CN114501616B - Improved weighted centroid positioning method based on KL divergence and adjacent relation - Google Patents
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Abstract
The application relates to an improved weighted centroid positioning method based on KL divergence and adjacent relation, belonging to the field of wireless communication. The method comprises the following steps: deploying a node to be positioned and an anchor node, and initializing node setting; the anchor node propagates signals to the node to be positioned to establish an RSS matrix and calculate the KL divergence value measurement approximation degree; the KL divergence value is observed and accumulated with a plurality of sample values to obtain a KL divergence matrix; determining an anchor node for positioning by utilizing self-adaptive neighbor selection threshold setting; setting a weight for a neighbor anchor node to be positioned according to the KL divergence matrix; and obtaining the coordinates of the node to be positioned by using the weighted value and the coordinates of the anchor node.
Description
Technical Field
The application belongs to the field of wireless communication, and particularly relates to an improved weighted centroid positioning method based on KL divergence and adjacent relation.
Background
A wireless sensor network is an event-oriented detection network that deploys a large number of low-power, low-cost miniature wireless sensor nodes to a target task area. The sensor nodes quickly form a distributed network in a self-organizing mode, measure and collect required data, and return the data to a user in a wireless mode, so that target tracking and monitoring tasks can be realized, and the sensor nodes have wide application prospects in civil and military fields. The self-positioning of the sensor nodes is a foundation and a premise for realizing large-scale application, so that the positioning technology plays a vital role in the effective effect of the sensor network.
Current node location algorithms can be broadly divided into distance-based algorithms and non-distance-based algorithms depending on whether accurate measurement of the distance or angle between nodes is required. The distance-based algorithm needs to obtain the distance or angle information between the nodes, so that the hardware requirement is high, the power consumption is high, the corresponding positioning accuracy is high, and common algorithms include time of arrival (TOA), time difference of arrival (TDOA) and the like. Non-range-based positioning algorithms typically utilize network connectivity to achieve an estimate of the location of an unknown node, and commonly used positioning algorithms include: a positioning algorithm (DV-Hop) based on jump distance estimation, a positioning algorithm (MDS-MAP) based on multidimensional scale, a positioning Algorithm (APIT) based on a triangle interior point test method, and the like. Since most wireless communication modules can directly provide Received Signal Strength (RSS) values, the non-distance-based positioning algorithm needs less devices, has low requirements on device hardware, and has relatively low computational complexity.
In order to improve the node positioning precision in a wireless sensor network, the application provides an improved weighted centroid algorithm based on KL divergence and neighbor relation, namely KL-divergence based WCL (KLD-WCL). In this algorithm, anchor nodes deployed in the wireless sensor network are used to assist in estimating coordinates of the location-unaware nodes. Unlike conventional centroid algorithms, the RSS information used by KLD-WCL in the positioning process is not limited to from between the unknown node and the anchor node, but takes the distance between the anchor nodes and the RSS information into account. And measuring the approximation degree of the anchor node and the unknown node by calculating KL divergence values of RSS distribution of the unknown node and all anchor nodes.
The application provides a self-adaptive neighbor selection algorithm, which adaptively sets a proper threshold value for each unknown node to select an optimal neighbor anchor node. And finally, setting a proper weight for each neighbor anchor node participating in positioning based on the KL divergence value, so that the distribution of the weights is more reasonable, and different anchor nodes reflect different influences on a final positioning result through different weights.
Disclosure of Invention
Aiming at the problems existing in the prior art, the application provides an improved weighted centroid positioning method based on KL divergence and adjacent relation, and the positioning precision and robustness are improved higher than those of the traditional node positioning algorithm under the same scene.
In a first aspect, an improved weighted centroid positioning method based on KL divergence and adjacency relation is provided, the method comprising: deploying a node to be positioned and an anchor node, and initializing node setting; the anchor node propagates signals to the node to be positioned to establish an RSS matrix and calculate the KL divergence value measurement approximation degree; the KL divergence value is observed and accumulated with a plurality of sample values to obtain a KL divergence matrix; determining an anchor node for positioning by utilizing self-adaptive neighbor selection threshold setting; setting a weight for a neighbor anchor node to be positioned according to the KL divergence matrix; and obtaining the coordinates of the node to be positioned by using the weighted value and the coordinates of the anchor node.
