CN110674917B - Long-time differential deployment method of mobile radar monitoring platform under maneuvering constraint - Google Patents

Long-time differential deployment method of mobile radar monitoring platform under maneuvering constraint Download PDF

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CN110674917B
CN110674917B CN201910972533.9A CN201910972533A CN110674917B CN 110674917 B CN110674917 B CN 110674917B CN 201910972533 A CN201910972533 A CN 201910972533A CN 110674917 B CN110674917 B CN 110674917B
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杨晓波
汤窈颖
王尧
杨琪
刘克柱
张鹏辉
易伟
孔令讲
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Abstract

The invention discloses a long-time differential deployment method of a mobile radar monitoring platform under mechanical constraint, which belongs to the technical field of radar signal processing and aims at solving the problem that the monitoring of different task importance areas with different monitoring strength cannot be realized because different task importance of the monitoring areas is not considered in the process of executing a monitoring task by the traditional mobile radar monitoring platform; the method comprises the steps of describing importance and effectiveness of information contained in a monitored area in different timeliness stages by using a trapezoidal curve model, updating an acquired information matrix based on detection probability of a mobile radar monitoring platform to each position of the monitored area at each moment, establishing an optimization problem by taking maximized acquired information rate as an optimization target, and finally solving the optimization problem with constraint through a constraint particle swarm optimization algorithm based on virtual force; therefore, long-time differential deployment of the mobile radar monitoring platform is realized.

Description

Long-time differential deployment method for mobile radar monitoring platform under maneuvering constraint
Technical Field
The invention belongs to the technical field of radar signal processing, and particularly relates to a deployment technology of a mobile radar monitoring platform.
Background
The mobile platform has the characteristics of strong self-organization, high maneuverability, strong flexibility and the like, and is widely applied to environmental monitoring, real-time monitoring of dangerous goods, target tracking and the like in disaster areas. Taking the monitoring task as an example, the mobile platform moves to and from each position in the monitoring area to monitor, so that the reasonable planning of the path of the mobile platform has important significance for improving the monitoring performance. However, because of the mobility and flexibility of the mobile platform, it is difficult to manually plan the moving path. In summary, mobile platforms have a wide range of development in both the civilian and military fields as a new means of surveillance, tracking and rescue.
For the mobile radar monitoring platform, the mobile radar monitoring platform goes to and fro each position in the monitoring area to acquire effective information contained in corresponding positions at different moments, and the best monitoring performance is obtained by reasonably planning the moving path of the platform within the task time. However, as the importance and the effectiveness of information contained in each position in the monitored area are different, the monitoring strength of the mobile radar monitoring platform on the information will be changed. Meanwhile, the deployment of all nodes in the path needs to be considered comprehensively, which results in high computational complexity and even dimension disaster, and in addition, each node needs to meet maneuvering constraints, and for such high-dimensional non-convex problems, such complex constraints are difficult to handle, which all cause the problem of long-time deployment of the mobile radar monitoring platform to be difficult to solve, so that currently, related research on the problem is also less. The document "Multi-period coverage path planning and scheduling for air turbine coverage," IEEE Transactions on aeronautical and Electronic Systems,2018, pp.2257-2273 "studies the long-time deployment of a mobile platform under the monitoring of multiple disjoint areas, but does not consider that the task importance of the sub-monitoring areas is different from the moving path of the platform when the areas are crossed. A station arrangement method for monitoring areas with different importance degrees by MIMO radar is proposed in the document' MIMO radar optimization station arrangement algorithm [ J ] with controllable preference of multiple monitoring areas modern radar, 2017,39(06):23-26 ], but the method is only suitable for the deployment of a static radar monitoring platform. The method cannot be used for long-time deployment of the mobile radar monitoring platform under the monitoring of different task importance areas.
Disclosure of Invention
The invention provides a long-time differential deployment method of a mobile radar monitoring platform under the maneuvering constraint, aiming at solving the problem that different task importance of a monitoring area cannot be monitored by different monitoring strength because different task importance of the monitoring area is not considered in the process of executing a monitoring task by the existing mobile radar monitoring platform.
