目标的定位方法，采用聚类思想实现目标的聚类，然后利用RSSI测距模型和加权多边质心算法计算被动式目标的最终位置坐标，降低了被动式多目标定位的误差，仿真结果的平均误差为1.18，多目标的定位结果轨迹和实际位置轨迹趋势基本吻合。可应用于大规模随机散布的野外应用场景，各传感节点并不需要精确的定位，利用信标节点作为参考点实现目标生物的定位。各传感节点在初始化阶段会自适应地建立无线传感网中各个节点的相对位置坐标，即可满足实际应用需求，并且实现要求的硬件成本低本、定位过程的通信开销小、功耗低。The invention is an outdoor self-adaptive passive target positioning method, which adopts the clustering idea to realize the clustering of the targets, and then uses the RSSI ranging model and the weighted polygon centroid algorithm to calculate the final position coordinates of the passive target, which reduces the cost of passive multi-target positioning. The average error of the simulation results is 1.18, and the trajectory of the multi-target positioning results is basically consistent with the actual position trajectory. It can be applied to large-scale random distribution of field application scenarios. Each sensor node does not need precise positioning, and the beacon node is used as a reference point to achieve the positioning of target organisms. In the initialization stage, each sensor node will adaptively establish the relative position coordinates of each node in the wireless sensor network, which can meet the actual application requirements, and realize the required hardware cost, low cost of hardware, low communication overhead in the positioning process, and low power consumption. .

Description

一种室外自适应的被动式目标的定位方法An Outdoor Adaptive Passive Target Location Method

技术领域technical field

本发明属于无线通信领域，尤其涉及一种室外自适应的被动式目标的定位方法。The invention belongs to the field of wireless communication, and in particular relates to an outdoor adaptive passive target positioning method.

背景技术Background technique

无线传感网络作为物联网时代下的核心技术，被广泛运用于军事、安防、环境监测、搜索救援、目标追踪等短距离无线组网的各种应用场景。基于各传感节点和环境的交互场景，节点通常随机散布在一定的监测区域内，因此自身的定位是大多数应用场景下的基础和前提。As the core technology in the era of the Internet of Things, wireless sensor networks are widely used in various application scenarios of short-range wireless networking such as military, security, environmental monitoring, search and rescue, and target tracking. Based on the interaction scene between each sensor node and the environment, the nodes are usually randomly scattered in a certain monitoring area, so their own positioning is the basis and premise of most application scenarios.

目前，目标定位技术主要分为主动式目标定位和被动式目标定位两类，其本质区别在于目标是否携带设备参与无线信号的接收或发送。主动式定位技术通常要求定位目标携带信号收发设备，通过信号的衰减或相位的变化来实现定位；被动式定位则不要求目标携带任何数据收发设备，这给被动式定位可行性研究与定位精度提升带来极大困难。At present, target positioning technology is mainly divided into two categories: active target positioning and passive target positioning. The essential difference is whether the target carries equipment to participate in the reception or transmission of wireless signals. Active positioning technology usually requires the positioning target to carry signal transceiver equipment, and achieve positioning through signal attenuation or phase changes; passive positioning does not require the target to carry any data transceiver equipment, which brings the feasibility study of passive positioning and the improvement of positioning accuracy. extremely difficult.

而从定位算法上，也可以分为基于测距和基于非测距的两大类。基于测距的定位通过测量节点间点到点的距离或角度信息,并使用三边测量法、三角测量法或最大似然估计法计算节点位置。常用的测距技术有RSSI、TOA、TDOA和AOA。基于RSSI模型的测距技术中，无线传感网通过接收到的信号强度值RSSI，获得接收到的数据包信息，计算节点之间的距离，从而确定每个目标节点的位置。无需测距的定位算法中，则不需要距离和角度信息，算法根据网络的连通性等信息来实现节点的定位。上述的定位技术和定位算中，针对多目标的被动式定位则暂时还没有成熟算法。From the positioning algorithm, it can also be divided into two categories: ranging-based and non-ranging-based. Ranging-based positioning measures the point-to-point distance or angle information between nodes, and uses trilateration, triangulation, or maximum likelihood estimation to calculate node positions. Commonly used ranging techniques are RSSI, TOA, TDOA and AOA. In the ranging technology based on the RSSI model, the wireless sensor network obtains the received data packet information through the received signal strength value RSSI, calculates the distance between nodes, and determines the location of each target node. In the positioning algorithm without ranging, distance and angle information are not required, and the algorithm realizes the positioning of nodes according to information such as network connectivity. Among the above-mentioned positioning technologies and positioning algorithms, there is no mature algorithm for passive positioning for multiple targets.

发明内容SUMMARY OF THE INVENTION

本发明的目的在于提出了一种室外自适应的被动式目标的定位方法，通过基于RSSI测距模型的节点自定位、凝聚型层次目标聚类以及三边和多边质心算法，实现被动式随机目标的定位。The purpose of the present invention is to propose an outdoor adaptive passive target positioning method, which realizes passive random target positioning through node self-positioning based on RSSI ranging model, agglomerative hierarchical target clustering and trilateral and multilateral centroid algorithms. .

