CN107222925A - A kind of node positioning method based on cluster optimization - Google Patents

Abstract

The invention discloses a node positioning method based on clustering optimization, which includes: randomly selecting three beacon nodes in the network as a combination of beacon nodes, and obtaining Beacon node combinations, and determine whether each beacon node combination meets the collinearity threshold, and add the beacon node combination that meets the collinearity threshold to the container ColSet; for each beacon node combination in the container ColSet, judge Whether the combination of beacon nodes passes the PIT test, remove the combination of beacon nodes that cannot pass the PIT test from the container ColSet; use the three beacon nodes in the combination of beacon nodes to perform preliminary positioning estimation on unknown nodes, and the preliminary positioning estimation results Add it to the positioning candidate set LocSet; use clustering algorithm to cluster and optimize the positioning candidate set LocSet, classify the data points and remove the noise data points in the positioning candidate set LocSet, find the largest core data point cluster, and determine the estimation of unknown nodes Position coordinates. This method achieves improved positioning accuracy.

Description

A Node Location Method Based on Clustering Optimization

technical field

The invention relates to the technical field of wireless positioning, in particular to a node positioning method based on clustering optimization.

Background technique

In recent years, with the development of the Internet of Things, wireless sensor networks have been widely used, such as in smart home, industrial control, intelligent transportation, smart city, medical and health, military and national defense, etc. Wireless sensor networks are also changing our lives and gradually becoming an integral part of our lives. In the wireless sensor network, we sometimes need to interact with the monitoring objects on the network. In order to take corresponding measures for the monitoring objects, we need the physical location of the monitoring objects. If the location of the monitored object is not clear, research work related to this may be meaningless. Therefore, positioning is a basic and important function in a wireless sensor network system.

Node positioning technology in wireless sensor networks is one of the hot research issues at present. The current positioning algorithms can be roughly divided into two categories, one is based on ranging and the other is based on non-ranging. These two types of algorithms have their own advantages and disadvantages. The first type of algorithm is mainly based on distance measurement, and uses trilateration or maximum likelihood estimation methods to locate unknown nodes; its advantages are small positioning errors and accurate positioning. The degree is higher, but its disadvantage is that it has higher requirements on the hardware of the nodes and increases the cost of the network. Since the second type of algorithm has nothing to do with distance, the hardware requirements for sensor nodes are relatively low, and the cost of the entire network is also reduced, but the positioning accuracy is not as good as the first type of algorithm. Since the requirements for positioning accuracy in wireless sensor networks are usually related to applications, different positioning algorithms are usually adopted according to different applications.

In large-scale wireless sensor networks, considering the characteristics of economic cost and the simplicity of node hardware, most of the positioning algorithms based on non-ranging are used on the network. Among them, the DV-Hop node positioning algorithm has received special attention from many scholars. focus on. Because DV-Hop algorithm has many deficiencies in positioning, DV-Hop (Distance Vector-Hop) positioning algorithm is a distance-independent distributed positioning algorithm similar to the distance vector routing mechanism in the network. Use the distance vector positioning mechanism to realize the minimum number of hops between the unknown node and the beacon node, then estimate the average distance of each hop through the minimum number of hops, and then calculate the distance between the unknown node and the beacon node through the product of the minimum number of hops and the average distance per hop Finally, calculate the coordinate position of the unknown node with the help of maximum likelihood estimation method or trilateration method. However, there are still positioning noise points in the positioning using the DV-Hop algorithm, the positioning effect is low, and there are many errors, so the positioning accuracy is not high.

Contents of the invention

The purpose of the present invention is to provide a node positioning method based on cluster optimization, so as to improve the positioning accuracy.

