CN108521626A - A multi-sensor network-based positioning method for maritime search and rescue - Google Patents
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Abstract
本发明公开了一种基于多传感网络的水上搜救定位方法,属于多传感器网络技术领域。本发明包括:传感器节点位置的获取,通过GPS和无线通信实时获取并交换自身位置坐标信息;建立传感器节点受力模型,将传感器节点抽象成势力场中的粒子,分析传感器节点间力的作用控制传感器节点的移动方向和距离;基于改进虚拟力算法的多传感器网络区域覆盖。本发明可以使多传感器网络实现区域的无缝覆盖,并对覆盖区域进行数据收集;光电成像系统利用灰度及温度差异确定搜索目标,利用三边定位算法确定并反馈目标坐标位置信息。
The invention discloses a multi-sensor network-based water search and rescue positioning method, which belongs to the technical field of multi-sensor networks. The present invention includes: acquiring the position of the sensor node, acquiring and exchanging its own position coordinate information in real time through GPS and wireless communication; establishing a force model of the sensor node, abstracting the sensor node into particles in the force field, and analyzing the action control of the force between the sensor nodes Moving direction and distance of sensor nodes; Multi-sensor network area coverage based on improved virtual force algorithm. The invention enables the multi-sensor network to realize the seamless coverage of the area, and collects data on the covered area; the photoelectric imaging system determines the search target by using the gray scale and temperature difference, and determines and feeds back the target coordinate position information by using the trilateration positioning algorithm.
Description
技术领域technical field
本发明属于多传感器网络技术领域,具体涉及一种基于多传感网络的水上搜救定位方法。The invention belongs to the technical field of multi-sensor networks, and in particular relates to a multi-sensor network-based search and rescue positioning method on water.
背景技术Background technique
多传感器网络综合了传感器技术、嵌入式计算技术、微机电技术和无线通信技术,由部署在监测区域内的大量传感器节点组成,能够协同地完成实时监测、传感和采集目标对象的信息,并对其进行处理,传送给需要信息的用户。随着微机电系统、无线通信技术和大规模集成电路技术的发展,传感器信息技术的获取渐渐走向集成化、微型化和网络化的方向,多传感器网络也被认为是本世纪最重要的技术之一,它的发展和广泛应用,将对人们的社会生活和产业变革带来极大的影响。The multi-sensor network integrates sensor technology, embedded computing technology, micro-electromechanical technology and wireless communication technology. It is processed and transmitted to the users who need the information. With the development of micro-electromechanical systems, wireless communication technology and large-scale integrated circuit technology, the acquisition of sensor information technology is gradually moving towards the direction of integration, miniaturization and networking, and multi-sensor networks are also considered to be one of the most important technologies in this century. First, its development and wide application will have a great impact on people's social life and industrial transformation.
由于多传感器网络中的传感器节点成本低、功耗低、微型等优点,可以减少布置网络的时间和成本,可搭载平台广泛,能够适应恶劣的条件,因此被广泛应用于军事战场、戈壁沙漠、宇宙太空、海底深处等地域。本发明提出了一种运用改进虚拟力算法及三边定位算法将多传感器网络应用于水面无人搜救定位的方法。Due to the advantages of low cost, low power consumption, and miniature sensor nodes in multi-sensor networks, it can reduce the time and cost of network deployment, can be equipped with a wide range of platforms, and can adapt to harsh conditions, so it is widely used in military battlefields, Gobi Desert, Cosmic space, deep seabed and other areas. The invention proposes a method for applying a multi-sensor network to unmanned search and rescue positioning on the water surface by using an improved virtual force algorithm and a trilateration positioning algorithm.
发明内容Contents of the invention
本发明的目的在于利用改进虚拟力算法控制传感器节点分布,使多传感器网络覆盖搜救水域,全面收集信息,定位并反馈目标位置,实现对水面目标的无人搜救定位。The purpose of the present invention is to use the improved virtual force algorithm to control the distribution of sensor nodes, so that the multi-sensor network covers the search and rescue waters, collects information comprehensively, locates and feeds back the target position, and realizes unmanned search and rescue positioning of water surface targets.
