阅读说明：本技术 一种基于wsn的道路车辆三维无源被动定位方法及系统 (WSN-based three-dimensional passive positioning method and system for road vehicle ) 是由 王满意 黄炳华 杨佳星 于 2020-04-30 设计创作，主要内容包括：本发明公开了一种基于WSN的道路车辆三维无源被动定位方法及系统,首先在道路两侧部署无线传感器节点拓扑网络；再对所部署的无线传感器节点拓扑网络建立三维空间模型：建立与无线传感器节点拓扑网络一致的三维空间模型,并对其监测区域划分等距离的网格；然后接收节点接收到的信号强度值；消除噪声的影响：利用核函数的方法消除噪声的影响；将有效的衰减链路对应画在所建立的三维空间模型中,得到特征图；最后生成车辆定位结果三维热图：将所有的有效衰减链路的特征图进行叠加拟合,生成车辆的定位结果热图；本发明可对在监测区内的车辆进行三维无线定位。(The invention discloses a WSN-based three-dimensional passive positioning method and a WSN-based three-dimensional passive positioning system for a road vehicle, wherein a wireless sensor node topological network is deployed on two sides of a road; then establishing a three-dimensional space model for the deployed wireless sensor node topological network: establishing a three-dimensional space model consistent with a wireless sensor node topological network, and dividing monitoring areas of the three-dimensional space model into equidistant grids; then receiving a signal strength value received by the node; eliminating the influence of noise: eliminating the influence of noise by using a kernel function method; correspondingly drawing the effective attenuation links in the established three-dimensional space model to obtain a characteristic diagram; and finally generating a three-dimensional heat map of the vehicle positioning result: performing superposition fitting on the characteristic graphs of all the effective attenuation links to generate a positioning result heat map of the vehicle; the invention can carry out three-dimensional wireless positioning on the vehicles in the monitoring area.)

1. A WSN-based three-dimensional passive positioning method for a road vehicle is characterized by comprising the following steps:

step 1, deploying wireless sensor node topological networks on two sides of a road: a plurality of sensor nodes are symmetrically arranged on two sides of a road; a plurality of groups are arranged on each side to form a communication network; placing a receiving node for receiving communication data of a communication network;

step 2, establishing a three-dimensional space model for the deployed wireless sensor node topological network: establishing a three-dimensional space model consistent with a wireless sensor node topological network, and dividing a monitoring area of the wireless sensor node network into equidistant grids;

and 3, receiving the RSS value received by the node, and eliminating the influence of noise: eliminating the influence of noise by using a kernel function method;

step 4, correspondingly drawing the effective attenuation links in the established three-dimensional space model to obtain a characteristic diagram: dividing the weight of each communication link between nodes according to the information entropy of each link, dividing the weight of each link on the established grids, obtaining the correlation coefficient between pixels according to the position relation of each small grid, and performing covariance processing to generate a pixel feature map;

step 5, generating a three-dimensional heat map of the vehicle positioning result: and performing superposition fitting on the characteristic maps of all the effective attenuation links to generate a positioning result heat map of the vehicle.

2. The WSN-based three-dimensional passive positioning method for road vehicles according to claim 1, wherein the positioning method is characterized in that step 3 utilizes a kernel function method to eliminate the influence of noise, specifically:

when the vehicle is at a specific position, counting the RSS values of all links of the vehicle at the position and the occurrence frequency of each RSS value, and counting the size and the occurrence frequency of the RSS values on a histogram; then, the difference between the histogram of the link i when the vehicle is at a certain position in the monitoring area and the histogram of the link i when no vehicle exists in the monitoring area is measured by using the kernel distance, and the kernel distance formula is as follows:

D_i＝d(h_i(x),h_i(0))

wherein D is_iDenotes the nuclear distance, h_i(x) RSS histogram, h, representing the ith link in the presence of a vehicle_i(0) An RSS histogram representing the ith link without a vehicle, d (-) being the matrix to real mapping;

and when the core distance is greater than a set threshold value, the link is considered to be shielded, otherwise, the link is considered to be not shielded.

3. The WSN-based three-dimensional passive positioning method for road vehicles according to claim 1, wherein the positioning method is characterized in that the step 4 of obtaining a feature map specifically comprises the following steps:

step 4.1, dividing the weight of each communication link to obtain a preliminary characteristic diagram:

dividing the weight of each communication link between the nodes according to the link information entropy, and projecting the established three-dimensional space model to three surfaces of the space respectively; defining a weight function similar to the information entropy distribution curve to determine the weight of different links, wherein the weight function adopts an exponential decay function

Wherein, w_iRepresents the link weight, α and σ_lAs a characteristic parameter value, S_iThe effective projection area of the link i is shown, and S is the total area of the projection surface; dividing the link weight on the established grid pixels, wherein the depth of the pixels represents the weight;

and 4.2, processing the preliminary characteristic graph again, carrying out correlation analysis on the pixels, taking a corresponding correlation coefficient between each pixel, and then adding a covariance model for processing to generate a pixel characteristic graph.