本发明一种室外自适应的被动式目标的定位方法，包括如下步骤：An outdoor adaptive passive target positioning method of the present invention includes the following steps:

步骤1、基于RSSI计算信标节点之间的距离Step 1. Calculate the distance between beacon nodes based on RSSI

采用自由空间传播模型和对数-常态分布模型相结合的方式计算信标节点之间的距离，自由空间无线传播损耗模型如下：The distance between beacon nodes is calculated by combining the free-space propagation model and the log-normal distribution model. The free-space wireless propagation loss model is as follows:

Figure 513180DEST_PATH_IMAGE001
(
Figure 961479DEST_PATH_IMAGE002
)
Figure 513180DEST_PATH_IMAGE001
(
Figure 961479DEST_PATH_IMAGE002
)

其中，d为相距信源的距离，f是无线频率，k为路径衰减因子；Among them, d is the distance from the source , f is the wireless frequency, and k is the path attenuation factor;

对数-常态分布无线传播损耗模型为：The log-normal distribution wireless propagation loss model is:

Figure 147741DEST_PATH_IMAGE003
(2)
Figure 147741DEST_PATH_IMAGE003
(2)

其中，

Figure 808529DEST_PATH_IMAGE004
是经过距离d后的路径损耗，
Figure 527961DEST_PATH_IMAGE005
是高斯随机分布变数，取d0=1m，代入公式（1），求得loss即为
Figure 881582DEST_PATH_IMAGE006
的值，根据公式（2）可得出各未知节点与信标节点的信号强度RSSI值为：in,
Figure 808529DEST_PATH_IMAGE004
is the path loss after distance d ,
Figure 527961DEST_PATH_IMAGE005
is a Gaussian random distribution variable, take d 0 =1m, substitute it into formula (1), and find the loss as
Figure 881582DEST_PATH_IMAGE006
According to formula (2), the RSSI value of the signal strength of each unknown node and beacon node can be obtained:

Figure 555140DEST_PATH_IMAGE007
（3）
Figure 555140DEST_PATH_IMAGE007
(3)

依据公式（1）至（3），根据信标节点收到任意一个节点的信号强度RSSI，即可求得该节点与信标节点之间的距离；According to formulas (1) to (3), according to the signal strength RSSI of any node received by the beacon node, the distance between the node and the beacon node can be obtained;

步骤2、参考节点自定位Step 2. Self-positioning of reference nodes

无线传感网中由无线模块和传感模块构成的各节点统称为参考节点，将参考节点分为中心节点和监测节点，各节点之间的距离通过接收的RSSI计算求得；通过参考节点的自定位，自适应地建立无线传感网中各个节点位置信息，位置信息用平面坐标表示；In the wireless sensor network, the nodes composed of wireless modules and sensing modules are collectively referred to as reference nodes. The reference nodes are divided into central nodes and monitoring nodes. The distance between each node is calculated by the received RSSI; Self-positioning, establishes the location information of each node in the wireless sensor network adaptively, and the location information is represented by plane coordinates;

2.1各参考节点依据其收到其他参考节点的信号强度RSSI值，建立自身与其他参考节点的距离映射，建立如下两个集合：2.1 Each reference node establishes the distance mapping between itself and other reference nodes according to the signal strength RSSI value it receives from other reference nodes, and establishes the following two sets:

参考节点集合：

Figure 285199DEST_PATH_IMAGE008
，其中，
Figure 360602DEST_PATH_IMAGE009
标识中心节点，
Figure 885124DEST_PATH_IMAGE010
标识监测节点
Figure 45978DEST_PATH_IMAGE011
；Reference node collection:
Figure 285199DEST_PATH_IMAGE008
,in,
Figure 360602DEST_PATH_IMAGE009
identify the central node,
Figure 885124DEST_PATH_IMAGE010
Identify monitoring nodes
Figure 45978DEST_PATH_IMAGE011
;

参考节点间的距离集合：

Figure 48569DEST_PATH_IMAGE012
，其中，
Figure 103113DEST_PATH_IMAGE013
表示参考节点i和参考节点j之间的距离,n表示无线传感网中参考节点的个数；A collection of distances between reference nodes:
Figure 48569DEST_PATH_IMAGE012
,in,
Figure 103113DEST_PATH_IMAGE013
represents the distance between reference node i and reference node j, n represents the number of reference nodes in the wireless sensor network;

2.2建立上述两个集合后，各参考节点开始自定位过程，建立各自的位置坐标：2.2 After the above two sets are established, each reference node starts the self-positioning process and establishes its own position coordinates:

中心节点

Figure 438017DEST_PATH_IMAGE009
的坐标初始化为（0，0），作为平面坐标系原点，中心节点
Figure 945222DEST_PATH_IMAGE009
根据参考节点间的距离集合，选择距离自身最近的两个监测节点
Figure 626870DEST_PATH_IMAGE014
,
Figure 801500DEST_PATH_IMAGE015
,以
Figure 543191DEST_PATH_IMAGE009
和
Figure 537691DEST_PATH_IMAGE014
的连线为X轴，建立平面坐标系，
Figure 147664DEST_PATH_IMAGE014
坐标为
Figure 786587DEST_PATH_IMAGE016
,
Figure 89393DEST_PATH_IMAGE017
坐标为
Figure 945091DEST_PATH_IMAGE018
,
Figure 358755DEST_PATH_IMAGE019
为直线
Figure 976818DEST_PATH_IMAGE020
和直线
Figure 325891DEST_PATH_IMAGE021
间的夹角,
Figure 294984DEST_PATH_IMAGE022
；central node
Figure 438017DEST_PATH_IMAGE009
The coordinates are 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, select the two closest monitoring nodes to itself
Figure 626870DEST_PATH_IMAGE014
,
Figure 801500DEST_PATH_IMAGE015
,by
Figure 543191DEST_PATH_IMAGE009
and
Figure 537691DEST_PATH_IMAGE014
The connection line is the X-axis, and a plane coordinate system is established.
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
as a straight line
Figure 976818DEST_PATH_IMAGE020
and straight line
Figure 325891DEST_PATH_IMAGE021
the angle between,
Figure 294984DEST_PATH_IMAGE022
;