本发明的目的是这样实现的:The purpose of the present invention is achieved like this:
一种基于多传感网络的水上搜救定位方法,其特征在于,包含以下步骤:A water search and rescue positioning method based on a multi-sensor network, characterized in that it comprises the following steps:
步骤一传感器节点位置的获取;Step 1: Obtaining the position of the sensor node;
传感器节点根据其搭载平台的特点,选取通过GPS方式实时获取其自身定位的位置坐标,并通过无线通信方式与其他节点交换位置信息;According to the characteristics of its carrying platform, the sensor node chooses to obtain its own location coordinates in real time through GPS, and exchanges location information with other nodes through wireless communication;
步骤二建立传感器节点受力模型;Step 2 establishes the force model of the sensor node;
将传感器节点抽象成势力场中的粒子,某个传感器节点对周围传感器节点有力的作用,根据距离的大小区别为引力和斥力;当两个传感器节点的距离小于距离阈值时,两者之间存在斥力,两者之间距离增大;当两个传感器节点的距离大于距离阈值时,两者之间则表现为引力,二者之间的距离缩短;The sensor nodes are abstracted into particles in the force field, and a certain sensor node has a strong effect on the surrounding sensor nodes, which are distinguished as attraction and repulsion according to the distance; when the distance between two sensor nodes is less than the distance threshold, there is Repulsion, the distance between the two increases; when the distance between the two sensor nodes is greater than the distance threshold, the two are acting as attraction, and the distance between the two is shortened;
传感器节点间的受力关系Fij:Force relationship F ij between sensor nodes:
式中,wA表示引力系数;wR表示斥力系数;dij表示节点之间的欧式距离;dth表示距离阀值;aij是传感器节点的方位角;Rc表示节点的通信半径;In the formula, w A represents the attraction coefficient; w R represents the repulsion coefficient; d ij represents the Euclidean distance between nodes; d th represents the distance threshold; a ij represents the azimuth angle of the sensor node; R c represents the communication radius of the node;
若Fij为斥力,则If F ij is the repulsive force, then
Fxij=|Fij|(xi-xj)/dij F xij =|F ij |(x i -x j )/d ij
Fyij=|Fij|(yi-yj)/dij F yij =|F ij |(y i -y j )/d ij
若Fij为引力,则If F ij is gravity, then
Fxij=|Fij|(xj-xi)/dij F xij =|F ij |(x j -x i )/d ij
Fyij=|Fij|(yj-yi)/dij F yij =|F ij |(y j -y i )/d ij
两个传感器节点之间的受力关系,Fij为:The force relationship between two sensor nodes, F ij is:
Fij=Fxij+Fyij F ij =F xij +F yij
水平方向的虚拟力和为:The virtual force sum in the horizontal direction is:
Fx=∑Fxij F x =∑F xij
竖直方向的虚拟力和为:The virtual force sum in the vertical direction is:
Fy=∑Fyij F y =∑F yij
水平方向与竖直方向合力的大小为:The resultant force in horizontal direction and vertical direction is:
式中若Fxij,Fyij为负数,表明水平方向的分力向左,竖直方向的分力向下;In the formula, if F xij and F yij are negative numbers, it indicates that the component force in the horizontal direction is to the left, and the component force in the vertical direction is downward;
根据以上分析得到无线传感器节点的受力详细情况,根据传感器节点的受力情况决定传感器节点受到虚拟力时移动的方向和距离:According to the above analysis, the details of the force of the wireless sensor node are obtained, and the direction and distance of the sensor node moving when it is subjected to a virtual force are determined according to the force of the sensor node:
式中,(xi yi)为传感器节点初始的位置;(xi',yi')为移动后的位置;Fth为虚拟力分力的阈值,当受到的虚拟力的分力小于该值时,在该分力方向上传感器节点不移动;step为传感器节点移动的最大距离;In the formula, (xi y i ) is the initial position of the sensor node; (xi ' , y i ') is the position after movement; F th is the threshold of the virtual force component, when the received virtual force component is less than When this value is set, the sensor node does not move in the direction of the component force; step is the maximum distance that the sensor node moves;
步骤三基于改进虚拟力算法的多传感器网络区域覆盖;Step 3: Multi-sensor network area coverage based on improved virtual force algorithm;
