CN116359858A - Collaborative interference resource scheduling method based on improved sparrow search algorithm - Google Patents
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Abstract
The invention relates to a cooperative interference resource scheduling method based on an improved sparrow search algorithm, and belongs to the technical field of cooperative interference. The method comprises the following steps: 1) Constructing an interference decision matrix and a power distribution matrix; 2) Constructing a first suppression probability model of the networking radar by taking uniform distribution of beam power of the jammer as a principle, and solving an interference decision matrix corresponding to the maximum suppression probability by utilizing a sparrow search algorithm; 3) Constructing a second suppression probability model based on the interference decision matrix obtained by solving, and solving a power distribution matrix which enables the second suppression probability to be the largest by utilizing a sparrow search algorithm; in the step of updating the position of the discoverer in the sparrow searching algorithm, if the early warning value is larger than the safety value, the discoverer after the encoding ordering is subjected to the cross operation randomly. And finally, a cooperative interference resource scheduling scheme is obtained. The scheduling method has high scheduling efficiency and more reasonable scheduling scheme, and forms the optimal interference effect on the networking radar under the condition of limited interference resources.
Description
Technical Field
The invention belongs to the technical field of cooperative interference, and particularly relates to a cooperative interference resource scheduling method based on an improved sparrow search algorithm.
Background
In recent years, as the complexity of the electromagnetic environment of the battlefield is continuously increased, modern electronic warfare has evolved from single combat to systematic combat. Networking radars are widely deployed because of their excellent "four-antibody" characteristics, which pose a great threat to the target's task of performing the defense. To enhance interference to radar networking, coordinated interference may be implemented with multiple jammers. Under the condition of limited interference resources, how to optimally allocate the interference resources to form optimal interference on the networking radar becomes an important military research subject.
The optimal allocation of the cooperative interference resources is a process of reasonably scheduling the interference resources to achieve the optimal interference effect on the premise of knowing the working parameters of each radar. The interference resource optimization allocation method at the present stage mainly comprises two types: classical combinatorial optimization algorithms and intelligent optimization algorithms. The traditional algorithms of 0-1 integer planning, closeness, fuzzy multi-attribute dynamic planning and the like for radar interference resource allocation research based on 0-1 planning (authors: shenyang and the like, journal: soldier school newspaper, 2007, 5 th period, 528-532 pages), cooperative electronic interference resource allocation method based on fuzzy multi-attribute (authors: luo Juanjuan and the like, journal: electronic information countermeasure technology, 2019, 34, 5 th period, 65-70 pages) and radar interference resource allocation strategy research based on closeness (authors: lv Yongsheng and the like, journal: system engineering and electronic technology, 2005, 11 th period, 79-80, 160 pages) are increased along with the allocation scale of interference resources, and the discreteness, non-convexity and algorithm performance of the optimization problem appear severely, so that the problem is difficult to solve.
The genetic algorithm is used for solving the problem of optimizing and distributing interference resources, the running time is correspondingly increased while the optimizing probability is improved, and the real-time performance is not realized; the document 'application of improved cuckoo algorithm in cooperative interference resource allocation' (author: liu Xiang, etc., journal: modern radar, 2019, volume 41, 2 nd, pages 84-90) applies the cuckoo algorithm to the problem of interference resource allocation, improves convergence stability at the expense of convergence speed, and only considers the condition that the number of radars is equal to the number of jammers.
In summary, the existing interference resource optimization allocation method generally has the problems of low convergence rate, weak optimizing capability and the like, and cannot meet the needs of practical problems. Meanwhile, the problem of beam power distribution of an jammer is not generally focused, and a combined optimization model of beam distribution and power distribution of the jammer is established by adopting detection probability as an interference evaluation index and solving the combined optimization model by using a PSO algorithm, wherein the problem of distribution of interference patterns is not considered in a literature (the author: zhang Dalin and the like, journal: radar journal, 2021, volume 10, period 4, pages 595-606).
Disclosure of Invention
The invention aims to provide a cooperative interference resource scheduling method based on an improved sparrow search algorithm, which aims to solve the problems of poor interference effect caused by low convergence speed and weak optimizing capability of the existing interference resource optimal allocation method.
