Quam-following cooperative support interference resource management and control method capable of embedding swarm intelligence optimization algorithm

Abstract

The invention provides a queue-following cooperative support interference resource management and control method capable of embedding a swarm intelligence optimization algorithm. The method comprises the following steps: inputting parameter classification; constructing an interference resource management and control optimization model; implementing a dynamic self-adaptive solver; and outputting a result. In order to solve the problems of low interference resource cooperation efficiency, poor dynamic adaptability and the like when the formation carries out a queue support interference technology in a complex electromagnetic environment, a dynamic multi-target interference resource management and control model is constructed, and cooperative optimization of interference efficiency and interference identification risks is realized in combination with a swarm intelligence optimization algorithm and an online adjustment mechanism.

Description

Technical Field

[0001] The present invention relates to the field of radar electronic countermeasure application technology, and more specifically to optimizing and improving a team support jamming method for a networked radar system in a complex electromagnetic environment, and more specifically to a team collaborative support jamming resource management and control method based on an embeddable swarm intelligence optimization algorithm, which is particularly suitable for dynamic resource allocation and jamming strategy optimization in a multi-jammer collaborative combat scenario. Background Art

[0002] As the modern electronic warfare environment becomes increasingly complex, networked radar systems have become the core equipment of battlefield situation awareness systems with their significant advantages such as spatial diversity, frequency domain agility, and information fusion. To cope with such advanced radar systems, Escort Support Jamming (ESJ) technology, as an important tactical means in the field of electronic warfare, can effectively improve the penetration combat effectiveness and battlefield survivability of the formation.

[0003] However, in complex electromagnetic environments, traditional interference resource management methods face the following technical bottlenecks:

[0004] (1) Insufficient dynamic confrontation modeling: Existing methods are mostly based on static threat assessment models, which make it difficult to perceive the dynamic reconstruction characteristics of networked radar systems in real time. In particular, when the radar node topology changes, the working mode switches, and the anti-interference strategy is activated, the interference effectiveness exhibits nonlinear attenuation.

[0005] (2) Complex coupling of multi-dimensional constraints: Interference resource management needs to comprehensively consider multi-dimensional parameter constraints such as allocation efficiency, spatial coverage, and spectrum matching. Traditional combinatorial optimization methods (0-1 planning, sticking to schedule, etc.) are prone to fall into local optimality when solving high-dimensional non-convex optimization problems, resulting in low resource utilization.

[0006] (3) Lack of intelligence in the coordination mechanism: Existing coordinated jamming strategies mostly adopt a preset rule base approach and lack the ability to learn the electromagnetic situation online. They are unable to achieve autonomous coordination and efficiency emergence between jamming units and are unable to meet the real-time response requirements in complex battlefield environments.

[0007] (4) Poor adaptability of hardware resources: Traditional optimization algorithms have high computational complexity and are difficult to embed into the embedded processors of existing ESJ platforms to achieve millisecond-level decision-making, which restricts the engineering implementation of tactical-level applications.

[0008] In response to the above problems, although some scholars have proposed an improved optimization algorithm based on swarm intelligence in recent years, there is still a contradictory imbalance between jamming effectiveness and solution quality in practical applications, and a coupling model with the dynamic characteristics of radar confrontation has not been established. Therefore, it is urgent to develop an intelligent optimization framework with online learning capabilities to achieve multi-objective dynamic optimization and distributed collaborative control of jamming resources. Summary of the Invention

[0009] To overcome the deficiencies of the prior art, the present invention provides a method for managing and controlling interference resources for team-based collaborative support that can embed swarm intelligence optimization algorithms. The specific steps are as follows:

[0010] Step 1) Classify input parameters;

[0011] Divide the input parameters according to the networked radar system feature layer, formation ability layer, and algorithm configuration layer, specifically as follows:

[0012] A. Networked radar system feature layer:

[0013] Basic parameters: number of radars N, radar operating wavelength λ R , pulse width τ R , bandwidth Δf R ;

[0014] Detection characteristic parameters: radar transmit power P R , radar antenna gain G R , non-coherent integration pulse number n p , detection threshold V T , noise factor F n , transmission loss L R , false alarm probability P fa , azimuth main lobe width elevation main lobe width

[0015] Information fusion parameter: information fusion criterion threshold K;

[0016] B. Formation ability layer:

[0017] Interference resource parameters: number of jammers M, jammer transmit power P J , jammer operating wavelength λ J , jammer antenna gain G J , optional interference style set {0, 1, 2,..., I}, where I represents the number of interference styles;