改进虚拟力算法:Improved virtual force algorithm:
为减少虚拟力算法迭代次数,在传感器节点的受力模型上添加与距离相关的系数;节点在距离较近时,虚拟力足够大,使其快速扩散;节点间距离趋于距离阈值时,虚拟力足够小,使节点易于达到平衡;改进的节点间的虚拟力方程为:In order to reduce the number of iterations of the virtual force algorithm, a distance-related coefficient is added to the force model of the sensor node; when the distance between nodes is close, the virtual force is large enough to make it spread rapidly; when the distance between nodes tends to the distance threshold, the virtual force The force is small enough to make the nodes easy to reach equilibrium; the improved virtual force equation between nodes is:
式中,d(si,sj)是节点i与节点j之间的欧几里德距离;U1为处于节点i通信范围内的节点的集合;In the formula, d(s i , s j ) is the Euclidean distance between node i and node j; U 1 is the set of nodes within the communication range of node i;
步骤四定位未知位置目标;Step 4 locates the unknown location target;
光电成像搜索系统利用灰度及温度的差异来搜索确定搜寻目标;确定目标后,已知自身位置坐标的传感器节点,通过三边定位算法,对未知位置目标进行定位;The photoelectric imaging search system uses the difference in grayscale and temperature to search and determine the search target; after the target is determined, the sensor node that knows its own position coordinates uses the trilateration positioning algorithm to locate the unknown position target;
目标位置根据以下方程求解:The target position is solved according to the following equation:
式中,(x,y)为目标位置坐标;(x1,y2)、(x2,y2)、(x3,y3)分别为三个已知节点的坐标;d1、d2、d3分别为目标与三个节点间的距离。In the formula, (x, y) is the target position coordinates; (x 1 , y 2 ), (x 2 , y 2 ), (x 3 , y 3 ) are the coordinates of three known nodes respectively; d 1 , d 2 and d 3 are the distances between the target and the three nodes respectively.
与现有技术相比,本发明的有益效果是:Compared with prior art, the beneficial effect of the present invention is:
1、本发明完成了对指定目标的水面无人搜救的任务,利用传感器节点的机动性,自动完成对勘察水域的覆盖,收集并分析信息,确定并反馈目标位置坐标,达到了无人搜救的目的;1. The present invention completes the task of unmanned search and rescue on the water surface of the designated target, and uses the mobility of sensor nodes to automatically complete the coverage of the surveyed waters, collect and analyze information, determine and feed back the coordinates of the target position, and achieve unmanned search and rescue. Purpose;
2、本发明使用改进的虚拟力算法控制传感器节点区域内的运动分布,克服了原始虚拟力算法运行速度慢的问题,使得传感器节点迅速分散并稳定在相应位置,减小能耗,增强多传感器网络的稳定性,并延长了多传感器网络的存活时长;2. The present invention uses the improved virtual force algorithm to control the motion distribution in the sensor node area, which overcomes the problem of slow running speed of the original virtual force algorithm, makes the sensor nodes quickly disperse and stabilize at corresponding positions, reduces energy consumption, and enhances multi-sensor The stability of the network and prolong the survival time of the multi-sensor network;
3、本发明通过光电成像搜索系统对收集的监测水面成像进行处理,确定未知目标。这样无需目标自身携带传感器节点,减小对目标本身的要求,可以扩大目标适用范围。3. The present invention processes the collected monitoring water surface images through the photoelectric imaging search system to determine unknown targets. In this way, there is no need for the target to carry sensor nodes, which reduces the requirements for the target itself and can expand the scope of application of the target.
4、本发明通过改进虚拟力算法完成了多传感器网络对勘察水面的覆盖,通过三边定位算法确定并反馈目标位置,实现了对目标的水面无人搜救定位。4. The present invention completes the coverage of the surveyed water surface by the multi-sensor network by improving the virtual force algorithm, determines and feeds back the target position by the trilateral positioning algorithm, and realizes unmanned search and rescue positioning of the target on the water surface.