The technical scheme of the cooperative interference resource scheduling method based on the improved sparrow search algorithm provided by the invention for solving the technical problems is as follows: the method comprises the following steps:
1) Constructing an interference resource allocation model according to each radar set, each jammer pattern set and the power allocation of each jammer beam of the networking, wherein the interference resource allocation model comprises an interference decision matrix and a power allocation matrix;
2) Setting a power distribution matrix in an interference resource distribution model by taking uniform distribution of beam power of each jammer as a principle, constructing a first suppression probability model of the networking radar according to the interference resource distribution model, taking the first suppression probability model as a first objective function, and solving an interference decision matrix corresponding to the first objective function to the maximum by utilizing a sparrow search algorithm;
3) Constructing a second suppression probability model of the networking radar based on the optimal interference decision matrix obtained by solving in the step 2), taking the second suppression probability model as a second objective function, and solving a power distribution matrix corresponding to the second objective function when the second objective function is maximum by utilizing the sparrow search algorithm to obtain an optimal power distribution matrix;
when the first objective function and the second objective function are solved by utilizing the sparrow search algorithm, if the early warning value is larger than the safety value in the step of updating the position of the discoverer of the sparrow search algorithm, randomly grouping the discoverers after encoding and sorting in pairs to form parent chromosomes, and then intersecting to generate offspring individuals;
4) And carrying out cooperative interference resource scheduling based on the obtained optimal interference decision matrix and the optimal power allocation matrix.
The beneficial effects of the invention are as follows: aiming at a multi-jammer cooperative interference scene, the method utilizes the suppression probability of the networking radar as an objective function, establishes an interference benefit model, and solves a transmitting power distribution result on the basis of solving an optimal distribution mode. Improving sparrow search algorithm increases the cross operation in genetic algorithm, and enhances the iterative speed and optimizing ability of algorithm while guaranteeing the feasibility of solution. The scheduling method based on the improved sparrow search algorithm is high in scheduling efficiency, the scheduling scheme is more reasonable, and the optimal interference effect is formed on the networking radar after the interference resources are reasonably distributed under the condition of limited interference resources.
Further, in the step of updating the finder position of the sparrow search algorithm, if the early warning value is greater than the safety valueWhen the value is full, the encoding is allocated to the interference object, the ordered discoverers are randomly grouped in pairs to form parent chromosomes, arithmetic crossover operators are adopted, child individuals are generated through linear recombination, and the formula is as follows:
wherein->
And->
For the progeny individuals produced by linear recombination, c is the crossover parameter, the value range is (0, 1), -the value range is (0)>
And->
Is the parent chromosome, and t is the current iteration number.
Further, in the step of updating the position of the finder in the sparrow search algorithm, if the early warning value is larger than the safety value, selecting codes for the interference patterns, and performing multi-point cross processing on the parent chromosome in the genetic algorithm to obtain offspring individuals.
Further, in the step of updating the positions of the alerters in the sparrow search algorithm, the positions of the alerters are updated to perform adaptive mutation of the positions of the individuals, and the mutation probability increases with the increase of the iteration times. And the adaptive mutation operation is introduced in the vigilant stage, when the mutation probability is high, the population diversity is increased, and the optimizing capability is enhanced.
Further, the formula of the adaptive variation is:
wherein P is m (T) is the variation probability of the current iteration stage, T is the total iteration number, T is the current iteration number, P m Is the maximum variation value.
Further, in the step of initializing the population of the sparrow searching algorithm, a Tent chaotic sequence is adopted to initialize the population. The Tent chaotic sequence is utilized to initialize the population, so that initial individuals are distributed in a solution space as uniformly as possible, the diversity of the population is maintained, and the global optimizing capability is enhanced.
Further, the expressions of the first suppression probability model and the second suppression model of the networking radar are respectively:
and->
Wherein C is 1 And C 2 The first suppression probability and the second suppression probability of the networking radar are respectively, R is the distance between the jammer and the radar, F is an interference decision matrix, F Optimum for the production of a product For the optimal interference decision matrix, P is the power distribution matrix, P Uniformity of For power distribution matrix, ps, set on the principle of uniform distribution of beam power of each jammer net (R,F,P Uniformity of ) Representing a first probability of detection, ps, of a networked radar net (R,F Optimum for the production of a product P) represents a second detection probability of the networking radar, ω (R) represents a weight corresponding to the suppression probability when the distance R between the jammer and the radar is equal to R min 、R max Respectively minimum and maximum distances between the jammer and the radar.
Further, the weight is calculated in the following manner:
the method comprises the steps of dividing the defense process of an interference machine into Q navigation segments at intervals of delta R, wherein Q represents the Q navigation segments, alpha is a weight factor, alpha epsilon (0, 1), and the value of alpha is related to the setting of a distance interval and R.
Further, the constraint condition of the objective function is:
wherein P is max To the maximum power of the interfering beam, P min Denoted as the minimum power of the interfering beam,
the total power of the interference beam emitted by the jammer.
Further, the interference decision matrix F is:
the power distribution matrix P is:
wherein P is m,n The power allocated to radar N by jammer M is represented by M, N being the numbers of jammer and radar, m=1, 2,3, …, M, n=1, 2,3, …, N, respectively.
