CN113453183B - Remote perception monitoring global target space coverage optimization method - Google Patents
Remote perception monitoring global target space coverage optimization method Download PDFInfo
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Abstract
The invention discloses a space coverage optimization method for remotely sensing and monitoring global targets, which can effectively sense the aerial targets in a certain height range. The invention can be realized by the following scheme: firstly, screening out sensors capable of sensing a target space to be sensed to form a sensing covered three-dimensional space directed sensor network, and establishing a set covering model; counting the coverage range of the three-dimensional space discrete points to be sensed, calculating the sensing coverage rate, and iteratively optimizing a fitness function; the method comprises the steps of adjusting the directional sensors to obtain the sensing coverage rate of the whole directional sensor network to a space to be sensed, utilizing a random search algorithm to iteratively optimize the pitch angle and the azimuth angle of each sensor, sensing and covering a specified three-dimensional aerial target on the earth surface until the coverage rate requirement or the iteration frequency requirement is met, utilizing the intelligent optimization algorithm to output optimization parameters to adjust the azimuth angle and the pitch angle of the sensors, and obtaining the global optimal space coverage through optimizing the main sensing direction.
Description
Technical Field
The invention relates to the field of sensor network coverage sensing widely applied to the fields of environment monitoring, traffic monitoring and the like, in particular to an optimization method for the three-dimensional space remote sensing coverage of the earth surface by a three-dimensional directional sensor.
Technical Field
Nowadays, a 'human-machine object' ternary object widely exists in a network space, wherein a wireless multimedia sensor realizes compression of multimedia content and data transmission with low communication traffic, and generated data becomes an important component in the network space object and provides a theoretical basis and powerful guarantee for the application of the wireless multimedia sensor network in practice. The coverage is an important index for measuring the monitoring capability of the sensor network, which is directly related to the service quality of the network, and the coverage rate of the effective space of the wireless multimedia sensor network reflects the capability of effectively participating in subsequent work such as target tracking and identification in the monitoring space, and is an important index for checking whether the wireless multimedia sensor network can work normally. As a more advanced sensor network form, the method is easy to extend the recognition angle of people to the general physical environment, and the interaction form of the information world and the physical world is deeply influenced. When people need to monitor a certain area, wireless sensor nodes are often used. The sensor nodes are deployed in the detection area in a random or determined mode according to different environments and requirements. These numerous, functionally independent Sensor nodes form a monitoring Network, which is also referred to as a Wireless Sensor Network WSN (Wireless Sensor Network). The WSN is composed of a large number of sensor nodes with limited resources (such as a CPU, battery energy, communication bandwidth and the like), the sensor nodes monitor and sense a target area in real time, various information (such as air humidity, ground temperature, pollutant concentration and the like) of a sensing object is collected, then calculation processing is carried out on collected information data, and the processed data is transmitted to a network sink or an observer terminal in a wireless communication multi-hop mode, so that the physical world, a calculation network and the real world are effectively linked, and a brand-new capability of observing the physical world is provided for people. The WSN technology mainly aims at sensing and monitoring a target area, and the primary work is to perform effective coverage control on the target area in order to sense and monitor various environments or objects in the target area. The coverage control problem of the WSN is that the sensor node completes monitoring of a target area by adjusting the coordinate position of the sensor node, executes a sensing task of a target object, and fundamentally reflects the monitoring sensing degree of the WSN to a specified area. The nodes of the wireless sensor network are a miniaturized embedded system, which is composed of four parts, namely a data collection unit (one or more sensor devices), a data processing unit (a miniature microcontroller), a data transmission unit (a radio transceiver) and a power management unit (usually a battery). The sensor nodes can perform various tasks such as data acquisition, data processing, data transmission and the like, and different sensor nodes also have different functions. The sensor nodes with independent functions cooperate with each other to detect and collect information in a sensing area in real time through physical sensing, and the sensor nodes communicate and exchange information in a wireless mode. In the wireless sensor network, a sensing node transmits data information to a gateway node in a step-by-step jumping mode, the gateway node is responsible for information interaction with an external network, and the data is transmitted to a management node by utilizing the Internet or a satellite network. The gateway node is different from the sensing node, the energy of the gateway node is not limited generally, and energy consumption is not required to be considered, so that the data processing energy, the communication capability and the storage capability of the gateway node are far beyond those of the sensing node, and the gateway node is a connection point between the wireless sensor network WSN and an external network such as a satellite network or the Internet. The user can complete the monitoring of the target and the data acquisition through the management node, so that the physical world, the computing world and the human society are connected into a whole. The WSN converts objective world information into an information world consisting of monitoring data, so that human beings can perceive the world from different angles, and a means for the human beings to know the world is increased. The WSN has very wide practical application, and can be applied to various monitoring systems, such as habitat environment monitoring and indoor climate control monitoring; monitoring things (structure monitoring, equipment state maintenance monitoring); monitoring of things to things and surrounding environment; natural disaster prediction, medical application and the like. And a solution is provided for key technologies such as low-power-consumption signal processing and low-power-consumption calculation of the sensor nodes in the same system. Wireless sensor network architecture in a wireless sensor network WSN, each sensor node is independent. Each node typically has some data processing, storage, and communication capabilities, but the energy is limited. Because the perception area is covered by a large number of sensor nodes, each node collects the data collected in the perception range to the gateway node in a multi-hop mode. Information interaction is carried out between the gateway node and the sensor node in the same way, and wireless communication is generally carried out between the nodes. The gateway node transmits the data to the task management node via the Internet or a satellite network. The sensor node is low in price, convenient to deploy and high in concealment. The wireless sensor network is arranged in the field area or the area which is not suitable for manual monitoring, so that long-term unmanned uninterrupted monitoring can be carried out. The sensor nodes are deployed in the agricultural ecological environment, so that the climate change, the land appropriateness and the like can be monitored in real time, and farmers can prevent natural disasters according to information acquired by the wireless sensor network and improve the crop yield. The wireless sensor nodes are deployed in a three-dimensional space to form a three-dimensional monitoring network, and monitoring information can be collected for various three-dimensional environments. The nodes are mainly used for collecting data, and are the source of the data flow of the whole network. From the network layer point of view, it is both a network terminal and a router, it both sends the sensing data and needs to forward the received data, and the functional sensor node involved in the data management, storage and fusion in the forwarding process is composed of a power supply (general battery), a communication device (radio transceiver), a sensor or sensors, a plurality of analog-to-digital converters (ADCs), a microprocessor and data memory. The sensor is positioned at the most front end and mainly has the functions of monitoring targets and acquiring data, and the obtained data is analog data. The primary function of the ADC is to be responsible for the conversion between analog and digital data. The processor unit is connected with the sensing unit, the storage unit and the communication unit and is mainly responsible for collecting, processing and storing the data of the control node and receiving and transmitting the data through a network. The communication part is responsible for connecting with surrounding network equipment, including receiving and sending data. The energy supply part is responsible for supplying energy for the operation of the nodes, and a miniature battery is usually adopted.