附图说明Description of drawings
图1是本发明传感器节点受力模型;Fig. 1 is the stress model of the sensor node of the present invention;
图2是本发明基于改进虚拟力算法的传感器节点移动过程仿真结果;Fig. 2 is the simulation result of the sensor node moving process based on the improved virtual force algorithm in the present invention;
图3是本发明三边定位算法的原理图。Fig. 3 is a schematic diagram of the trilateral positioning algorithm of the present invention.
具体实施方式Detailed ways
下面结合附图对本发明的新概念减摇减阻船舶作出以下详细说明:Below in conjunction with the accompanying drawings, the new concept anti-rolling and drag-reducing ship of the present invention is described in detail as follows:
为使本发明的上述目的、特征和优点能够更加明显易懂,下面结合图1至图3和具体实施方式对本发明作进一步详细的说明,其中,图1为传感器节点受力模型;图2是基于改进虚拟力算法的传感器节点移动过程仿真结果;图3是三边定位算法的原理图;In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the present invention will be further described in detail below in conjunction with FIGS. The simulation results of the sensor node movement process based on the improved virtual force algorithm; Figure 3 is the schematic diagram of the trilateral positioning algorithm;
具体实施例一Specific embodiment one
本发明的目的按以下步骤实现:The purpose of the present invention is achieved by the following steps:
1.建立传感器节点受力模型1. Establish the force model of the sensor node
将传感器节点抽象成势力场中的粒子,某个传感器节点对周围传感器节点有力的作用,可以根据距离的大小区别为引力和斥力。当两个传感器节点的距离小于距离阈值时,两者之间存在斥力,两者之间距离增大;当两个传感器节点的距离大于距离阈值时,两者之间则表现为引力,二者之间的距离缩短。据此,建立传感器节点受力模型,如图1所示。The sensor nodes are abstracted into particles in the force field. A certain sensor node has a strong effect on the surrounding sensor nodes, which can be distinguished as attraction and repulsion according to the size of the distance. When the distance between two sensor nodes is less than the distance threshold, there is a repulsive force between them, and the distance between them increases; when the distance between two sensor nodes is greater than the distance threshold, there is an attractive force between them, and The distance between them is shortened. Accordingly, the force model of the sensor node is established, as shown in Figure 1.
传感器节点间的受力关系Fij:Force relationship F ij between sensor nodes:
式中,wA表示引力系数;In the formula, w A represents the gravitational coefficient;
wR表示斥力系数;w R represents the repulsion coefficient;
dij表示节点之间的欧式距离;d ij represents the Euclidean distance between nodes;
dth表示距离阀值;d th represents the distance threshold;
aij是传感器节点的方位角;a ij is the azimuth angle of the sensor node;
Rc表示节点的通信半径。R c represents the communication radius of the node.
若Fij为斥力,则If F ij is the repulsive force, then
Fxij=|Fij|(xi-xj)/dij F xij =|F ij |(x i -x j )/d ij
Fyij=|Fij|(yi-yj)/dij F yij =|F ij |(y i -y j )/d ij
若Fij为引力,则If F ij is gravity, then
Fxij=|Fij|(xj-xi)/dij F xij =|F ij |(x j -x i )/d ij
Fyij=|Fij|(yj-yi)/dij F yij =|F ij |(y j -y i )/d ij
两个传感器节点之间的受力关系,Fij为The force relationship between two sensor nodes, Fi j is
Fij=Fxij+Fyij F ij =F xij +F yij
水平方向的虚拟力和为:The virtual force sum in the horizontal direction is:
Fx=∑Fxij F x =∑F xij
竖直方向的虚拟力和为:The virtual force sum in the vertical direction is:
Fy=∑Fyij F y =∑F yij
水平方向与竖直方向合力的大小为:The resultant force in horizontal direction and vertical direction is:
式中若Fxij,Fyij为负数,表明水平方向的分力向左,竖直方向的分力向下。In the formula, if F xij and F yij are negative numbers, it indicates that the component force in the horizontal direction is to the left, and the component force in the vertical direction is downward.
