Unmanned aerial vehicle track and interference resource joint optimization method for composite interference networking radar

Abstract

The unmanned aerial vehicle flight path and interference resource joint optimization method for the composite interference networking radar comprises the following steps: introducing suppressing interference while flight path deception is carried out, and determining an unmanned aerial vehicle cluster composite interference networking radar model; the method comprises the following steps of: establishing an unmanned aerial vehicle track and interference resource joint optimization model for the composite interference networking radar by jointly optimizing and configuring beam pointing, track and power resources of an unmanned aerial vehicle and taking motion parameters of the unmanned aerial vehicle as constraints and minimizing the detection probability of the networking radar on the unmanned aerial vehicle for implementing an interference task as an optimization target; and solving the optimization model by using a hybrid optimization algorithm combining a particle swarm optimization algorithm and an interior point method to realize composite interference on the networking radar. According to the method, the difficulty of implementing flight path deception by the unmanned aerial vehicle is reduced, the feasibility of flight path deception is effectively improved, the suppression interference is introduced on the basis of deception interference, the interference effect on the networking radar is further improved by reasonably distributing interference resources, and the probability that the unmanned aerial vehicle is found is reduced.

Description

Technical Field

[0001] The present invention relates to radar electronic countermeasure technology, and in particular to a method for jointly optimizing the track and jamming resources of an unmanned aerial vehicle (UAV) for a composite jamming network radar. Background Art

[0002] In the modern military and defense fields, radar detection systems are key means of information acquisition, and their performance and reliability are directly related to battlefield situation perception and decision-making. With the continuous development of radar technology, compared with traditional single radars, networked radar systems have enhanced radar detection performance, expanded detection range, and improved anti-interference capabilities due to their advantages of multi-radar node collaboration, information sharing and fusion. Therefore, networked radars have brought new challenges to electronic countermeasures.

[0003] At present, there are various electronic jamming methods for networked radars, but traditional single jamming methods often fail to achieve ideal jamming effects, especially in the face of complex electromagnetic environments where multiple types of jamming coexist and interweave. As an emerging aerial platform, drones have shown great application potential in the field of electronic countermeasures due to their flexibility, low cost, and ability to carry multiple mission payloads. At present, suppression jamming and deception jamming are the two main methods of active jamming. Suppression jamming drowns or suppresses echo signals carrying target status information through noise or pseudo-noise jamming signals, making it difficult for networked radars to detect targets and measure target parameters normally; while deception jamming forwards radar signals by storing delayed forwarding, and deception signals can form false targets on radars, thereby achieving the effect of deceiving networked radars. However, when drones perform complex jamming on networked radars, how to plan the flight track of drones to maximize the jamming effect and reasonably allocate limited jamming resources has become a technical problem that needs to be solved urgently. Traditional drone track planning methods are mostly based on static or simple dynamic environmental models, which are difficult to adapt to the complex and changeable detection characteristics of networked radars and the rapid changes in electronic jamming environments. At the same time, the allocation of interference resources lacks systematicity and scientificity, resulting in low interference efficiency and difficulty in forming an effective interference system. Summary of the invention

[0004] Purpose of the invention: The purpose of the present invention is to provide a method for jointly optimizing the UAV track and interference resources for composite interference networking radar, so as to realize composite interference to the networking radar.

[0005] Technical solution: The method for jointly optimizing the UAV track and jamming resources for composite jamming network radar of the present invention comprises the following steps:

[0006] M drones are used to spoof the track of the networked radar system composed of N radars, forming P false tracks. At the same time, drones are introduced to suppress and interfere with the radar, and a drone cluster composite interference model for the networked radar is established.

[0007] By jointly optimizing the configuration of the UAV's beam pointing, track and power resources, and implementing track deception on the networked radar, the UAV's motion parameters are used as constraints, and the optimization goal is to minimize the detection probability of the networked radar for the UAV implementing the jamming mission. A joint optimization model of the UAV's track and jamming resources for the composite jamming networked radar is established.

[0008] A hybrid optimization algorithm combining the particle swarm optimization algorithm and the interior point method is used to solve the optimization model, and the UAV cluster beam pointing, trajectory and power resource allocation results that meet the constraints are obtained. In this way, composite interference to the networked radar is achieved while satisfying the pre-set UAV dynamic constraints and generating an effective deception trajectory.

[0009] Furthermore, the motion control equation of the UAV when implementing the deception jamming mission is:

[0010]

[0011] Among them, β e is the flight pitch angle of the UAV, θ is the pitch angle of the UAV in the direction of the radar, α e is the flight angle of the UAV, φ is the angle of the UAV in the direction of the radar, φ' is the derivative of φ, θ' is the derivative of θ, r' is the derivative of r, r is the distance from the UAV to the radar, is the square of the UAV’s flight speed;

[0012] The UAV always satisfies the coupling relationship between the radar, the UAV and the false target during the implementation of the track deception jamming mission, and the false target position and the radar position are known a priori. The motion variables of the UAV will be constrained in one dimension, that is, only by controlling its flight direction angle α e It is possible to realize track control under track deception coupling constraints;

[0013] The suppression interference adopts noise interference, and the noise interference signal is expressed as:

[0014]

[0015] Where J(t) is the noise interference signal, is the noise interference signal amplitude, f j is the center frequency of the noise interference signal, K FM is the frequency modulation slope, μ(t') is the modulation noise, ψ is the phase function of the noise interference signal, exp is the exponential operation, j represents the imaginary operation, t represents the signal changing with time, and ['] is the derivative operation; the above formula is simplified to:

[0016]

[0017] Among them, s m,n,k (t) is the normalized complex envelope of the suppressed interference signal.