Drawings
FIG. 1 is a schematic diagram of a cooperative interference model according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an interference decision matrix model according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a networked radar countermeasure model according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a multi-point cross-over process of a finder stage in an SSA algorithm according to an embodiment of the present invention;
FIG. 5 is a flowchart for solving interference resource allocation by ISSA algorithm according to the embodiment of the invention;
FIG. 6 is a schematic diagram of a radar countermeasure model according to an embodiment of the present invention;
FIG. 7 is a diagram showing the comparison of single convergence between ISSA and SSA algorithms according to the present invention;
FIG. 8 is a graph comparing multiple convergence of ISSA algorithm and SSA algorithm according to the present invention;
FIG. 9 is a chart of the convergence error of the ISSA algorithm and the SSA algorithm according to the invention;
FIG. 10 is a graph of objective function value convergence in power optimization under ISSA algorithm of the present invention;
fig. 11 is a schematic diagram of beam power allocation of the jammer 1 according to the ISSA algorithm of the present invention;
fig. 12 is a schematic diagram of a beam power distribution result of each jammer under the ISSA algorithm of the present invention;
fig. 13 is a schematic diagram of the probability of radar network detection under different interference conditions in the ISSA algorithm of the present invention.
Detailed Description
The following describes the embodiments of the present invention further with reference to the drawings.
The technical conception of the invention is as follows: and constructing an interference resource allocation model, wherein each interference resource allocation mode corresponds to a group of suppression probability values, synthesizing the suppression probability values in the whole burst prevention process, obtaining the suppression probability C corresponding to the whole burst prevention process, taking the suppression probability C as an objective function, searching an interference decision matrix corresponding to the maximum value maxC of the objective function through an improved sparrow search algorithm, namely an optimal allocation object and an interference pattern, and searching a power allocation matrix corresponding to the maximum value maxC of the objective function through the improved sparrow search algorithm on the basis of the optimal interference decision matrix, so as to obtain a final interference resource allocation strategy. The improved sparrow search algorithm is an improvement based on the standard sparrow search algorithm, and the main improvements are as follows: 1) Introducing a Tent chaotic mapping initialization population; 2) Introducing crossover operations of the genetic algorithm into the finder position update; 3) The adaptive mutation operation of the genetic algorithm is introduced into the position update of the alerter. The improvement of any one of the three aspects can ensure that the sparrow search algorithm improves the iteration efficiency and optimizing probability of the algorithm while ensuring the feasibility of the solution. The cooperative interference resource is scheduled based on the improved sparrow search algorithm, so that the optimal interference effect can be achieved.
1. And (5) constructing an interference resource allocation model.
The interference resource scheduling problem can be understood as a multi-constraint nonlinear discrete integer programming problem, and mainly comprises three aspects of allocation of interference objects, selection of interference patterns and power allocation of interference beams. Fig. 1 shows a cooperative interference model of a networking radar.
Suppose the jammer set is j= { J 1 ,J 2 ,…,J M Each radar set of the networking is R= { R } 1 ,R 2 ,…,R N The selectable interference pattern set is s= { S } 1 ,S 2 ,…,S K The power allocation of each jammer beam is g= { G 1 ,G 2 ,…,G L }。
Establishing an interference object allocation matrix D, D mn Representing whether the jammer m interferes with the radar n, and a plurality of interference rules D mn =1, otherwise D mn ＝0。
Establishing an interference pattern selection matrix Y, Y mk Representing whether the jammer m selects the interference pattern k, if so, Y mk =1, otherwise Y mk ＝0。
Final interference decision matrix available F m,n,k ＝F{D mn ,Y mk And the interference decision matrix model is shown in figure 2. F (F) m,n,k =1 represents that jammer m performs interference on radar n by selecting interference pattern k; f (F) m,n,k =0 represents jammer m does not interfere with radar n or does not select interference pattern k.
The overall interference decision matrix can be expressed as:
to represent the power allocation of each beam of the jammer, an interference power allocation matrix is further defined:
if the jammer m distributes the wave beam to interfere with the radar n, P m,n > 0; otherwise P m,n ＝0。
Therefore, when the networking radars are subjected to cooperative interference, the interference decision matrix F and the power distribution matrix P can be used for describing the whole interference system.
2. And constructing a suppression probability model of the networking radar.
The searching and early warning of the target is the most basic and important processing link of the radar work. Only after the target signal is detected will the next stage of positioning and tracking and fire guidance be performed. The challenge model of the networked radar is shown in fig. 3.
In the absence of interference, the target echo signal-to-noise ratio is known from the radar equation:
wherein P is t For radar transmitting power, G t (θ)、G r (θ) is the antenna transmitting and receiving gain, λ is the radar signal wavelength, σ is the target RCS (radar cross-sectional area), pc is the radar matching pulse pressure gain, R t For radar to target spacing, k is Boltzmann constant, T 0 For effective noise temperature, B is receiver bandwidth, N f For the noise figure of the receiver, L t Is the sum of radar losses.