根据以上分析可以得到无线传感器节点的受力详细情况,根据传感器节点的受力情况决定传感器节点受到虚拟力时移动的方向和距离:According to the above analysis, the details of the force of the wireless sensor node can be obtained, and the direction and distance of the sensor node moving when it is subjected to a virtual force can be determined according to the force of the sensor node:
式中,(xi yi)为传感器节点初始的位置;In the formula, (xi y i ) is the initial position of the sensor node;
(xi',yi')为移动后的位置;(x i ', y i ') is the moved position;
Fth为虚拟力分力的阈值,当受到的虚拟力的分力小于该值时,在该分力方向上传感器节点不移动;F th is the threshold of the virtual force component. When the component of the virtual force received is less than this value, the sensor node does not move in the direction of the component force;
step为传感器节点移动的最大距离。step is the maximum distance the sensor node moves.
2.改进虚拟力算法,并利用其控制传感器节点无缝覆盖勘察水域2. Improve the virtual force algorithm and use it to control the sensor nodes to seamlessly cover the surveyed waters
(1)改进虚拟力算法(1) Improved virtual force algorithm
为减少虚拟力算法迭代次数,在传感器节点的受力模型上添加与距离相关的系数。节点在距离较近时,虚拟力足够大,使其快速扩散;节点间距离趋于距离阈值时,虚拟力足够小,使节点易于达到平衡。改进的节点间的虚拟力方程为:In order to reduce the number of iterations of the virtual force algorithm, a distance-related coefficient is added to the force model of the sensor node. When the distance between nodes is close, the virtual force is large enough to make it diffuse rapidly; when the distance between nodes tends to the distance threshold, the virtual force is small enough to make the nodes easy to reach equilibrium. The improved virtual force equation between nodes is:
式中,d(si,sj)是节点i与节点j之间的欧几里德距离;In the formula, d(s i , s j ) is the Euclidean distance between node i and node j;
U1为处于节点i通信范围内的节点的集合。U 1 is the set of nodes within the communication range of node i.
(2)改进虚拟力算法运行过程(2) Improve the running process of the virtual force algorithm
虚拟力算法按照如下过程进行迭代:初始状态为传感器节点随机分布在监测区域,传感器节点受到其他节点力的作用,然后根据受到力的大小和方向决定移动的距离和方向,移动到新位置后再根据新的受力大小和方向进行下一次移动,直到达到满意的覆盖要求。其具体运行过程如下:The virtual force algorithm iterates according to the following process: the initial state is that the sensor nodes are randomly distributed in the monitoring area, and the sensor nodes are affected by the force of other nodes, and then the distance and direction of movement are determined according to the magnitude and direction of the force received, and after moving to a new position, the virtual force algorithm is iterated. Carry out the next move according to the new force magnitude and direction until the satisfactory coverage requirement is achieved. Its specific operation process is as follows:
步骤1:将传感器节点随机分布在监测区域;Step 1: Randomly distribute sensor nodes in the monitoring area;
步骤2:传感器节点计算与周围节点的距离和相对位置,计算虚拟力的大小和方向,并以此决定移动距离和方向;Step 2: The sensor node calculates the distance and relative position to the surrounding nodes, calculates the magnitude and direction of the virtual force, and determines the moving distance and direction accordingly;
步骤3:计算覆盖率;Step 3: Calculate coverage;
步骤4:当覆盖率满足要求,停止算法迭代,输出结果。Step 4: When the coverage rate meets the requirements, stop the algorithm iteration and output the result.
图2反映了传感器节点逐步从随机分布状态移动到水面无缝覆盖的状态,并利用曲线图反映出覆盖率与算法迭代次数之间的关系。Figure 2 reflects the gradual movement of sensor nodes from the state of random distribution to the state of seamless coverage of the water surface, and uses the graph to reflect the relationship between the coverage rate and the number of iterations of the algorithm.