[0018] Furthermore, the radar adopts a self-transmitting and self-receiving working mode. Each radar only receives and processes the echo signal sent by itself and scattered by the target. Each radar can detect M drones at time k, so each radar will receive M echo signals. Under the condition of suppressing interference, the radar will also receive interference signals emitted by multiple drones. The signal received by radar n at time k is:

[0019]

[0020] Among them, r n,k (t) represents the signal received by radar n at the kth moment, r m,n,k (t) represents the echo signal of the mth UAV received by radar n at the kth moment; j m,n,k (t) indicates that at the kth moment, radar n receives the suppression jamming signal from the mth UAV; w n,k (t) represents the radar receiver’s own thermal noise of radar n at the kth moment, and t indicates that these signals all vary with time;

[0021] When M UAVs cooperate to suppress a single radar, the total interference signal power received by the radar is the sum of the power of each interference signal. According to the power superposition principle, the total interference signal power received by radar n when detecting UAV m is for:

[0022]

[0023] in, is the suppression jamming signal power received by radar n from the mth UAV at the kth moment, u m,n,k is the beam pointing situation of the mth UAV to the nth radar at the kth moment, P m,n,k is the interference power of the mth UAV on the nth radar at the kth moment.

[0024] Furthermore, the expression of the joint optimization model of UAV track and jamming resources for composite jamming network radar is:

[0025]

[0026] in, To improve the detection performance of networked radar on drone clusters, Pd k (u k ,P k ,R k ) is the detection probability of the networked radar to the drone cluster at the kth moment, u kis the UAV cluster beam pointing matrix at the kth moment, P k is the interference power matrix of the UAV cluster at the kth moment, R k is the distance matrix between the drone cluster and the radar at the kth moment, is the detection probability requirement, P m,n,k is the interference power of the mth UAV to the nth radar at the kth moment, P min is the lower limit of the UAV beam interference power, P max is the upper limit of the UAV beam interference power, u m,n,k is the beam pointing situation of the mth UAV to the nth radar at the kth moment, is the total power of the jammer, L is the number of beams generated by each drone, S is the maximum number of beams interfered, △α e is the change in the flight angle of the drone, △α max is the maximum value of the change in the flight direction angle of the drone, △α min is the minimum value of the change in the flight direction angle of the drone, △β e is the change in the pitch angle of the UAV, △β max is the maximum value of the change in the pitch angle of the UAV flight, △β min is the minimum value of the change in the pitch angle of the UAV flight, v e is the flight speed of the drone, v max is the upper limit of the UAV flight speed, v min The lower limit of the UAV flight speed.

[0027] Furthermore, the networked radar adopts the KN criterion, that is, when the number of radar nodes that discover UAV m in the networked radar exceeds the detection threshold K, 1≤K≤N, it is determined that the networked radar has discovered UAV m, otherwise it is determined that the UAV has not been discovered; as shown in the following formula:

[0028]

[0029] in, Indicates the judgment result of the network radar on the drone m; represents the decision result of radar n and According to the KN criterion, the detection probability of the networked radar to the UAV m at time k is:

[0030]

[0031] in, is the detection probability of the networked radar to UAV m at the kth moment, is the equivalent expression of the detection probability of the networked radar to the UAV m at the kth moment, u k is the UAV cluster beam pointing matrix at the kth moment, P kis the interference power matrix of the UAV cluster at the kth moment, R k is the distance matrix between the drone cluster and the radar at the kth moment, is the detection probability of radar n to drone m at the kth moment, is the decision of radar n on drone m at the kth moment, It represents the permutation and combination whose sum of the judgment results of N radar nodes on UAV m is j;

[0032] The vector Pd formed by the detection probability of the networked radar for M drones k As a performance evaluation indicator for suppressing jamming network radars:

[0033]

[0034] Among them, Pd k (u k ,P k ,R k ) is the detection probability matrix of the networked radar to the drone cluster at the kth moment;

[0035] Detection probability requirements for multiple targets for:

[0036]

[0037] in, To meet the detection requirements for the first drone, To meet the detection requirements for the second drone, The detection requirement for the Mth UAV;

[0038] Based on the principle of quality of service, a global cost function is established:

[0039]

[0040]

[0041] in, To improve the detection performance of networked radar on drone clusters, Pd k (u k ,P k ,R k ) is the detection probability matrix of the networked radar to the drone cluster at the kth moment, The detection performance of the networked radar on the mth UAV, is the detection probability of the mth drone cluster by the networked radar at the kth moment, is the detection requirement for the mth UAV.

[0042] Furthermore, the optimization model solution method is:

[0043] (1) Based on the preset false targets and radar position prior information, the optimal trajectory of the UAV that satisfies the dynamic constraints of the optimization model in this scenario is obtained using the particle swarm optimization algorithm, and the optimal distance correspondence between the UAV and the radar at each moment is obtained.