In order to solve the above-mentioned technical problems, the present invention provides a node location method based on clustering optimization, the method comprising:

In the network, three beacon nodes are arbitrarily selected as beacon node combinations, and C _m ³ beacon node combinations are obtained, and it is judged whether each beacon node combination satisfies the collinearity threshold, and the beacons that meet the collinearity threshold The node combination is added to the container ColSet; among them, m is the number of beacon nodes in the network;

For each beacon node combination in the container ColSet, determine whether the beacon node combination passes the PIT test, and remove the beacon node combination that cannot pass the PIT test from the container ColSet;

For each beacon node combination in the container ColSet, use three beacon nodes in the beacon node combination to perform preliminary location estimation on unknown nodes, and add the preliminary location estimation results to the location candidate set LocSet;

The clustering algorithm is used to cluster and optimize the positioning candidate set LocSet, classify the data points and remove the noise data points in the positioning candidate set LocSet, find the largest core data point cluster, and determine the estimated position coordinates of unknown nodes.

Preferably, for each combination of beacon nodes in the container ColSet, three beacon nodes in the combination of beacon nodes are used to perform preliminary positioning estimation on unknown nodes, and the preliminary positioning estimation results are added to the positioning candidate set LocSet, including :

For the three beacon nodes in the beacon node combination, the weighted average hop distance method is used to estimate the distance between the unknown node and any two beacon nodes;

Through the unknown relationship between the beacon triangle and the unknown node, the triangle property is used to calculate the distance between the unknown node and the remaining beacon node in the combination of beacon nodes, and the preliminary position estimation of the unknown node is carried out by means of trilateration;

Through each beacon node combination in the container ColSet, the unknown node is estimated three times for preliminary location estimation, and each preliminary location estimation result is added to the location candidate set LocSet of the unknown node.

Preferably, after removing the combination of beacon nodes that cannot pass the PIT test from the container ColSet, it further includes:

Update container ColSet.

Preferably, the clustering algorithm includes DBSCAN clustering algorithm.

Preferably, the clustering algorithm is used to cluster and optimize the positioning candidate set LocSet, classify the data points and remove the noise data points in the positioning candidate set LocSet, find the largest core data point cluster, and determine the estimated position coordinates of the unknown nodes ,include:

Run the DBSCAN clustering algorithm on the location candidate set LocSet, classify the data points with reachable density into one category, remove the noise points in the LocSet at the same time, and obtain one or more clusters after removing the noise data points;

The largest cluster is found from the obtained clusters, the average value of the largest cluster is calculated, and the average value is used as the final position coordinate of the unknown node.

Preferably, before the DBSCAN clustering algorithm is run on the positioning candidate set LocSet, it also includes:

Set the data point density threshold parameter Minpts in the DBSCAN algorithm;

Establish the K-dist graph according to the data point density threshold parameter Minpts and the location candidate set LocSet, and determine the data point field radius parameter Eps in the DBSCAN algorithm;

Use Minpts, Eps and LocSet as the input values before the DBSCAN algorithm runs.

Preferably, the PIT test is a best triangle interior point test.

In the node positioning method based on clustering optimization provided by the present invention, three beacon nodes are arbitrarily selected as the combination of beacon nodes in the network, and the obtained Beacon node combinations, and determine whether each beacon node combination meets the collinearity threshold, and add the beacon node combination that meets the collinearity threshold to the container ColSet; where m is the number of beacon nodes in the network; For each beacon node combination in the container ColSet, determine whether the beacon node combination passes the PIT test, and remove the beacon node combination that cannot pass the PIT test from the container ColSet; for each beacon node combination in the container ColSet, use The three beacon nodes in the beacon node combination perform preliminary positioning estimation on unknown nodes, and add the preliminary positioning estimation results to the positioning candidate set LocSet; use clustering algorithm to cluster and optimize the positioning candidate set LocSet, and classify the data points And remove the noise data points in the positioning candidate set LocSet, find the largest cluster of core data points, and determine the estimated position coordinates of unknown nodes. It can be seen that compared with the traditional DV-Hop positioning algorithm, this method first uses the collinearity discrimination method and the best triangle interior point test method to screen the beacon node groups participating in the positioning, and then estimates the distance between the unknown node and any two beacon nodes. Calculate the distance between the unknown node and the remaining beacon nodes, and then perform a preliminary positioning estimate for the unknown node. Each group of beacon nodes can generate three preliminary positioning estimates for the unknown node, and add each preliminary positioning estimation result to the positioning candidate Finally, the clustering algorithm is used to optimize the positioning candidate set, remove the positioning noise points, leave the positioning core data points, and find the best possible position of the unknown node. This method has a better positioning effect than the DV-Hop positioning algorithm. The error is reduced and the positioning accuracy is improved.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only It is an embodiment of the present invention, and those skilled in the art can also obtain other drawings according to the provided drawings without creative work.