The technical scheme adopted by the invention is as follows: a long-time differential deployment method of a mobile radar monitoring platform under the maneuvering constraint comprises the steps of S1, describing the importance and the timeliness of information contained in a monitoring area by utilizing a trapezoidal curve model; recording importance values of all Points of Interest (POI) in the current monitoring area by adopting an information matrix;
s2, updating an information collection matrix based on the detection probability of the mobile radar monitoring platform to each position of the monitoring area at the current moment;
s3, updating the information matrix according to the updated acquired information matrix;
s4, repeating the step S1 and the step S3 to update the acquired information matrixes at all the moments, and based on the updated acquired information matrixes, establishing an optimization problem by taking the maximum acquired information rate as an optimization target;
and S5, solving the optimization problem by using a constraint particle swarm optimization algorithm based on the virtual force.
Further, the timeliness of the information in step S1 is divided into the following stages:
from 0 to the disappearance initial time, from the disappearance initial time to the disappearance end time, from the disappearance end time to the new birth start time, and from the new birth start time to the new birth end time.
Further, the expression of a single element in the information matrix is:
Figure BDA0002232566480000021
wherein the content of the first and second substances,
Figure BDA0002232566480000022
the importance value of the j-th interest point located in the i interest areas at the k-th time point is shown,
Figure BDA0002232566480000023
for this purpose the position of the POI, αiFor this purpose, the information in the POI is increased by an importance value, delta, within the delta T during the disappearance and the regenerationi 1、δi 2、
Figure BDA0002232566480000024
The information is lost initial time, lost end time, new generation start time and new generation end time respectively, and epsilon is the remainder of the current time and the corresponding updating period.
Further, step S2 is specifically: if the detection probability of the mobile radar monitoring platform to a certain POI at the current moment is greater than or equal to the set threshold value, the mobile radar monitoring platform is considered to acquire the information of the POI at the current moment, and the importance value of the POI in the current-moment monitoring area determined by the trapezoidal curve model in the step S1 is used for updating the importance value of the information acquired by the mobile radar monitoring platform from the POI in the current-moment acquisition information matrix; otherwise, updating the importance value of the information collected from the POI by the mobile radar monitoring platform in the current time collection information matrix to be 0.
Further, step S3 is specifically: the result of the difference between the updated collected information matrix of step S2 and the information matrix of step S1 is used as the updated information matrix.
Further, in step S4, the acquisition information rate is: the mobile radar monitoring platform acquires the percentage of the sum of the importance values of the information and the sum of the importance values of the information appearing in the monitoring area.
The invention has the beneficial effects that: the method of the invention obtains long-time differential deployment of the mobile radar monitoring platform under the maneuvering constraint by utilizing a constraint particle swarm optimization algorithm based on the virtual force, firstly, the importance and the timeliness of tasks in a monitoring area are described by utilizing a trapezoidal curve model, then, an acquired information matrix is updated based on the detection probability of the mobile radar monitoring platform to each position in the monitoring area at each moment, then, an optimization problem is established by taking the maximum acquired information rate as an optimization target, and finally, the constrained optimization problem is solved by utilizing the constraint particle swarm optimization algorithm based on the virtual force, thereby solving the problem of monitoring different task importance areas by the mobile radar monitoring platform under the maneuvering constraint with different monitoring forces. The method has the advantages of realizing long-time differential deployment of the mobile radar monitoring platform, along with simple solving process and low calculation complexity. The invention can be applied to the fields of multi-region monitoring, military application and the like.
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FIG. 1 is a block flow diagram of a method provided by the present invention.
Fig. 2 is a flowchart of an algorithm of a constrained particle swarm optimization algorithm based on virtual force according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a long-term deployment of a mobile radar surveillance platform during a mission time, as utilized by an embodiment of the present invention.
FIG. 4 is a trapezoidal curve model used in embodiments of the present invention to describe the importance and effectiveness of information contained in a surveillance area.
FIG. 5 is a long-time deployment result graph of a single mobile radar monitoring platform for monitoring tasks of the same shape but different task importance at different times within a task time and a total collected information matrix before the current time, which are adopted by the embodiment of the present invention;
fig. 5(a) is a deployment result diagram at the k-th time point 6, fig. 5(b) is a deployment result diagram at the k-th time point 12, fig. 5(c) is a deployment result diagram at the k-th time point 18, and fig. 5(d) is a deployment result diagram at the k-th time point 24.
Fig. 6 is a long-time deployment result diagram of a single mobile radar monitoring platform for a monitoring task with different shapes and task importance at different times within a task time and a total collected information matrix before the current time, which are adopted by the specific embodiment of the present invention;
fig. 6(a) is a deployment result graph at the time k-6, fig. 6(b) is a deployment result graph at the time k-12, fig. 6(c) is a deployment result graph at the time k-18, and fig. 6(d) is a deployment result graph at the time k-24.