When the jammer selects different interference patterns, the signal to noise ratio is different due to the large energy difference in time, frequency, space, processing domain and the like. Thus, according to the interference equation, the interference signal JNR is obtained while considering the influence of each interference factor:
wherein P is j 、G j The transmitting power and the antenna gain of the jammer are respectively; gamma ray j Is a polarization mismatch factor; l (L) j For the comprehensive loss of the jammer E j ＝E t E f E s E p The relative energy differences of the interference signals in the time, frequency, space and processing domains are respectively E t Is the time domain energy difference of the interference signal, E f Is the energy difference of the interference signal frequency domain, E s Is the spatial energy difference of interference signals, E p Is the interference signal processing domain energy difference, and is related to the interference pattern.
Therefore, when the same radar is interfered by a plurality of jammers, the interference signals of the jammers are assumed to be mutually independent, the total power of the interference signals received by the radar is equal to the sum of the power of the interference signals of the jammers, and the signal-to-interference ratio of the target echo of the nth radar under the interference condition can be obtained by combining the above steps:
wherein F is m,n,k And E is m,n,k Respectively representing interference decision value of interference pattern k to radar n and interference signal gain R m,n Representing the distance between jammer m and radar n, P m,n Representing the power allocated to radar n by jammer m, G m,n Representing the antenna gain of jammer m.
The radar detection principle shows that the detection probability of the radar on the target is related to the target echo signal-to-interference-and-noise ratio and the false alarm probability. In the case of a swering type II target, the probability of detection of the target by the radar is given by:
wherein V is T Representation inspectionThreshold measurement and false alarm probability P fa Related, n p Represents the pulse accumulation number Γ I Representing an incomplete gamma function, defined by DiFranco and Rubin as:
under the interference environment, the SNR can be replaced by the signal-to-interference ratio SJR, and the detection probability of the radar to the target when the radar is interfered can be obtained. In general, the weaker the detection capability of the radar after interference, the better the interference effect is proved. The suppression probability for radar in an interference environment can be expressed as:
Ps＝1-Pd (9)
obviously, when the interference intensity is larger, the radar detection probability is reduced more rapidly, and the suppression probability is larger. Therefore, the suppression probability Ps can be used to evaluate the suppression effect on the networking radar under co-interference.
For the nth radar:
to know the pressing probability Ps n And F is equal to m,n,k P m,n Therefore, the interference decision matrix F and the power distribution matrix P of the jammer are jointly optimized, the suppression probability of cooperative interference on the networking radar can be effectively enhanced, and the benefit of limited interference resources can be furthest exerted.
From the above analysis, it can be seen that the detection probability of the networking radar is related to the interference signal power received by each radar, and thus the detection probability Pd of the networking radar net Can be expressed as:
Pd net ＝f(Pd 1 (SJR 1 ),Pd 1 (SJR 2 ),…,Pd n (SJR N )) (10)
f represents membership function from single radar detection probability to networking suppression probability, pd n Membership functions representing the detection probability of the interference signal SJR received by the nth radar, n=1, 2, …, N.
Jammer accompanied target machine burst preventionIn the process, the positions of the two are changed at any time, so that the pressing probability is changed. Ps at a single fixed point net The value is not reasonable enough as the whole pressing effect, so the sudden prevention process can be discretized into a plurality of navigation segments, a plurality of track points are selected, and the pressing probability values of the track points are weighted and summed to obtain the final function value. Different weights are divided according to the distance between the line segment and the attack destination, and the closer the target is to the destination, the greater the threat is, so the weights of all points are reduced along with the increase of the distance and are nonlinear functions. If the burst prevention process [ R min ,R max ]The delta R is taken as an interval to be divided into Q sections, and the change relation of the weights is as follows:
wherein, alpha is E (0, 1), the specific size is related to the distance interval and R setting, so that the pressing probability of the final networking radar, namely, the objective function is:
constraint conditions concerning the fight relationship are set as follows:
constraint (1) indicates that each jammer generates at most L beams and selects one interference pattern.
Constraint (2) indicates that the radar may not be interfered, and may also be interfered by multiple jammers at the same time.
Constraint (3) indicates that the total number of jammers allocated to each radar does not exceed the number of existing jammers.
Constraint (4) indicates that the interfering beams have maximum and minimum power constraints and that the total power is met.
Constraint (5) indicates that the interference decision matrix element is defined.
Each interference resource allocation mode corresponds to a group of suppression probability values, the suppression probability values in the whole burst prevention process are synthesized, the suppression probability C in the whole corresponding navigation section is obtained and used as an objective function, and an interference resource allocation strategy corresponding to the maximum value maxC of the objective function is searched.
There are several difficulties with the optimization problem of equation (12) and (13):
(1)F m,n,k is a ternary variable that is non-convex.
(2) Interference decision matrix F m,n,k And power distribution matrix P m,n The mutual coupling is in the form of product, and the synchronous solving is too complex.
