CN117041993A - Multi-unmanned aerial vehicle formation aware resource joint scheduling method, device and system - Google Patents
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Abstract
The embodiment of the invention discloses a multi-unmanned aerial vehicle formation aware resource joint scheduling method, device and system. The method comprises the following steps: s1: deducing the signal joint detection probability of the multiple unmanned aerial vehicles; s2: maximizing the signal joint detection probability of the multiple unmanned aerial vehicles, and constructing an original problem P1 of the multi-unmanned aerial vehicle perception resource joint scheduling; s3: converting the original problem P1 into a secondary problem P2 with optimized multi-unmanned aerial vehicle antenna pointing and a primary problem P3 with optimized multi-unmanned aerial vehicle deployment; s4: solving the slave problem P2 by adopting a block coordinate descent algorithm to obtain the optimal multi-unmanned aerial vehicle antenna pointing direction; s5: and solving the main problem P3 by adopting a block coordinate descent algorithm based on the obtained optimal multi-unmanned aerial vehicle antenna pointing, so as to obtain an optimal multi-unmanned aerial vehicle deployment position. The invention solves the problem of many-to-many detection in a three-dimensional scene, realizes the effective scheduling of the formation sensing resources of the multiple unmanned aerial vehicles, and realizes the optimization of the sensing performance of multiple targets.
Description
Technical Field
The invention relates to the technical field of communication awareness, in particular to a multi-unmanned aerial vehicle formation awareness resource joint scheduling method, device and system.
Background
With the rapid development of wireless sensing technology, a multi-unmanned aerial vehicle formation cooperative sensing method is getting more and more attention. The cooperative operation of the plurality of perception unmanned aerial vehicles improves the target perception efficiency, and is widely applied to the fields of military reconnaissance, target recognition, electronic countermeasure, battlefield evaluation and the like. Therefore, the multi-unmanned aerial vehicle formation sensing requirements with high precision and low delay are based, the requirements of airspace, fuzzy areas, crowd-sourcing characteristics and the like are combined, the inherent evolution rules of the unmanned aerial vehicle optimized deployment position and antenna direction are analyzed, and the multi-level sensing resource joint scheduling method is researched. By establishing a perceived node topological relation and perceived network utility mapping model under a typical scene, an efficient resource scheduling algorithm is designed, and accurate perception and detection of a target are realized.
Through searching the prior art, wang.Weijia published article "Optimal Configuration Analysis of AOA Localization and Optimal Heading Angles Generation Method for UAV Swarms" on the journal IEEE ACCESS in 2019 presents the problem of the space domain optimization of the sensing node in two-dimensional space. However, in practical military scenarios, the problem considered by this article is not applicable. Firstly, as the actual perceived scene is a three-dimensional scene, the perceived problem in three dimensions is considered; secondly, the actual perceived scene needs to carry out joint detection on a plurality of targets, and the detection problem of many-to-many needs to be considered.
Disclosure of Invention
The embodiment of the invention provides a multi-unmanned aerial vehicle formation aware resource joint scheduling method, device and system, which can realize optimization of multiple target awareness performances in a three-dimensional scene.
According to a first aspect of the present invention, there is provided a multi-unmanned aerial vehicle formation aware resource joint scheduling method, the method being applied to cluster unmanned aerial vehicle reconnaissance, comprising the steps of:
s1: deducing the signal joint detection probability of multiple unmanned aerial vehicles based on the set number of unmanned aerial vehicles in formation, the range of the sensing frequency bands of the unmanned aerial vehicles, the deployable areas of the unmanned aerial vehicles, the distance constraint among the unmanned aerial vehicles, the number of targets to be sensed, the set of target frequency points to be sensed, the positions of the targets to be sensed and the environmental parameters;
s2: maximizing the signal joint detection probability of the multiple unmanned aerial vehicles, and constructing an original problem P1 of the multi-unmanned aerial vehicle perception resource joint scheduling;
s3: converting the original problem P1 into a secondary problem P2 with optimized multi-unmanned aerial vehicle antenna pointing and a primary problem P3 with optimized multi-unmanned aerial vehicle deployment;
s4: solving the slave problem P2 by adopting a block coordinate descent algorithm to obtain the optimal multi-unmanned aerial vehicle antenna pointing direction;
s5: and solving the main problem P3 by adopting a block coordinate descent algorithm based on the obtained optimal multi-unmanned aerial vehicle antenna pointing, so as to obtain an optimal multi-unmanned aerial vehicle deployment position.