3.传感器收集水域信息,基于三边定位算法确定目标位置3. The sensor collects water area information and determines the target position based on the trilateral positioning algorithm
传感器节点通过GPS及无线通信实时获取并与相邻节点交换自身坐标位置,由传感器收集通讯范围内信息,利用三边定位算法确定并反馈目标位置坐标。The sensor nodes obtain and exchange their own coordinate positions with adjacent nodes in real time through GPS and wireless communication. The sensors collect information within the communication range, and use the trilateration positioning algorithm to determine and feed back the target position coordinates.
三边定位算法原理如图3所示,目标位置根据以下方程求解:The principle of trilateration positioning algorithm is shown in Figure 3, and the target position is solved according to the following equation:
式中,(x,y)为目标位置坐标;In the formula, (x, y) is the target position coordinates;
(x1,y2)、(x2,y2)、(x3,y3)分别为三个已知节点的坐标;(x 1 ,y 2 ), (x 2 ,y 2 ), (x 3 ,y 3 ) are the coordinates of three known nodes respectively;
d1、d2、d3分别为目标与三个节点间的距离。d 1 , d 2 , and d 3 are the distances between the target and the three nodes, respectively.
4.基于多传感网络的水上搜救定位方法4. Water search and rescue positioning method based on multi-sensor network
基于多传感器网络的水上搜救定位方法设计步骤如下:The design steps of the maritime search and rescue positioning method based on the multi-sensor network are as follows:
步骤1:将传感器节点随机分布在监测水域;Step 1: Randomly distribute the sensor nodes in the monitoring waters;
步骤2:利用改进的虚拟力算法控制传感器节点无缝覆盖侦察水域;Step 2: Use the improved virtual force algorithm to control the sensor nodes to seamlessly cover the reconnaissance waters;
步骤3:传感器节点通过GPS及无线通信实时获取并交流自身位置坐标,并收集覆盖区域内信息,利用光电成像搜索系统确定找寻目标;Step 3: The sensor nodes obtain and exchange their own position coordinates in real time through GPS and wireless communication, collect information in the coverage area, and use the photoelectric imaging search system to determine the target;
步骤4:利用三边定位算法,传感器节点确定目标与三个已知节点间的距离,计算并反馈目标位置信息。Step 4: Using the trilateral positioning algorithm, the sensor node determines the distance between the target and three known nodes, calculates and feeds back the target position information.
具体实施例二Specific embodiment two
一种基于多传感网络的水上搜救定位方法,其特征在于:该方法利用虚拟力算法控制具有机动特性的传感器节点,使其由初始的随机分布状态移动,实现对指定水域的无缝覆盖,同时收集覆盖区域内数据,利用光电成像系统及数字图像处理分析并确定海面上的目标,采取三边定位算法确定并反馈目标位置,可以实现对目标的搜救及定位。A water search and rescue positioning method based on a multi-sensor network, characterized in that: the method uses a virtual force algorithm to control sensor nodes with maneuvering characteristics, so that it moves from an initial random distribution state to achieve seamless coverage of designated water areas, At the same time, collect data in the coverage area, use photoelectric imaging system and digital image processing to analyze and determine the target on the sea surface, and use trilateration positioning algorithm to determine and feed back the target position, so as to realize the search and rescue and positioning of the target.
(1)传感器节点位置的获取(1) Obtaining the position of the sensor node
传感器节点可根据其搭载平台的特点,选取通过GPS等方式实时获取其自身定位的位置坐标,并通过无线通信方式与其他节点交换位置信息。According to the characteristics of its carrying platform, the sensor node can choose to obtain its own location coordinates in real time through GPS and other methods, and exchange location information with other nodes through wireless communication.
(2)建立传感器节点受力模型(2) Establish the force model of the sensor node
将传感器节点抽象成势力场中的粒子,建立传感器节点受力模型,通过传感器节点间力的作用控制传感器节点移动方向和距离。The sensor nodes are abstracted into particles in the force field, the force model of the sensor nodes is established, and the movement direction and distance of the sensor nodes are controlled by the force between the sensor nodes.