In recent years, the Internet of Things (the Internet of Things) is receiving attention and attention from more and more countries because it can widely interconnect the real objects with each other through a network. Taking a wireless sensor network WSN as an example, the WSN includes functions of data acquisition, data transceiving and data processing, is a highly integrated intelligent network system, and is often considered as a "nerve ending" of the internet of things in the industry. WSNs are composed of a large number of different miniature sensor nodes with sensing, computing, and communication capabilities. The WSN is composed of a plurality of low-price isomorphic or heterogeneous small sensor nodes, the sensor nodes have data calculation and wireless communication capabilities, the sensor nodes are manually deployed in a detection area which is not easy to reach by human beings or is in a severe environment, the nodes can autonomously complete a specified monitoring task, and meanwhile, the sensor nodes are sent to a system display interface of a remote user through a wireless communication function. The image sensor and the video sensor are two representative sensor forms in a wireless sensor network, and are widely applied to environment monitoring and traffic monitoring. The whole process realizes information interaction between the information world and the physical world, and from the theoretical level, the existing related theoretical technology is not enough to be capable of overcoming all scenes of the physical world, such as complex planes, space coverage and other problems, and is not mature enough.
The sensing coverage of the wireless sensor network to the hot spot area and the hot spot target is the premise of comprehensively and effectively monitoring and controlling the hot spot area and the hot spot target. Multiple factors are often considered together to achieve full coverage of the sensor network. The coverage problem is one of the key points in the research of the field of the wireless sensor network, and mainly relates to a deployment environment, a node perception model, a deployment algorithm and the like, wherein the perception model directly reflects the coverage range and the perception capability of the wireless multimedia sensor network node. The specific implementation may involve node-aware models, area coverage methods, data processing and transmission issues. Sensor-aware coverage is generally divided into target coverage, area coverage, and fence coverage: target coverage refers to monitoring limited target points in a monitoring area to ensure that each target point is perceived and covered by at least one sensor node; the area coverage refers to the complete coverage of the monitoring area, namely, any event and any position in the area are monitored, so that all points in the area are perceived and covered by the sensor nodes; fence coverage ensures that sensors deployed in the monitored area can detect targets of intrusion that traverse the area, primarily by appropriately allocating sensor resources. However, the visibility of the intrusion target has uncertainty in time and position, and deploying the sensor network for the target with area coverage maximization is more capable of finding the target quickly to the greatest extent.
In a wireless sensor network, the influence of irregular three-dimensional structures of space targets on deployment needs to be considered in the full coverage of the three-dimensional targets in the three-dimensional space, and how to keep the maximum coverage of the surface area of the targets on the premise of avoiding the collision of nodes. The coverage perception of the three-dimensional space on the surface of the earth needs to consider the curvature factor of the earth. The method comprises the steps of carrying out remote sensing on a three-dimensional space with a certain thickness on the earth surface, considering the sight line sensing of a sensor to the target three-dimensional space, and enabling the sensor not to sense the space below the sight line due to the curvature factor of the earth. The existing three-dimensional directional sensor mainly considers the close range perception of a multimedia sensor to a space to be perceived without considering the earth curvature factor, and the visibility of the sensor to the sight of a certain target point in the three-dimensional space on the earth surface is not only limited by the perception distance and the perception field angle of a sensor perception model, but also limited by the earth curvature. Therefore, the optimal perception coverage strategy cannot be obtained by directly applying the algorithm in the existing literature to deploy the sensor network. And the existing related theoretical technology is not enough to be used for all scenes of the physical world, such as complex planes, space coverage and the like. From the practical application, the coverage overlapping and the coverage blind area generated when the three-dimensional node-oriented directed heterogeneous sensor network nodes with different sensing radii are randomly deployed are difficult to improve by the traditional algorithm because the randomly deployed sensor nodes are in the real three-dimensional space, and the existing multimedia sensor network optimization algorithm has the problem that the existing multimedia sensor network optimization algorithm is easy to fall into the local optimal solution. After the directed heterogeneous sensor nodes are randomly deployed in the three-dimensional space, the positions of the sensor nodes are fixed, and the coverage performance of the network can be improved only by changing the main perception direction of the nodes. Moreover, a large number of three-dimensional coverage overlap areas and three-dimensional boundary nodes are generated after deployment, which results in low coverage of the network. In addition, existing coverage enhancement algorithms are mostly directed to common Wireless Sensor Networks (WSNs), data collected by the WSNs is relatively simple and an omnidirectional perception model, data collected by WMSNs is relatively complex, and due to constraints of camera view angles, WMSNs is mostly a directional perception model, but in a practical environment, WMSNs is mostly in a three-dimensional environment. At present, most of coverage research on multimedia sensor networks is focused on two-dimensional planes, and the coverage optimization algorithm of most of the two-dimensional planes has a poor optimization effect on network coverage performance in a three-dimensional space.