[0018] Platform characteristic parameters: radar cross section of the protected target σ, jammer operating frequency f J , jammer mismatch loss L J ;

[0019] Hardware constraint parameter: maximum number of transmit beams of the jammer P;

[0020] C. Algorithm configuration layer:

[0021] Maximum number of iterations Max_T, population size N p ;

[0022] Step 2) Construction of the interference resource control optimization model;

[0023] step1. Definition of decision variables;

[0024] (01) Beam allocation variable: Construct a binary interference beam allocation matrix where the matrix element is used to represent the beam allocation variable at time k; indicates that jammer m allocates an interference beam to radar n at time k, indicates that jammer m does not allocate an interference beam to radar n at time k;

[0025] (02) Jamming pattern selection variable: Define a discrete jamming pattern selection vector The elements in the vector represent the jamming pattern variable selected by jammer m at time k;

[0026] step 2. Optimization index design;

[0027] (11) Calculation of the fusion detection probability of the networked radar system;

[0028] Under jamming conditions, the detection probability of a single radar n at time k is related to the interference-to-signal ratio received by the radar at time k The calculation formulas for the two are respectively

[0029]

[0030] where P Je represents the effective received power of the radar for the interference signal, and P Re represents the effective received power of the radar for the radar signal; represents the path transmission gain of the signal transmitted by radar n being received by it, represents the path transmission gain of the interference signal transmitted by jammer m arriving at radar n. The calculation formulas for the two are shown in Equation (5-6), represents the channel gain of radar n in working mode when the jamming pattern is adopted at time k. The coefficient k0 = 1.38×10 -23 represents the Boltzmann constant, and T t = 290K represents the equivalent noise temperature of the radar receiver. C3, C4, and C6 are Gram-Charlier series coefficients determined by the target fluctuation type; the calculation methods of the process function φ(z) and the first process variable V in Equation (2) are shown in Equation (3-4);

[0031]

[0032] Among them, represents the included angle between the jammer m and the protected target relative to the radar n at time k; λ R represents the radar operating wavelength, λ J represents the jammer operating wavelength, and the first process coefficient represents a constant, represents the distance between the radar n and the target, represents the distance between the radar n and the jammer m. Since the relative positions of the jammer, the target, and the radar are constantly changing, define as the interference direction mismatch factor for the interference signal to enter the radar receiver from the direction deviating from the maximum gain direction of the radar antenna, and the calculation is as follows:

[0033]

[0034] Among them, the second process coefficient k1 = 0.05 is a constant;

[0035] Suppose the networked radar system adopts the rank-K information fusion criterion. When the number of radar nodes that simultaneously determine the discovery of the target in the network reaches the information fusion criterion threshold K, the networked radar system determines the discovery of the target. Then, the fusion detection probability of the networked radar system is calculated as

[0036]

[0037] Among them, d n represents the local decision made by the radar n, taking values 0 or 1. d n = 1 indicates that the radar discovers the existence of the target, and d n = 0 indicates that the radar does not discover the target; represents the permutation and combination of the sum of the decision results of all radars being the second process variable q;

[0038] (12) Calculation of the tracking mean square error of the networked radar system

[0039] Select the three parameters of distance, azimuth angle, and elevation angle as the main measurement parameters of the radar. According to the interference-to-signal ratio, the measurement mean square errors of the three parameters are

[0040]

[0041] Among them, c = 3.0×10 8 m / s represents the speed of light, and respectively represent the mean square errors of the radar n measuring the distance, azimuth angle, and elevation angle at time k. From this, the measurement error vector of the radar n for the target parameters at time k can be obtained Then, the covariance matrix of the single radar measurement error vector is calculated as

[0042]

[0043] Among them, P e,n (k) represents the covariance matrix of the radar n measurement error vector, E[*] represents the expectation calculation, and T represents the transpose of the matrix;

[0044] Suppose each radar in the network uses Kalman filtering to perform track association and tracking on the target. After integrating the filtering information of each radar node on the target, the fusion equation obtained is

[0045]

[0046] Among them, x(k) represents the filtering information of the networked radar system after fusion, and x n (k) represents the filtering information of radar n at time k, and P e (k) represents the measurement error covariance matrix of the networked radar system at time k; According to Equation (11), the tracking mean square error of the networked radar system at time k can be Calculated as

[0047]

[0048] Among them, tr[*] represents calculating the trace of the matrix;

[0049] (13) Calculation of radar interference recognition probability

[0050] The interference power recognition coefficient of jammer m at time k Is calculated as follows