4. A WSN based three dimensional passive positioning method of road vehicles according to claim 3, characterized by the step 4.2 covariance matrix C expressed as:

wherein d is_i,jIs the Euclidean distance, σ, between the center points of two pixels_cIs a space constant, σ_iIs the difference value of each pixel, [ C]_i,jRepresents the ith row and jth column element of the covariance matrix C.

5. A WSN-based three-dimensional passive positioning system for a road vehicle is characterized by comprising a wireless sensor node topology network and a processor for processing received data; the processor is provided with a three-dimensional space model establishing module, a noise eliminating module, a feature generating module and a three-dimensional heat map generating module;

the wireless sensor node topology network comprises a plurality of sensor nodes and a receiving node which are symmetrically arranged on two sides of a road, and is used for collecting RSS value data of the sensor nodes when a vehicle runs on the road;

the three-dimensional space model building module is used for building a three-dimensional space model consistent with the wireless sensor node topological network and dividing the monitoring area of the wireless sensor node network into equidistant grids;

the noise elimination module is used for eliminating the influence of noise by utilizing a kernel function method on the received RSS value of the node communication;

the characteristic generating module is used for dividing the weight of each communication link among the nodes according to the information entropy of each link, dividing the weight of each link on the established grids, obtaining the correlation coefficient among the pixels according to the position relation of each small grid, and performing covariance processing to generate a pixel characteristic diagram;

and the three-dimensional heat map generation module is used for performing superposition fitting on the characteristic maps of all the effective attenuation links to generate a positioning result heat map of the vehicle.

6. The positioning system according to claim 5, wherein the noise elimination module eliminates the influence of noise by:

counting the RSS values of all links of the vehicle at the position and the occurrence frequency of each RSS value, and counting the size and the occurrence frequency of the RSS values on a histogram; then, the difference between the histogram of the link i when the vehicle is at a certain position in the monitoring area and the histogram of the link i when no vehicle exists in the monitoring area is measured by using the kernel distance, and the kernel distance formula is as follows:

D_i＝d(h_i(x),h_i(0))

wherein D is_iDenotes the nuclear distance, h_i(x) RSS histogram, h, representing the ith link in the presence of a vehicle_i(0) An RSS histogram representing the ith link without a vehicle, d (-) being the matrix to real mapping;

and when the core distance is greater than a set threshold value, the link is considered to be shielded, otherwise, the link is considered to be not shielded.

7. The localization system according to claim 5, characterized in that the feature generation module comprises a preliminary feature map unit and a pixel feature map unit;

the preliminary characteristic diagram unit is used for dividing the weight of the link to obtain a preliminary characteristic diagram:

dividing the weight of each communication link between the nodes according to the link information entropy, and projecting the established three-dimensional space model to three surfaces of the space respectively; defining a weight function similar to the information entropy distribution curve to determine the weight of different links, wherein the weight function adopts an exponential decay function

Wherein, w_iRepresents the link weight, α and σ_lAs a characteristic parameter value, S_iThe effective projection area of the link i is shown, and S is the total area of the projection surface; dividing the link weight on the established grid pixels, wherein the depth of the pixels represents the weight;

the pixel feature map unit is used for processing the preliminary feature map again, performing correlation analysis on the pixels, taking the corresponding correlation coefficient among the pixels, and then adding a covariance model for processing to generate the pixel feature map.

8. The positioning system of claim 6, wherein the covariance calculation formula is:

where C is the covariance matrix, d_i,jIs the Euclidean distance, σ, between the center points of two pixels_cIs a space constant, σ_iIs the difference value of each pixel, [ C]_i,jRepresents the ith row and jth column element of the covariance matrix C.

Technical Field

The invention belongs to the field of three-dimensional positioning of targets, and particularly relates to a WSN-based road vehicle three-dimensional passive positioning method and system.