The directional sensor has a focusing sensing range and the capability of increasing the sensing distance, so that the target area can be remotely monitored. The sensing range and the sensing capability of a three-dimensional directional sensor in reality are described by using a proper three-dimensional directional sensing model, for example, the sensing model of a radar and a directional receiving antenna is described as a spherical top cone, the sensing model of a multimedia sensor is described as a spherical top quadrangular pyramid, and then a sensor network is adjusted to perform sensing coverage on a three-dimensional space to be sensed, so that an intrusion target in the space to be sensed can be effectively found and monitored. At present, a wireless sensor network represented by a directed sensor network mainly starts from two aspects, the most important problem is the network coverage problem, the purpose of maximizing coverage of a target area is the network coverage problem, and effective monitoring can be carried out only by realizing maximization; the sensing model matched with the network coverage is also indispensable, a reasonable coverage mechanism of the sensing model of the directed sensor network is also very important, and the sensing model is different from the traditional two-dimensional sensing model and a coverage control algorithm, and currently, the coverage control of the sensor network mainly comprises an omnidirectional sensing model of a traditional sensor node and two directed sensing models facing to a novel multimedia sensor node. The two different perception models are used in different ranges, and each perception model has advantages. The directional sensing model refers to a sensing area of a node with a point of view, which is usually a sector area, and if the node can rotate, the sensing area changes along with the change of time. The omnidirectional sensing model is not limited by the range, and for a two-dimensional plane, the sensing area is a circular area with the node as the center of a circle; the sensor node is a spherical structure for a three-dimensional space, and the self characteristics of the sensor node determine the sensing distance and also determine the sensing distance sensed by different dimensions.
As a hot research field, a directional sensor network faces a lot of difficulties and tests, and the technology is expected to be developed and applied, and many problems need to be solved. Network coverage is the most typical problem, and the conventional method has its own advantages and some disadvantages. The existing research method of the directed sensor network mainly comprises a directed sensor network node perception model and a coverage control method thereof. The node perception model is divided into a two-dimensional perception model and a three-dimensional perception model. The two-dimensional directional perception model is a fan-shaped perception area, the node position is taken as the center of a fan, and the perception radius is taken as the radius of the fan. The method can lead the sensing area to cover different places by adjusting the main sensing direction of the fan-shaped area. The two-dimensional perception model uses a fan-shaped perception model, and the randomly deployed nodes are low in coverage rate and poor in operation efficiency. Different from a two-dimensional directional perception model, the three-dimensional directional perception model is a cone area which is composed of space coordinates of sensor nodes, perception radius, horizontal direction vectors and vertical direction vectors of the sensor visual angle direction vectors. The cone area sensing method can enable the sensing area to be switched to different directions around the vertex of the cone area by adjusting the main sensing direction of the cone area. Can be applied to generally simple indoor three-dimensional scenes. Three-dimensional perceptual models most commonly are those based on three-dimensional vertebral bodies. Decomposing the three-dimensional space problem into a vertical plane and a horizontal plane based on a directional sensor perception model of the three-dimensional vertebral body, searching a geometric relation between the vertical plane and the horizontal plane by utilizing a quadruple, judging the condition that a target point is covered, and calculating the coverage rate. By setting the same characteristic parameters, three-dimensional space can be basically covered. In a real physical environment, a sensor and a target to be monitored are located in a three-dimensional space, and although the three-dimensional directional sensor can focus on a sensing range, the sensing distance is increased. However, the problem of coverage of overlapping areas and blind areas caused by randomly deploying nodes in a three-dimensional directed heterogeneous sensor network is solved, and the accuracy and the coverage rate are not quite satisfactory. In the three-dimensional area U, after the initial random deployment is completed, nodes whose distance to the boundary is smaller than the sensing radius are generally generated, and these nodes are called three-dimensional boundary nodes. Most coverage areas of the three-dimensional boundary nodes are outside the boundary of the research area, so that the coverage rate of the network is low due to the existence of the three-dimensional boundary nodes, and particularly in the directed heterogeneous sensor network, the radius of the three-dimensional boundary nodes may be large, so that great waste of network resources is caused. Therefore, the traditional two-dimensional sensor perception model and the two-dimensional to-be-perceived area model obviously cannot effectively describe a real sensor perception scene.
The traditional coverage method of the directed sensor network and the sensor sensing model are all-directional sensing models, for area coverage, effective sensing can be carried out on a to-be-sensed area only when a sensor of the all-directional sensing model is located in the to-be-sensed area, sensing coverage is carried out on the to-be-sensed area by utilizing the all-directional sensing sensor outside the to-be-sensed area, waste of the sensor on effectiveness of sensing space can be caused, and sensing distance can be reduced.
Disclosure of Invention
The invention aims to provide a remote sensing optimization method for covering a three-dimensional target space to be sensed on the surface of the earth, which can effectively sense an aerial target in a certain height range and greatly improve the coverage rate of a directed sensor network, aiming at the defect that the existing sensing model can not effectively describe the real sensing capability of a wireless multimedia sensor, the optimization problem of the three-dimensional directed sensor on the three-dimensional space remote sensing coverage on the surface of the earth and the defect of the existing three-dimensional coverage sensing optimization method.
The above object of the present invention can be achieved by the following scheme, a method for optimizing spatial coverage of a remote sensing monitoring global target, which is characterized in that: firstly, screening out sensors capable of sensing a space to be sensed, and combining azimuth angles and pitch angles of sensing directions of the sensors as individuals and using the individuals as optimization parameters; sensor resources and a space to be sensed are given, sensing coverage optimization scenes and population quantity are set, and the population is initialized and parameters required by a particle swarm algorithm are set; forming a plurality of oriented three-dimensional sensors with fixed positions into a three-dimensional space oriented sensor network for sensing and covering a three-dimensional space to be sensed with a certain thickness and with an arbitrary shape above the surface of the earth; adjusting the azimuth angle and the pitch angle of the center sensing direction of each sensor in the directed sensor network, and establishing an aggregate coverage model for effectively tracking and identifying a moving target in a monitoring space for the network after the main sensing direction of the sensor nodes is adjusted; according to the capability of the directional sensor and the curvature of the earth, performing coverage analysis of a three-dimensional directional sensor network on each discrete point, performing equidistant discretization processing on a three-dimensional target space to be sensed on the surface of the earth, counting the coverage range of the discrete points of the three-dimensional space to be sensed, calculating the sensing coverage rate, taking the ratio of the number of the discrete points of the air target which can be covered to the total number of the discrete points as the coverage rate and taking the coverage rate as a fitness function, then iteratively optimizing the fitness function, and updating the individual best fitness, the individual best position, the population best position, the individual moving speed and the individual position to calculate the individual fitness; according to the sensing distance constraint, the sensing angle constraint and the earth curvature constraint of the directional sensor, the directional sensor is adjusted to obtain the sensing coverage rate of the whole directional sensor network to the space to be sensed, and an intelligent optimization algorithm is utilized: and performing iterative optimization on the pitch angle and the azimuth angle of each sensor by using a particle swarm algorithm, a genetic algorithm or other heuristic random search algorithms, performing sensing coverage on a specified three-dimensional aerial target on the earth surface until the coverage rate requirement or the iteration frequency requirement is met, outputting optimization parameters by using the intelligent optimization algorithm to adjust the azimuth angle and the pitch angle of the sensors, and optimizing the main sensing direction to obtain the global optimal spatial coverage.