Fig. 1 is a flow chart of a node location method based on cluster optimization provided by the present invention;

Figure 2 is a flow chart of DV-Hop positioning algorithm based on DBSCAN clustering optimization;

Fig. 3 (a) is the simulation diagram of the impact of the proportion of beacon nodes on the average positioning error in the method of the present invention;

Fig. 3 (b) is the emulation diagram of the impact of the point communication radius on the average positioning error in the method of the present invention;

Fig. 3(c) is a simulation diagram of the influence of the total number of nodes on the average positioning error in the method of the present invention.

detailed description

The core of the present invention is to provide a node positioning method based on clustering optimization, so as to improve the positioning accuracy.

In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

Please refer to Fig. 1, Fig. 1 is the flowchart of a kind of node localization method based on clustering optimization provided by the present invention, and this method comprises:

S11: Randomly select three beacon nodes in the network as beacon node combinations, obtain C _m ³ beacon node combinations, and judge whether each beacon node combination satisfies the collinearity threshold, and will satisfy the collinearity threshold The combination of beacon nodes is added to the container ColSet;

Among them, m is the number of beacon nodes in the network;

S12: For each beacon node combination in the container ColSet, determine whether the beacon node combination passes the PIT test, and remove the beacon node combination that cannot pass the PIT test from the container ColSet;

S13: For each beacon node combination in the container ColSet, use three beacon nodes in the beacon node combination to perform preliminary location estimation on the unknown node, and add the preliminary location estimation result to the location candidate set LocSet;

S14: Use a clustering algorithm to cluster and optimize the location candidate set LocSet, classify data points and remove noise data points in the location candidate set LocSet, find the largest core data point cluster, and determine the estimated position coordinates of unknown nodes.

It can be seen that compared with the traditional DV-Hop positioning algorithm, this method first uses the collinearity discrimination method and the best triangle interior point test method to screen the beacon node groups participating in the positioning, and then estimates the distance between the unknown node and any two beacon nodes. Calculate the distance between the unknown node and the remaining beacon nodes, and then perform a preliminary positioning estimate for the unknown node. Each group of beacon nodes can generate three preliminary positioning estimates for the unknown node, and add each preliminary positioning estimation result to the positioning candidate Finally, the clustering algorithm is used to optimize the positioning candidate set, remove the positioning noise points, leave the positioning core data points, and find the best possible position of the unknown node. This method has a better positioning effect than the DV-Hop positioning algorithm. The error is reduced and the positioning accuracy is improved.

Based on the above method, further, step S13 specifically includes the following steps:

S21: For the three beacon nodes in the beacon node combination, use the weighted average hop distance method to estimate the distance between the unknown node and any two beacon nodes;

S22: through the unknown relationship between the beacon triangle and the unknown node, use the triangle property to calculate the distance between the unknown node and the remaining beacon node in the combination of beacon nodes, and use the trilateration method to estimate the initial position of the unknown node;

S23: Perform three preliminary location estimates on the unknown node through each combination of beacon nodes in the container ColSet, and add each preliminary location estimation result to the location candidate set LocSet of the unknown node.