求得任意一个监测节点

Figure 856546DEST_PATH_IMAGE023
的坐标为
Figure 860274DEST_PATH_IMAGE024
，其中
Figure 380248DEST_PATH_IMAGE025
为直线
Figure 571058DEST_PATH_IMAGE026
和直线
Figure 326525DEST_PATH_IMAGE021
间的夹角，监测节点
Figure 293081DEST_PATH_IMAGE027
轴坐标正负号通过
Figure 108591DEST_PATH_IMAGE028
确定，若Find any monitoring node
Figure 856546DEST_PATH_IMAGE023
The coordinates of are
Figure 860274DEST_PATH_IMAGE024
,in
Figure 380248DEST_PATH_IMAGE025
as a straight line
Figure 571058DEST_PATH_IMAGE026
and straight line
Figure 326525DEST_PATH_IMAGE021
The angle between the monitoring nodes
Figure 293081DEST_PATH_IMAGE027
Axis coordinate positive and negative signs are passed
Figure 108591DEST_PATH_IMAGE028
sure, if

Figure 662063DEST_PATH_IMAGE029
，则
Figure 221220DEST_PATH_IMAGE023
的坐标为
Figure 809328DEST_PATH_IMAGE030
，否则，监测节点
Figure 264580DEST_PATH_IMAGE023
的坐标为
Figure 695561DEST_PATH_IMAGE031
；
Figure 662063DEST_PATH_IMAGE029
,but
Figure 221220DEST_PATH_IMAGE023
The coordinates of are
Figure 809328DEST_PATH_IMAGE030
, otherwise, monitor the node
Figure 264580DEST_PATH_IMAGE023
The coordinates of are
Figure 695561DEST_PATH_IMAGE031
;

步骤3、目标定位Step 3. Target positioning

目标定位是指通过无线传感网监测到随机生物，进而给出随机生物相对参考节点的位置信息的过程，先利用聚类算法对目标聚类，再通过加权质心法确定不同类别目标的最终位置坐标。Target positioning refers to the process of monitoring random creatures through wireless sensor networks, and then giving the position information of random creatures relative to the reference node. First, the clustering algorithm is used to cluster the targets, and then the weighted centroid method is used to determine the final positions of different types of targets. coordinate.

另外，所述的先利用聚类算法对目标聚类，再通过加权质心法确定不同类别目标的最终位置坐标，包括如下步骤：In addition, firstly using the clustering algorithm to cluster the targets, and then determining the final position coordinates of the different categories of targets by the weighted centroid method, including the following steps:

3.1目标聚类3.1 Target Clustering

采用凝聚型层次聚类算法，对目标进行分类，采用最大距离度量法作为簇间距离度量方法，即两两簇最大距离

Figure 402617DEST_PATH_IMAGE032
，
Figure 501023DEST_PATH_IMAGE033
和
Figure 766657DEST_PATH_IMAGE034
分别为簇
Figure 419355DEST_PATH_IMAGE035
中的对象，该
Figure 195681DEST_PATH_IMAGE036
上限取值为
Figure 617435DEST_PATH_IMAGE037
，
Figure 680069DEST_PATH_IMAGE038
为无线传感网中监测节点的监测半径，设有
Figure 695430DEST_PATH_IMAGE039
个监测节点监测到目标，具体的目标聚类过程如下：The agglomerative hierarchical clustering algorithm is used to classify the targets, and the maximum distance measurement method is used as the distance measurement method between clusters, that is, the maximum distance between two clusters
Figure 402617DEST_PATH_IMAGE032
,
Figure 501023DEST_PATH_IMAGE033
and
Figure 766657DEST_PATH_IMAGE034
cluster
Figure 419355DEST_PATH_IMAGE035
object in the
Figure 195681DEST_PATH_IMAGE036
The upper limit value is
Figure 617435DEST_PATH_IMAGE037
,
Figure 680069DEST_PATH_IMAGE038
is the monitoring radius of the monitoring nodes in the wireless sensor network, with
Figure 695430DEST_PATH_IMAGE039
Each monitoring node monitors the target, and the specific target clustering process is as follows:

Figure 400081DEST_PATH_IMAGE002
．将
Figure 551707DEST_PATH_IMAGE039
个监测节点都单独视为一个簇，计算两两簇之间的最大距离；
Figure 400081DEST_PATH_IMAGE002
． Will
Figure 551707DEST_PATH_IMAGE039
Each monitoring node is regarded as a cluster independently, and the maximum distance between two clusters is calculated;

Figure 50822DEST_PATH_IMAGE040
．将所有最大距离小于
Figure 52014DEST_PATH_IMAGE037
的两个簇合并成一个新簇；
Figure 50822DEST_PATH_IMAGE040
． Set all maximum distances less than
Figure 52014DEST_PATH_IMAGE037
The two clusters of are merged into a new cluster;

Figure 29197DEST_PATH_IMAGE041
．重新分别计算新簇与所有簇之间的距离；
Figure 29197DEST_PATH_IMAGE041
. Recalculate the distances between the new cluster and all clusters separately;