[0044] (2) In seeking Based on this, it is assumed that the UAV can interfere with all radar nodes at the same time and the transmission power of each beam is evenly distributed. is the interference power of the UAV under the condition of uniform power distribution, is the total power of the jammer; for the convenience of solving, the beam is directed to the variable u k Relaxed to a continuous variable in the interval [0,1]

[0045] At this point, the optimization problem is simplified to:

[0046]

[0047]

[0048] in, In order to simplify the detection performance of the rear network radar for drone clusters, is the detection probability matrix of the networked radar to the drone cluster, is the interference power matrix of drone cluster under average radiation power, is the direction of the mth UAV to the nth radar under the average radiated power;

[0049] The interior point method is used to solve the above equation to obtain the beam pointing variable u k The relaxation result And initialize the beam pointing result Under the constraints, The maximum value is Set 1 in the middle and repeat the process of The maximum value is Set to 1 in the middle until all beam resources of the M jammers are exhausted, and finally the optimal allocation method of beam pointing is obtained

[0050] (3) In seeking and Based on this, the interference power allocation result is solved At this point, the optimization problem has been simplified to:

[0051]

[0052] Similarly, the interior point method is used to obtain the optimal interference power allocation result

[0053] Furthermore, step (1) includes the following steps:

[0054] (a) Initialize population size, population inertia, particle state and fitness function;

[0055] (b) Iteratively optimizing the flight speed, flight direction angle, and flight pitch angle parameters of the UAV at that moment;

[0056] (c) Return to repeat step (b) until the absolute value of the difference between the two optimization objective functions is less than any minimum value ε>0, thereby obtaining the optimal UAV flight speed, flight direction angle and flight pitch angle motion state parameters at the current moment;

[0057] (d) Based on the coupling relationship between the false target, the UAV and the radar, the initial position of the UAV at the next moment is solved;

[0058] (e) Repeat steps (b) to (d) until the distributed online decision-making of the flight trajectory of the UAV cluster is completed, that is, the optimal distance correspondence between the UAV and the radar at each moment is obtained.

[0059] The system corresponding to the above method includes:

[0060] The composite jamming model building unit is used to implement track deception on a networked radar system consisting of N radars using M drones to form P false tracks, while introducing suppression jamming of the radar by drones to establish a drone cluster composite jamming networked radar model;

[0061] The optimization model building unit is used to configure the beam pointing, track and power resources of the UAV through joint optimization. On the basis of implementing track deception on the networked radar, the UAV motion parameters are used as constraints, and the detection probability of the networked radar for the UAV implementing the interference mission is minimized as the optimization goal. A joint optimization model of the UAV track and interference resources for the composite interference networked radar is established;

[0062] The optimization model solving unit is used to solve the optimization model by adopting a hybrid optimization algorithm that combines the particle swarm optimization algorithm with the interior point method, and obtain the UAV cluster beam pointing, track and power resource allocation results that meet the constraints, thereby achieving composite interference to the networked radar under the premise of satisfying the pre-set UAV dynamic constraints and generating an effective deception track.

[0063] The control device of the above method comprises a processor, a communication interface, a memory and a communication bus;

[0064] Wherein, the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0065] The memory is used to store computer programs;

[0066] The processor is used to implement the steps of the method for jointly optimizing the UAV track and interference resources for the composite interference networking radar when executing the program stored in the memory.

[0067] A storage medium for storing the above method, wherein a computer program is stored on the storage medium, and when the computer program is executed by at least one processor, the steps of the above-mentioned method for jointly optimizing the UAV track and interference resources for the composite interference networking radar are implemented.

[0068] Beneficial effects: Compared with the prior art, the significant technical effects of the present invention are as follows: (1) Under the condition of limited interference resources, by jointly optimizing the configuration of the UAV's beam pointing, track and power resources, on the basis of implementing track deception on the networked radar, the probability of the networked radar detecting the UAV implementing the interference mission is minimized, thereby reducing the exposure risk of the UAV and effectively improving the success rate of the interference mission and the survivability of the UAV; (2) The two interference methods of deception interference and suppression interference are integrated to effectively weaken and disrupt the detection and tracking capabilities of the radar system, which not only meets the pre-set strict UAV dynamic constraints, reduces the difficulty of the UAV to implement track deception, and effectively improves the feasibility of track deception, but also introduces suppression interference on the basis of deception interference. By reasonably allocating interference resources, the interference effect on the networked radar is further improved, and the probability of the UAV being discovered is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 is a flow chart of the method of the present invention;

[0070] Figure 2 It is a schematic diagram of composite interference;

[0071] Figure 3 It is a composite interference simulation scene diagram;

[0072] Figure 4 Schematic diagram of UAV motion parameters, where (a) is the flight direction angle of the UAV, (b) is the flight pitch angle of the UAV, and (c) is the flight speed of the UAV;

[0073] Figure 5 This is a schematic diagram of the distance between the drone and the radar;

[0074] Figure 6Schematic diagram of the optimization result of suppressing interference resources, where (a) is the interference resource allocation result of the drone cluster to radar 1, (b) is the interference resource allocation result of the drone cluster to radar 2, and (c) is the interference resource allocation result of the drone cluster to radar 3;

[0075] Figure 7 Schematic diagram of the probability of radar detecting UAV when suppression interference is not implemented;

[0076] Figure 8 Schematic diagram of the detection probability of each radar to the UAV after the suppression interference is implemented;

[0077] Fig. 9 Schematic diagram of the detection probability of the networked radar to the drone after the suppression interference is implemented;

[0078] Fig.10 Schematic diagram of networked radar detection performance after implementing composite interference. DETAILED DESCRIPTION

[0079] The structure and working process of the present invention are further described below in conjunction with specific embodiments and drawings.

[0080] like Figure 1 As shown, the optimization method of the present invention comprises the following steps:

[0081] 1. Establish the radar model of drone cluster composite interference network:

[0082] M drones are used to spoof the track of a networked radar system composed of N radars, forming P false tracks. In order to reduce the probability of the enemy's networked radar detecting its own drone, the drone is used to perform deceptive interference on the networked radar while exerting suppression interference. Assuming that the parameters of each enemy radar (such as radar position, carrier frequency, pulse width, etc.) have been obtained, and the radar position, drone position, and false target position are all known, the composite interference diagram is as follows: Figure 2 shown.