Therefore, solving the optimization problem firstly considers that the optimal interference decision matrix F corresponding to maxC is obtained under the condition of beam power average m,n,k Then power optimization is carried out on the basis to obtain a power distribution matrix P corresponding to the maximum objective function value m,n 。
3. And searching an interference resource allocation strategy corresponding to the maximum value maxC of the objective function by utilizing an improved sparrow search algorithm.
The interference resource allocation is a multi-constraint multi-dimensional multi-selection discrete integer programming problem, and along with the increase of allocation scale, a classical combination optimization algorithm is not applicable any more, and an intelligent algorithm is needed to carry out optimization solution. The SSA algorithm (sparrow search algorithm) performs optimization by updating the position of a finder-a joiner and adding a reconnaissance early warning mechanism.
1. Standard SSA algorithm theory framework.
In the standard SSA algorithm, the method mainly comprises three parts of a finder, an enrollee and an alerter.
(1) The discoverer can provide foraging position information for the joiner, and the position updating formula is as follows:
wherein: t is the current iteration number, T is the total iteration number, x id For the ith individualD-th dimensional information of a is (0, 1)]The random number between QL is a sum x id Identical dimensions at [0,1 ]]A random matrix between; r is R 2 ∈[0,1]，ST∈[0.5,1]，R 2 ST are respectively early warning values and warning values. When R is 2 < ST, the population does not find danger, the discoverer can search in a larger range so as to obtain a higher fitness value; conversely, if a hazard is found, the population contracts toward a safe area.
(2) The change of the position of the joiner is related to the discoverer, and the position updating formula is as follows:
wherein X is best And X worst Respectively the optimal and worst position information in the current stage, num represents the population number, L j Meaning 1 row d column full 1 matrix, A + Represents a d-dimensional vector with random values of 1 and-1, and A + ＝A T (AA T ) -1 I > Num/2 indicates that individuals with low fitness need to find new food positions for further searching; otherwise, in the current optimal position X best Nearby seeking locations to feed.
(3) The alerter refers to individuals in the population who are aware of the threat of predators, generally account for 10% -20% of the population, and are aware of the danger and rapidly remind the population to counter predation. The location update is as follows:
wherein W and beta are step control parameters, W E [ -1,1]Beta obeys a normal distribution with a mean value of 0 and a variance of 1, epsilon is a minimum value, f i For the current fitness value, f b And f w Respectively the optimal and worst fitness values of the current population.
2. Improved Sparrow Search Algorithm (ISSA).
On the premise of keeping the integral framework of a standard sparrow search algorithm, the invention introduces a Tent chaotic mapping initialization population, and simultaneously introduces the crossover and mutation evolution operation of a genetic algorithm into a standard SSA algorithm, thereby providing an improved sparrow search algorithm (ISSA for short). The improved sparrow search algorithm generates an effective feasible solution, balances the diversity and the directivity of the algorithm, and further improves the iteration speed and the optimizing capability of the algorithm.
For the model condition of the cooperative interference resource allocation constraint, an adaptive ISSA algorithm is mainly set from the following key aspects.
(1) Initializing a population.
The standard SSA algorithm randomly initializes the population, and uniformity of population position distribution can influence convergence accuracy. The chaotic variable is widely applied to the optimization problem due to randomness, ergodic property and regularity. Initializing a population by using a Tent chaotic sequence, so that initial individuals are distributed in a solution space as uniformly as possible, the diversity of the population is maintained, the global optimizing capability is enhanced, and the sequence expression is as follows
Wherein: n is the number of particles in the chaotic sequence, z i The number of chaotic sequences, z, representing the current state i+1 The number of chaotic sequences of the next state.
(2) Discoverer stage
For the problem of discrete integer distribution of interference resource distribution, when R is more than ST, the position of a finder is basically unchanged, searching is trapped and stagnated, the purpose of the finder is mainly to conduct neighborhood searching, cross operation in a genetic algorithm is adopted, population diversity is increased, and optimizing capability is improved.
For the distribution codes of the interference objects, the ordered discoverers can be randomly grouped into groups to form parent chromosomes, and offspring individuals are generated through linear recombination by adopting arithmetic crossover operators.
Wherein the method comprises the steps of
And->
For the progeny individuals produced by linear recombination, c is the crossover parameter, the value range is (0, 1), -the value range is (0)>
And
is the parent chromosome, and t is the current iteration number. For interference pattern selection coding, because the number of seeds of the solution is small, a chromosome multipoint crossover process can be adopted, and the crossover process is shown in fig. 4.
(3) Stage of joiner
For individuals with low fitness, at X best And for other joiners, observing discoverers so as to conveniently compete food or search positions nearby, adding cross processing with the global optimal solution to update positions, and randomly selecting part of subscripts of the global optimal solution to replace the subscripts of the part of joiners, so that the development performance and global optimizing capability of the algorithm are enhanced.