Figure 425543DEST_PATH_IMAGE042
．重复上述2、3，直到不存在簇间距离小于
Figure 705346DEST_PATH_IMAGE043
的情况；在本步骤用聚类形成的簇的个数来表示当前时刻需要定位的目标的个数，意味着每个簇中的所有节点都监测到了目标，不同的簇监测了不同的目标；
Figure 425543DEST_PATH_IMAGE042
． Repeat 2 and 3 above until there is no inter-cluster distance less than
Figure 705346DEST_PATH_IMAGE043
In this step, the number of clusters formed by clustering is used to represent the number of targets that need to be located at the current moment, which means that all nodes in each cluster have monitored the target, and different clusters have monitored different targets;

3.2目标定位计算3.2 Target positioning calculation

目标定位则是在上述目标聚类之后，计算每类目标最终位置坐标的过程，使用加权的三边和多边质心法计算每类目标的质心，从而确定该目标的最终位置坐标；利用红外测距模型，计算出目标距其对应目标簇中所有监测节点的距离，作为定位时的权值；

Figure 819933DEST_PATH_IMAGE044
个监测节点经过聚类形成了若干个簇，每个簇中的监测节点数用n表示,n≧1：Target positioning is the process of calculating the final position coordinates of each type of target after the above target clustering, using the weighted trilateral and multilateral centroid method to calculate the centroid of each type of target, thereby determining the final position coordinates of the target; using infrared ranging The model calculates the distance between the target and all monitoring nodes in its corresponding target cluster as the weight during positioning;
Figure 819933DEST_PATH_IMAGE044
The monitoring nodes are clustered to form several clusters, and the number of monitoring nodes in each cluster is represented by n, n≧1:

若n=1，则用该簇中的监测节点的坐标

Figure 476173DEST_PATH_IMAGE045
作为目标的坐标位置，即
Figure 727026DEST_PATH_IMAGE046
；If n=1, use the coordinates of the monitoring nodes in the cluster
Figure 476173DEST_PATH_IMAGE045
as the coordinate position of the target, i.e.
Figure 727026DEST_PATH_IMAGE046
;

若n=2，则目标的坐标为簇中两个监测节点坐标的均值，即

Figure 567943DEST_PATH_IMAGE047
；If n=2, the coordinate of the target is the mean of the coordinates of the two monitoring nodes in the cluster, that is
Figure 567943DEST_PATH_IMAGE047
;

若

Figure 45192DEST_PATH_IMAGE048
，则
Figure 364177DEST_PATH_IMAGE049
个节点可构成
Figure 109017DEST_PATH_IMAGE050
个三角形，依据红外测距模型，可求得目标距离三角形顶点的距离分别为
Figure 120836DEST_PATH_IMAGE051
、
Figure 678856DEST_PATH_IMAGE052
和
Figure 942478DEST_PATH_IMAGE053
，先用加权三边质心法求出
Figure 167923DEST_PATH_IMAGE050
个三角形的质心,三边质心公式为
Figure 960430DEST_PATH_IMAGE054
,其中
Figure 536905DEST_PATH_IMAGE055
、
Figure 338639DEST_PATH_IMAGE056
、
Figure 153011DEST_PATH_IMAGE057
表示定位因子，表示距离目标越近的监测节点的坐标的影响力越大；然后利用
Figure 772211DEST_PATH_IMAGE050
个三角形质心，通过多边质心法计算得到目标的最终坐标
Figure 678725DEST_PATH_IMAGE058
。like
Figure 45192DEST_PATH_IMAGE048
,but
Figure 364177DEST_PATH_IMAGE049
nodes can form
Figure 109017DEST_PATH_IMAGE050
According to the infrared ranging model, the distance between the target and the vertex of the triangle can be obtained as:
Figure 120836DEST_PATH_IMAGE051
,
Figure 678856DEST_PATH_IMAGE052
and
Figure 942478DEST_PATH_IMAGE053
, first use the weighted trilateral centroid method to find
Figure 167923DEST_PATH_IMAGE050
The centroid of a triangle, the formula for the centroid of the three sides is
Figure 960430DEST_PATH_IMAGE054
,in
Figure 536905DEST_PATH_IMAGE055
,
Figure 338639DEST_PATH_IMAGE056
,
Figure 153011DEST_PATH_IMAGE057
Represents the positioning factor, which indicates that the coordinates of the monitoring nodes that are closer to the target have greater influence; then use
Figure 772211DEST_PATH_IMAGE050
The centroid of a triangle is calculated by the polygon centroid method to obtain the final coordinates of the target.
Figure 678725DEST_PATH_IMAGE058
.

本发明应用于大规模随机散布的野外应用场景，各传感节点并不需要精确的定位，利用信标节点作为参考点实现目标生物的定位。各传感节点在初始化阶段会自适应地建立无线传感网中各个节点的相对位置坐标，即可满足实际应用需求，并且实现要求的硬件成本低本、定位过程的通信开销小、功耗低。The present invention is applied to a large-scale random distribution of field application scenarios, each sensing node does not need precise positioning, and uses beacon nodes as reference points to achieve the positioning of target organisms. In the initialization stage, each sensor node will adaptively establish the relative position coordinates of each node in the wireless sensor network, which can meet the actual application requirements, and realize the required hardware cost, low cost of hardware, low communication overhead in the positioning process, and low power consumption. .