Further, the deploying the node to be located and the anchor node, and initializing the node setting, includes: in a geographical area of a wireless sensor network with the area of L (M) multiplied by L (M), n+M sensor nodes are randomly distributed, wherein N represents the number of nodes to be positioned, and M represents the number of anchor nodes; the coordinate of the anchor node is X a ＝[x a ，y a ] T The coordinate of the node to be positioned is X u ＝[x u ，y u ] T The method comprises the steps of carrying out a first treatment on the surface of the The communication radius of the anchor node is set to r; the Euclidean distance between the (u) th anchor node to be positioned and the (a) th anchor node is l ua The Euclidean distance from the a-th anchor node to other anchor nodes is [ v ] a1 ，v a2 ，…，v aa ，…，v aM ]。
Further, the anchor node propagates a signal to the node to be positioned to establish an RSS matrix and calculate a KL divergence value measurement approximation degree, including: the propagation environment is a power law path loss channel model plus a lognormal shadow environment; according to l ua And v a Constructing RSS matrices P and Q between nodes to be positioned and anchor nodes and between anchor nodes M×M The method comprises the steps of carrying out a first treatment on the surface of the Using P and Q M×M Obtaining KL divergence value C ua 。
Further, the KL divergence value is obtained by observing and accumulating a plurality of sample values, and the KL divergence matrix comprises the following steps: and accumulating the number of t observations and the number of I samples to obtain the KL divergence matrix D.
Further, the determining the anchor node for positioning by using the adaptive neighbor selection threshold setting includes: recording minimum KL divergence D of each to-be-positioned based on KL divergence matrix D min，u The method comprises the steps of carrying out a first treatment on the surface of the By D and D min，u Respectively calculating KL divergence threshold D of the u-th node to be positioned th，u 。
Further toSetting weights for neighbor anchor nodes to be positioned according to the KL divergence matrix, wherein the steps comprise: the KL divergence value reflects the similarity of the RSS distribution of the anchor node and the node to be positioned, and an appropriate weight w is distributed to each neighbor anchor node based on the KL divergence value u，n 。
Further, the obtaining the coordinates of the node to be located by using the weighted value and the coordinates of the anchor node includes: by means of weights w u，n Number K of adjacent anchor nodes u Obtaining the estimated coordinates of the u-th node to be positioned
In a second aspect, the present application provides an electronic device comprising: a processor and a memory coupled to the processor, the memory for storing computer program code comprising computer instructions that, when read from the memory by the processor, cause the electronic device to perform the improved weighted centroid localization method based on KL-divergence and adjacency as described in the first aspect.
In a fourth aspect, the present application provides a computer storage medium comprising computer instructions which, when run on a terminal, cause the terminal to perform the improved weighted centroid positioning method based on KL-divergence and adjacency relation as described in the first aspect.
Additional advantages, objects, and features of the application will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the application. The objects and other advantages of the application may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the specification.
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For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the following detailed description of preferred embodiments of the present application will be made with reference to the accompanying drawings, in which it is apparent that the drawings in the following description are some embodiments of the present application and other drawings can be obtained from these drawings without inventive efforts to those skilled in the art.
FIG. 1 is a communication scenario diagram of an application of an embodiment of the present application;
FIG. 2 is a flowchart of an improved weighted centroid positioning method based on KL divergence and adjacency relation provided by an embodiment of the application;
FIG. 3 is a schematic view of a device according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that, as shown in fig. 1, the application scenario of the present application includes n+m sensor nodes in a geographical area of a wireless sensor network with an area of L (M) ×l (M), where N nodes to be located and M anchor nodes.
It should also be noted that, for any one anchor node, if it is used to locate a node to be located, if l is between them ua R, two nodes are considered to be able to communicate, the two nodes communicating via a Communication Channel (CCH); otherwise, no communication is possible between the two nodes.
Fig. 2 is a flowchart of an improved weighted centroid positioning method based on KL divergence and adjacency relation according to an embodiment of the present application, as shown in fig. 2, where the method is as follows:
s201: and deploying the node to be positioned and the anchor node, and initializing node setting.
Note that the coordinate of the anchor node is X a ＝[x a ，y a ] T The coordinate of the node to be positioned is X u ＝[x u ，y u ] T The Euclidean distance between the (u) th anchor node to be positioned and the (a) th anchor node is as follows:
s202: the anchor node propagates signals to the node to be positioned to establish an RSS matrix and calculate the KL divergence value measurement approximation degree.
It should be noted that, the radio propagation path loss has a great influence on the positioning accuracy of the RSS positioning algorithm. Consider a lognormal shadow path loss propagation environment in which the strength of a received signal received by the nth unknown node from the nth anchor node is expressed as
wherein PTx For the transmission power of the anchor node, P l (d 0 ) Representing a reference distance d 0 The corresponding path loss, path loss coefficient y,representing shadow fading and small scale fading complex factors.