(4) Stage of alerter
When the adaptive mutation operation is introduced in the vigilance stage and the mutation probability is high, the population diversity is increased, the optimizing capability is enhanced, the convergence speed is slow, and otherwise, the convergence speed is fast, but the local optimum is easily trapped. By introducing a sigmoid function, the variation probability is gradually changed from small to large along with the iteration times:
wherein P is m (T) is the variation probability of the current iteration stage, T is the total iteration number, T is the current iteration number, P m Is thatMaximum variance.
Variation rule: and selecting a gene segment at a random position of an individual to perform mutation, namely, inverting the chromosome.
The improved ISSA algorithm comprises the following operation steps:
The interference decision matrix is divided into two coding segments of interference object allocation and interference pattern selection, X p ＝[x 1 ,…,x M ] Τ Representing the allocation of interfering objects, Y p ＝[y 1 ,…,y M ] Τ Representing the selection of the interference pattern. Wherein x is 0.ltoreq.x i ≤2 N -1 and is an integer, the allocation of the corresponding interfering object after conversion into binary, if x 1 ＝52,y 1 =3, which is converted to binary 110100, i.e. jammer 1 selects interference pattern 3 to interfere with radars 1,2, 4.
And initializing a population by using the Tent chaotic sequence, then coding, evaluating and sequencing the fitness of the individual by using the solved objective function formula (12), and storing the optimal fitness value and the corresponding position thereof.
According to the early warning value R 2 Updating the selection discoverer if R 2 Smaller than ST discoverers can search more widely to obtain higher fitness value if R 2 When the search strategy is larger than ST, the search strategy needs to be adjusted, information sharing can be carried out on other discoverers at the moment, the discoverers after sequencing are randomly grouped in pairs to form parent chromosomes, and offspring chromosomes are generated after intersecting.
And updating the position of the joiner. For individuals with i > n/2, at X best And for other joiners, adding cross processing with the globally optimal solution to update the positions, and randomly selecting part of subscripts of the globally optimal solution to replace the subscripts of the part of joiners.
And randomly selecting a plurality of sparrows for reconnaissance and warning, and updating the positions of the sparrows according to the step (19) for self-adaptive variation.
And (3) calculating the fitness value, comparing the fitness value of the sparrow and the sparrow of the previous generation generated in the step (5), preferentially keeping, and updating the optimal individual position and the fitness at the present stage.
If the maximum iteration number has evolved, stopping the algorithm, otherwise continuing to execute the step 3 until the iteration is finished, and determining an optimal interference allocation decision F m,n,k 。
Determining optimal interference allocation decision F by the same method m,n,k Then, solving the optimal power distribution matrix P by the ISSA algorithm m,n . The flow chart of the cooperative interference resource optimization allocation based on the ISSA is shown in fig. 5 below.
The specific implementation process of the technical scheme of the invention is further described by taking the example that 4 jammers surround a target machine to perform burst prevention on 6 radars of the enemy.
The radar, the working parameters of the target and the jammer and the position information are known, the position coordinates (unit: km) of the radar are (-8, -2, 0), (8,2,0), (-12, -6, 0), (-16, -12, 0), (16, -12, 0), the target flying azimuth θ=30°, pitch angle
The radial distance from the origin is in the range of 10-100km. The 4 jammers fly along with the target at the position 2km away from the target, the total power of the jammers is 30W, the anti-collision route is a straight line, and 3 interference patterns of noise frequency modulation interference, smart noise convolution, intermittent sampling repeated forwarding interference can be selected for cooperative interference. Wherein, radar operating parameters, targets and route parameters of the jammer are shown in tables 1 and 2.
Table 1 radar operating parameters
TABLE 2 targets and jammer route parameters
The interference pattern matching energy difference matrix of each interference machine to each radar through detection is as follows:
working parameters and position information of a target and an jammer are known, JSR of each radar at different positions is obtained by combining the jammer style matching energy difference matrix, and the Ps value of each track point is weighted and summed to obtain a networking radar positioning error C net . The two-party countermeasure scenario is shown in fig. 6.
Simulation parameter setting: setting the population scale as 50, the maximum iteration number as 200, the ratio of discoverers to additioners as 2:8, the early warning value ST=0.6, the warning sparrow number as 0.2 of the population number and the variation probability as 0.1; the ISSA algorithm is independently operated for 100 times, and the interference decision matrix allocation result is shown in table 3:
TABLE 3 interference decision matrix assignment results
Because the radar 1 is interfered by the jammer 3 in the interference mode 3, the interference effect is good, other jammers can better remove interference to other radars needing interference, three jammers respectively carry out cooperative suppression interference to the radars 3 and 6, and therefore, a good interference effect can be generated on networking radars.