According to a second aspect of the present invention, there is provided a multi-unmanned aerial vehicle formation aware resource joint scheduling device, the device being applied to cluster unmanned aerial vehicle reconnaissance, comprising:
the detection probability deduction module is used for deducting the signal joint detection probability of multiple unmanned aerial vehicles based on the set number of unmanned aerial vehicles in the formation, the unmanned aerial vehicle sensing frequency range, the unmanned aerial vehicle deployable area, the distance constraint among unmanned aerial vehicles, the number of targets to be sensed, the set of target frequency points to be sensed, the position of the targets to be sensed and the environmental parameters;
the original problem construction module is used for maximizing the signal joint detection probability of the multiple unmanned aerial vehicles and constructing an original problem P1 of the multi-unmanned aerial vehicle perception resource joint scheduling;
the problem conversion module is used for converting the original problem P1 into a secondary problem P2 with optimized multi-unmanned aerial vehicle antenna pointing and a primary problem P3 with optimized multi-unmanned aerial vehicle deployment;
the slave problem solving module is used for solving the slave problem P2 by adopting a block coordinate descent algorithm to obtain the optimal multi-unmanned aerial vehicle antenna orientation;
and the main problem solving module is used for solving the main problem P3 by adopting a block coordinate descent algorithm based on the obtained optimal multi-unmanned aerial vehicle antenna orientation to obtain an optimal multi-unmanned aerial vehicle deployment position.
According to a third aspect of the invention, a multi-unmanned aerial vehicle formation perception resource joint scheduling system is provided, which comprises a fusion center, a plurality of unmanned aerial vehicles and a plurality of targets to be perceived, wherein the targets to be perceived are positioned in detection ranges of the unmanned aerial vehicles, the fusion center is in communication connection with each unmanned aerial vehicle, and signals from different targets to be perceived, which are transmitted by each unmanned aerial vehicle, are received in real time;
the fusion center comprises a memory and a processor, wherein a computer program is stored in the memory, and the computer program is loaded and executed by the processor to realize the multi-unmanned aerial vehicle formation aware resource joint scheduling method.
According to a fourth aspect of the present invention, there is provided a computer readable storage medium storing one or more computer programs which, when executed by a processor, implement the aforementioned multi-drone formation aware resource joint scheduling method.
The beneficial effects of the embodiments of the invention are as follows:
aiming at a plurality of targets to be perceived in a three-dimensional scene, the method, the device and the system for jointly scheduling the perceived resources of the multi-unmanned-aerial-vehicle formation, provided by the embodiment of the invention, firstly deduces the signal joint detection probability of the multi unmanned aerial vehicles based on signal energy detection based on the set number of unmanned aerial vehicles in the formation, the range of unmanned aerial vehicle perceived frequency bands, the deployable areas of the unmanned aerial vehicles, the distance constraint among the unmanned aerial vehicles, the number of targets to be perceived, the set of frequency points of the targets to be perceived, the positions of the targets to be perceived and the environmental parameters; the original problem of multi-unmanned aerial vehicle perception resource joint scheduling is constructed by analyzing each perception target, optimization variable and environmental constraint and maximizing the signal joint detection probability of the multi-unmanned aerial vehicle; and then, simplifying the original problem into a master problem and a slave problem, solving the slave problem by adopting a block coordinate descent algorithm to obtain the optimal multi-unmanned aerial vehicle antenna pointing direction, and solving the master problem by adopting the block coordinate descent algorithm to obtain the optimal multi-unmanned aerial vehicle deployment position, thereby solving the many-to-many detection problem in a three-dimensional scene and realizing the effective scheduling of the multi-unmanned aerial vehicle formation perception resources. According to the scheme provided by the invention, under the condition that resources such as a plurality of unmanned aerial vehicles are limited in frequency domain, airspace and beam domain, the optimization of the sensing performance of a plurality of targets can be realized, the scheme can be well adapted to the future wireless sensing technology, and the overall performance of a sensing network is improved. In addition, the method and the device particularly explain that the block coordinate descent algorithm is adopted in the scheme of the invention when solving the master problem and the slave problem, the prior gibbs sampling algorithm is replaced, and as the gibbs sampling algorithm has the bottleneck that the probability transfer is not easy to converge in each iteration process, the method and the device can ensure that the objective function is stably descended in each iteration process by adopting the block coordinate descent algorithm, thereby accelerating the operation rate and improving the algorithm performance.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to these drawings for those of ordinary skill in the art. In the drawings:
fig. 1 is a schematic flow chart of a multi-unmanned aerial vehicle formation aware resource joint scheduling method provided by an embodiment of the invention;
FIG. 2 is a system model diagram of a multi-unmanned aerial vehicle formation aware resource joint scheduling method employed in an embodiment of the present invention;
fig. 3 is a schematic diagram of a perceived network deployment when the number of unmanned aerial vehicles is 5 and the number of targets is 3 according to the embodiment of the present invention;
FIG. 4 is a graph comparing the performance of a deployment scenario of an embodiment of the present invention with two baseline deployment scenarios;
fig. 5 is a schematic structural diagram of a multi-unmanned aerial vehicle formation aware resource joint scheduling device according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a framework of a multi-unmanned aerial vehicle formation aware resource joint scheduling system according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. These embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein.