[0083] The motion control equation of the drone when implementing the deception jamming mission is as follows:

[0084]

[0085] Among them, β e is the flight pitch angle of the UAV, θ is the pitch angle of the UAV in the direction of the radar, α e is the flight angle of the UAV, φ is the angle of the UAV in the direction of the radar, φ' is the derivative of φ, θ' is the derivative of θ, r' is the derivative of r, r is the distance from the UAV to the radar, is the square of the UAV’s flight speed.

[0086] From formula (1), we can see that since the UAV needs to always satisfy the coupling relationship between the radar, the UAV and the false target during the implementation of the track deception jamming mission, and the false target position and the radar position are known a priori, the UAV's motion variables will be constrained in one dimension, that is, only by controlling its flight direction angle α e The track control under the track deception coupling constraint can be realized.

[0087] On the basis of implementing track deception on networked radar, the present invention introduces suppression interference to improve the success rate of deception, thereby enhancing the interference effect on networked radar. The present invention mainly uses noise interference, and the noise interference signal implemented by drone m on radar n can be expressed as:

[0088]

[0089] Where J(t) is the noise interference signal, is the noise interference signal amplitude, f j is the center frequency of the noise interference signal, K FM is the frequency modulation slope, μ(t') is the modulation noise, ψ is the phase function of the noise interference signal, exp is the exponential operation, j represents the imaginary operation, t represents the signal changing with time, and ['] represents the derivative operation;. Equation (2) can be simplified as:

[0090]

[0091] Among them, s m,n,k (t) is the normalized complex envelope of the suppressed interference signal.

[0092] The present invention considers that the radar adopts a self-transmitting and self-receiving working mode. Each radar only receives and processes the echo signal sent by itself and scattered by the target. Each radar can detect M drones at time k, so each radar will receive M echo signals. Under the condition of suppressing interference, the radar will also receive interference signals emitted by multiple drones. It can be obtained that the signal received by radar n at time k is:

[0093]

[0094] Among them, r n,k (t) represents the signal received by radar n at the kth moment, r m,n,k (t) represents the echo signal of the mth UAV received by radar n at the kth moment; j m,n,k (t) indicates that at the kth moment, radar n receives the suppression jamming signal from the mth UAV; w n,k (t) represents the radar receiver’s own thermal noise of radar n at the kth moment, and t represents the variation of the signal over time.

[0095] When M UAVs cooperate to suppress a single radar, the total interference signal power received by the radar is the sum of the power of each interference signal. According to the power superposition principle, the total interference signal power received by radar n when detecting UAV m is for:

[0096]

[0097] in, is the suppression jamming signal power received by radar n from the mth UAV at the kth moment, u m,n,k is the beam pointing situation of the mth UAV to the nth radar at the kth moment, P m,n,k is the interference power of the mth UAV on the nth radar at the kth moment.

[0098] 2. Construct a joint optimization model of UAV tracks and jamming resources for composite jamming network radar:

[0099] Considering that the drone uses a multi-beam jammer system, it can generate multiple power-controllable beams to interfere with multiple radar nodes at the same time. and To represent the variables of the jamming suppression task. Define the beam pointing vector of the drone cluster Indicates whether UAV m is assigned beam jammer radar n,u m,n,k is the beam pointing situation of the mth UAV to the nth radar at the kth time, n=1,…,N; define the transmission power vector of the UAV represents the interference power emitted by each UAV at the kth moment, P m,n,k is the interference power of the mth UAV on the nth radar at the kth moment; [·] T represents the transpose operation, u m,n,k and P m,n,k The following constraints need to be met:

[0100]

[0101] Among them, L is the number of beams generated by each drone, S is the maximum number of beams interfered, and P min is the lower limit of the UAV beam interference power, P max is the upper limit of the UAV beam jammer power, is the total jammer power.

[0102] Among them, considering the limitations of UAV equipment, constraint 1 means that each UAV can generate at most L beams; considering that all radar nodes can be interfered, constraint 2 means that the number of beams allocated to each radar does not exceed S; constraint 3 means that the transmission power of the beam must be within the set power range; constraint 4 means that the total power of each UAV is certain; constraints 5 and 6 mean that when UAV m allocates a beam to interfere with radar n, the power of this beam is not 0; otherwise the transmission power is 0.

[0103] Consider the KN criterion (rank K criterion) used by the networked radar. That is, when the number of radar nodes that discover drone m in the networked radar exceeds the detection threshold K (1≤K≤N), it is determined that the networked radar has discovered drone m, otherwise it is determined that the target has not been discovered. As shown in the following formula:

[0104]

[0105] in, represents the decision result of the networked radar on UAV m at the kth moment; represents the decision result of radar n on drone m at the kth moment and According to the rank K fusion criterion, the detection probability of the networked radar to the drone m at time k is:

[0106]

[0107] in, is the detection probability of the networked radar to UAV m at the kth moment, is the equivalent expression of the detection probability of the networked radar to the UAV m at the kth moment, u k is the UAV cluster beam pointing matrix at the kth moment, P k is the interference power matrix of the UAV cluster at the kth moment, R k is the distance matrix between the drone cluster and the radar at the kth moment, is the detection probability of radar n to drone m at the kth moment, is the decision of radar n on drone m at the kth moment, It represents the permutation and combination in which the sum of the judgment results of N radar nodes on UAV m is j.

[0108] The vector Pd formed by the detection probability of the networked radar for M drones k Can be used as a performance evaluation indicator for suppressing interference network radars:

[0109]

[0110] Among them, Pd k (u k ,P k ,R k) is the detection probability matrix of the networked radar to the drone cluster at the kth moment.