It should also be noted that the RSS matrix between the N nodes to be located and the M anchor nodes is:
wherein For the M multiplied by N dimension full 1 matrix, s is the shadow influence factor matrix corresponding to the node to be positioned and the anchor node, L is the distance matrix between all the nodes to be positioned and all the anchor nodes, and the anchor node can be obtainedThe distance matrix V between the anchor nodes is obtained by the same method M×M 。
It should also be noted that the RSS vector p between the unknown node and the anchor node u ＝[p u1 ，p u2 ，…，p uM ] T RSS vector q between u E N and anchor node a ＝[q a1 ， qa2 ，…，q aM ] T The KL divergence corresponding to a E M is:
wherein
S203: the KL divergence value is observed and accumulated with a plurality of sample values to obtain the KL divergence matrix.
It should be noted that the KL divergence matrix of the unknown node and the anchor node is observed at the t-th timeIn order to reduce the influence of RSS instability on positioning accuracy, I is defined as the number of samples, and in the positioning process, the KL divergence matrix is calculated I times, and then the average value is taken as the final KL divergence matrix. Defining the final calculated KL divergence matrix as:
s204: an anchor node for positioning is determined using an adaptive neighbor selection threshold setting.
It should be noted that, based on the KL divergence matrix D, for the u-th node to be located, KL divergence values of the u-th node and all anchor nodes are sorted in ascending order. Recording the KL divergence value corresponding to the anchor node at the first position, wherein the KL divergence value is the minimum KL divergence value and is recorded as D min，u . The minimum value D is then min，u Expanding sigma times to obtain the (u) th waitingKL divergence threshold value corresponding to positioning node:
D th，u ＝D min，u ×σ，u＝1，2，…，N
it is also noted that D th，u By the setting method, each node to be positioned can adaptively set a KL divergence threshold value when selecting the neighbor anchor node, so that the optimal neighbor node is found to perform position estimation.
S205: and setting weights for the neighbor anchor nodes to be positioned according to the KL divergence matrix.
It should be noted that, based on the KL divergence matrix, the present application proposes a new weighting method, and sets a weight for each neighbor anchor node of the node to be located. Since KL divergence values of each node to be positioned and all anchor nodes are stored in the KL divergence matrix, the similarity of RSS distribution of the anchor nodes and the nodes to be positioned is reflected. When the KL divergence value is smaller, the fact that the distance between the node to be positioned and the anchor node in the space is more similar is indirectly reflected, namely the contribution of the anchor node to the final determination of the coordinates of the node to be positioned is larger. Specifically, the weight of the nth anchor node in the neighbor anchor node set obtained by the nth node to be located may be expressed as
wherein Du (n) represents the KL divergence value, K corresponding to the nth neighbor anchor node in the neighbor anchor node set obtained by the nth unknown node u And the number of the anchor nodes in the neighbor anchor node set is represented.
S206: and obtaining the coordinates of the node to be positioned by using the weighted value and the coordinates of the anchor node.
It should be noted that, based on the weight w u，n The influence degree of each neighbor anchor node on the unknown node position can be determined, and then the estimated coordinates of the u-th node to be positioned can be expressed as
wherein Estimated coordinates for the u-th unknown node, X u，n And representing the coordinates of the nth neighbor anchor node corresponding to the nth unknown node.
In the case of an integrated unit, fig. 3 shows a schematic structural diagram of an improved weighted centroid locating device based on KL divergence and adjacency relation as referred to in the above embodiment. The device comprises: an obtaining unit 301, a setting unit 302.
An obtaining unit 301, configured to establish an RSS matrix by using an anchor node to propagate a signal to a node to be located and calculate a KL divergence value measurement approximation degree;
the obtaining unit 301 is further configured to obtain a KL divergence matrix by observing a KL divergence value and accumulating multiple sample values;
a setting unit 302, configured to deploy a node to be located and an anchor node, and initialize node setting;
an obtaining unit 301, configured to determine an anchor node for positioning by using the adaptive neighbor selection threshold setting;
the setting unit 302 is further configured to set a weight for the neighbor anchor node to be located according to the KL divergence matrix;
the obtaining unit 301 is further configured to obtain coordinates of the node to be located by using the weighted value and coordinates of the anchor node.
It should be further noted that, for convenience and brevity of description, the specific working process of the wireless sensor node apparatus described above may refer to a corresponding process in the foregoing method embodiment, which is not described herein again.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application, and as shown in fig. 4, the apparatus may include a processor 401 and a memory 402, where the memory 402 is coupled to the processor 401, and the memory is configured to store computer program codes, where the computer program codes include computer instructions, when the processor reads the computer instructions from the memory, so that the electronic device performs the wireless sensor node positioning method provided by the embodiment.