Fig. 1 is a flow chart of a multi-unmanned aerial vehicle formation aware resource joint scheduling method provided by an embodiment of the invention, and fig. 2 is a system model diagram of the multi-unmanned aerial vehicle formation aware resource joint scheduling method adopted by the embodiment of the invention. Referring to fig. 1 and 2, the method of the present invention is implemented by a system of a plurality of unmanned aerial vehicles (UAV 1, UAV 2, … UAV M) and a plurality of targets to be perceived (Target 1,Target 2,Target 3, … Target K), and the method of the present invention includes the following steps S1 to S5:
s1: deducing the signal joint detection probability of multiple unmanned aerial vehicles based on the set number of unmanned aerial vehicles in formation, the unmanned aerial vehicle perception frequency range, the deployable area of the unmanned aerial vehicles, the distance constraint among the unmanned aerial vehicles, the number of targets to be perceived, the set of target frequency points to be perceived, the positions of the targets to be perceived and the environmental parameters.
The step S1 specifically includes:
in a three-dimensional plane, the number of unmanned aerial vehicles is assumed to be M, and the position of the ith unmanned aerial vehicle is assumed to be q i ＝[x i ,y i ,H] T Wherein x is i ,y i Represents the abscissa of the position, and H represents the position height; let the number of targets to be perceived be K and the position of the kth target be q t,k ＝[x t,k ,y t,k ,z t,k ] T The distance from a certain unmanned plane i to the target k to be perceived and the horizontal angleAnd pitch angle are as follows:
d i,k ＝||q t,k -q i || 2
ψ i,k ＝arctan(x t,k -x i ,y t,k -y i )
each unmanned aerial vehicle is provided with a directional antenna to obtain the antenna gain G of a certain unmanned aerial vehicle i which receives the k signal of the target to be perceived i,k (q i ,ψ i ) Signal to noise ratio gamma i,k (q i ,ψ i ) The method comprises the following steps:
wherein θ is expressed as antenna main lobe width, β 0 For channel gain, P, when distance is 1 k Transmit power, N for target signal to be perceived t The number of the directional antenna array elements;
based on signal-to-noise ratio gamma i,k (q i ,ψ i ) Obtaining energy detection probability P of unmanned plane i on target k to be perceived i,k (q i ,ψ i ) The method comprises the following steps:
wherein Q is represented as a right-tail function of a standard normal distribution, N is the number of samples of the received signal, P fa The false alarm probability of the sensing system;
assume that the spectrum sensing range of a certain unmanned aerial vehicle i is (f i min ,f i max ) Assume that the transmission frequency point set of the target k to be perceived is F k Obtaining the signal detection probability of the target k to be perceived on the frequency point f by the unmanned plane iThe method comprises the following steps:
further obtaining the signal joint detection probability of M unmanned aerial vehicles on the frequency point f for the target k to be perceivedThe method comprises the following steps:
s2: and maximizing the signal joint detection probability of the multiple unmanned aerial vehicles, and constructing the original problem P1 of the multi-unmanned aerial vehicle perception resource joint scheduling.
The step S2 specifically includes:
defining the detection probability and function of the system as follows:
popularization to actual scenes, and construction of the original problem P1 of multi-unmanned aerial vehicle perception resource joint scheduling by taking the detection probability and function of a maximized system as targets is as follows:
P1:
wherein Q is { Q } 1 ,q 2 ,...,q M The position set of the unmanned aerial vehicle is represented by the formula (I), the range of the deployable area is represented by the formula (D), and the formula (S) l Is the minimum distance limit between the unmanned aerial vehicle and the target, R l Is the minimum distance limit between unmanned aerial vehicles.