Meanwhile, the ISSA algorithm is compared with the SSA algorithm, so that the effectiveness of the algorithms is compared in more detail, simulation parameters are kept consistent, the algorithms are independently operated for 100 times respectively, and the convergence condition of single operation and multiple operation of each algorithm is shown in fig. 7 and 8:
as can be seen from fig. 7, the optimization problem is a nonlinear multimodal function, and the ISSA algorithm initializes the population by means of Tent chaotic map, so that the average fitness of the primary population is higher than that of the SSA algorithm. In a single operation, the ISSA algorithm is iterated 42 times and then converged to a global optimal solution, and the SSA is iterated 123 times and then converged to the global optimal solution. In multiple operations, the ISSA algorithm converges the curve more rapidly and tends to be stable, and the final convergence value is higher than SSA algorithm, so that the optimizing capability is stronger, and the convergence speed is faster.
The difference between the single-run optimizing solution and the global optimizing solution is regarded as reaching global optimization within 1 per mill, and the performance comparison conditions of the two algorithms are shown in table 4.
TABLE 4 Table 4
Meanwhile, the difference between the global optimal solution and each running convergence value in the two algorithms is obtained, and the obtained convergence error is shown in fig. 9.
As can be seen, the optimal solution times and worst convergence errors of the ISSA algorithm in 100 times of operation are 72 times and 0.0063 respectively, the convergence errors of the ISSA algorithm are all below 0.01, and the optimal solution times and worst convergence errors of the SSA algorithm are 60 times and 0.0208 respectively.
For the problem of optimal allocation of interference beam power, further analysis and discussion are carried out, when an optimal allocation object and an optimal interference pattern under uniform power allocation are determined, the detection probability of the networking radar to the target is reduced more effectively by reasonably allocating the power of each beam, and the cooperative interference suppression capability of the multi-interference machine is improved.
The optimization problem at this time becomes:
the ISSA algorithm is also utilized to solve the problem, under the condition that an interference decision matrix is known, the power of each beam is optimized to obtain the optimal power distribution of the corresponding interference beam, and a target value function in the optimizing and optimizing process is shown in figure 10.
According to the optimizing result, the early-stage objective function value optimizing is divided into a plurality of stages, on one hand, the objective function of power distribution is a nonlinear multimodal function, and on the other hand, the ISSA algorithm has good global optimizing capability. When the iteration of single operation evolves to 13 generations, the objective function value converges to the global optimal solution 0.5507, and compared with the maximum objective function value 0.4679 of power sharing, the objective function value is greatly improved, and the reasonable distribution of beam power can more effectively implement interference.
Taking the jammer 1 as an example, fig. 11 shows the corresponding beam power allocation situation of the jammer in each iteration process, and different colors represent different power values. The power distribution results corresponding to each of the optimized jammers are shown in fig. 12, and different colors and sizes represent different power values.
For the radar 1, the jammer 3 adopts the interference pattern 3 to perform interference, only partial power is required to be allocated because of good interference effect, for the radar 3, the jammer 2 with better interference effect allocates more power, and the jammers 1 and 4 allocate less power to the radar 3 for interfering other radars which need to be interfered, so that the actual requirements are met.
And simultaneously calculating the detection probability of the radar network under different interference conditions, and verifying the interference effect after the interference resource is optimized. Fig. 13 shows the radar network monitoring probability under the following four conditions. Four conditions are divided into:
(1) Without interference.
(2) Interference resource random allocation (power average).
(3) Optimal allocation of interference resources (power averaging).
(4) Interference resource optimal allocation (power optimization).
As can be seen from the simulation result of FIG. 13, after the interference machine performs cooperative interference, the detection probability of the radar network is greatly reduced, the effective detection distance of the random interference strategy is 120km, and the interference decision distribution matrix is optimized and then groupedThe effective detection distance of the network radar is shortened to 45km, and the effective detection distance of the interference beam power is shortened to 35km after the combined optimization is carried out, so that an ISSA algorithm is adopted to carry out the interference decision matrix F m,n,k And power distribution matrix P m,n The scheme for jointly optimizing the interference resources is feasible, reasonable allocation of the resources can be realized, and a better interference effect is achieved.
In summary, aiming at a multi-jammer cooperative interference scene, a networking radar suppression probability Ps is selected as an objective function, a cooperative interference benefit mathematical model is constructed, an improved sparrow search algorithm is utilized for optimizing and solving, and compared with an SSA algorithm, simulation shows that the ISSA algorithm has stronger optimizing capability and higher convergence speed. And the transmitting power distribution result is further solved on the basis of solving the optimal distribution mode, so that the optimal interference effect is realized, and the cooperative interference capability of the multi-interference machine is improved.