[0051]

[0052] Among them, p s Represents the safety power threshold; step(x) represents the step function;

[0053] The interference time recognition coefficient of jammer m at time k Is calculated as follows

[0054]

[0055] Among them, Represents the beam allocation variable at time l, and t s Represents the safety time threshold;

[0056] Correlate the interference power recognition coefficient And the interference time recognition coefficient To obtain the interference recognition probability of the networked radar system for jammer m as

[0057] Step 3. Design of objective function and constraint conditions

[0058] Establish an optimized objective function \(F\) for weighted summation, which is calculated as follows

[0059]

[0060] where \(w\) m represents the importance coefficient of jammer \(m\), and the coefficients are \(k_2 = 0.9\), \(k_3 = 4\), \(k_4 = 1\), \(c_1 = 0.6\), \(c_2 = 0.4\);

[0061] Add the following constraints in the design:

[0062] (1) Limitation of the jammer beam ability; at the current moment, the jammer can only jam a limited number of radar nodes simultaneously, which is expressed as

[0063]

[0064] where \(P\) represents the maximum number of radars that the jammer can jam simultaneously;

[0065] (2) Limitation of the interference target allocation; at the current moment, the number of jammers allocated to each radar node is also limited, which is expressed as

[0066]

[0067] where \(Q\) represents the maximum number of jammers allocated to each radar node;

[0068] (3) Limitation of the decision variable coupling; the jammer can only select the interference pattern after transmitting the interference beam, which is expressed as

[0069]

[0070] where \(\odot\) represents the exclusive NOR operation;

[0071] Step 3) Implementation of the dynamic adaptive solver;

[0072] The solver adopts a serial structure and consists of two parts. The first part is the dynamic adjustment module, and the second part is the intelligent optimization module for the interference strategy. The specific implementation steps are as follows:

[0073] STEP 1. Dynamic adjustment module

[0074] The specific steps are as follows:

[0075] (31) Observe the phase of the radar signal in real time and construct an observation sequence of the radar signal where represents the phase of the radar signal observed at time \(k\);

[0076] (32) Update the belief probability of the radar state using Bayes' rule, and the calculation method is

[0077]

[0078] Among them, b(s T ) indicates that the radar was in state s at the last moment T The belief probability, b′(s T ) represents the updated belief probability, T(s T ,s′ T ) indicates that the radar is in state s T Transition to state s′ T The transition probability, Indicates the radar state is s′ T Observed The probability of state transition T(s T ,s′ T ) and the observation probability It is obtained by using the EM algorithm to calculate the reconnaissance radar signal intelligence data accumulated in the early stage.

[0079] (33) Select the belief probability b(s) at the current moment T )The maximum state is taken as the estimated state of the radar;

[0080] (34) When the interference recognition probability of jammer m at time k reaches the upper limit 1, the objective function weight w is triggered m renew:

[0081]

[0082] Among them, M a is a very large positive real number;

[0083] STEP 2. Interference strategy intelligent optimization module

[0084] The specific steps are as follows:

[0085] (41)Will U k Randomly initialized to an M×P dimensional real number vector That is, the initial beam allocation scheme after encoding at time k, V k Initialized to an M-dimensional real vector That is, the initial interference style selection scheme after encoding at time k; vector The element x in i and vector The element y in i Use formula (22-23) to obtain respectively;

[0086] x i =x L +(x U -x L)·rand(22)

[0087] y i =y L +(y U -y L )·rand(23)

[0088] where x U and x L are the upper and lower limits of x i respectively, and y U and y L are the upper and lower limits of y i respectively; rand represents a random number uniformly distributed in the range [0,1], and let the iteration number t = 0;

[0089] (42) Substitute the beam allocation scheme into formula (16), and use the embedded swarm intelligence algorithm to solve the single-variable model formula (24) only for the interference pattern selection variable

[0090]

[0091] where F1 represents the objective function of model (24);

[0092] (43) Assign the solution result of (42) to for updating, that is Then substitute into formula (16), and use the embedded swarm intelligence algorithm to solve the single-variable model formula (25) only for the interference beam allocation variable

[0093]

[0094] where F2 represents the objective function of model (25);

[0095] (44) Assign the solution result of formula (25) to for updating, that is

[0096] (45) Judge whether the iteration number t reaches the maximum iteration number Max_T. If it reaches, enter step 4); if not, let the iteration number t = t + 1, and return to step (42);

[0097] Step 4) Result output;