Further, in step S12, after removing the combination of beacon nodes that cannot pass the PIT test from the container ColSet, the method further includes: updating the container ColSet.

In detail, the clustering algorithm includes a DBSCAN clustering algorithm.

Further, step S14 includes the following steps:

S31: Run the DBSCAN clustering algorithm on the positioning candidate set LocSet, classify the data points with reachable density into one category, remove the noise points in the LocSet at the same time, and obtain one or more clusters after removing the noise data points;

S32: Find the largest cluster from the obtained clusters, calculate the average value of the largest cluster, and use the average value as the final position coordinate of the unknown node.

Wherein, before the step S31, it also includes: setting the data point density threshold parameter Minpts in the DBSCAN algorithm; establishing a K-dist graph according to the data point density threshold parameter Minpts and the positioning candidate set LocSet, and determining the data point field radius parameter Eps in the DBSCAN algorithm; Use Minpts, Eps and LocSet as the input values before the DBSCAN algorithm runs.

Among them, the beacon node is also called an anchor node or a reference node. The characteristic of this type of node is that the location information is known. The reason why the location information is known is that this type of node has a GPS positioning module or is determined by a manual preset method. The self-location information of the node does not need to be obtained by positioning the node through other means.

Among them, the average hop distance is the ratio of the sum of the estimated distance between any two nodes to the sum of the number of hops between nodes in the entire network, which is called the average hop distance of the entire network. The two obtained according to different hop distance calculation rules The average hop distance between nodes may vary.

Wherein, the PIT test is the best triangle interior point test. The best triangle inner point test first uses the area with relatively high node density in the network to simulate the movement of nodes to find out whether the unknown node is in the triangle determined by any three beacon nodes. If the unknown node is inside the triangle, calculate all Satisfy the overlapping part of the unknown node in the triangular area determined by multiple groups of three beacon nodes, and then obtain the centroid in the polygonal overlapping part as the estimated position of the unknown node. Assuming that there are n beacon nodes in the deployment network, then there are Different selection methods in Test whether the unknown node is inside each triangle in different selections in turn, and repeat this operation until the accuracy required for positioning is met or all possible combinations are exhausted.

Specifically, under the known data set and Minpts, the distance between each data point and the kth nearest data point is calculated by calculation, and then sorted from small to large. This process is to establish k-dist picture. The value of Minpts is determined by a heuristic method. Generally, in order to reduce the amount of calculation, it is more appropriate to set Minpts to 4 in advance. For the convenience of calculation, the value of Minpts is set to 4, and the k-dist graph is established for LocSet to determine the value of parameter Eps, and Minpts, Eps and LocSet are used as the input values before the clustering algorithm DBSCAN runs.

The specific steps to create a k-dist graph are as follows:

(1) Calculate the distance between each data point and other data points, construct a distance matrix distn with a size of N×N, and each row of the matrix represents the distance between a data point and other data points;

(2) Find the minimum value for each row of the distance matrix distn, and then replace it with an infinity, while other data of the distance matrix remain unchanged;

(3) Go to step (2) and continue until the kth closest distance of each data point is found, that is, the number of times step 2 is executed is k+1 times, because when step 2 is executed for the first time, the found The minimum distance is the distance between the data point and itself, all of which are 0 data;

(4) After obtaining the k-th closest distance of all data points, sort the data from small to large, the x-axis is the sequence of data points, and the y-axis is the k-th closest distance value.