(3)基于改进虚拟力算法的多传感器网络区域覆盖(3) Multi-sensor network area coverage based on improved virtual force algorithm
传感器节点初始状态下随机分布在区域内,感知区域相互重叠,通过节点之间力的作用,控制节点移动方向和距离,算法多次迭代,直到对区域实现无缝覆盖,可对覆盖区域进行数据全面收集。由于原始的虚拟力算法迭代次数多,会增加传感器节点能量的消耗,本发明针对此问题,提出通过在传感器节点的受力模型上添加与距离相关的系数的方式,来提升算法速度,减小能耗。The sensor nodes are randomly distributed in the area in the initial state, and the sensing areas overlap each other. Through the action of the force between the nodes, the movement direction and distance of the nodes are controlled. The algorithm iterates multiple times until the area is seamlessly covered, and the data of the covered area can be collected. Comprehensive collection. Due to the large number of iterations of the original virtual force algorithm, the energy consumption of sensor nodes will be increased. To solve this problem, the present invention proposes to increase the speed of the algorithm and reduce the energy consumption.
(4)定位未知位置目标(4) Position unknown location target
光电成像搜索系统利用灰度及温度的差异来搜索确定搜寻目标。确定目标后,已知自身位置坐标的传感器节点,通过三边定位算法,对未知位置目标进行定位。The photoelectric imaging search system uses the difference in gray scale and temperature to search and determine the search target. After the target is determined, the sensor node, which knows its own position coordinates, uses the trilateration positioning algorithm to locate the unknown position target.
将传感器节点抽象成势力场中的粒子,某个传感器节点对周围传感器节点有力的作用,可以根据距离的大小区别为引力和斥力。当两个传感器节点的距离小于距离阈值时,两者之间存在斥力,两者之间距离增大;当两个传感器节点的距离大于距离阈值时,两者之间则表现为引力,二者之间的距离缩短。据此,建立传感器节点受力模型:The sensor nodes are abstracted into particles in the force field. A certain sensor node has a strong effect on the surrounding sensor nodes, which can be distinguished as attraction and repulsion according to the size of the distance. When the distance between two sensor nodes is less than the distance threshold, there is a repulsive force between them, and the distance between them increases; when the distance between two sensor nodes is greater than the distance threshold, there is an attractive force between them, and The distance between them is shortened. Accordingly, the force model of the sensor node is established:
传感器节点间的受力关系Fij:Force relationship F ij between sensor nodes:
式中,wA表示引力系数;In the formula, w A represents the gravitational coefficient;
wR表示斥力系数;w R represents the repulsion coefficient;
dij表示节点之间的欧式距离;d ij represents the Euclidean distance between nodes;
dth表示距离阀值;d th represents the distance threshold;
aij是传感器节点的方位角;a ij is the azimuth angle of the sensor node;
Rc表示节点的通信半径。R c represents the communication radius of the node.
若Fij为斥力,则If F ij is the repulsive force, then
Fxij=|Fij|(xi-xj)/dij F xij =|F ij |(x i -x j )/d ij
Fyij=|Fij|(yi-yj)/dij F yij =|F ij |(y i -y j )/d ij
若Fij为引力,则If F ij is gravity, then
Fxij=|Fij|(xj-xi)/dij F xij =|F ij |(x j -x i )/d ij
Fyij=|Fij|(yj-yi)/dij F yij =|F ij |(y j -y i )/d ij
两个传感器节点之间的受力关系,Fij为The force relationship between two sensor nodes, F ij is
Fij=Fxij+Fyij F ij =F xij +F yij
水平方向的虚拟力和为:The virtual force sum in the horizontal direction is:
Fx=∑Fxij F x =∑F xij
竖直方向的虚拟力和为:The virtual force sum in the vertical direction is:
Fy=∑Fyij F y =∑F yij
水平方向与竖直方向合力的大小为:The resultant force in horizontal direction and vertical direction is:
式中若Fxij,Fyij为负数,表明水平方向的分力向左,竖直方向的分力向下。In the formula, if F xij and F yij are negative numbers, it indicates that the component force in the horizontal direction is to the left, and the component force in the vertical direction is downward.