Detailed Description
The invention mainly adopts a simulation experiment method for verification, and all the steps and conclusions are verified to be correct on Matlab 2017. The present invention will now be further explained with reference to the accompanying figures 1-6, which illustrate a more detailed description of the invention with respect to specific embodiments.
For the convenience of describing the contents of the present invention, the following terms are first explained:
the term 1: maneuvering restraint
The maneuvering constraints refer to maneuvering performance constraints of the mobile radar monitoring platform, and comprise speed constraints and deflection angle constraints.
The term 2: importance value
The importance value is a value describing the degree of importance of information.
The term 3: task importance of a region
The task importance of a region refers to the importance of the information contained in the region, and can be represented by an initial importance value.
The term 4: information timeliness
The information timeliness means that the information in the region in the task time periodically disappears and newly grows, and the updating frequency is different along with the difference of the task importance.
The term 5: monitoring performance
The monitoring performance is an important index for measuring the long-time deployment effect of the mobile radar monitoring platform, and the total importance value of the information collected by the mobile radar monitoring platform in the task time can be represented.
The term 6: long-term differentiated deployment
The long-time differential deployment means that the position of the mobile radar monitoring platform at each moment in the task time is reasonably planned according to different task importance of the monitoring area so as to achieve the optimal monitoring performance.
Fig. 1 is a flowchart of a long-time differentiated deployment method of a mobile radar monitoring platform under a maneuvering constraint according to the present invention, including the following steps:
s1, describing the importance and the effectiveness of the information contained in the monitoring area by using a trapezoidal curve model; recording importance values of all interest points in the monitored area at the current moment by using an information matrix;
s2, updating an information collection matrix based on the detection probability of the mobile radar monitoring platform to each position of the monitoring area at the current moment;
s3, updating the information matrix according to the updated acquired information matrix;
s4, repeating the step S1 and the step S3 to update the acquired information matrixes at all the moments, and based on the updated acquired information matrixes, establishing an optimization problem by taking the maximum acquired information rate as an optimization target;
and S5, solving the optimization problem by using a constraint particle swarm optimization algorithm based on the virtual force.
The realization process of the invention is as follows:
step 1: describing the importance and the effectiveness of information contained in the monitored area by utilizing a trapezoidal curve model, and recording the importance values of all interest points in the monitored area at the current moment by adopting an information matrix;
step 1.1: initializing system parameters of the mobile radar monitoring platform,
FIG. 3 is a schematic diagram of long-time deployment of the mobile radar monitoring platform in a task time of [0, T]Selecting equal time interval delta T to discretize the task time into K (K > 0) subintervals which are divided into [ T [0；T1]，…，[Tk；Tk+1]，…，[TK-1；TK](ii) a The station area is
Figure BDA0002232566480000051
Having an area of Dx×DyThe monitoring area is
Figure BDA0002232566480000052
Wherein the content of the first and second substances,
Figure BDA0002232566480000053
is the ith Area of Interest (AOI) with a center position of
Figure BDA0002232566480000054
Area is AiI is the total number of AOIs; the station distribution area and each AOI are all formed by the size of gx×gyThe grid of (2) discretizing it; the central positions of all grids in the monitoring area are used for representing the positions of points of Interest (POI), and the position of the jth POI is represented as
Figure BDA0002232566480000055
And the delta T is properly valued according to the specific task time and the speed of the mobile platform, the integral division by the task time is required, and in a proper range, the smaller the value is, the closer the value is to the actual motion condition, and the value is suggested to be 5-25 minutes.
At the kth moment, the position of the mobile radar monitoring platform is pk＝(xk,yk) Wherein x isk、ykRespectively as its x-axis coordinate, y-axis coordinate, velocity vkIn the range of [ vmin,vmax]。θmaxMaximum deflection angle, θ ', for a mobile radar surveillance platform'0Is the initial heading angle (the angle from the positive x-axis). The path of the mobile radar surveillance platform is denoted P during the whole mission time0:K＝{p0,p1,…,pk,…,pKIn which the initial position p0And (4) fixing.