Background

With the development of scientific technology, position information plays an extremely important role in human production activities, and the demand for position information in the fields of smart home, target navigation, mobile commerce, military war and the like is increasing day by day, so that the positioning technology has huge development potential and wide application prospect. In recent years, researchers have explored different methods applied to positioning and activity perception of targets, such as bluetooth positioning, infrared positioning, computer vision positioning, Wi-Fi positioning, and the like, however, these positioning technologies have problems of requiring target cooperation (wearing identification cards), being greatly interfered by environmental factors such as walls or weather, and having complex positioning equipment structures and high costs, and the like, so that the application of the positioning technologies is greatly limited. Therefore, a positioning method which does not need target cooperation, has low cost and low power consumption and is not influenced by various factors such as environment and the like is explored, and the method becomes an urgent development demand and research hotspot of the future positioning technology.

Currently, a passive localization (DFL) technology developed based on a Wireless Sensor Network (WSN) provides a new method for target detection, localization and tracking. The DFL positioning technology can complete the positioning and tracking of the target in the area by only deploying the wireless sensor nodes around the monitoring area without any electronic tag carried by the target to be detected. Compared with other positioning technologies, the DFL positioning technology has the advantages of low power consumption, low cost, no need of carrying any equipment by a target, no influence of factors such as light, temperature, smoke, rain, snow and the like, strong self-organization and the like, and has huge application and research values in the field of target positioning. However, the existing DFL positioning technology still has some problems: (1) the positioning method is mostly carried out on the basis of a two-dimensional plane which is deployed in a two-dimensional surrounding manner in a wireless sensor node network, and a mature positioning method is not available in the field of three-dimensional positioning of targets; (2) related positioning algorithms and weight models are mostly used for positioning a human body under two-dimensional network topology, and a three-dimensional positioning method related to a large-volume target under a wireless sensor three-dimensional topology network is provided.

Disclosure of Invention

The invention aims to provide a WSN-based road vehicle three-dimensional passive positioning method and a WSN-based road vehicle three-dimensional passive positioning system, and a positioning method for three-dimensional wireless positioning of vehicles in a monitoring area and small environmental influence.

The technical solution for realizing the purpose of the invention is as follows:

a WSN-based three-dimensional passive positioning method for a road vehicle comprises the following steps:

step 1, deploying wireless sensor node topological networks on two sides of a road: a plurality of conveyors are symmetrically arranged on both sides of the road

A sensor node; a plurality of groups are arranged on each side to form a communication network; placing a receiving node for receiving communication data of a communication network;

step 2, establishing a three-dimensional space model for the deployed wireless sensor node topological network: establishing a three-dimensional space model consistent with a wireless sensor node topological network, and dividing a monitoring area of the wireless sensor node network into equidistant grids;

and 3, receiving the RSS value received by the node, and eliminating the influence of noise: eliminating the influence of noise by using a kernel function method;

step 4, correspondingly drawing the effective attenuation links in the established three-dimensional space model to obtain a characteristic diagram: dividing the weight of each communication link between nodes according to the information entropy of each link, dividing the weight of each link on the established grids, obtaining the correlation coefficient between pixels according to the position relation of each small grid, and performing covariance processing to generate a pixel feature map;

step 5, generating a three-dimensional heat map of the vehicle positioning result: and performing superposition fitting on the characteristic maps of all the effective attenuation links to generate a positioning result heat map of the vehicle.

Compared with the prior art, the invention has the following remarkable advantages:

(1) the normal working state of the deployed wireless sensor node network is not influenced by factors such as light, temperature, smoke, rain, snow and the like, and is not influenced by surrounding environments such as buildings, wall trees and the like, so that the vehicle three-dimensional positioning method provided by the invention can complete positioning under various environments and conditions.

(2) The WSN-based road vehicle three-dimensional wireless positioning method provided by the invention explores an effective positioning method for the field of three-dimensional wireless positioning of large-volume targets, the average positioning error is 0.2m, and the positioning accuracy is very high.

(3) The deployed wireless sensor node network is simple in structure, compared with the traditional positioning technology, the sensor node is small in size, can be powered by a battery, is convenient to use, and can be rapidly deployed during positioning; positioning can be completed only by arranging a wireless sensor space network, and the cost is lower than that of other positioning technologies;

(4) for the target vehicles in the monitoring area, the three-dimensional positioning of the target vehicles can be realized without carrying any equipment, and the privacy of the positioned target vehicles is fully ensured not to be invaded.

Drawings

FIG. 1 is a flow chart of the method of the present invention.

Fig. 2 is a top view of a wireless sensor network node arrangement.

Fig. 3 is a front view of a wireless sensor network node arrangement.

Fig. 4 is a positioning preliminary feature diagram obtained according to the communication link weight division principle.

Fig. 5 is a top view of the resulting positioning result heat map.

Fig. 6 is a front view of the resulting positioning result heat map.

Detailed Description

The invention is further described with reference to the following figures and embodiments.