Compared with the prior art, the invention has the following beneficial effects:
according to the method, sensing coverage is carried out on the earth target space according to longitude, latitude and height discretization three-dimensional zone sensing target space on the earth surface, according to the geodetic coordinate of a sensor, the azimuth angle and the pitch angle of a center sensing direction, the geocentric rectangular coordinate of the center sensing direction of the sensor is calculated, the three-dimensional directional sensor takes the earth curvature factor as the potential constraint condition of the visibility analysis of discrete points of a space to be sensed according to the sensor capacity and the earth curvature, the visibility coverage of each aerial target discrete point of the three-dimensional space to be sensed is analyzed, the coverage range of the discrete points of the three-dimensional space to be sensed is counted, and the coverage rate of a directional sensor network to the aerial target of the sensing space is calculated; the optimal orientation of the sensor is obtained through an optimization algorithm, effective sensing coverage is carried out on the three-dimensional space on the surface of the given earth, and then the aerial target in a certain height range can be effectively sensed, the effect that the monitoring target is completely covered by a small number of sensor nodes is achieved, and the limitation that the aerial target visibility analysis is restricted by the curvature of the earth or the sensing distance restriction and the sensing opening angle restriction of a sensor sensing model is overcome. Since the coverage rate calculation and the coverage optimization are not limited by the shape of the space to be perceived. Therefore, the aerial target in a certain height range can be effectively sensed, and the sensing coverage rate of the sensor network can be rapidly calculated. The coverage rate and the network coverage degree of the directed sensor network are improved; iterative optimization is carried out on the pitch angle and the azimuth angle pointed by the three-dimensional directional sensor through a particle swarm optimization algorithm, so that the sensing coverage rate of the sensor network to the space to be sensed tends to be optimal, and the optimization speed and the convergence are good. The three-dimensional space aerial target coverage on the earth surface can be more accurately optimized.
According to the distance sensing range and the angle sensing range of each sensor, whether each target space discrete point of a traversal space to be sensed can be sensed and covered by the sensor network or not is judged, the discrete points are taken according to the longitude, latitude and high-level intervals, the target discrete points of the three-dimensional space to be sensed on the earth surface are subjected to equally-spaced discretization processing according to certain granularity, the sensing coverage rate is calculated, and the ratio of the number of the discrete points of the air target which can be covered to the total number of the discrete points is taken as the coverage rate. The three-dimensional space target to be sensed on the earth surface is subjected to visibility analysis by utilizing a directional sensor network by taking discrete points according to the warp, weft and high-grade intervals, and the sensing coverage rate is calculated without obtaining the accurate sensing coverage rate by calculating the sensing space integral, so that the method is not limited by the specific shape of the space to be sensed. The coverage rate of the directed sensor network is improved. The calculation amount of the particle swarm optimization algorithm is greatly reduced, and the global optimal solution can be rapidly obtained. Simulation results show that compared with a space division algorithm, the method has the advantages of stronger coverage capability and lower energy consumption.
The method is based on coverage perception of a particle swarm algorithm, a directed sensor network is adopted to give directed sensor network resources and a three-dimensional space to be perceived, an optimized scene, the number of aerial target populations and population parameter initialization are set, perception coverage rate is calculated, the coverage rate is used as a fitness function, an intelligent optimization algorithm or a heuristic random search algorithm is used for carrying out iterative optimization on a pitch angle and an azimuth angle of each sensor, perception coverage is carried out on a specified three-dimensional aerial target on the earth surface until the coverage rate requirement or the iteration frequency requirement is met, and the algorithm is used for outputting optimization parameters to adjust the azimuth angle and the pitch angle of the sensor. The intelligent optimization algorithm adjusts the pitch angle of the main perception direction of the node according to the position of the node in the monitoring area and scene information, the perception coverage rate of the space to be perceived is maximized as a target, so that the invasion target in the three-dimensional space can be found to the maximum extent, the air invasion target with great uncertainty in the invasion direction, the invasion route and the visible position can be found as much as possible, and the follow-up tracking and monitoring are guaranteed. The deviation angle of the main sensing direction is adjusted by applying an intelligent optimization algorithm, so that the coverage capability of the wireless sensor node on a monitoring area is enhanced, and the redundant coverage rate of a network to be monitored is greatly reduced.
The node can autonomously complete a designated monitoring task, simultaneously sends the task to a system display interface of a remote user through a wireless communication function, and is close to a practical application environment. The related optimization problem of the three-dimensional directional sensor for the remote sensing coverage of the three-dimensional space on the earth surface can obtain the optimal direction of the sensor through an optimization algorithm, so that the three-dimensional space on the earth surface can be effectively sensed and covered, and further, an aerial target in a certain height range can be effectively sensed.
Drawings
FIG. 1 is a flow chart of the present invention for remotely sensing and monitoring coverage optimization of the earth's surface three-dimensional space;
FIG. 2 is a calculation flow of three-dimensional space perception coverage rate of the earth surface by the three-dimensional directional sensor network.