Step 1.2: describing the importance and the timeliness of information contained in the monitoring area by utilizing a trapezoidal curve model, and calculating each element value in the information matrix at the kth moment, namely the importance value of each POI at the current moment;
the importance and the effectiveness of the information contained in the monitored area, i.e. the updating process, is described by a trapezoidal curve model as shown in fig. 4. n iskAnd the k-th time information matrix is used for recording importance values of all POI at the current time. The importance value of the jth POI in the ith AOI at the kth (k > 0) time
Figure BDA0002232566480000056
Comprises the following steps:
Figure BDA0002232566480000057
wherein alpha isiFor this purpose, the information in the POI is increased by an importance value, delta, within the delta T during the disappearance and the regenerationi 1、δi 2、
Figure BDA0002232566480000061
Respectively as the disappearance initial time, the disappearance end time, the new generation start time and the new generation end time of the information,
Figure BDA0002232566480000062
for this reason, the information update period of the POI is reduced as the task importance of the area increases, and ∈ mod (k, T)i) The remainder of the current time and the corresponding update period.
Step 2: calculating element values in the information matrix acquired at the kth moment, namely importance values of the information acquired by the mobile radar monitoring platform at the current moment;
mkand the information acquisition matrix for the kth moment mobile radar monitoring platform is used for recording the importance value of the information acquired by the current moment mobile radar monitoring platform. At the kth moment, the importance value of the information collected by the mobile radar monitoring platform from the jth POI is represented as:
Figure BDA0002232566480000063
in the formula (I), the compound is shown in the specification,
Figure BDA0002232566480000064
monitoring platform detection probability for j POI for mobile radardtTo set the threshold value when
Figure BDA0002232566480000065
Meanwhile, the mobile radar monitoring platform is considered to acquire the information of the POI at the kth moment.
pdtThe value range is [0,1 ]]The higher the value is, the smaller the information acquisition area of the mobile radar monitoring platform at each moment is, and the value is generally 0.6-0.8.
Figure BDA0002232566480000066
The calculation is as follows:
Figure BDA0002232566480000067
wherein Q represents a MarcumQ function,
Figure BDA0002232566480000068
for the k moment the moving radar monitors the S/N, gamma of the platform to the j POITIs the false alarm probability pfaRelative detection threshold, gammaTAnd pfaSatisfy the relation
Figure BDA0002232566480000069
Figure BDA00022325664800000610
The calculation is as follows:
Figure BDA00022325664800000611
in the formula (I), the compound is shown in the specification,
Figure BDA00022325664800000612
is the Euclidean distance, P, between the kth time radar and the jth POItIs the transmitted power of the radar system,. tau.is the pulse width, GtIs the transmit antenna gain, GrIs the receive antenna gain, σ is the target scattering cross-sectional area, λ ═ c/F is the wavelength, c is the speed of light, F is the pulse repetition frequency, F is the frequency of the pulsetMode propagation factor, F, for the transmitting antenna and the target pathrIs the mode propagation factor of the target and receive antenna paths, k' is the Boltzmann parameter, T0For the noise temperature of the receiving system, FnIs the noise coefficient, CBIs the bandwidth correction factor and L' is the system loss factor.
And 3, step 3: the information matrix is updated and the information matrix is updated,
at the kth moment, after the mobile radar monitoring platform acquires partial region information, an information matrix needs to be updated, and the information matrix is represented as:
nk＝nk-mk (5)
repeating the steps 1.2 to 3 to calculate the information matrix and the collected information matrix at each moment;
and 4, step 4: constructing a long-time differential deployment optimization problem of a mobile monitoring platform,
step 4.1: the objective function value, namely the acquisition information rate is calculated,
during the task time, the information in the AOIs (all the interest areas) is changed periodically along with the time, and the information is acquired by the mobile radar monitoring platform, so that the more the information acquired by the mobile radar monitoring platform is, the better the information is. Thereby, an acquisition information rate C is proposedR(P0:K) Reflecting the monitoring performance, is expressed as:
Figure BDA0002232566480000071
in the formula, N is the total importance value of the information appearing in the AOIs, and M is the total importance value of the information collected by the mobile radar monitoring platform, which are respectively:
Figure BDA0002232566480000072
Figure BDA0002232566480000073
in the formula, J, JiThe number of all POIs in the whole surveillance area and the ith AOI, respectively.
And 4.2: and (4) the expression of an optimization problem,
under the maneuvering constraint, the problem of maximizing the monitoring performance of the mobile radar monitoring platform on different task importance areas within the task time is described as follows:
Figure BDA0002232566480000074
in the formula (d)min＝vmin*ΔT、dmax＝vmaxΔ T is the minimum and maximum travel distance constraints, dk、θkThe movement distance and deflection angle, respectively, at the kth time instant are expressed as:
dk＝||pk-pk-1|| (10)
Figure BDA0002232566480000081
in the formula, | | · | | is an euclidean norm.