[0111] Considering that in the actual execution of the composite jamming task, each drone has the same importance, that is, the detection probability requirement for each drone is approximately the same, the detection probability requirement for multiple targets is defined as for:

[0112]

[0113] in, To meet the detection requirements for the first drone, To meet the detection requirements for the second drone, The detection requirement for the Mth drone.

[0114] Based on the principle of quality of service, a global cost function is established:

[0115]

[0116]

[0117] in, To improve the detection performance of networked radar on drone clusters, Pd k (u k ,P k ,R k ) is the detection probability matrix of the networked radar to the drone cluster at the kth moment, The detection performance of the networked radar on the mth UAV, is the detection probability of the mth drone cluster by the networked radar at the kth moment, The detection requirement of the networked radar for the mth UAV at the kth moment.

[0118] The composite jamming algorithm for networked radar proposed in the present invention aims to minimize the detection probability of the networked radar for the UAV implementing the jamming task by jointly optimizing the configuration of the UAV's beam pointing, track and power resources under the condition of limited jamming resources, on the basis of implementing track deception on the networked radar. The optimization model can be established as follows:

[0119]

[0120] in, is the total power of the jammer, △α e is the change in the flight angle of the drone, △α max is the maximum value of the change in the flight direction angle of the drone, △α min is the minimum value of the change in the flight direction angle of the drone, △β e is the change in the pitch angle of the UAV, △βmax is the maximum value of the change in the pitch angle of the UAV flight, △β min is the minimum value of the change in the pitch angle of the UAV flight, v e is the flight speed of the drone, v max is the upper limit of the UAV flight speed, v min is the lower limit of the UAV flight speed; constraints 1 and 2 indicate that the interference beam pointing and interference power are coupled with each other, and the interference power has upper and lower limit constraints; constraint 3 indicates that each UAV has a corresponding interference power constraint; constraint 4 indicates that the number of interference beams generated by each UAV does not exceed L, and each radar will be interfered with and will be interfered with by at most S beams; constraints 5, 6, and 7 represent the kinematic constraints of the UAV, and the change values ​​of the heading angle and pitch angle of the UAV cannot exceed the upper and lower limits of the constraints.

[0121] 3. Use particle swarm optimization algorithm and interior point method to solve the optimization model:

[0122] The present invention adopts a three-step solution method, and the specific steps are as follows:

[0123] (1) Based on the prior information such as the preset false target and the radar position, the optimal trajectory that satisfies the UAV dynamics constraint of formula (13) is obtained according to the particle swarm optimization algorithm, and the optimal distance correspondence between the UAV and the radar at each moment is obtained.

[0124] The specific solution steps are as follows:

[0125] (a) Initialize population size, population inertia, particle state and fitness function;

[0126] (b) Iteratively optimizing the parameters such as the flight speed, flight direction angle, and flight pitch angle of the UAV at that moment;

[0127] (c) Return to repeat step (b) until the absolute value of the difference between the two optimization objective functions is less than any minimum value ε>0, thereby obtaining the optimal UAV flight speed, flight direction angle, flight pitch angle and other motion state parameters at the current moment;

[0128] (d) Based on the coupling relationship between the false target, the UAV and the radar, the initial position of the UAV at the next moment is solved;

[0129] (e) Repeat steps (b)-(d) until the distributed online decision-making of the flight trajectory of the UAV cluster is completed, that is, the optimal distance correspondence between the UAV and the radar is obtained.

[0130] (2) In seeking Based on this, it is assumed that the UAV can interfere with all radar nodes at the same time and the transmission power of each beam is evenly distributed. is the interference power of the UAV under the condition of uniform power distribution, is the total power of the jammer. To facilitate the solution, the beam needs to be pointed to the variable u k Relaxed to a continuous variable in the interval [0,1]

[0131] At this time, the optimization problem (13) is simplified to:

[0132]

[0133]

[0134] in, In order to simplify the detection performance of the rear network radar for drone clusters, is the detection probability matrix of the networked radar to the drone cluster, is the interference power matrix of drone cluster under average radiation power, This is the direction of the mth UAV towards the nth radar under average radiated power.

[0135] The interior point method is used to solve equation (14) to obtain the beam pointing variable u k Relaxation results And initialize the beam pointing result Under the constraint condition of formula (6), The maximum value is Set 1 in the middle and repeat the above steps (the above steps refer to continuously setting The maximum value is 1) until all beam resources of the M jammers are exhausted, and finally the optimal allocation method of beam pointing is obtained

[0136] The specific solution steps are as follows:

[0137] (a) Initialization

[0138] (b) Solve equation (14) using the interior point method and obtain the relaxed optimal

[0139] (c) Find the The maximum value in , update the corresponding It represents the optimal allocation result of the beam pointing of the m-th UAV to the radar n at the k-th time;

[0140] (d) Repeat step (c) cyclically until the jammer beam resources are exhausted under the constraints;

[0141] (e) Output optimal beam pointing

[0142] (3) In seeking and Based on this, the interference power allocation result is solved At this point, the optimization problem has been simplified to:

[0143]

[0144] Similarly, the interior point method is used to obtain the optimal interference power allocation result

[0145] The system corresponding to the above method includes:

[0146] The composite jamming model building unit is used to implement track deception on a networked radar system consisting of N radars using M drones to form P false tracks, while introducing suppression jamming of the radar by drones to establish a drone cluster composite jamming networked radar model;

[0147] The optimization model building unit is used to configure the beam pointing, track and power resources of the UAV through joint optimization. On the basis of implementing track deception on the networked radar, the UAV motion parameters are used as constraints, and the detection probability of the networked radar for the UAV implementing the interference mission is minimized as the optimization goal. A joint optimization model of the UAV track and interference resources for the composite interference networked radar is established;

[0148] The optimization model solving unit is used to solve the optimization model by adopting a hybrid optimization algorithm that combines the particle swarm optimization algorithm with the interior point method, and obtain the UAV cluster beam pointing, track and power resource allocation results that meet the constraints, thereby achieving composite interference to the networked radar under the premise of satisfying the pre-set UAV dynamic constraints and generating an effective deception track.