Detailed Description
Refer to fig. 1 and 2. According to the invention, firstly, sensors capable of sensing a space to be sensed are screened out, and azimuth angles and pitch angles of sensing directions of the sensors are combined to be used as individuals and used as optimization parameters; sensor resources and a space to be sensed are given, sensing coverage optimization scenes and population quantity are set, and the population is initialized and parameters required by a particle swarm algorithm are set; forming a plurality of oriented three-dimensional sensors with fixed positions into a three-dimensional space oriented sensor network for sensing and covering a three-dimensional space to be sensed with a certain thickness and with an arbitrary shape above the surface of the earth; adjusting the azimuth angle and the pitch angle of the sensing direction of each sensor center in the directed sensor network, and establishing an aggregate coverage model for effectively tracking and identifying a moving target in a monitoring space for the network after the main sensing direction of the sensor nodes is adjusted; according to the capability of the directional sensor and the curvature of the earth, performing coverage analysis of a three-dimensional directional sensor network on each discrete point, performing equidistant discretization processing on a three-dimensional target space to be sensed on the surface of the earth, counting the coverage range of the discrete points of the three-dimensional space to be sensed, calculating the sensing coverage rate, taking the ratio of the number of the discrete points of the air target which can be covered to the total number of the discrete points as the coverage rate and taking the coverage rate as a fitness function, then iteratively optimizing the fitness function, and updating the individual best fitness, the individual best position, the population best position, the individual moving speed and the individual position to calculate the individual fitness; according to the perception distance constraint, the perception angle constraint and the earth curvature constraint of the directional sensor, the directional sensor is adjusted to solve the perception coverage rate of the whole directional sensor network to the space to be perceived, and an intelligent optimization algorithm is utilized: and performing iterative optimization on the pitch angle and the azimuth angle of each sensor by using a particle swarm algorithm, a genetic algorithm or other heuristic random search algorithms, performing sensing coverage on a specified three-dimensional aerial target on the earth surface until the coverage rate requirement or the iteration frequency requirement is met, outputting optimization parameters by using the intelligent optimization algorithm to adjust the azimuth angle and the pitch angle of the sensors, and optimizing the main sensing direction to obtain the global optimal spatial coverage.
When a perception coverage optimization scene is set, the maximum perception distance and the maximum perception angle of the directed sensor network are set according to the perception capability of each sensor, the longitude, the latitude and the altitude are quantized with certain granularity, so that the target space to be perceived is discretized, the sensors of the directed sensor network are used as an individual, and the number of all the sensors is set to be N s At the perceived pitch angle θ of each sensor i And an azimuth angle gamma i For optimizing the parameters, the constraint conditions for obtaining the parameters are
The three-dimensional directional sensor network senses and covers three-dimensional air targets to be sensed on the earth surface according to longitude, latitude and height, discretizes three-dimensional target spaces to be sensed, calculates the geocentric rectangular coordinates of the sensing direction of the sensor center according to the geodetic coordinates of the sensor, the azimuth angle and the pitch angle of the center sensing direction, and establishes a set coverage model for the network after the main sensing direction of the sensor node is adjusted.
In a discretized space to be sensed, a directed sensor network is provided with a discrete point set omega covered by a sensor at least once 1 Discrete point set omega of region to be sensed q Judging whether the discrete points in the space to be sensed can be covered, calculating the sensing coverage rate, finding a group of vector groups (B1 (t), B2 (t), \8230;, bi (t), BN (t)) with N node sensing directions to maximize the point set of N node coverage areas, and counting the number of covered discrete points | omega |, wherein the vector groups are distributed in the sensing direction of N nodes, and the vector groups are distributed in the sensing direction of N nodes 1 And total quantity | omega of discrete points of space to be sensed q The ratio of | is the perceived coverage C, C = | Ω 1 |/|Ω q And taking the perception coverage rate of the three-dimensional space to be perceived on the earth surface as a fitness function of coverage optimization.
In the optimization process of the directed sensor network, the directed sensor network gives a sensor center sensing direction and an azimuth angle and a pitch angle which are converted between a geocentric rectangular coordinate and a geodetic coordinate, and adjusts a pitch angle theta and an azimuth angle gamma set of each sensor to obtain an optimization target with a maximized sensing coverage rate C:
the directed sensor network takes all sensors in the sensor network as an individual, sets the number of the individuals in a population, initializes population parameters and optimization parameters of each individual, calculates the perception coverage rate of each individual as fitness according to the pitch angle and the azimuth angle of each three-dimensional directed sensor, updates the individual optimal fitness, the individual historical optimal position and the population historical optimal position, updates the individual moving speed and the individual position optimization parameters, and calculates the fitness of each individual and the new population optimal fitness according to the updated individual historical optimal position optimization parameters and the population historical optimal position optimization parameters; and checking the fitness or the iteration times by the directed sensor network according to the calculated fitness, judging whether the population optimal fitness or the particle swarm algorithm iteration times meet the algorithm stop condition, if so, outputting an optimization parameter corresponding to the optimal fitness, adjusting the azimuth angle and the pitch angle of the sensor network, otherwise, continuously updating the individual optimal fitness, the individual historical optimal optimization parameter and the population historical optimal parameter, and continuing the iteration. After iteration is finished, the coverage condition is obviously improved, the coverage rate is finally improved to 22%, meanwhile, the time is short, and the efficiency of the algorithm is proved to be very high.
In an optional embodiment, the set coverage model screens out sensors capable of sensing a space to be sensed firstly, and takes a pitch angle theta and an azimuth angle gamma of sensing directions of the sensors as optimization parameters; a combination of a pitch angle theta and an azimuth angle gamma of a sensor network is used as an individual, and parameters required by a population and a particle swarm algorithm are initialized; calculating the perception coverage rate of a sensor network to-be-perceived space, taking the space coverage rate as the fitness, and updating the individual historical optimal fitness, the individual historical optimal azimuth angle and pitch angle, and the population historical optimal pitch angle theta and azimuth angle gamma; calculating the updating speed of each individual optimization variable, and updating the pitch angle theta and the azimuth angle gamma of each sensor in each individual; and checking whether the current iteration times exceed the maximum iteration times or reach the coverage rate requirement, if so, finishing the algorithm, taking the historical optimal pitch angle theta and the historical optimal azimuth angle gamma of the population as optimal solutions, adjusting the pitch angle theta and the azimuth angle gamma of the sensor by using the optimal solutions, otherwise, calculating the individual fitness, updating the historical optimal fitness of the individual and the population, continuing the iteration, and outputting the optimized parameters to adjust the azimuth angle and the pitch angle of the sensor after the iteration is finished.
In one embodiment of the invention, the particle swarm optimization algorithm is adopted to search the optimal sensor sensing direction, but the sensor sensing direction searching method is not limited to the particle swarm optimization algorithm. The sensor perception model in the sensor network is three-dimensional directional, and can be an active sensor (such as a radar) or a passive sensor (such as a directional receiving antenna); the shape of the three-dimensional space on the earth surface is arbitrary, and discretization processing is required for the longitude, the latitude, the altitude and other intervals in the three-dimensional space.