In more detail, the DBSCAN clustering algorithm is used to optimize the location candidate set, and the specific steps to classify the data points and remove the noise data points in the location candidate set LocSet are as follows:

1. First determine the value of Minpts, and then select the distance of the kth neighbor data point of each data point to establish a k-dist graph. By observing the k-dist graph, find the distance value corresponding to the depression in the graph as Eps value;

2. Randomly select a data point that does not belong to any cluster from the data set as the starting point for the establishment of the cluster label C, and then perform query and statistics on the data point in the Eps field, and compare the number of judgment statistics with Minpts. If it is greater than or equal to Minpts , it means that the data point is a core data point, and all data points in its field are marked with the same type of cluster label C, and then the core data points in the field of the data point _Eps are added to the container list; if less than Minpts, temporally labeled as noisy data points;

3. Take a data point from the container list, then query and count the data points in the Eps field of the data point, mark all the data points in the field with C, and finally add the core data points in the field to the container list;

4. Repeat step 3, and so on, continue to expand cluster C until no new data point is marked as C. At this time, cluster C has been completely established, and the next step is to continue to select other possible clusters. Go to step 1 to continue;

5. When no data points that do not belong to any cluster can be found, and all data points have been tested, the remaining data points that do not belong to any cluster have been marked as noise data points.

It can be seen that the present invention adopts an improved strategy based on density clustering optimization. First, the traditional DV-Hop positioning algorithm adopts a weighted method to improve the average jump distance, and uses the collinearity discrimination method and the best triangle interior point test method to screen the beacons participating in the positioning. secondly, estimate the distance between the unknown node and any two beacon nodes, use the triangle property to calculate the distance between the unknown node and the remaining beacon nodes, and then use the trilateration method to estimate the initial location of the unknown node. Each target node can generate three preliminary positioning estimates for unknown nodes, add each preliminary positioning estimation result to the positioning candidate set, and use the DBSCAN clustering algorithm to optimize the positioning candidate set, remove the positioning noise points, and leave the positioning The core data points are used to find the best possible position of the unknown node. This method has a better positioning effect than the DV-Hop positioning algorithm, reduces the error, and improves the positioning accuracy.

In this method, the improved DV-Hop positioning algorithm is used to obtain the positioning candidate set LocSet for the unknown node in the deployment network, and the density-based clustering algorithm DBSCAN is used to cluster and optimize it to find the largest core data point cluster, and then determine the unknown node. The estimated location coordinates of the node.

Figure 2 is a flowchart of the DV-Hop positioning algorithm based on DBSCAN clustering optimization. In detail, the DV-Hop positioning algorithm process is divided into three steps:

The purpose of the first step is to make each node in the connected network record the minimum hop number information with each beacon node and the location information of each beacon node. In order to achieve this goal, firstly, each beacon node broadcasts its own positioning beacon data packet to the entire network through a controllable flooding mechanism. The amount of information in the data packet includes the timestamp, the serial number of the beacon node, the current hop value h, and the position (x, y) of the beacon node itself. The initial value of the field of the current hop value h is zero. Then, when its neighbor node receives the data packet, it compares and analyzes it with the currently saved data packet, so as to decide whether to update and save the data in the data packet previously saved by the node, so that the node can save the optimal data in the current situation at any time. The record in the data packet, the current hop value of the record is the smallest, and the required time is also the shortest, and then forwarded to the surrounding neighbor nodes, before forwarding, first modify the value of the current hop information field so that value plus one. After such continuous forwarding, each node in the connected network will obtain the above two key information.

The second step is to calculate the estimated distance between the unknown node and each beacon node. First estimate the average hop distance of the beacon node:

Among them, (x _i , y _i ), (x _j , y _j ) are the coordinates of beacon nodes i and j, and h _ij is the minimum hop value between beacon nodes i and j.

Then, after each beacon node obtains its own average hop distance, it sends its average hop distance information to surrounding neighbor nodes through a controllable flooding mechanism, and the unknown node only saves the average hop distance of the nearest beacon node information. The unknown node then uses this information to calculate an estimated distance to the beacon node:

d _i =h _i ·hopsize

Among them, h _i represents the minimum hop value between the unknown node and the beacon node i, d _i represents the estimated distance between the unknown node and the beacon node i, and the Hopsize value is the average hop distance of the beacon node closest to the unknown node.