根据以上分析可以得到无线传感器节点的受力详细情况,根据传感器节点的受力情况决定传感器节点受到虚拟力时移动的方向和距离:According to the above analysis, the details of the force of the wireless sensor node can be obtained, and the direction and distance of the sensor node moving when it is subjected to a virtual force can be determined according to the force of the sensor node:
式中,(xi yi)为传感器节点初始的位置;In the formula, (xi y i ) is the initial position of the sensor node;
(xi',yi')为移动后的位置;(x i ', y i ') is the moved position;
Fth为虚拟力分力的阈值,当受到的虚拟力的分力小于该值时,在该分力方向上传感器节点不移动;F th is the threshold of the virtual force component. When the component of the virtual force received is less than this value, the sensor node does not move in the direction of the component force;
step为传感器节点移动的最大距离。step is the maximum distance the sensor node moves.
为减少虚拟力算法迭代次数,在传感器节点的受力模型上添加与距离相关的系数。节点在距离较近时,虚拟力足够大,使其快速扩散;节点间距离趋于距离阈值时,虚拟力足够小,使节点易于达到平衡。改进的节点间的虚拟力方程为:In order to reduce the number of iterations of the virtual force algorithm, a distance-related coefficient is added to the force model of the sensor node. When the distance between nodes is close, the virtual force is large enough to make it diffuse rapidly; when the distance between nodes tends to the distance threshold, the virtual force is small enough to make the nodes easy to reach equilibrium. The improved virtual force equation between nodes is:
式中,d(si,sj)是节点i与节点j之间的欧几里德距离;In the formula, d(s i , s j ) is the Euclidean distance between node i and node j;
U1为处于节点i通信范围内的节点的集合。U 1 is the set of nodes within the communication range of node i.
虚拟力算法按照如下过程进行迭代:初始状态为传感器节点随机分布在监测区域,传感器节点受到其他节点力的作用,然后根据受到力的大小和方向决定移动的距离和方向,移动到新位置后再根据新的受力大小和方向进行下一次移动,直到达到满意的覆盖要求。The virtual force algorithm iterates according to the following process: the initial state is that the sensor nodes are randomly distributed in the monitoring area, and the sensor nodes are affected by the force of other nodes, and then the distance and direction of movement are determined according to the magnitude and direction of the force received, and after moving to a new position, the virtual force algorithm is iterated. Carry out the next move according to the new force magnitude and direction until the satisfactory coverage requirement is achieved.
目标位置根据以下方程求解:The target position is solved according to the following equation:
式中,目标位置坐标为(x,y);In the formula, the coordinates of the target position are (x, y);
三个已知节点的坐标分别为(x1,y2)、(x2,y2)、(x3,y3);The coordinates of the three known nodes are (x 1 ,y 2 ), (x 2 ,y 2 ), (x 3 ,y 3 );
目标与三个节点见得距离分别为d1、d2、d3。The visible distances between the target and the three nodes are d 1 , d 2 , and d 3 .
基于多传感器网络的水上搜救定位方法设计步骤如下:The design steps of the maritime search and rescue positioning method based on the multi-sensor network are as follows:
步骤1:将传感器节点随机分布在监测水域;Step 1: Randomly distribute the sensor nodes in the monitoring waters;
步骤2:利用改进的虚拟力算法控制传感器节点无缝覆盖侦察水域;Step 2: Use the improved virtual force algorithm to control the sensor nodes to seamlessly cover the reconnaissance waters;
步骤3:传感器节点通过GPS及无线通信实时获取并交流自身位置坐标,并收集覆盖区域内信息,利用光电成像搜索系统确定找寻目标;Step 3: The sensor nodes obtain and exchange their own position coordinates in real time through GPS and wireless communication, collect information in the coverage area, and use the photoelectric imaging search system to determine the target;
步骤4:利用三边定位算法,传感器节点确定目标与三个已知节点间的距离,计算并反馈目标位置信息。Step 4: Using the trilateral positioning algorithm, the sensor node determines the distance between the target and three known nodes, calculates and feeds back the target position information.
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