具体实施方式Detailed ways

本发明一种室外自适应的被动式目标的定位方法，包括如下步骤：An outdoor adaptive passive target positioning method of the present invention includes the following steps:

步骤1、无线传输损耗模型Step 1. Wireless Transmission Loss Model

RSSI定位算法的精度很大程度上取决于无线电传播路径损耗。常用的无线传播路径损耗模型有自由空间传播模型（free space propagation model）、对数距离路劲损耗模型（log-distance path loss model）、哈它模型（Hata model）、对数-常态分布模型（log-distance distribution）等。野外实际环境下，由于多径、绕射、障碍物等因素的影响，无线电穿薄损耗与在自由空间下传播的理论值相比会有变化。The accuracy of the RSSI positioning algorithm largely depends on the radio propagation path loss. Commonly used wireless propagation path loss models include free space propagation model, log-distance path loss model, Hata model, log-normal distribution model ( log-distance distribution), etc. In the actual field environment, due to the influence of factors such as multipath, diffraction, obstacles, etc., the radio penetration loss will change compared with the theoretical value of propagation in free space.

本发明采用自由空间传播模型和对数-常态分布模型相结合的方式，自由空间无线传播损耗模型如下：The present invention adopts the combination of the free space propagation model and the logarithmic-normal distribution model, and the free space wireless propagation loss model is as follows:

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(
Figure 218608DEST_PATH_IMAGE002
)
Figure 408783DEST_PATH_IMAGE001
(
Figure 218608DEST_PATH_IMAGE002
)

其中，d为相距信源的距离，单位为km ；f是无线频率，单位为MHZ ；k为路径衰减因子；Among them, d is the distance from the source, the unit is km ; f is the wireless frequency, the unit is MHZ; k is the path attenuation factor;

对数-常态分布无线传播损耗模型为：The log-normal distribution wireless propagation loss model is:

Figure 743130DEST_PATH_IMAGE003
(2)
Figure 743130DEST_PATH_IMAGE003
(2)

其中，

Figure 28618DEST_PATH_IMAGE004
是经过距离d后的路径损耗，单位dB；
Figure 172154DEST_PATH_IMAGE005
是高斯随机分布变数，标准差范围在410；k为路径衰减因子范围在2~5；取d=1m，代入公式（1），求得loss即为
Figure 961119DEST_PATH_IMAGE006
的值，根据公式（2）可得出各未知节点与信标节点的信号强度为：in,
Figure 28618DEST_PATH_IMAGE004
is the path loss after the distance d , in dB;
Figure 172154DEST_PATH_IMAGE005
is a Gaussian random distribution variable, and the standard deviation is in the range of 410; k is the path attenuation factor in the range of 2~5; take d = 1m, substitute it into formula (1), and obtain the loss as
Figure 961119DEST_PATH_IMAGE006
The value of , according to formula (2), the signal strength of each unknown node and beacon node can be obtained as:

Figure 797488DEST_PATH_IMAGE007
（3）
Figure 797488DEST_PATH_IMAGE007
(3)

依据公式（1）至（3），信标节点收到任意一个节点的RSSI，即可求得该节点与信标节点之间的距离；According to formulas (1) to (3), when the beacon node receives the RSSI of any node, the distance between the node and the beacon node can be obtained;

在无线传感网中，理论上都是通过目标结点与多个信标节点之间的欧式距离来测量出目标结点的位置，常用三边测量法。在本发明的野外应用场景中，目标节点是指能被热释电红外传感器感应的随机目标，其本身并不具有无线信号，因此目标节点的位置信息只能通过参考节点的位置信息和传感器的检测范围求得。本发明通过聚类与加权三边和多边质心算法，实现目标聚类和目标最终位置坐标的计算；In the wireless sensor network, theoretically, the position of the target node is measured by the Euclidean distance between the target node and multiple beacon nodes, and the trilateration method is commonly used. In the field application scenario of the present invention, the target node refers to a random target that can be sensed by a pyroelectric infrared sensor, which itself does not have a wireless signal, so the location information of the target node can only be obtained by referring to the location information of the node and the sensor's location information. The detection range is obtained. The invention realizes the calculation of target clustering and target final position coordinates through clustering and weighted trilateral and polygonal centroid algorithms;

步骤2、参考节点自定位Step 2. Self-positioning of reference nodes

无线传感网中由无线模块和传感模块构成的各节点统称为参考节点，将参考节点分为中心节点和监测节点，各节点之间的距离通过接收的RSSI计算求得；In the wireless sensor network, each node composed of the wireless module and the sensor module is collectively referred to as the reference node, and the reference node is divided into a central node and a monitoring node, and the distance between each node is calculated by the received RSSI;

参考节点的自定位，目的是为了自适应地建立无线传感网中各个节点位置信息，位置信息用平面坐标表示。The purpose of the self-positioning of the reference node is to adaptively establish the location information of each node in the wireless sensor network, and the location information is represented by plane coordinates.

各参考节点，依据其收到其他参考节点的RSSI值，建立自身与其他参考节点的距离映射，建立如下两个集合：Each reference node establishes a distance mapping between itself and other reference nodes according to the RSSI values it receives from other reference nodes, and establishes the following two sets:

参考节点集合：

Figure 39113DEST_PATH_IMAGE008
，其中，
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标识中心节点，
Figure 659505DEST_PATH_IMAGE010
标识监测节点
Figure 260251DEST_PATH_IMAGE011
；Reference node collection:
Figure 39113DEST_PATH_IMAGE008
,in,
Figure 110974DEST_PATH_IMAGE009
identify the central node,
Figure 659505DEST_PATH_IMAGE010
Identify monitoring nodes
Figure 260251DEST_PATH_IMAGE011
;