The embodiment of the application also provides a computer readable storage medium, which can include a computer program or instructions, which when run on a computer, cause the computer to execute the wireless sensor node positioning method described in the above embodiment.
The above-described embodiments may be implemented in whole or in part by software, hardware, firmware, or any combination. When implemented using a software program, the embodiments described above may be wholly or partially in the form of a computer program product including one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions in accordance with embodiments of the present application are produced in whole or in part.
It should be understood that the disclosed systems, apparatus and methods may be implemented in other ways, and that the apparatus embodiments described above are merely illustrative, for example, the division of the elements is merely a logical function division, and that other divisions may be implemented in practice, for example, multiple elements or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Finally, it is noted that the above embodiments are only for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the present application, which is intended to be covered by the claims of the present application.
Claims (3)
1. An improved weighted centroid positioning method based on KL divergence and adjacency relation, the method comprising:
in a geographical area of the L (M) x L (M) wireless sensor network, N+M sensor nodes are included, wherein N nodes to be positioned and M anchor nodes are included;
deploying a node to be positioned and an anchor node, and initializing node setting; the coordinate of the anchor node is X a ＝[x a ，y a ] T The coordinate of the node to be positioned is X u ＝[x u ，y u ] T The Euclidean distance between the (u) th anchor node to be positioned and the (a) th anchor node is
Calculating KL divergence value C according to signals received by the node to be positioned ua The method comprises the steps of carrying out a first treatment on the surface of the The received signal strength of the nth unknown node received from the nth anchor node is expressed as wherein PTx For the transmission power of the anchor node, P l (d 0 ) Representing the reference distance d 0 Corresponding path loss with a path loss coefficient of γ, < >>Represents a shadow fading and small-scale fading composite factor; the RSS matrix between the N nodes to be positioned and the M anchor nodes is that wherein />For the M multiplied by N dimension full 1 matrix, S is a shadow influence factor matrix corresponding to the node to be positioned and the anchor node, and L is a distance matrix between all the nodes to be positioned and all the anchor nodes; the distance matrix V between the anchor nodes can also be obtained, and the RSS matrix Q between the anchor nodes can be obtained by the same method M×M The method comprises the steps of carrying out a first treatment on the surface of the RSS vector p between unknown node and anchor node u ＝[p u1 ，p u2 ，…，p uM ] T RSS vector q between u E N and anchor node a ＝[q a1 ，q a2 ，…，q aM ] T KL divergence corresponding to a E M is
wherein ,
obtaining a KL divergence matrix D through accumulation of t times of observation and I times of sample number; the KL divergence matrix of the unknown node and the anchor node is observed at the t timeDefining I as the number of samples, calculating the KL divergence matrix I times in the positioning process, taking the average value as a final KL divergence matrix, and finally calculating the KL divergence matrix as follows
Determining a neighbor anchor node of the ith node to be positioned by utilizing self-adaptive neighbor selection threshold setting; based on the KL divergence matrix D, for the ith node to be positioned, firstly carrying out ascending sorting on KL divergence values of the ith node and all anchor nodes; recording the KL divergence value corresponding to the anchor node arranged at the first position, wherein the KL divergence value is the minimum KL divergence value and is marked as D min，u Then the minimum value D min，u Expanding sigma times to obtain a KL divergence threshold value corresponding to the ith node to be positioned,D th，u ＝D min，u ×σ，u＝1，2，…，N；
determining the weight of the neighbor anchor node according to the KL divergence matrix D; the weight of the nth anchor node in the neighbor anchor node set obtained by the nth node to be positioned is expressed as wherein ,Du (n) represents the KL divergence value, K corresponding to the nth neighbor anchor node in the neighbor anchor node set obtained by the nth unknown node u Representing the number of anchor nodes in a neighbor anchor node set;
obtaining the coordinate of the u-th node to be positioned according to the weight and the coordinate of the neighbor anchor nodeBased on weight w u，n The influence degree of each neighbor anchor node on the unknown node position can be determined, and then the estimated coordinate of the u-th node to be positioned is expressed as
wherein ,estimated coordinates for the u-th unknown node, X u，n And representing the coordinates of the nth neighbor anchor node corresponding to the nth unknown node.
2. An electronic device, comprising: a processor and a memory coupled to the processor, the memory for storing computer program code comprising computer instructions that, when read from the memory by the processor, cause the electronic device to perform the improved weighted centroid localization method based on KL divergence and adjacency as set forth in claim 1.
3. A computer-readable storage medium comprising computer instructions that, when run on a terminal, cause the terminal to perform the improved weighted centroid positioning method based on KL divergence and adjacency relationship as recited in claim 1.
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