[0149] The control device of the above method comprises a processor, a communication interface, a memory and a communication bus;

[0150] Wherein, the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0151] The memory is used to store computer programs;

[0152] The processor is used to implement the steps of the method for jointly optimizing the UAV track and interference resources for the composite interference networking radar when executing the program stored in the memory.

[0153] A storage medium for storing the above method, wherein a computer program is stored on the storage medium, and when the computer program is executed by at least one processor, the steps of the above-mentioned method for jointly optimizing the UAV track and interference resources for the composite interference networking radar are implemented.

[0154] Simulation results:

[0155] Assume that the networked radar system consists of N = 3 radars, M = 12 drones interfere with the networked radar system, and the networked radar adopts the rank 2 fusion criterion. The composite interference simulation scenario is as follows: Figure 3 As shown. The upper and lower limits of the UAV beam interference power are and The parameters of each UAV and each radar are the same, as shown in Table 1 and Table 2 respectively.

[0156] Table 1 Jammer operating parameters

[0157]

[0158] Table 2 Radar operating parameters

[0159]

[0160] The motion trajectory, false track and spatial position of the UAV cluster when implementing composite interference on the networked radar are as follows: Figure 3 The motion parameters of the drone are shown in Figure 4 As shown in (a) to (c).

[0161] Depend on Figure 4 From (a) to (c), we can see that the heading angle of the drone is used as the control parameter of its navigation. Under the given conditions of the scene, the heading angle, pitch angle and speed of the drone are coupled. At the same time, it can be seen from Table 1 that since the objective function to be optimized has only one minimum value, Figure 4 In the [0,5] moment of (a), the change in the heading angle of the drone has a large fluctuation, and it approaches the optimal value of the objective function as much as possible within the constraint range; in the remaining moments, the drone adjusts its heading angle according to the spatial position relationship between the preset false target and the radar, and at this time, the change in the heading angle tends to be gentle. It can be seen from formula (1) that in the coupling relationship, the flight speed of the drone is positively correlated with its pitch angle, so, Figure 4 (b) and Figure 4 The changing trends of motion parameters in (c) are similar.

[0162] Figure 6 This is the optimization result of suppression interference resources for networked radar in this scenario. Figure 6 (a) is the result of the interference resource allocation of the UAV to the radar 1. Figure 6(b) is the result of the UAV's interference resource allocation to Radar 2. Figure 6 (c) shows the interference resource allocation result of the UAV to radar 3. Figure 7 The detection probability of the three radars for the 12 drones when no suppression interference is implemented. At this time, the detection probability of the networked radar for each drone is 100%.

[0163] Combination Figure 6 , Figure 7 It can be seen that the interference resource optimization result is related to the detection probability of each radar to the corresponding drone when the suppression interference is not implemented. For example, in the simulation scenario, radar 2 has the highest detection probability for drones 2, 5, 8, and 11. Therefore, these four drones will allocate more resources to suppress radar 2 to reduce the interference performance of the networked radar. In addition, combined with Figure 3 , Figure 5 and Figure 7 It can be seen that the distance between the UAV and each radar is between [50, 100] km. The spatial position change caused by the low-speed movement of the UAV has a negligible effect on the detection probability of the radar. Figure 6 The optimal jamming resource strategy of each UAV remains basically stable to achieve the optimal jamming effect.

[0164] Figure 8 The detection probability of each radar for the UAV after the suppression interference is implemented is shown. Fig. 9 The detection probability of 12 UAVs by a networked radar system consisting of three radar systems is shown. Fig.10 It shows the degradation of UAV detection performance of networked radar under composite interference.

[0165] Combination Figure 5 , Fig. 9 and Fig.10 It can be seen that since the distance between UAVs 3, 6, and 9 and the three radars is closer than that of other UAVs, the probability of detection of them by the networked radar is higher. When optimizing the detection performance of the networked radar for the UAV cluster, the algorithm prioritizes allocating more resources to reduce the detection probability of the networked radar for other UAVs to ensure the optimal algorithm performance. Therefore, Fig. 9 and Fig.10 In the figure, the detection probability of networked radar for UAVs 3, 6, and 9 is higher than that for other UAVs. The detection performance of networked radar for UAVs 3, 6, and 9 decreases by about 60%, and the detection performance for other UAVs decreases by about 80%.

[0166] Working principle and working process of the present invention:

[0167] The present invention takes into account the risk of drone clusters being discovered by networked radars in actual scenarios, and proposes to use M drones to attack a networked radar system composed of N early warning radars, and adopts a composite interference method that combines deception interference with suppression interference to solve this problem, that is, while implementing effective track deception on the networked radar, suppressive interference is applied to suppress the detection and tracking of the drone cluster by the networked radar system, which can improve the success rate of track deception, reduce the risk of drone clusters being discovered by networked radars, and enhance the interference effect on the networked radar. It is assumed that the parameters of each enemy radar (such as radar position, carrier frequency, pulse width, etc.) have been obtained, and the radar position, drone position, and false target position are all known, and the motion of the drone and the false track meets the dynamic constraints. The present invention is guided by the composite jamming effect, designs a joint optimization method for UAV tracks and jamming resources, takes certain UAV motion parameters as constraints, aims to minimize the detection probability of the networked radar for the UAV implementing the jamming task by jointly optimizing the configuration of the UAV's beam pointing, track and power resources under the condition of limited jamming resources, and establishes a joint optimization model of UAV tracks and jamming resources for composite jamming networked radar on the basis of implementing track deception on the networked radar. On this basis, a hybrid optimization algorithm combining the particle swarm optimization algorithm and the interior point method is used to solve the optimization model, and the UAV cluster beam pointing, track and power resource allocation results that meet the constraints are obtained. At the same time, the parameter design of the deception signal is completed in real time, so as to achieve composite jamming of the networked radar under the premise of meeting the pre-set UAV dynamic constraints and generating an effective deception track.