Claims (10)
1. The cooperative interference resource scheduling method based on the improved sparrow search algorithm is characterized by comprising the following steps of:
1) Constructing an interference resource allocation model according to each radar set, each jammer pattern set and the power allocation of each jammer beam of the networking, wherein the interference resource allocation model comprises an interference decision matrix and a power allocation matrix;
2) Setting a power distribution matrix in an interference resource distribution model by taking uniform distribution of beam power of each jammer as a principle, constructing a first suppression probability model of the networking radar according to the interference resource distribution model, taking the first suppression probability model as a first objective function, and solving an interference decision matrix corresponding to the first objective function to the maximum by utilizing a sparrow search algorithm to obtain an optimal interference decision matrix;
3) Constructing a second suppression probability model of the networking radar based on the optimal interference decision matrix obtained by solving in the step 2), taking the second suppression probability model as a second objective function, and solving a power distribution matrix corresponding to the second objective function when the second objective function is maximum by utilizing the sparrow search algorithm to obtain an optimal power distribution matrix;
when the first objective function and the second objective function are solved by utilizing the sparrow search algorithm, if the early warning value is larger than the safety value in the step of updating the position of the discoverer of the sparrow search algorithm, randomly grouping the discoverers after encoding and sorting in pairs to form parent chromosomes, and then intersecting to generate offspring individuals;
4) And carrying out cooperative interference resource scheduling based on the obtained optimal interference decision matrix and the optimal power allocation matrix.
2. The method for scheduling cooperative interference resources based on an improved sparrow search algorithm according to claim 1, wherein in the step of updating the position of the finder in the sparrow search algorithm, if the early warning value is greater than the safety value, the encoding is allocated to the interference objects, the sorted finder is randomly grouped into groups to form parent chromosomes, an arithmetic crossover operator is adopted, child individuals are generated through linear recombination, and the formula is as follows:
wherein->
And->
For the progeny individuals produced by linear recombination, c is the crossover parameter, the value range is (0, 1), -the value range is (0)>
And->
Is the parent chromosome, and t is the current iteration number.
3. The method for scheduling cooperative interference resources based on an improved sparrow search algorithm according to claim 1, wherein in the step of updating the position of the finder in the sparrow search algorithm, if the early warning value is greater than the safety value, selecting codes for the interference patterns, and performing multi-point cross processing in a genetic algorithm on parent chromosomes to obtain offspring individuals.
4. A co-interference resource scheduling method based on an improved sparrow search algorithm according to any one of claims 1 to 3, wherein in the step of updating the guard position of the sparrow search algorithm, the adaptive mutation of the individual position is performed after the guard position is updated, and the mutation probability increases with an increase in the number of iterations.
5. The method for scheduling co-interference resources based on an improved sparrow search algorithm of claim 4, wherein the adaptive variation is formulated as:
wherein P is m (T) is the variation probability of the current iteration stage, T is the total iteration number, T is the current iteration number, P m Is the maximum variation value.
6. The collaborative interference resource scheduling method based on the improved sparrow search algorithm according to any one of claims 1-3, wherein in the step of initializing the sparrow search algorithm, a Tent chaotic sequence is adopted to initialize the population.
7. The cooperative interference resource scheduling method based on the improved sparrow search algorithm according to claim 1, wherein the expressions of the first suppression probability model and the second suppression model of the networking radar are respectively:
and->
Wherein C is 1 And C 2 First suppression probability and second suppression probability of networking radar respectivelyProbability is made, R is the distance between the jammer and the radar, F is the interference decision matrix, F Optimum for the production of a product For the optimal interference decision matrix, P is the power distribution matrix, P Uniformity of For power distribution matrix, ps, set on the principle of uniform distribution of beam power of each jammer net (R,F,P Uniformity of ) Representing a first probability of detection, ps, of a networked radar net (R,F Optimum for the production of a product P) represents a second detection probability of the networking radar, ω (R) represents a weight corresponding to the suppression probability when the distance R between the jammer and the radar is equal to R min 、R max Respectively minimum and maximum distances between the jammer and the radar.
8. The method for scheduling co-interference resources based on the improved sparrow search algorithm of claim 7, wherein the weight is calculated by:
the method comprises the steps of dividing the defense process of an interference machine into Q navigation segments at intervals of delta R, wherein Q represents the Q navigation segments, alpha is a weight factor, alpha epsilon (0, 1), and the value of alpha is related to the setting of a distance interval and R.
9. The cooperative interference resource scheduling method based on the improved sparrow search algorithm according to claim 1, wherein the constraint condition of the objective function is:
wherein P is max To the maximum power of the interfering beam, P min Denoted as the minimum power of the interfering beam,
the total power of the interference beam emitted by the jammer.
10. The method for scheduling cooperative interference resources based on an improved sparrow search algorithm according to claim 1, wherein the interference decision matrix F is:
the power distribution matrix P is:
wherein P is m,n The power allocated to radar N by jammer M is represented by M, N being the numbers of jammer and radar, m=1, 2,3, …, M, n=1, 2,3, …, N, respectively.
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