S3: and converting the original problem P1 into a secondary problem P2 with optimized multi-unmanned aerial vehicle antenna pointing and a primary problem P3 with optimized multi-unmanned aerial vehicle deployment.
The step S3 specifically includes:
to simplify the problem, the original problem P1 is simplified into a slave problem P2 and a master problem P3 by equivalent conversion of the problem;
the slave problem P2 is: under the condition that the formation deployment position of the unmanned aerial vehicle is determined, the directional antenna pointing directions of all unmanned aerial vehicles are optimized; and is expressed as:
P2:
the main problem P3 is: under the condition that the optimal directional antenna pointing direction is known, optimizing the deployment positions of all unmanned aerial vehicles; and is expressed as:
P3:
wherein G is dpi (Q, ψ) is the function value after optimization from the problem P2 is performed each time.
S4: and solving the slave problem P2 by adopting a block coordinate descent algorithm to obtain the optimal multi-unmanned aerial vehicle antenna pointing direction.
The problem solving thought of the block coordinate descent algorithm is as follows: in the process of each iteration, only one variable is optimized and solved, and the rest variables are kept unchanged and then solved alternately.
The step S4 specifically includes:
s41, setting a feasible solution of initial unmanned aerial vehicle antenna pointing to be psi (0) Initializing an iteration variable t 1 =0, maximum iteration number T 1,max ；
S42. set antenna pointing ψ=ψ (1) Generating a random sequence of integers from 1 to MAnd set variable l 1 ＝0；
S43, settingL 1 ＝l 1 +1；
S44, fixing a variable set { ψ } 1 ,ψ 2 ,...,ψ m-1 ,ψ m+1 ,...,ψ M Finding the optimal antenna pointing direction of the mth unmanned aerial vehicle as
S45 if l 1 Not equal to M, repeat step S43-S45; otherwise set t 1 ＝t 1 +1, and is provided with
S46, repeating the steps S42-S46 until the objective function value of the condition is unchanged or t is met 1 ＝T 1,max 。
S5: and solving the main problem P3 by adopting a block coordinate descent algorithm based on the obtained optimal multi-unmanned aerial vehicle antenna pointing, so as to obtain an optimal multi-unmanned aerial vehicle deployment position.
The step S5 specifically includes:
s51, setting feasible solution of initial sensing node deployment position as Q (0) Initializing an iteration variable t 2 =0, maximum iteration number T 2,max ；
S52. initializing an iteration variable k=1, generating a random permutation of integer sequences of 1 to P
S53, setting
S54, initializing the position of the mth sensing node asWherein->Representation->An mth column vector;
s55, recording the maximum value of initial positioning performance asSetting a step length constant delta, and defining four possible position coordinate vectors of an mth sensing node in a constrained feasible domain as q respectively m,1 ＝q m,0 +Δ×[1,0,0] T 、q m,2 ＝q m,0 +Δ×[-1,0,0] T 、q m,3 ＝q m,0 +Δ×[0,1,0] T 、q m,4 ＝q m,0 +Δ×[0,-1,0] T The method comprises the steps of carrying out a first treatment on the surface of the Maximum value of positioning performance when respectively calculating corresponding positions
S56, searching the optimal moving direction of the sensing node
S57, if I is not equal to 0, let q m,0 ＝q m,l Repeating steps S55-S57; if i=0, updateM-th column of (2)Updating the iteration variable k=k+1 and repeating steps S53-S57 until the condition k=p is satisfied;
s58, updating the iteration variable t 2 ＝t 2 +1, repeating steps S52-S57 until condition t is satisfied 2 ＝T 2,max Or (b)
Fig. 3 is a schematic diagram of a perceived network deployment when the number of unmanned aerial vehicles is 5 and the number of targets is 3, wherein a depoyable area represents a deployable area of a multi-unmanned aerial vehicle formation, and a Minimum UAV-target1/2/3 distance represents a Minimum distance between the multi-unmanned aerial vehicle formation and each target. The parameters in fig. 3 are specifically set as follows: the minimum distance between the unmanned aerial vehicle and the target is 500 meters, and the minimum distance between the unmanned aerial vehicle and the target is 50 meters; the coordinates of the 3 targets (Target 1,Target 2,Target 3) are (2500, 3000, 450), (2000, 2000,450) and (3000,1500,450) respectively, the frequency band sets are {105,110, …,240,245}, {205,210, …,340,345}, {305,310,315, …,390,395}, and the transmitting powers are 20dBm, 20dBm and 20dBm respectively; the spectrum sensing ranges of the 5 unmanned aerial vehicles are [100,160], [160,220], [220,280], [280,340], [340,400] respectively. Of the three dotted lines led out from each unmanned aerial vehicle, the dotted line in the middle represents the direction of the directional antenna, and the dotted lines on the two sides represent the beam width of the directional antenna.