The third step will realize the localization of unknown nodes. After the unknown node obtains multiple estimated distance values such as 3 or more through the second step of the algorithm, the estimated coordinates of the unknown node are calculated by using the trilateration method or the maximum likelihood estimation method.

Specifically, the present invention uses the DBSCAN clustering algorithm to optimize the positioning candidate set, removes the positioning noise points, and leaves the positioning core data points. The DBSCAN clustering algorithm is to classify all data points that can be connected to each other in a cluster. The entire dataset may end up with one or more clusters, and may also contain noisy data points that are not in any of the clusters.

The present invention proposes to obtain the positioning candidate set for the unknown node through the improved DV-Hop positioning algorithm in the deployment network, and use the density-based clustering algorithm DBSCAN to cluster and optimize it to find the largest core data point cluster, and then determine the unknown nodes. The estimated location coordinates of the node.

In this method, after the traditional DV-Hop weights the average hop distance of the unknown node, the topological relationship between the unknown node and the beacon triangle is used to adjust the original positioning process, and the unknown node is calculated based on two estimated edges. Perform location estimation to form an improved DV-Hop location algorithm (An Improved DV-Hop Localization Algorithm, referred to as IDV-Hop). Since two beacon nodes can be arbitrarily selected among the three beacon nodes to use the improved DV-Hop positioning algorithm to locate unknown nodes, every three beacon nodes on the network may form a group, and each of them All beacon nodes in the group can perform three preliminary positioning estimates on unknown nodes, so the maximum possible number of preliminary positioning estimates is Among them, m is the total number of beacon nodes in the network. Based on these preliminary positioning estimates, the clustering algorithm is used to analyze this, and the most likely position of the unknown node is obtained. This process forms an improved model based on the clustering analysis strategy. DV-Hop algorithm (An Improved DV-Hop Algorithm Based on Clustering Analysis of DBSCAN, referred to as IDV-HopCAD).

The simulation results are shown in Fig. 3(a), Fig. 3(b) and Fig. 3(c), Fig. 3(a) is a simulation diagram of the influence of the proportion of beacon nodes on the average positioning error in the method of the present invention, and Fig. 3(b ) is the simulation diagram of the influence of the communication radius of the middle point of the present invention on the average positioning error, and Fig. 3 (c) is the simulation diagram of the influence of the total number of nodes on the average positioning error in the method of the present invention, and these figures are simulation models established in MATLAB, for DV -Hop algorithm and the improved algorithm proposed in this paper are compared and analyzed. Specifically, the influence of the average positioning error of the three algorithms is tested under the factors such as the proportion of beacon nodes, the communication radius of nodes and the total number of nodes. From Figure 3 (a), Fig. 3(b) and Fig. 3(c), it can be seen that compared with the other two algorithms, the node positioning algorithm based on cluster optimization of the present invention has smaller positioning error and better positioning effect.

Because the DV-Hop algorithm has many deficiencies in positioning, the present invention proposes an improvement strategy based on density clustering optimization. First, the traditional DV-Hop positioning algorithm is weighted to improve the average jump distance, and the collinearity discrimination method is used. and the best triangle interior point test method to screen the beacon node group participating in the positioning, secondly, estimate the distance between the unknown node and any two beacon nodes, use the triangle properties to calculate the distance between the unknown node and the remaining beacon nodes, and then use The trilateration method performs preliminary positioning estimation on unknown nodes, and repeats the above operations. Each group of beacon nodes can generate three preliminary positioning estimates for unknown nodes, and each preliminary positioning estimation result is added to the positioning candidate set. Finally, DBSCAN The clustering algorithm optimizes the positioning candidate set, removes the positioning noise points, leaves the positioning core data points, and finds the best possible position of the unknown node. Establish a simulation model by MATLAB, compare and analyze the DV-Hop algorithm and the improved algorithm proposed by the present invention, the simulation results show that the positioning algorithm based on density clustering optimization is better than the DV-Hop positioning algorithm positioning effect, reduces errors, and improves positioning accuracy.

