CN108521626A - A multi-sensor network-based positioning method for maritime search and rescue - Google Patents

Abstract

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

A multi-sensor network-based positioning method for maritime search and rescue

technical field

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.

Background technique

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.

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.

Contents of the invention

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.

The purpose of the present invention is achieved like this:

A water search and rescue positioning method based on a multi-sensor network, characterized in that it comprises the following steps:

Step 1: Obtaining the position of the sensor node;

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;

Step 2 establishes the force model of the sensor node;

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;

Force relationship F _ij between sensor nodes:

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;

If F _ij is the repulsive force, then

F _xij ＝|F _ij |(x _i -x _j )/d _ij

F _yij ＝|F _ij |(y _i -y _j )/d _ij

If F _ij is gravity, then

F _xij ＝|F _ij |(x _j -x _i )/d _ij

F _yij ＝|F _ij |(y _j -y _i )/d _ij

The force relationship between two sensor nodes, F _ij is:

F _ij =F _xij +F _yij

The virtual force sum in the horizontal direction is:

F _x ＝∑F _xij

The virtual force sum in the vertical direction is:

F _y =∑F _yij

The resultant force in horizontal direction and vertical direction is:

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;

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:

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;

Step 3: Multi-sensor network area coverage based on improved virtual force algorithm;

Improved virtual force algorithm:

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:

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;

Step 4 locates the unknown location target;

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;

The target position is solved according to the following equation:

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.

Compared with prior art, the beneficial effect of the present invention is:

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. 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. 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. 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.

Description of drawings

Fig. 1 is the stress model of the sensor node of the present invention;

Fig. 2 is the simulation result of the sensor node moving process based on the improved virtual force algorithm in the present invention;

Fig. 3 is a schematic diagram of the trilateral positioning algorithm of the present invention.

Detailed ways

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:

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;

Specific embodiment one

The purpose of the present invention is achieved by the following steps:

1. Establish the force model of the sensor node

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.

Force relationship F _ij between sensor nodes:

In the formula, w _A represents the gravitational coefficient;

w _R represents the repulsion coefficient;

d _ij represents the Euclidean distance between nodes;

d _th represents the distance threshold;

a _ij is the azimuth angle of the sensor node;

R _c represents the communication radius of the node.

If F _ij is the repulsive force, then

F _xij ＝|F _ij |(x _i -x _j )/d _ij

F _yij ＝|F _ij |(y _i -y _j )/d _ij

If F _ij is gravity, then

F _xij ＝|F _ij |(x _j -x _i )/d _ij

F _yij ＝|F _ij |(y _j -y _i )/d _ij

The force relationship between two sensor nodes, Fi _j is

F _ij =F _xij +F _yij

The virtual force sum in the horizontal direction is:

F _x ＝∑F _xij

The virtual force sum in the vertical direction is:

F _y =∑F _yij

The resultant force in horizontal direction and vertical direction is:

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.

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:

In the formula, (xi _{y i} ) is the initial position of the sensor node;

(x _i ', y _i ') is the moved position;

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 is the maximum distance the sensor node moves.

2. Improve the virtual force algorithm and use it to control the sensor nodes to seamlessly cover the surveyed waters

(1) Improved virtual force algorithm

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:

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.

(2) Improve the running process of the virtual force algorithm

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:

Step 1: Randomly distribute sensor nodes in the monitoring area;

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;

Step 3: Calculate coverage;

Step 4: When the coverage rate meets the requirements, stop the algorithm iteration and output the result.

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. The sensor collects water area information and determines the target position based on the trilateral positioning algorithm

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.

The principle of trilateration positioning algorithm is shown in Figure 3, and the target position is solved according to the following equation:

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.

4. Water search and rescue positioning method based on multi-sensor network

The design steps of the maritime search and rescue positioning method based on the multi-sensor network are as follows:

Step 1: Randomly distribute the sensor nodes in the monitoring waters;

Step 2: Use the improved virtual force algorithm to control the sensor nodes to seamlessly cover the reconnaissance waters;

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;

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.

Specific embodiment two

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) Obtaining the position of the sensor node

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) Establish the force model of the sensor node

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) Multi-sensor network area coverage based on improved virtual force algorithm

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) Position unknown location target

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.

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:

Force relationship F _ij between sensor nodes:

In the formula, w _A represents the gravitational coefficient;

w _R represents the repulsion coefficient;

d _ij represents the Euclidean distance between nodes;

d _th represents the distance threshold;

a _ij is the azimuth angle of the sensor node;

R _c represents the communication radius of the node.