And 5: solving the optimization problem by utilizing a constraint particle swarm optimization algorithm based on virtual force,
the total number of iterations is L, the total number of particles is S, and each particle contains 2K elements. Randomly generating the speed and position of each particle before iteration begins, taking each particle as its own individual optimum, and taking the particle with the highest objective function value as the global optimumThe advantages are excellent. In the l iteration, the s-th particle represents a deployment scenario of Xs(l) The individual is preferably Ys(l) Globally optimal is Yg(l) Velocity V of the node at the k-th times,k(l +1) and position Xs,kThe (l +1) update is:
Figure BDA0002232566480000082
Xs,k(l+1)＝Xs,k(l)+Vs,k(l+1) (13)
wherein ω (L) ═ 0.9-0.5 (L/L) is the inertial weight, and Δ v is the weights,k(l) The virtual constraint vector is moved along the vector, and the maneuvering constraint can be met through continuous iteration, and the length and the angle of the virtual constraint vector are respectively expressed as follows:
Figure BDA0002232566480000083
Figure BDA0002232566480000084
in the formula, ci(i ═ 1,2,3,4) is a constant relating to the optimization problem, ri(i-1, 2,3,4,5,6,7) is in the range of [0, 1%]Random variable of ds,k(l) Is the moving distance of the stage at the kth time, thetas,k(l) Is the deflection angle of the stage at the kth time, theta's,k(l) The heading angle of the platform at the kth time is expressed as:
Figure BDA0002232566480000091
the individual optimal update rule is as follows:
Figure BDA0002232566480000092
in the formula, c (X)s(l) Is used forAnd recording the total number of nodes which do not meet the constraint in the s particle in the ith iteration.
In this iteration, the optimal solution is:
Figure BDA0002232566480000093
the global optimal update rule is as follows:
Figure BDA0002232566480000094
repeating the steps (12) - (19) until the iteration is terminated, and finally obtaining the optimized deployment method Yg(L)。
Fig. 5 to 6 are a long-time deployment result and a total collected information matrix before the current time of a single mobile radar monitoring platform for monitoring tasks with the same shape and different task importance, and a long-time deployment result and a total collected information matrix before the current time of monitoring tasks with different shapes and different task importance at different times within a task time. The parameter tables corresponding to fig. 5 and 6 are table 1 and table 2, respectively. To more intuitively illustrate the effect of the long-term deployment in fig. 5 and 6, it is proposed that the average acquisition time interval reflects the monitoring frequency of the mobile surveillance radar platform for each AOI, and the specific data is shown in table 3, where table 3 shows that the monitoring frequency increases with the task importance of the sub-surveillance area. Those skilled in the art should understand that the monitoring frequency of the more important area is higher, which indicates that the frequency of the information collected by the mobile radar monitoring platform in the mission time is higher, and therefore, the deployment method capable of monitoring different mission importance areas with different monitoring frequencies is effective and reasonable.
The asterisks in fig. 5-6 represent the starting position of the platform and the triangles represent the final position of the platform.
TABLE 1 corresponding parameters in FIG. 5
Figure BDA0002232566480000101
Figure BDA0002232566480000111
Table 2 corresponding parameters in fig. 6
Figure BDA0002232566480000112
Figure BDA0002232566480000121
Figure BDA0002232566480000131
TABLE 3 average acquisition time interval for the method of the invention
AOIi AOI1 AOI2 AOI3
ri,1(min) 67 48 50
ri,2(min) 57 44 45
In Table 3, ri,1、ri,2The average acquisition time interval of each AOI in fig. 5 and 6, respectively, the average acquisition time interval of the ith AOI is:
Figure BDA0002232566480000132
in the formula (I), the compound is shown in the specification,
Figure BDA0002232566480000133
the average acquisition time interval for the jth POI located in the ith AOI is expressed as:
Figure BDA0002232566480000134
in the formula (I), the compound is shown in the specification,
Figure BDA0002232566480000135
respectively the epsilon-th acquisition time of the jth POI, the total acquisition times rmaxThe longest average acquisition time interval among all POIs.