Claims

1. A joint optimization method of UAV track and jamming resources for composite jamming network radar, characterized in that: The following steps are involved: M drones are used to spoof the track of the networked radar system composed of N radars, forming P false tracks. At the same time, drones are introduced to suppress and interfere with the radar, and a drone cluster composite interference model for the networked radar is established. By jointly optimizing the configuration of the UAV's beam pointing, track and power resources, and implementing track deception on the networked radar, the UAV's motion parameters are used as constraints, and the optimization goal is to minimize the detection probability of the networked radar for the UAV implementing the jamming mission. A joint optimization model of the UAV's track and jamming resources for the composite jamming networked radar is established. A hybrid optimization algorithm combining the particle swarm optimization algorithm and the interior point method is used to solve the optimization model, and the UAV cluster beam pointing, trajectory and power resource allocation results that meet the constraints are obtained. In this way, composite interference to the networked radar is achieved while satisfying the pre-set UAV dynamic constraints and generating an effective deception trajectory.

2. The method for joint optimization of UAV tracks and jamming resources for composite jamming network radar according to claim 1 is characterized in that: The motion control equation of the UAV when implementing the deception jamming mission is: Among them, β e is the flight pitch angle of the UAV, θ is the pitch angle of the UAV in the direction of the radar, α e is the flight angle of the UAV, φ is the angle of the UAV in the direction of the radar, φ' is the derivative of φ, θ' is the derivative of θ, r' is the derivative of r, r is the distance from the UAV to the radar, is the square of the UAV’s flight speed; The UAV always satisfies the coupling relationship between the radar, the UAV and the false target during the implementation of the track deception jamming mission, and the false target position and the radar position are known a priori. The motion variables of the UAV will be constrained in one dimension, that is, only by controlling its flight direction angle α e It is possible to realize track control under track deception coupling constraints; The suppression interference adopts noise interference, and the noise interference signal is expressed as: Where J(t) is the noise interference signal, is the noise interference signal amplitude, f j is the center frequency of the noise interference signal, K FM is the frequency modulation slope, μ(t') is the modulation noise, ψ is the phase function of the noise interference signal, exp is the exponential operation, j represents the imaginary operation, t represents the signal changing with time, and ['] is the derivative operation; the above formula is simplified to: Among them, s m,n,k (t) is the normalized complex envelope of the suppressed interference signal.

3. The method for joint optimization of UAV tracks and jamming resources for composite jamming network radar according to claim 1 is characterized in that: The radar adopts a self-transmitting and self-receiving working mode. Each radar only receives and processes the echo signal sent by itself and scattered by the target. Each radar can detect M drones at time k, so each radar will receive M echo signals; under the condition of suppressing interference, the radar will also receive interference signals emitted by multiple drones; the signal received by radar n at time k is: Among them, r n,k (t) represents the signal received by radar n at the kth moment, r m,n,k (t) represents the echo signal of the mth UAV received by radar n at the kth moment; j m,n,k (t) indicates that at the kth moment, radar n receives the suppression jamming signal from the mth UAV; w n,k (t) represents the radar receiver’s own thermal noise of radar n at the kth moment, and t indicates that these signals all vary with time; When M drones cooperate to suppress a single radar, the total interference signal power received by the radar is the sum of the power of each interference signal. According to the power superposition principle, the total interference signal power received by radar n when detecting drone m is for: in, is the suppression jamming signal power received by radar n from the mth UAV at the kth moment, u m,n,k is the beam pointing situation of the mth UAV to the nth radar at the kth moment, P m,n,k is the interference power of the mth UAV on the nth radar at the kth moment.

4. The method for joint optimization of UAV tracks and jamming resources for composite jamming network radar according to claim 1 is characterized in that: The expression of the joint optimization model of UAV track and jamming resources for composite jamming network radar is: in, To improve the detection performance of networked radar on drone clusters, Pd k (u k ,P k ,R k ) is the detection probability of the networked radar to the drone cluster at the kth moment, u k is the UAV cluster beam pointing matrix at the kth moment, P k is the interference power matrix of the UAV cluster at the kth moment, R k is the distance matrix between the drone cluster and the radar at the kth moment, is the detection probability requirement, P m,n,k is the interference power of the mth UAV to the nth radar at the kth moment, P min is the lower limit of the UAV beam interference power, P max is the upper limit of the UAV beam interference power, u m,n,k is the beam pointing situation of the mth UAV to the nth radar at the kth moment, is the total power of the jammer, L is the number of beams generated by each drone, S is the maximum number of beams interfered, △α e is the change in the flight angle of the drone, △α max is the maximum value of the change in the flight direction angle of the drone, △α min is the minimum value of the change in the flight direction angle of the drone, △β e is the change in the pitch angle of the UAV, △β max is the maximum value of the change in the pitch angle of the UAV flight, △β min is the minimum value of the change in the pitch angle of the UAV flight, v e is the flight speed of the drone, v max is the upper limit of the UAV flight speed, v min The lower limit of the UAV flight speed.