参考节点间的距离集合：

Figure 395697DEST_PATH_IMAGE059
，其中，
Figure 740091DEST_PATH_IMAGE013
表示参考节点i和参考节点j之间的距离，n表示无线传感网中参考节点的个数；A collection of distances between reference nodes:
Figure 395697DEST_PATH_IMAGE059
,in,
Figure 740091DEST_PATH_IMAGE013
represents the distance between reference node i and reference node j, and n represents the number of reference nodes in the wireless sensor network;

建立上述两个集合后，参考节点开始自定位过程，建立各自的位置坐标；After the above two sets are established, the reference node starts the self-positioning process and establishes the respective position coordinates;

中心节点

Figure 769226DEST_PATH_IMAGE009
的坐标初始化为（0，0），作为平面坐标系原点，中心节点
Figure 416240DEST_PATH_IMAGE009
根据参考节点间的距离集合，选择距离自身最近的两个监测节点
Figure 163616DEST_PATH_IMAGE014
,
Figure 452646DEST_PATH_IMAGE015
,以
Figure 70709DEST_PATH_IMAGE009
和
Figure 544416DEST_PATH_IMAGE014
的连线为X轴，建立平面坐标系，
Figure 621831DEST_PATH_IMAGE014
坐标为
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,
Figure 311755DEST_PATH_IMAGE017
坐标为
Figure 566150DEST_PATH_IMAGE018
,
Figure 22539DEST_PATH_IMAGE019
为直线
Figure 387793DEST_PATH_IMAGE020
和直线
Figure 246027DEST_PATH_IMAGE021
间的夹角,
Figure 795957DEST_PATH_IMAGE022
；central node
Figure 769226DEST_PATH_IMAGE009
The coordinates are 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, select the two closest monitoring nodes to itself
Figure 163616DEST_PATH_IMAGE014
,
Figure 452646DEST_PATH_IMAGE015
,by
Figure 70709DEST_PATH_IMAGE009
and
Figure 544416DEST_PATH_IMAGE014
The connection line is the X-axis, and a plane coordinate system is established.
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
as a straight line
Figure 387793DEST_PATH_IMAGE020
and straight line
Figure 246027DEST_PATH_IMAGE021
the angle between,
Figure 795957DEST_PATH_IMAGE022
;

求得任意一个监测节点

Figure 615009DEST_PATH_IMAGE023
的坐标为
Figure 908587DEST_PATH_IMAGE024
，其中
Figure 729650DEST_PATH_IMAGE025
为直线
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和直线
Figure 22408DEST_PATH_IMAGE021
间的夹角，监测节点
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轴坐标正负号通过
Figure 421345DEST_PATH_IMAGE028
确定，若Find any monitoring node
Figure 615009DEST_PATH_IMAGE023
The coordinates of are
Figure 908587DEST_PATH_IMAGE024
,in
Figure 729650DEST_PATH_IMAGE025
as a straight line
Figure 716061DEST_PATH_IMAGE026
and straight line
Figure 22408DEST_PATH_IMAGE021
The angle between the monitoring nodes
Figure 854098DEST_PATH_IMAGE027
Axis coordinate positive and negative signs are passed
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sure, if

Figure 188444DEST_PATH_IMAGE029
，则
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的坐标为
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，否则，监测节点
Figure 39223DEST_PATH_IMAGE023
的坐标为
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；
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,but
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The coordinates of are
Figure 617469DEST_PATH_IMAGE030
, otherwise, monitor the node
Figure 39223DEST_PATH_IMAGE023
The coordinates of are
Figure 367436DEST_PATH_IMAGE031
;

步骤3、目标定位Step 3. Target positioning

目标定位是指通过无线传感网监测到随机生物，进而给出随机生物相对参考节点的位置信息的过程。在实际的应用环境中，目标的位置精度和无线传感网中监测节点的监测半径、具有随机分量的RSSI值以及无线传感网的随机分布相关，可以用质心法或最大似然估计法来改善目标的定位精度。但是在实际的应用场景中，可能同时出现多个目标，因此简单使用质心法或最大似然估计法并不能保证定位的正确性。本发明先利用聚类算法对目标聚类，再通过加权质心法确定不同类别目标的最终位置坐标。Target positioning refers to the process of monitoring random creatures through wireless sensor network, and then giving the location information of random creatures relative to the reference node. In the actual application environment, the location accuracy of the target is related to the monitoring radius of the monitoring nodes in the wireless sensor network, the RSSI value with random components, and the random distribution of the wireless sensor network. The centroid method or the maximum likelihood estimation method can be used to determine Improve the positioning accuracy of targets. However, in practical application scenarios, multiple targets may appear at the same time, so simply using the centroid method or the maximum likelihood estimation method cannot guarantee the correctness of the positioning. The present invention first uses the clustering algorithm to cluster the targets, and then uses the weighted centroid method to determine the final position coordinates of the targets of different categories.