According to the invention, long-time differentiated deployment of the mobile radar monitoring platform to different task importance areas under the maneuvering constraint can be well realized.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims (5)

1. A long-time differential deployment method for a mobile radar monitoring platform under the action of maneuvering constraints is characterized by comprising the following steps:
s1, describing the importance and the effectiveness of information contained in the monitoring area by utilizing a trapezoidal curve model; recording importance values of all interest points in the monitored area at the current moment by using an information matrix; step S1 specifically includes the following substeps:
s11, recording the task time as 0, T]Selecting equal time interval delta T to discretize task time into K sub-intervals, K>0, is divided into [ T0；T1]，…，[Tk；Tk+1]，…，[TK-1；TK](ii) a The station area is
Figure FDA0003648058640000011
Having an area of Dx×DyThe monitoring area is
Figure FDA0003648058640000012
Wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003648058640000013
for the ith region of interest AOI,
Figure FDA0003648058640000014
the central position is
Figure FDA0003648058640000015
Area is AiI is the total number of AOIs; the station distribution area and each AOI are all formed by the size of gx×gyThe grid of (2) discretizing it; the central positions of all grids in the monitoring area are used for representing the positions of POI (point of interest), and the position of the jth POI is represented as
Figure FDA0003648058640000016
At the kth moment, the position of the mobile radar monitoring platform is pk＝(xk,yk) Wherein x isk、ykIts x-axis coordinate, y-axis coordinate, and velocity respectivelyvkIn the range of [ vmin,vmax]；θmaxMaximum deflection angle, theta, for moving radar surveillance platforms0' is the initial course angle; the path of the mobile radar surveillance platform during the whole mission time is denoted as P0:K＝{p0,p1,…,pk,…,pKIn which the initial position p0Fixing;
s12, describing the importance and the effectiveness of information contained in the monitored area by using a trapezoidal curve model, and calculating each element value in the kth moment information matrix, namely the importance value of each POI at the current moment; specifically, the method comprises the following steps:
describing the importance and the timeliness of information contained in the monitoring area through a trapezoidal curve model; note nkIs the k time information matrix for recording the importance value of each POI at the current time, the k time, k>0, importance value of j POI in i AOI
Figure FDA0003648058640000017
Comprises the following steps:
Figure FDA0003648058640000018
wherein alpha isiFor this purpose the information in the POI is incremented by the value of importance within deltat during the disappearance and the regeneration,
Figure FDA0003648058640000019
Figure FDA00036480586400000110
respectively as the disappearance initial time, the disappearance end time, the new generation start time and the new generation end time of the information,
Figure FDA00036480586400000111
for this reason, the information update period of the POI decreases as the task importance of the area increases, and ∈ mod (k, T)i) Is the current timeEtching the remainder of the corresponding update period;
s2, updating an information collection matrix based on the detection probability of the mobile radar monitoring platform to each position of the monitoring area at the current moment;
s3, updating the information matrix according to the updated acquired information matrix;
s4, repeating the steps S1 to S3 to update the information matrixes and the acquired information matrixes at all the moments, and constructing a long-time differentiated deployment optimization problem of the mobile monitoring platform by taking the maximum acquired information rate as an optimization target on the basis of the updated information matrixes and the acquired information matrixes;
s5, solving an optimization problem by a constraint particle swarm optimization algorithm based on the virtual force, and finally obtaining the optimized deployment method.
2. The method for long-time differentiated deployment of mobile radar surveillance platforms under mechanical constraints as claimed in claim 1, wherein the timeliness of the information of step S1 is divided into the following stages:
from 0 to the disappearance initial time, from the disappearance initial time to the disappearance end time, from the disappearance end time to the new birth start time, and from the new birth start time to the new birth end time.
3. The long-time differentiated deployment method for the mobile radar monitoring platform under the maneuvering constraint according to claim 2, wherein the step S2 is specifically as follows: if the detection probability of the mobile radar monitoring platform to a certain interest point at the current moment is greater than or equal to the set threshold, the mobile radar monitoring platform is considered to acquire the information of the interest point at the current moment, and the importance value of the information acquired by the mobile radar monitoring platform from the interest point in the current moment acquisition information matrix is updated according to the importance value of the interest point in the current moment monitoring area determined by the trapezoidal curve model in the step S1; otherwise, updating the importance value of the information collected from the interest point by the mobile radar monitoring platform in the current time collection information matrix to be 0.
4. The long-time differentiated deployment method for the mobile radar monitoring platform under the maneuvering constraint according to claim 3, wherein the step S3 is specifically as follows: the result of the difference between the collected information matrix updated in step S2 and the information matrix of step S1 is used as the updated information matrix.
5. The method for long-time differentiated deployment of mobile radar surveillance platforms under mechanical constraints as claimed in claim 4, wherein the information acquisition rate in step S4 is: the mobile radar monitoring platform collects the percentage of the sum of the importance values of the information and the sum of the importance values of the information appearing in the monitoring area.
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