The earth is a flat sphere with slightly long diameter of equator and slightly short distance between two poles. The network coverage rate of the three-dimensional space is relatively complex to calculate, and the realization difficulty is high. The set coverage model of the embodiment simplifies the calculation of the network coverage rate into: discrete points are selected at equal intervals in three latitudes of latitude B, longitude L and altitude H, and the network coverage rate is approximately calculated by utilizing the proportion of the number of covered discrete points to the total number of discrete points. When the coverage rate is calculated, conversion between earth coordinates and earth center rectangular coordinates is needed, the earth center rectangular coordinate system points to a north earth polar from the earth center by a Z axis, the X axis points to the intersection point of the initial meridian plane and the equator from the earth center, and the Y axis is orthogonal to the X axis and the Z axis to form a right-hand coordinate system. In a rectangular coordinate system of the earth's center, the earth's longer half axis a (equatorial radius) and shorter half axis b (polar radius) are expressed as the earth's groundSpherical surface equation (x) 2 +y 2 )/a 2 +z 2 /b 2 =1, three-dimensional coordinates under the geodetic coordinate system: obtaining a sensing direction vector group consisting of sensing directions of N nodes under the latitude B, the longitude L and the altitude H to obtain a conversion function (X, Y, Z) = blh2xyz (B, L, H) from geodetic coordinates (B, L, H) to geocentric rectangular coordinates (X, Y, Z), wherein the geocentric rectangular coordinates (X, Y, Z) are respectively realized by the following formulas: x = (N + H) cos B cos L, Y = (N + H) cos B sin L,
Z＝[N(1-e 2 )+H]sin B, where N = α/(1-e) 2 sin 2 b) I/2 ，e 2 ＝(α 2 -b 2 )/α 2 Is the first eccentricity.
The expression of geodetic coordinates (B, L, H) with respect to geocentric rectangular coordinates (X, Y, Z) is:
L＝arctan(Y/X)
the longitude L can be solved exactly, and the latitude B and the altitude H are coupled to each other and cannot be solved directly. The exact conversion of the geocentric rectangular coordinates into geodetic coordinates can be done iteratively by the conversion function (B, L, H) = xyz2blh (X, Y, Z), as follows:
1) And (3) adopting a direct method to approximately solve the following steps: latitude coordinate vector
The set coverage model is converted into a function through a corresponding objective function
Approximately calculating the geocentric rectangular coordinate; considering that the sensor and the space to be sensed are both at east longitude 90-180 DEGWithin the range, the X-axis component error of the geocentric rectangular coordinate is calculated to be
Given a sufficiently small positive real number epsilon, update
To be updated
And (5) carrying out the step 2, stopping iteration when the geocentric rectangular coordinate error delta X meets the requirement, and outputting an accurate geocentric rectangular coordinate vector
Wherein the content of the first and second substances,
is an altitude vector.
The direction vector of the center sensing direction under the geocentric rectangular coordinate system is obtained by knowing the geodetic coordinate of the three-dimensional directional sensor, the azimuth angle gamma and the pitch angle theta of the center sensing direction. And setting the geodetic coordinates (B, L and H) of the sensor, the azimuth angle gamma and the pitch angle theta of the central sensing direction. The centroid rectangular coordinate of the sensor is P s = blh2xyz (B, L, H), sensor true north direction vector V under geocentric rectangular coordinate system N ＝blh2xyz(B+0.0001,L,H)-P s The east-ward direction vector V E ＝blh2xyz(B,L+0.0001,H)-P s Azimuth vector V of horizontal line of sight Az ＝V N /|V N |cos(γ)+V E /|V E |sin(γ)。
Set coverage model establishes a point C in the sensor center perception direction s The earth's center rectangular coordinates (x, y, z), the central perception direction vector V c ＝C s -P s The included angle between the center sensing direction and the earth tangent plane of the sensor is a pitch angle theta, and the earth center rectangular coordinate P of the sensor s ＝(x s ,y s ,z s ) Normal vector of the earth tangent plane
Equation b of the earth tangent plane passing through the sensor node 2 x s (x-x s )+b 2 y s (y-y s )+a 2 z s (z-z s ) =0, inner product equation C of central perception direction vector and eye-level direction vector s ·V Az ＝cos(θ)|C s ||V Az Length equation of central perceptual direction vector | C s | 2 And (= C), taking the earth tangent plane passing through the sensor as a datum plane, taking a direction vector of a point on the same side of the geocentric as a depression angle vector, taking a direction vector of a point excessively measured by the geocentric anomaly as an elevation angle vector, and simultaneously solving an equation set by three equations to obtain two groups of real number solutions, wherein C is a normal number, and the elevation angle theta is a point in the central sensing direction respectively.
The set coverage model discretization is carried out on a three-dimensional space to be sensed, whether discrete points can be sensed and covered by the sensor or not is judged according to sensing distance, sensing angle and earth curvature constraint conditions, sensing coverage of the sensor network on the space to be sensed is obtained, the space to be sensed is discretized at equal intervals in three dimensions of latitude B, longitude L and altitude H with certain granularity, and the geocentric rectangular coordinate of the discrete points is P g = blh2xyz (B, L, H), checking whether the line of sight is occluded by the earth: because the length of the major semi-axis and the minor semi-axis of the ellipsoid of the earth is similar, the earth is approximately assumed to be a regular sphere, and then the earth center is over the center of the earth and P is over the center of the earth s P g Straight line perpendicular to P s P g Is a straight line P s P g Setting the coordinate (x, y, z) of the intersection point at the point closest to the ground, and then crossing the origin point, the normal is a vector
Has the plane equation of
Passing point P s And P g Has the linear equation of
Solving the system of linear equations can result in an intersectionPoint coordinates; judging the line of sight P by the following logic s P g Whether or not the earth is sheltered:
(1) If (x-P) s (1))(x-P g (1) Greater than 0), the intersection point is on line segment P s P g Besides, the sight is not shielded by the earth;
(2) If the intersection point is on the line segment P s P g And calculating the altitude (~, H) = xyz2blh (x, y, z) of the intersection point, wherein if H is larger than 0, the intersection point is not shielded, and otherwise, the intersection point is shielded. Let the maximum sensing distance of the sensor be R and the sensing distance constraint condition be | P s P g < R. The sensor perception model is a spherical top cone model, the perception half angle is alpha, and the perception angle constraint condition is
If the line segment from the sensor to the discrete point is not occluded by the earth and satisfies the perceptual distance constraint and the perceptual angle constraint, then the discrete point is visible to the sensor. Each sensor in the sensor network performs visibility analysis on discrete points in a space to be sensed to obtain the coverage condition of the sensor network on each discrete point. And counting the number of discrete points covered by the sensor network to obtain the sensing coverage rate.