3.1目标聚类3.1 Target Clustering

目前有大量的聚类算法。而对于具体应用，聚类算法的选择取决于数据的类型、聚类的目的。主要的聚类算法可以划分为如下几类：划分方法、层次方法、基于密度的方法、基于网格的方法以及基于模型的方法。本发明采用凝聚型层次聚类算法，对目标进行分类，簇间距离度量方法采用的是最大距离度量法，

Figure 615752DEST_PATH_IMAGE060
，其中，
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和
Figure 596664DEST_PATH_IMAGE034
分别为簇
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中的对象，簇间最大距离
Figure 598435DEST_PATH_IMAGE036
上限取值为
Figure 185405DEST_PATH_IMAGE037
，
Figure 847331DEST_PATH_IMAGE038
为无线传感网中监测节点的监测半径，设有
Figure 392712DEST_PATH_IMAGE039
个监测节点监测到目标，具体的目标聚类过程如下：There are a large number of clustering algorithms currently available. For specific applications, the choice of clustering algorithm depends on the type of data and the purpose of clustering. The main clustering algorithms can be divided into the following categories: partition methods, hierarchical methods, density-based methods, grid-based methods, and model-based methods. The invention adopts agglomerative hierarchical clustering algorithm to classify the target, and the distance measurement method between clusters adopts the maximum distance measurement method,
Figure 615752DEST_PATH_IMAGE060
,in,
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and
Figure 596664DEST_PATH_IMAGE034
cluster
Figure 971144DEST_PATH_IMAGE035
objects in , maximum distance between clusters
Figure 598435DEST_PATH_IMAGE036
The upper limit value is
Figure 185405DEST_PATH_IMAGE037
,
Figure 847331DEST_PATH_IMAGE038
is the monitoring radius of the monitoring nodes in the wireless sensor network, with
Figure 392712DEST_PATH_IMAGE039
Each monitoring node monitors the target, and the specific target clustering process is as follows:

Figure 241720DEST_PATH_IMAGE002
．将
Figure 22594DEST_PATH_IMAGE039
个监测节点都单独视为一个簇，计算两两簇之间的最大距离；
Figure 241720DEST_PATH_IMAGE002
. Will
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Each monitoring node is regarded as a cluster independently, and the maximum distance between two clusters is calculated;

Figure 647348DEST_PATH_IMAGE040
．将所有最大距离小于
Figure 488265DEST_PATH_IMAGE037
的两个簇合并成一个新簇；
Figure 647348DEST_PATH_IMAGE040
. Set all maximum distances less than
Figure 488265DEST_PATH_IMAGE037
The two clusters of are merged into a new cluster;

Figure 965514DEST_PATH_IMAGE041
．重新分别计算新簇与所有簇之间的距离；
Figure 965514DEST_PATH_IMAGE041
． Recalculate the distances between the new cluster and all clusters separately;

Figure 284500DEST_PATH_IMAGE042
．重复上述2、3，直到不存在簇间距离小于
Figure 655438DEST_PATH_IMAGE043
的情况；在本步骤用聚类形成的簇的个数来表示当前时刻需要定位的目标的个数，意味着每个簇中的所有节点都监测到了目标，不同的簇监测了不同的目标；
Figure 284500DEST_PATH_IMAGE042
. Repeat 2 and 3 above until there is no inter-cluster distance less than
Figure 655438DEST_PATH_IMAGE043
In this step, the number of clusters formed by clustering is used to represent the number of targets that need to be located at the current moment, which means that all nodes in each cluster have monitored the target, and different clusters have monitored different targets;

3.2目标定位计算3.2 Target positioning calculation

目标定位是在上述目标聚类之后，计算每类目标的最终位置坐标的过程。使用加权三边和多边质心法计算每类目标的质心，从而确定该目标的最终位置坐标。利用红外测距模型，计算出目标距其对应目标簇中所有监测节点的距离，作为定位时的权值。Target positioning is the process of calculating the final position coordinates of each type of target after the above target clustering. The centroid of each type of target is calculated using the weighted trilateral and polygonal centroid method to determine the final position coordinates of that target. Using the infrared ranging model, the distance between the target and all the monitoring nodes in the corresponding target cluster is calculated as the weight during positioning.

红外测距可用于温度高于绝对零度的目标，电磁辐射能量是检测Infrared ranging can be used for targets with temperatures above absolute zero, and electromagnetic radiation energy is the

目标距离的重要参数，它取决于目标表面温度T和波长。依据普朗克定律，可以知道波长λ、温度T、发射率

Figure 277044DEST_PATH_IMAGE061
和辐出度
Figure 100643DEST_PATH_IMAGE062
之间的关系。An important parameter of the target distance, which depends on the target surface temperature T and wavelength. According to Planck's law, we can know the wavelength λ, temperature T, emissivity
Figure 277044DEST_PATH_IMAGE061
and radiance
Figure 100643DEST_PATH_IMAGE062
The relationship between.

Figure 364265DEST_PATH_IMAGE063
Figure 364265DEST_PATH_IMAGE063

但测距原理一般是通过一个较低频率的红外线，然后测量回波和发射波之间的相位差

Figure 324131DEST_PATH_IMAGE064
，依据相位差计算出回波时间
Figure 506851DEST_PATH_IMAGE065
，即：But the principle of ranging is generally to pass a lower frequency infrared ray, and then measure the phase difference between the echo and the transmitted wave
Figure 324131DEST_PATH_IMAGE064
, calculate the echo time according to the phase difference
Figure 506851DEST_PATH_IMAGE065
,which is:

Figure 191648DEST_PATH_IMAGE066
Figure 191648DEST_PATH_IMAGE066

最后求得目标距离。

Figure 118016DEST_PATH_IMAGE067
为红外信号周期。Finally, the target distance is obtained.
Figure 118016DEST_PATH_IMAGE067
is the period of the infrared signal.