Simulation verification is carried out on the perceived network deployment Scheme designed in the figure 3 through Matlab software, and the performance of the deployment Scheme provided by the invention is compared with that of two reference deployment schemes to obtain figure 4, wherein figure 4 is a performance comparison diagram of the deployment Scheme provided by the embodiment of the invention and the two reference deployment schemes, a deployment Scheme provided by the embodiment of the invention is represented by a Proposed Scheme, a Scheme based on a Gibbs sampling algorithm is represented by a Baseline Scheme 1, and a Scheme based on random deployment is represented by a Baseline Scheme 2. In fig. 4, sum detection Probability on the ordinate represents the detection performance and rate of the perceived network, and Transmission power of targets on the abscissa represents the transmission power of the target. As can be seen from fig. 4: as the target's transmit power increases, the target perceived performance of the system becomes progressively better. Meanwhile, it can be seen that, for the same network scenario, the system performance of the deployment scheme of the embodiment of the invention is better than that of the other two reference deployment schemes. Because the Gibbs sampling algorithm has the bottleneck that the probability transfer is not easy to converge in each iteration process, the Scheme adopts the block coordinate descent algorithm when solving the master problem and the slave problem, and compared with the Scheme based on the Gibbs sampling algorithm of Baseline Scheme 1, the method can ensure that the objective function is stably descended in each iteration process, thereby accelerating the operation rate and improving the algorithm performance. Table 1 compares the run times of the two algorithms as the number of drones increases under the two algorithms. As can be seen from table 1: compared with the Gibbs sampling algorithm, the block coordinate descent algorithm provided by the invention has higher efficiency.
Table 1:
as can be seen from comparison of performance simulation of FIG. 4, the method of the invention analyzes each perception target, optimization variable and environment constraint aiming at a plurality of targets to be perceived, and builds deployment and beam direction optimization problems of multi-perception unmanned aerial vehicle formation; and a perceived resource joint scheduling optimization algorithm is designed based on a block coordinate descent algorithm, so that the effective scheduling of perceived resources of the multi-unmanned aerial vehicle formation is realized. The method can still realize the optimization of the perception performance of a plurality of targets under the condition of limited resources such as a plurality of unmanned aerial vehicle frequency domains, airspace, beam domains and the like, and can be expected to be well suitable for future wireless perception technologies, so that the overall performance of a perception network is improved.
The method belongs to the same technical conception as the multi-unmanned aerial vehicle formation aware resource joint scheduling method shown in fig. 1, and the embodiment of the invention also provides a multi-unmanned aerial vehicle formation aware resource joint scheduling device. Fig. 5 is a schematic structural diagram of a multi-unmanned aerial vehicle formation aware resource joint scheduling device according to an embodiment of the present invention, where the device includes:
the detection probability deduction module 510 is configured to deduct a signal joint detection probability of multiple unmanned aerial vehicles based on a set number of unmanned aerial vehicles in a formation, an unmanned aerial vehicle sensing frequency range, an unmanned aerial vehicle deployable area, an inter-unmanned aerial vehicle distance constraint, a number of targets to be sensed, a set of target frequency points to be sensed, a target position to be sensed and an environmental parameter;
the original problem construction module 520 is configured to maximize the signal joint detection probability of the multiple unmanned aerial vehicles, and construct an original problem P1 of the multi unmanned aerial vehicle perceived resource joint scheduling;
the problem conversion module 530 is configured to convert the original problem P1 into a secondary problem P2 with optimized multi-unmanned aerial vehicle antenna pointing and a primary problem P3 with optimized multi-unmanned aerial vehicle deployment;
the slave problem solving module 540 is configured to solve the slave problem P2 by using a block coordinate descent algorithm, so as to obtain an optimal multi-unmanned aerial vehicle antenna pointing direction;
and the main problem solving module 550 is configured to solve the main problem P3 by using a block coordinate descent algorithm based on the obtained optimal multi-unmanned aerial vehicle antenna pointing direction, so as to obtain an optimal multi-unmanned aerial vehicle deployment position.