If F _ij is the repulsive force, then

F _xij ＝|F _ij |(x _i -x _j )/d _ij

F _yij ＝|F _ij |(y _i -y _j )/d _ij

If F _ij is gravity, then

F _xij ＝|F _ij |(x _j -x _i )/d _ij

F _yij ＝|F _ij |(y _j -y _i )/d _ij

The force relationship between two sensor nodes, F _ij is

F _ij =F _xij +F _yij

The virtual force sum in the horizontal direction is:

F _x ＝∑F _xij

The virtual force sum in the vertical direction is:

F _y =∑F _yij

The resultant force in horizontal direction and vertical direction is:

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.

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:

In the formula, (xi _{y i} ) is the initial position of the sensor node;

(x _i ', y _i ') is the moved position;

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 is the maximum distance the sensor node moves.

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:

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.

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.

The target position is solved according to the following equation:

In the formula, the coordinates of the target position are (x, y);

The coordinates of the three known nodes are (x ₁ ,y ₂ ), (x ₂ ,y ₂ ), (x ₃ ,y ₃ );

The visible distances between the target and the three nodes are d ₁ , d ₂ , and d ₃ .

The design steps of the maritime search and rescue positioning method based on the multi-sensor network are as follows:

Step 1: Randomly distribute the sensor nodes in the monitoring waters;

Step 2: Use the improved virtual force algorithm to control the sensor nodes to seamlessly cover the reconnaissance waters;

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;

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.

Claims (1)

1. a kind of search and rescue localization method waterborne based on more sensing networks, which is characterized in that comprise the steps of：

The acquisition of step 1 sensor node position；

The characteristics of sensor node is according to its carrying platform chooses the position seat for obtaining its own positioning in real time by GPS modes Mark, and mode and other node switching location informations by radio communication；

Step 2 establishes sensor node stress model；

Sensor node is abstracted into the particle in force field, some sensor node work strong to ambient sensors node With being distinguished as gravitation and repulsion according to the size of distance；When the distance of two sensor nodes be less than distance threshold when, the two it Between there are repulsion, distance increases between the two；When the distance of two sensor nodes is more than distance threshold, then table between the two It is now gravitation, Distance Shortened between the two；

Stress relationship F between sensor node_ij：

In formula, w_AIndicate gravitational coefficients；w_RIndicate repulsion coefficient；d_ijIndicate the Euclidean distance between node；d_thIt indicates apart from valve Value；a_ijIt is the azimuth of sensor node；R_cIndicate the communication radius of node；

If F_ijFor repulsion, then

F_xij=| F_ij|(x_i-x_j)/d_ij

F_yij=| F_ij|(y_i-y_j)/d_ij

If F_ijFor gravitation, then

F_xij=| F_ij|(x_j-x_i)/d_ij

F_yij=| F_ij|(y_j-y_i)/d_ij

Stress relationship between two sensor nodes, F_ijFor：

F_ij=F_xij+F_yij

The fictitious force of horizontal direction and it is：

F_x=∑ F_xij

The fictitious force of vertical direction and it is：

F_y=∑ F_yij

The size of horizontal direction and vertical direction resultant force is：

If F in formula_xij, F_yijFor negative, show the component of horizontal direction to the left, the component of vertical direction is downward；

The stress details of wireless sensor node is obtained according to the above analysis, is determined according to the stressing conditions of sensor node The direction moved when sensor node is by fictitious force and distance：

In formula, (x_i y_i) it is the initial position of sensor node；(x_i',y_i') be it is mobile after position；F_thFor fictitious force component Threshold value, when the component for the fictitious force being subject to be less than the value when, sensor node does not move on the component direction；Step is to pass The maximum distance of sensor node movement；

Step 3 is based on the multiple-sensor network region overlay for improving fictitious force algorithm；

Improve fictitious force algorithm：

To reduce fictitious force algorithm iteration number, added on the stress model of sensor node and apart from relevant coefficient；Section For point when distance is closer, fictitious force is sufficiently large, it is made quickly to spread；When euclidean distance between node pair tends to distance threshold, fictitious force is enough It is small, so that node is easily reached balance；Fictitious force equation between improved node is：

In formula, d (s_i,s_j) it is Euclidean distance between node i and node j；U₁For the section in node i communication range The set of point；

Step 4 positions unknown position target；

Photoelectronic imaging search system searches for determining searching target using the difference of gray scale and temperature；After determining target, it is known that from The sensor node of body position coordinates positions unknown position target by three side location algorithms；

Target location is solved according to following equation：

In formula, (x, y) is target location coordinate；(x₁,y₂)、(x₂,y₂)、(x₃,y₃) be respectively three known nodes coordinate；d₁、 d₂、d₃Respectively target between three nodes at a distance from.