假设

Figure 932388DEST_PATH_IMAGE044
个监测节点经过聚类形成了若干个簇，簇中的监测节点数用
Figure 426954DEST_PATH_IMAGE068
表示：Assumption
Figure 932388DEST_PATH_IMAGE044
The monitoring nodes are clustered to form several clusters, and the number of monitoring nodes in the cluster is determined by
Figure 426954DEST_PATH_IMAGE068
express:

若n=1，则用该簇中的监测节点的坐标

Figure 225146DEST_PATH_IMAGE045
作为目标的坐标位置，即
Figure 299412DEST_PATH_IMAGE069
；If n=1, use the coordinates of the monitoring nodes in the cluster
Figure 225146DEST_PATH_IMAGE045
as the coordinate position of the target, i.e.
Figure 299412DEST_PATH_IMAGE069
;

若n=2，则目标的坐标为簇中两个监测节点的坐标均值，即

Figure 499449DEST_PATH_IMAGE070
；If n=2, the coordinates of the target are the mean coordinates of the two monitoring nodes in the cluster, that is
Figure 499449DEST_PATH_IMAGE070
;

若

Figure 164917DEST_PATH_IMAGE048
，则
Figure 919246DEST_PATH_IMAGE049
个监测节点可构成
Figure 452996DEST_PATH_IMAGE050
个三角形，依据红外测距模型，求得目标距离三角形顶点的距离分别为
Figure 904879DEST_PATH_IMAGE051
、
Figure 600302DEST_PATH_IMAGE052
和
Figure 982873DEST_PATH_IMAGE053
，先用加权三边质心法求出
Figure 54734DEST_PATH_IMAGE050
个三角形的质心,该三边质心公式为：like
Figure 164917DEST_PATH_IMAGE048
,but
Figure 919246DEST_PATH_IMAGE049
monitoring nodes can be composed of
Figure 452996DEST_PATH_IMAGE050
According to the infrared ranging model, the distance between the target and the vertex of the triangle is obtained as
Figure 904879DEST_PATH_IMAGE051
,
Figure 600302DEST_PATH_IMAGE052
and
Figure 982873DEST_PATH_IMAGE053
, first use the weighted trilateral centroid method to find
Figure 54734DEST_PATH_IMAGE050
The centroid of a triangle, the formula for the centroid of the three sides is:

Figure 839151DEST_PATH_IMAGE054
,其中
Figure 971055DEST_PATH_IMAGE055
、
Figure 840922DEST_PATH_IMAGE056
、
Figure 450895DEST_PATH_IMAGE057
表示定位因子，表示距离目标越近的监测节点，其坐标的影响力越大；然后再利用
Figure 480030DEST_PATH_IMAGE050
个三角形质心，通过多边质心法计算得到目标的最终位置坐标
Figure 891158DEST_PATH_IMAGE071
。
Figure 839151DEST_PATH_IMAGE054
,in
Figure 971055DEST_PATH_IMAGE055
,
Figure 840922DEST_PATH_IMAGE056
,
Figure 450895DEST_PATH_IMAGE057
Indicates the positioning factor, indicating that the closer the monitoring node is to the target, the greater the influence of its coordinates; then use
Figure 480030DEST_PATH_IMAGE050
The centroid of a triangle is calculated by the polygon centroid method to obtain the final position coordinates of the target.
Figure 891158DEST_PATH_IMAGE071
.

被动式多目标定位是目前无线传感网中定位技术中的难题，本发明基于聚类和RSSI的室外自适应定位算法，提出采用聚类思想实现目标的聚类，然后利用RSSI测距模型和加权多边质心算法计算被动式目标的最终位置坐标，降低了被动式多目标定位的误差，仿真结果的平均误差为1.18，多目标的定位结果轨迹和实际位置轨迹趋势基本吻合。接近85%的概率下本发明的定位方法的误差在0.8m以内，优于传统的RSSI和红外定位方法，而在累积概率分布趋于1时，本发明的误差明显大于传统两种方法，这些较大误差都发生在监测区域的边界，是因为无线传感网的边界节点的监测范围相交密度低造成的，这也因此增加了方法的平均误差，但可以通过扩大传感网监测范围和提高节点的监测范围相交密度的方式解决，即让无线传感网的监测范围大于实际的监测边界。Passive multi-target positioning is a difficult problem in the current positioning technology in wireless sensor networks. The present invention is based on the outdoor adaptive positioning algorithm of clustering and RSSI, and proposes to use the clustering idea to realize the clustering of targets, and then use the RSSI ranging model and weighted The polygonal centroid algorithm calculates the final position coordinates of the passive target, which reduces the error of passive multi-target positioning. The average error of the simulation results is 1.18, and the trajectory of the multi-target positioning results is basically consistent with the actual position trajectory. Under the probability of close to 85%, the error of the positioning method of the present invention is within 0.8m, which is better than the traditional RSSI and infrared positioning methods, and when the cumulative probability distribution tends to 1, the error of the present invention is significantly larger than the traditional two methods. Larger errors occur at the boundary of the monitoring area, which is caused by the low intersection density of the monitoring range of the boundary nodes of the wireless sensor network, which also increases the average error of the method. The intersection density of the monitoring range of the node is solved, that is, the monitoring range of the wireless sensor network is larger than the actual monitoring boundary.

以上所述，仅是本发明较佳实施例而已，并非对本发明的技术范围作任何限制，故凡是依据本发明的技术实质对以上实施例所作的任何细微修改、等同变化与修饰，均仍属于本发明技术方案的范围内。The above are only preferred embodiments of the present invention, and do not limit the technical scope of the present invention. Therefore, any minor modifications, equivalent changes and modifications made to the above embodiments according to the technical essence of the present invention are still within the scope of the present invention. within the scope of the technical solution 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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