The implementation process of each module in the apparatus shown in fig. 5 may refer to the foregoing method embodiment, and will not be described herein.
The invention also provides a multi-unmanned aerial vehicle formation aware resource joint scheduling system, which belongs to the technical conception as well as the multi-unmanned aerial vehicle formation aware resource joint scheduling method. Fig. 6 is a schematic diagram of a framework of a multi-unmanned aerial vehicle formation aware resource joint scheduling system according to an embodiment of the present invention, where, as shown in fig. 6, the system according to an embodiment of the present invention includes:
a fusion center 600, a plurality of unmanned aerial vehicles (611, 612, 613, 614, …) and a plurality of targets (621, 622, 623, …), wherein the plurality of targets (621, 622, 623, …) are located in the detection range of the plurality of unmanned aerial vehicles (611, 612, 613, 614, …), and the fusion center 600 is in communication connection with each unmanned aerial vehicle (611, 612, 613, 614, …) and receives signals transmitted by each unmanned aerial vehicle from different targets to be perceived in real time;
the fusion center 600 includes a memory and a processor, where the memory stores a computer program, and the computer program is loaded and executed by the processor to implement the aforementioned multi-unmanned aerial vehicle formation aware resource joint scheduling method.
In actual cluster drone reconnaissance, the fusion center 600 may be deployed within a ground director or within one of the drone clusters. For the implementation process of the fusion center 600 in the system shown in fig. 6, reference may be made to the foregoing method embodiment, which is not described herein.
The embodiment of the invention also provides a computer readable storage medium, which stores one or more computer programs, and when the one or more computer programs are executed by a processor, the method for jointly scheduling the multi-unmanned aerial vehicle formation sensing resources is realized, and is not repeated herein.
In summary, according to the multi-unmanned aerial vehicle formation sensing resource joint scheduling method, device and system provided by the embodiment of the invention, for a plurality of targets to be sensed in a three-dimensional scene, the signal joint detection probability of a plurality of unmanned aerial vehicles based on signal energy detection is firstly deduced based on the set number of unmanned aerial vehicles in the formation, the range of sensing frequency bands of the unmanned aerial vehicles, the deployable areas of the unmanned aerial vehicles, the distance constraint among the unmanned aerial vehicles, the number of targets to be sensed, the set of frequency points of the targets to be sensed, the positions of the targets to be sensed and environmental parameters; the original problem of multi-unmanned aerial vehicle perception resource joint scheduling is constructed by analyzing each perception target, optimization variable and environmental constraint and maximizing the signal joint detection probability of the multi-unmanned aerial vehicle; and then, simplifying the original problem into a master problem and a slave problem, solving the slave problem by adopting a block coordinate descent algorithm to obtain the optimal multi-unmanned aerial vehicle antenna pointing direction, and solving the master problem by adopting the block coordinate descent algorithm to obtain the optimal multi-unmanned aerial vehicle deployment position, thereby solving the many-to-many detection problem in a three-dimensional scene and realizing the effective scheduling of the multi-unmanned aerial vehicle formation perception resources. According to the scheme provided by the invention, under the condition that resources such as a plurality of unmanned aerial vehicles are limited in frequency domain, airspace and beam domain, the optimization of the sensing performance of a plurality of targets can be realized, the scheme can be well adapted to the future wireless sensing technology, and the overall performance of a sensing network is improved. In addition, the method and the device particularly explain that the block coordinate descent algorithm is adopted in the scheme of the invention when solving the master problem and the slave problem, the prior gibbs sampling algorithm is replaced, and as the gibbs sampling algorithm has the bottleneck that the probability transfer is not easy to converge in each iteration process, the method and the device can ensure that the objective function is stably descended in each iteration process by adopting the block coordinate descent algorithm, thereby accelerating the operation rate and improving the algorithm performance.
Finally it is pointed out that the term "comprising," "including," or any other variation thereof, is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
The foregoing is merely exemplary of the present invention and is not intended to limit the present invention. Those of ordinary skill in the art, with access to the present disclosure, may implement the invention in a variety of other embodiments. Therefore, all designs which adopt the design structure and thought of the invention and do some simple changes or modifications are required to fall within the protection scope of the claims of the invention.