5. The method for joint optimization of UAV tracks and jamming resources for composite jamming network radar according to claim 4 is characterized in that: The networked radar adopts the KN criterion, that is, when the number of radar nodes that discover UAV m in the networked radar exceeds the detection threshold K, 1≤K≤N, it is determined that the networked radar has discovered UAV m, otherwise it is determined that the UAV has not been discovered; As shown below: in, Indicates the judgment result of the network radar on the drone m; represents the decision result of radar n and According to the KN criterion, the detection probability of the networked radar to the UAV m at time k is: in, is the detection probability of the networked radar to UAV m at the kth moment, is the equivalent expression of the detection probability of the networked radar to the UAV m at the kth moment, u k is the UAV cluster beam pointing matrix at the kth moment, P k is the interference power matrix of the UAV cluster at the kth moment, R k is the distance matrix between the drone cluster and the radar at the kth moment, is the detection probability of radar n to drone m at the kth moment, is the decision of radar n on drone m at the kth moment, It represents the permutation and combination whose sum of the judgment results of N radar nodes on UAV m is j; The vector Pd formed by the detection probability of the networked radar for M drones k As a performance evaluation indicator for suppressing jamming network radars: Among them, Pd k (u k ,P k ,R k ) is the detection probability matrix of the networked radar to the drone cluster at the kth moment; Detection probability requirements for multiple targets for: in, To meet the detection requirements for the first drone, To meet the detection requirements for the second drone, The detection requirement for the Mth UAV; Based on the principle of quality of service, a global cost function is established: in, To improve the detection performance of networked radar on drone clusters, Pd k (u k ,P k ,R k ) is the detection probability matrix of the networked radar to the drone cluster at the kth moment, The detection performance of the networked radar on the mth UAV, is the detection probability of the mth drone cluster by the networked radar at the kth moment, is the detection requirement for the mth UAV.

6. The method for joint optimization of UAV tracks and jamming resources for composite jamming network radar according to claim 1 is characterized in that: The optimization model solution method is: (1) Based on the preset false targets and radar position prior information, the optimal trajectory of the UAV that satisfies the dynamic constraints of the optimization model in this scenario is obtained using the particle swarm optimization algorithm, and the optimal distance correspondence between the UAV and the radar at each moment is obtained. (2) In seeking Based on this, it is assumed that the UAV can interfere with all radar nodes at the same time and the transmission power of each beam is evenly distributed. is the interference power of the UAV under the condition of uniform power distribution, is the total power of the jammer; for the convenience of solving, the beam is directed to the variable u k Relaxed to a continuous variable in the interval [0,1] At this point, the optimization problem is simplified to: in, In order to simplify the detection performance of the rear network radar for drone clusters, is the detection probability matrix of the networked radar to the drone cluster, is the interference power matrix of drone cluster under average radiation power, is the direction of the mth UAV to the nth radar under the average radiated power; The interior point method is used to solve the above equation to obtain the beam pointing variable u k The relaxation result And initialize the beam pointing result Under the constraints, The maximum value is Set 1 in the middle and repeat the process of The maximum value is Set to 1 in the middle until all beam resources of the M jammers are exhausted, and finally the optimal allocation method of beam pointing is obtained (3) In seeking and Based on this, the interference power allocation result is solved At this point, the optimization problem has been simplified to: Similarly, the interior point method is used to obtain the optimal interference power allocation result 7. The method for joint optimization of UAV tracks and jamming resources for composite jamming network radar according to claim 6 is characterized in that: Step (1) comprises the following steps: (a) Initialize population size, population inertia, particle state and fitness function; (b) Iteratively optimizing the flight speed, flight direction angle, and flight pitch angle parameters of the UAV at that moment; (c) Return to repeat step (b) until the absolute value of the difference between the two optimization objective functions is less than any minimum value ε>0, thereby obtaining the optimal UAV flight speed, flight direction angle and flight pitch angle motion state parameters at the current moment; (d) Based on the coupling relationship between the false target, the UAV and the radar, the initial position of the UAV at the next moment is solved; (e) Repeat steps (b) to (d) until the distributed online decision-making of the flight trajectory of the UAV cluster is completed, that is, the optimal distance correspondence between the UAV and the radar at each moment is obtained.

8. The UAV track and jamming resource joint optimization system for composite jamming network radar is characterized by: include: The composite jamming model building unit is used to implement track deception on a networked radar system consisting of N radars using M drones to form P false tracks, while introducing suppression jamming of the radar by drones to establish a drone cluster composite jamming networked radar model; The optimization model building unit is used to configure the beam pointing, track and power resources of the UAV through joint optimization. On the basis of implementing track deception on the networked radar, the UAV motion parameters are used as constraints, and the detection probability of the networked radar for the UAV implementing the interference mission is minimized as the optimization goal. A joint optimization model of the UAV track and interference resources for the composite interference networked radar is established; The optimization model solving unit is used to solve the optimization model by adopting a hybrid optimization algorithm that combines the particle swarm optimization algorithm with the interior point method, and obtain the UAV cluster beam pointing, track and power resource allocation results that meet the constraints, thereby achieving composite interference to the networked radar under the premise of satisfying the pre-set UAV dynamic constraints and generating an effective deception track.

9. A control device, characterized in that: including a processor, a communication interface, a memory and a communication bus; Wherein, the processor, the communication interface, and the memory communicate with each other via the communication bus; The memory is used to store computer programs; The processor is used to execute the program stored in the memory to implement the steps of the method for jointly optimizing the UAV track and interference resources for the composite interference networking radar as described in any one of claims 1-7.

10. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by at least one processor, implements the steps of the method for jointly optimizing the UAV track and interference resources for a composite interference networking radar as described in any one of claims 1 to 7.

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