A node location method based on cluster optimization provided by the present invention has been introduced in detail above. In this paper, specific examples are used to illustrate the principle and implementation of the present invention, and the descriptions of the above embodiments are only used to help understand the method and core idea of the present invention. It should be pointed out that for those skilled in the art, without departing from the principle of the present invention, some improvements and modifications can be made to the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

Claims (7)

1. a kind of node positioning method based on cluster optimization, it is characterised in that including：

Arbitrarily three beaconing nodes of selection are combined as beaconing nodes in a network, are obtainedIndividual beaconing nodes combination, and judge Whether each beaconing nodes combination meets conllinear degree threshold value, and the beaconing nodes combination for meeting conllinear degree threshold value is added into container ColSet；Wherein, m is the number of beaconing nodes in network；

To each beaconing nodes combination in container ColSet, judge whether beaconing nodes combination is tested by PIT, it is impossible to logical The beaconing nodes for crossing PIT tests combine the rejecting from container ColSet；

To each beaconing nodes combination in container ColSet, three beaconing nodes in being combined using beaconing nodes are to unknown section Point carries out Primary Location estimation, and Primary Location estimated result is added into positioning Candidate Set LocSet；

Cluster optimization is carried out to positioning Candidate Set LocSet using clustering algorithm, data point is sorted out and positioning Candidate Set is removed Noise data point in LocSet, finds maximum kernel heart data points cluster, determines the estimated location coordinate of unknown node.

2. the method as described in claim 1, it is characterised in that each beaconing nodes combination in the ColSet to container, Three beaconing nodes in being combined using beaconing nodes carry out Primary Location estimation to unknown node, by Primary Location estimated result It is added to positioning Candidate Set LocSet, including：

Three beaconing nodes in being combined for beaconing nodes, are jumped using weighted average and estimate unknown node and any two away from method The distance between individual beaconing nodes；

By beacon triangle and the unknown relation of unknown node, unknown node and beaconing nodes group are calculated using triangular nature The distance between remaining beaconing nodes in conjunction, rough location estimation is carried out by trilateration to unknown node；

The Primary Location that three times are carried out to unknown node by the combination of each beaconing nodes in container ColSet estimates, will be each Secondary Primary Location estimated result is added in the positioning Candidate Set LocSet of unknown node.

3. the method as described in claim 1, it is characterised in that the beaconing nodes combination that will not pass through PIT tests from After being rejected in container ColSet, in addition to：

More new container ColSet.

4. the method as described in claim 1, it is characterised in that the clustering algorithm includes DBSCAN clustering algorithms.

5. method as claimed in claim 4, it is characterised in that the use clustering algorithm is carried out to positioning Candidate Set LocSet Cluster optimization, data point is sorted out and the noise data point in positioning Candidate Set LocSet is removed, maximum kernel calculation strong point is found Cluster, determines the estimated location coordinate of unknown node, including：

DBSCAN clustering algorithms are run on positioning Candidate Set LocSet, the reachable data point of density is classified as a class, gone simultaneously Except the noise spot in LocSet, the one or more class clusters removed after noise data point are obtained；

The class cluster of maximum is found from the class cluster of acquisition, the average value of the maximum class cluster is asked for, the average value is made For the final position coordinate of unknown node.

6. method as claimed in claim 5, it is characterised in that the operation DBSCAN clusters on positioning Candidate Set LocSet Before algorithm, in addition to：

Data point density threshold parameter Minpts in DBSCAN algorithms is set；

K-dist figures are set up according to data point density threshold parameter Minpts and positioning Candidate Set LocSet, DBSCAN algorithms are determined In data point field radius parameter Eps；

Input value before Minpts, Eps and LocSet are run as DBSCAN algorithms.

7. the method as described in any in claim 1 to 6, it is characterised in that the PIT tests are surveyed for point in optimal triangle Examination.