Claims (10)
1. The multi-unmanned aerial vehicle formation aware resource joint scheduling method is characterized by being applied to cluster unmanned aerial vehicle reconnaissance and comprises the following steps of:
s1: deducing the signal joint detection probability of multiple unmanned aerial vehicles based on the set number of unmanned aerial vehicles in formation, the range of the sensing frequency bands of the unmanned aerial vehicles, the deployable areas of the unmanned aerial vehicles, the distance constraint among the unmanned aerial vehicles, the number of targets to be sensed, the set of target frequency points to be sensed, the positions of the targets to be sensed and the environmental parameters;
s2: maximizing the signal joint detection probability of the multiple unmanned aerial vehicles, and constructing an original problem P1 of the multi-unmanned aerial vehicle perception resource joint scheduling;
s3: converting the original problem P1 into a secondary problem P2 with optimized multi-unmanned aerial vehicle antenna pointing and a primary problem P3 with optimized multi-unmanned aerial vehicle deployment;
s4: solving the slave problem P2 by adopting a block coordinate descent algorithm to obtain the optimal multi-unmanned aerial vehicle antenna pointing direction;
s5: and solving the main problem P3 by adopting a block coordinate descent algorithm based on the obtained optimal multi-unmanned aerial vehicle antenna pointing, so as to obtain an optimal multi-unmanned aerial vehicle deployment position.
2. The method according to claim 1, wherein the step S1 specifically includes:
in a three-dimensional plane, the number of unmanned aerial vehicles is assumed to be M, and the position of the ith unmanned aerial vehicle is assumed to be q i ＝[x i ,y i ,H] T Wherein x is i ,y i Represents the abscissa of the position, H represents the positionSetting the height; let the number of targets to be perceived be K and the position of the kth target be q t,k ＝[x t,k ,y t,k ,z t,k ] T The distance, horizontal angle and pitch angle from a certain unmanned plane i to the target k to be perceived are as follows:
d i,k ＝||q t,k -q i || 2
ψ i,k ＝arctan(x t,k -x i ,y t,k -y i )
each unmanned aerial vehicle is provided with a directional antenna to obtain the antenna gain G of a certain unmanned aerial vehicle i which receives the k signal of the target to be perceived i,k (q i ,ψ i ) Signal to noise ratio gamma i,k (q i ,ψ i ) The method comprises the following steps:
wherein θ is expressed as antenna main lobe width, β 0 For channel gain, P, when distance is 1 k Transmit power, N for target signal to be perceived t The number of the directional antenna array elements;
based on signal-to-noise ratio gamma i,k (q i ,ψ i ) Obtaining energy detection probability P of unmanned plane i on target k to be perceived i,k (q i ,ψ i ) The method comprises the following steps:
wherein Q is represented as a right tail function of standard normal distribution and N is a junctionNumber of received signal samples, P fa The false alarm probability of the sensing system;
assume that the spectrum sensing range of a certain unmanned aerial vehicle i is (f i min ,f i max ) Assume that the transmission frequency point set of the target k to be perceived is F k Obtaining the signal detection probability of the target k to be perceived on the frequency point f by the unmanned plane iThe method comprises the following steps:
further obtaining the signal joint detection probability of M unmanned aerial vehicles on the frequency point f for the target k to be perceivedThe method comprises the following steps:
3. the method according to claim 2, wherein the step S2 specifically includes:
defining the detection probability and function of the system as follows:
popularization to actual scenes, and construction of the original problem P1 of multi-unmanned aerial vehicle perception resource joint scheduling by taking the detection probability and function of a maximized system as targets is as follows:
wherein Q is { Q } 1 ,q 2 ,...,q M The position set of the unmanned aerial vehicle is represented by the formula (I), the range of the deployable area is represented by the formula (D), and the formula (S) l Is the minimum distance limit between the unmanned aerial vehicle and the target, R l Is the minimum distance limit between unmanned aerial vehicles.
4. A method according to claim 3, wherein said step S3 comprises:
simplifying the original problem P1 into a slave problem P2 and a master problem P3 through equivalent conversion of the problem;
the slave problem P2 is: under the condition that the formation deployment position of the unmanned aerial vehicle is determined, the directional antenna pointing directions of all unmanned aerial vehicles are optimized; and is expressed as:
the main problem P3 is: under the condition that the optimal directional antenna pointing direction is known, optimizing the deployment positions of all unmanned aerial vehicles; and is expressed as:
wherein G is dpi (Q, ψ) is the function value after optimization from the problem P2 is performed each time.