The set coverage model is N according to the number of individuals of the population s Iteratively optimizing the azimuth angle gamma and the pitch angle theta of each sensor by utilizing a particle swarm algorithm, optimizing the perception coverage rate of the sensor network to the space to be perceived, and obtaining the optimized parameter of each individual as
Randomly initializing the optimized parameters of each individual according to the boundary conditions of the optimized parameters, and initializing the optimal optimized parameters of the individual
And the population optimal optimization parameter gamma gb =0, obtaining the variation speed of the random initialization optimization parameter
The set coverage model sets hyper-parameters required by the particle swarm algorithm: inertial weight w, self-learning factor c 1 Group learning factor c 2 Maximum number of iterations N g Iterative optimization calculation of each individual fitness (namely the perception coverage rate of the sensor network to the space to be perceived) and comparison with the individual historical best fitness, updating the individual historical best fitness and the historical best azimuth angle of each individual
And a pitch angle
Best azimuth gamma of population history gb And a pitch angle theta gb (ii) a Updating the azimuth angle γ and the pitch angle θ in each individual: gamma ray i ＝γ i +v γi ，θ i ＝θ i +v θi If the updated value of the pitch angle gamma or the azimuth angle theta exceeds the boundary, the latest boundary value is taken as an updated value, and the updating speed of each optimized variable in each individual is calculated as follows:
wherein r belongs to a random number of (0, 1), a speed updating value exceeds a boundary, whether the current iteration number exceeds the maximum iteration number is checked, whether the fitness value of the generation is superior to the optimal fitness of the current population is judged, and if yes, the fitness of the generation is replaced by the optimal fitness of the current population; otherwise, keeping the current optimal fitness, if so, updating the step size factor, judging whether the iteration termination condition is met, and if so, ending the process; otherwise, iterative optimization is carried out, each iterative process carries out iterative optimization towards an optimization target, a more optimal solution is continuously iterated, the iterative optimization is completed, the current global optimal pitch angle and the global optimal azimuth angle of the objective function are calculated to be the optimal solution of the algorithm, otherwise, the generated individuals are merged into the population, the iteration is returned, the fitness of the generation is calculated until the global optimal value of the objective function is reached, and the coverage effect is optimal. And outputting parameters (the pitch angle and the direction angle of each sensor in the sensor network) corresponding to the optimal fitness, adjusting the direction of the corresponding sensor according to the parameters, and finishing the algorithm.
While there has been described and illustrated what are considered to be example embodiments of the present invention, those skilled in the art will recognize that many variations are possible in light of the above description, and thus the present embodiment is intended to be illustrative only of one or more specific embodiments. It will be apparent to those skilled in the art that various modifications can be made without departing from the spirit of the invention. In addition, many modifications may be made to adapt a particular situation to the teachings of the present invention without departing from the central concept described herein. Therefore, it is intended that the invention not be limited to the particular embodiments disclosed, but that the invention will include all embodiments and equivalents falling within the scope of the invention.
Claims (4)
1. A remote perception monitoring global target space coverage optimization method is characterized by comprising the following steps: firstly, screening out sensors capable of sensing a target space to be sensed, and combining azimuth angles and pitch angles of sensing directions of the sensors as individuals and using the individuals as optimization parameters; giving sensor resources and a target space, setting a sensing coverage optimization scene, a population quantity, initializing a population and parameters required by a particle swarm algorithm; forming a three-dimensional space directional sensor network for sensing and covering a three-dimensional space to be sensed by a plurality of directional three-dimensional sensors with fixed positions; adjusting the azimuth angle and the pitch angle of the sensing direction of each sensor center in the directed sensor network, and establishing an aggregate coverage model for effectively tracking and identifying a moving target in a monitoring space for the network after the sensing direction of the sensor node center is adjusted; according to the capability of the directional sensor and the curvature of the earth, the three-dimensional directional sensor network performs coverage analysis on each discrete point, performs equidistant discretization processing on a three-dimensional target space to be sensed on the surface of the earth, counts the coverage range of the discrete points of the three-dimensional space to be sensed, calculates the sensing coverage rate, takes the ratio of the number of the discrete points of the target space to be covered to the total number of the discrete points as the coverage rate and as a fitness function, then iteratively optimizes the fitness function, updates the optimal fitness of the individual, the optimal position of the population, the moving speed of the individual and the position of the individual, and calculates the individual fitness; adjusting the directional sensors to obtain the sensing coverage rate of the whole directional sensor network to a space to be sensed according to the sensing distance constraint, the sensing angle constraint and the earth curvature constraint of the directional sensors, iteratively optimizing the pitch angle and the azimuth angle of each sensor by using an intelligent optimization algorithm, sensing and covering a specified three-dimensional aerial target on the earth surface until the coverage rate requirement or the iteration frequency requirement is met, outputting optimization parameters by using the intelligent optimization algorithm to adjust the azimuth angle and the pitch angle of the sensors, and obtaining the global optimal space coverage by optimizing the main sensing direction;
when a perception coverage optimization scene is set, a directed sensor network sets the maximum perception distance and the maximum perception angle of each sensor according to the perception capability of each sensor, quantizes longitude, latitude and altitude according to certain granularity, discretizes a target space to be perceived, performs perception coverage on the three-dimensional target space to be perceived on the earth surface according to the longitude, the latitude and the altitude, calculates the center perception direction and the geocentric rectangular coordinate of the sensor according to the geodetic coordinate of the sensor, the azimuth angle and the pitch angle of the center perception direction, establishes an aggregate coverage model for the network after the main perception direction of the sensor node is adjusted, takes the sensors of the directed sensor network as an individual, and sets the number of all the sensors as N s With sensed pitch and azimuth angles γ of each sensor i For optimizing the parameters, the constraint condition of the obtained parameters is
In a discretized space to be sensed, a directed sensor network is provided with a discrete point set omega covered by a sensor at least once 1 Set omega of spatially discrete points to be perceived q Judging whether discrete points in the space to be perceived can be covered, calculating perception coverage rate, finding a group of vector groups (B1 (t), B2 (t), \8230;, bi (t), BN (t)) with N node perception directions, maximizing the point set of the N node coverage space, and covering the number | omega of the discrete points 1 | and total | omega of discrete points of space to be sensed q The ratio of | is the perceived coverage C, C = | Ω 1 |/|Ω q Taking the perception coverage rate of the three-dimensional space to be perceived on the earth surface as a fitness function of coverage optimization;
the set coverage model simplifies the calculation of the network coverage rate into: selecting discrete points at equal intervals in three dimensions of latitude B, longitude L and altitude H, converting the discrete points into geocentric rectangular coordinates, pointing the geocentric rectangular coordinates to the north polar earth by the axis Z from the geocentric, pointing the axis X from the geocentric to the intersection point of the initial meridian plane and the equator, and forming a right-hand coordinate system by the axis Y orthogonal to the axis X and the axis Z, and obtaining an equation (X) expressed as the earth spherical surface according to the longer half axis a and the shorter half axis B of the earth under the geocentric rectangular coordinates 2 +y 2 )/a 2 +z 2 /b 2 =1, three-dimensional coordinates under the geodetic coordinate system: at latitude B, longitude L and altitude H, the sensing directions of N nodes form a sensing direction vector group, and the sensing direction vector group is expressed by N = a/(1-e) 2 sin 2 b) 1/2 ，e 2 ＝(a 2 -b 2 )/a 2 For the first eccentricity, a conversion function (X, Y, Z) = blh2xyz (B, L, H) from the geodetic coordinates (B, L, H) to the geocentric rectangular coordinates (X, Y, Z) is obtained, and the accurate conversion from the geocentric rectangular coordinates to the geodetic coordinates is iteratively completed through the conversion function (B, L, H) = xyz2blh (X, Y, Z), and the geocentric rectangular coordinates (X, Y, Z) are respectively realized by the following formulas: x = (N + H) cosB cosL, Y = (N + H) cosB sinL, Z = [ N (1-e) 2 )+H]sinB；
The set coverage model is converted into a function through a corresponding objective function
Approximately calculating the geocentric rectangular coordinate, and calculating the X-axis component error of the geocentric rectangular coordinate as
Given a sufficiently small positive real number epsilon, update
To be updated
If the earth center rectangular coordinate error delta X meets the requirement, the iteration is stopped, and the accurate earth center rectangular coordinate vector is output
Wherein the content of the first and second substances,
is a latitude coordinate vector, L is longitude, N is the number of nodes,
is an altitude vector;
the set coverage model sets hyper-parameters required by the particle swarm algorithm: inertial weight w, self-learning factor c 1 Group learning factor c 2 Maximum number of iterations N g Iterative optimization calculation of each individual fitness, namely the perception coverage rate of the sensor network to the space to be perceived, is compared with the individual historical best fitness to update the individual historical best fitness and the historical best azimuth angle of each individual
And a pitch angle
Group history optimum azimuth angle gamma gb And a pitch angle theta gb (ii) a Updating the azimuth angle γ and the pitch angle θ in each individual: gamma ray i ＝γ i +v γi ，θ i ＝θ i +v θi If the updated value of the pitch angle gamma or the azimuth angle theta exceeds the boundary, the latest boundary value is taken as an updated value, and the updating speed of each optimized variable in each individual is calculated as follows:
wherein r belongs to the random number of (0, 1), the speed updating value exceeds the boundary, whether the current iteration number exceeds the maximum iteration number is checked, whether the fitness value of the generation is superior to the optimal fitness of the current population is judged, if yes, the fitness of the generation is replaced by the current optimal fitness, otherwise, the current optimal fitness is kept, whether the iteration termination condition is met is judged, and if yes, the process is ended; otherwise, iterative optimization is carried out, each iterative process carries out iterative optimization towards the optimization target, a more optimal solution is continuously iterated until the iterative optimization is completed, and the current global optimal pitch angle and the global optimal azimuth angle of the objective function are calculated to be the optimal solution of the algorithm, so that the coverage effect is optimal.
2. The remote sensing monitoring global target space coverage optimization method according to claim 1, characterized in that: in the optimization process of the directed sensor network, the directed sensor network gives a sensor center sensing direction and an azimuth angle and a pitch angle which are converted between a geocentric rectangular coordinate and a geodetic coordinate, and adjusts a pitch angle theta and an azimuth angle gamma set of each sensor to obtain an optimization target with a maximized sensing coverage rate C:
3. the remote sensing monitoring global target space coverage optimization method according to claim 1, characterized in that: according to the sensors which can sense the space to be sensed and are screened out by the coverage model, the pitch angle theta and the azimuth angle gamma of the sensing directions of the sensors are used as optimization parameters; a combination of a pitch angle theta and an azimuth angle gamma of a sensor network is used as an individual, and parameters required by a population and a particle swarm algorithm are initialized; calculating the perception coverage rate of a sensor network to-be-perceived space, taking the space coverage rate as the fitness, and updating the individual historical optimal fitness, the individual historical optimal azimuth angle and pitch angle, and the population historical optimal pitch angle theta and azimuth angle gamma; calculating the updating speed of each individual optimization variable, and updating the pitch angle theta and the azimuth angle gamma of each sensor in each individual; and checking whether the current iteration times exceed the maximum iteration times or reach the coverage rate requirement, if so, taking the historical optimal pitch angle theta and the azimuth angle gamma of the population as optimal solutions, adjusting the pitch angle theta and the azimuth angle gamma of the sensor by using the optimal solutions, otherwise, calculating the individual fitness, updating the historical optimal fitness of the individual and the population, continuing the iteration, and outputting the optimized parameters to adjust the azimuth angle and the pitch angle of the sensor after the iteration is finished.
4. The remote sensing monitoring global target space coverage optimization method according to claim 1, characterized in that: the sensor perception model in the sensor network is three-dimensional directional, and can be an active sensor or a passive sensor; the shape of the three-dimensional space on the earth surface is arbitrary, and discretization processing is required for the longitude, latitude, altitude and other intervals in the three-dimensional space.
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