5. The method according to claim 4, wherein the step S4 specifically includes:
s41, setting a feasible solution of initial unmanned aerial vehicle antenna pointing to be psi (0) Initializing an iteration variable t 1 =0, maximum iteration number T 1,max ；
S42. set antenna pointing ψ=ψ (1) Generating a randomly arranged integer sequence Θ of 1 to M (t1) And set variable l 1 ＝0；
S43, settingL 1 ＝l 1 +1；
S44, fixing a variable set { ψ } 1 ,ψ 2 ,...,ψ m-1 ,ψ m+1 ,...,ψ M Finding the optimal antenna pointing direction of the mth unmanned aerial vehicle as
S45 if l 1 Not equal to M, repeating steps S43-S45; otherwise set t 1 ＝t 1 +1, and is provided with
S46, repeating the steps S42-S46 until the objective function value of the condition is unchanged or t is met 1 ＝T 1,max 。
6. The method according to claim 5, wherein the step S5 specifically includes:
s51, setting feasible solution of initial sensing node deployment position as Q (0) Initializing an iteration variable t 2 =0, maximum iteration number T 2,max ；
S52. initializing an iteration variable k=1, generating a random permutation of integer sequences of 1 to P
S53, setting
S54, initializing the position of the mth sensing node asWherein->Representation->An mth column vector;
s55, recording the maximum value of initial positioning performance asSetting a step constant delta, and settingThe possible four position coordinate vectors of the mth sensing node in the constrained feasible domain are respectively q m,1 ＝q m,0 +Δ×[1,0,0] T 、q m,2 ＝q m,0 +Δ×[-1,0,0] T 、q m,3 ＝q m,0 +Δ×[0,1,0] T 、q m,4 ＝q m,0 +Δ×[0,-1,0] T The method comprises the steps of carrying out a first treatment on the surface of the Maximum value of positioning performance when respectively calculating corresponding positions
S56, searching the optimal moving direction of the sensing node
S57, if I is not equal to 0, let q m,0 ＝q m,l Repeating steps S55-S57; if i=0, updateM < th > column->Updating the iteration variable k=k+1 and repeating steps S53-S57 until the condition k=p is satisfied;
s58, updating the iteration variable t 2 ＝t 2 +1, repeating steps S52-S57 until condition t is satisfied 2 ＝T 2,max Or (b)
7. The utility model provides a many unmanned aerial vehicle formation perception resource joint scheduling device which characterized in that, the device is applied to cluster unmanned aerial vehicle reconnaissance, includes following module:
the detection probability deduction module is used for deducting the signal joint detection probability of multiple unmanned aerial vehicles based on the set number of unmanned aerial vehicles in the formation, the unmanned aerial vehicle sensing frequency range, the unmanned aerial vehicle deployable area, the distance constraint among unmanned aerial vehicles, the number of targets to be sensed, the set of target frequency points to be sensed, the position of the targets to be sensed and the environmental parameters;
the original problem construction module is used for maximizing the signal joint detection probability of the multiple unmanned aerial vehicles and constructing an original problem P1 of the multi-unmanned aerial vehicle perception resource joint scheduling;
the problem conversion module is used for converting the original problem P1 into a secondary problem P2 with optimized multi-unmanned aerial vehicle antenna pointing and a primary problem P3 with optimized multi-unmanned aerial vehicle deployment;
the slave problem solving module is used for solving the slave problem P2 by adopting a block coordinate descent algorithm to obtain the optimal multi-unmanned aerial vehicle antenna orientation;
and the main problem solving module is used for solving the main problem P3 by adopting a block coordinate descent algorithm based on the obtained optimal multi-unmanned aerial vehicle antenna orientation to obtain an optimal multi-unmanned aerial vehicle deployment position.
8. The multi-unmanned aerial vehicle formation perception resource joint scheduling system is characterized by comprising a fusion center, a plurality of unmanned aerial vehicles and a plurality of targets to be perceived, wherein the targets to be perceived are positioned in the detection ranges of the unmanned aerial vehicles, the fusion center is in communication connection with each unmanned aerial vehicle, and signals from different targets to be perceived, which are transmitted by each unmanned aerial vehicle, are received in real time;
the fusion center comprises a memory and a processor, wherein a computer program is stored in the memory, and the computer program is loaded and executed by the processor to realize the multi-unmanned aerial vehicle formation aware resource joint scheduling method of any one of claims 1 to 6.
9. The system of claim 8, wherein the fusion center is deployed within a ground director or within one of the drones in the cluster of drones.
10. A computer readable storage medium storing one or more computer programs which, when executed by a processor, implement the multi-drone formation aware resource joint scheduling method of any one of claims 1 to 6.
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