[0098] For the result obtained in step 3 and ​​Decode to obtain the optimized beam allocation scheme U at time k k and the interference pattern selection scheme V k ; The decoding methods for the two decision variables are as follows:

[0099] (51) Decoding of the interference beam allocation variable

[0100] For the vector Sort the elements in ascending order to obtain the sorted vector c sort = [c1, c2,..., c M×P , Take the first N elements to form a new vector [c1, c2,..., c N , Then the jammer number of the interference radar n is (c n % M) + 1; % is the remainder symbol;

[0101] (52) Decoding of the interference pattern selection variable

[0102] For the vector Round up the elements to obtain the interference pattern number selected by the jammer m

[0103] In step 1) of a specific embodiment of the present invention, set I = 3, Indicates not selecting an interference pattern, Select noise suppression interference, Select range gate pull-off interference, Select smart noise interference

[0104] In step 2) step3 of another specific embodiment of the present invention, set P = 2, Q = 1

[0105] In (34) of step 3) STEP1 of yet another specific embodiment of the present invention, set M a = 1000

[0106] In (41) of step 3) STEP 2 of still another specific embodiment of the present invention, set x U = 1, x L = -1; Set y U = 3, y L = 0

[0107] Aiming at the problems of low collaborative efficiency of interference resources and poor dynamic adaptability in the implementation of the follow-up support interference technology for formations in complex electromagnetic environments, the present invention provides a follow-up collaborative support interference resource management and control method that can be embedded with swarm intelligence optimization algorithms

[0108] The core of the present invention lies in constructing a dynamic multi-objective interference resource management and control model, combining swarm intelligence optimization algorithms with an online adjustment mechanism to achieve the collaborative optimization of interference effectiveness and interference recognition risk Description of the Drawings

[0109] Figure 1 Shows the overall workflow of the present invention;

[0110] Figure 2 Shows the encoding-decoding block diagram of the beam allocation variable;

[0111] Figure 3 Shows the encoding-decoding block diagram of the interference pattern selection variable. Detailed Description of the Invention

[0112] The present invention will be further described below in conjunction with the embodiments and the drawings.

[0113] The present invention provides a method for controlling interference resources in team cooperation and support that can embed a swarm intelligence optimization algorithm. The specific workflow is as shown in the appendix Figure 1 The specific steps to implement the method of the present invention are as follows:

[0114] Step 1) Classification of input parameters;

[0115] The input parameters are divided according to the networking radar system feature layer, formation ability layer, and algorithm configuration layer, specifically as follows:

[0116] 1. Networking radar system feature layer:

[0117] (1) Basic parameters: number of radars N, radar operating wavelength λ R , pulse width τ R , bandwidth Δf R ;

[0118] (2) Detection characteristic parameters: radar transmit power P R , radar antenna gain G R , non-coherent integration pulse number n p , detection threshold V T , noise factor F n , transmission loss L R , false alarm probability P fa , azimuth main lobe width elevation main lobe width

[0119] (3) Information fusion parameter: information fusion criterion threshold K.

[0120] 2. Formation ability layer:

[0121] (1) Interference resource parameters: number of jammers M, jammer transmit power P J , jammer operating wavelength λ J , jammer antenna gain G J, optional interference pattern set {0, 1, 2, …, I}, where I represents the number of interference patterns;

[0122] (2) Platform characteristic parameters: radar cross section σ of the protected target, operating frequency f of the jammer J , mismatch loss L of the jammer J ;

[0123] (3) Hardware constraint parameter: maximum number of transmitting beams P of the jammer;

[0124] 3. Algorithm configuration layer:

[0125] Maximum number of iterations Max_T, population size N p etc.

[0126] Step 2) Construction of the interference resource control and optimization model;

[0127] step 1. Definition of decision variables;

[0128] (01) Beam allocation variable: construction of a binary interference beam allocation matrix where the matrix element is used to represent the beam allocation variable at time k. means that jammer m allocates an interference beam to radar n at time k, means that jammer m does not allocate an interference beam to radar n at time k.

[0129] (02) Interference pattern selection variable: definition of a discrete interference pattern selection vector where the elements in the vector represent the interference pattern variable selected by jammer m at time k. In a specific embodiment of the present invention, it is set that I = 3, means not selecting an interference pattern, selecting noise suppression interference, selecting range gate pull-off interference, selecting smart noise interference.

[0130] step 2. Optimization index design;

[0131] (11) Calculation of the fusion detection probability of the networked radar system;

[0132] Under interference conditions, the detection probability of a single radar n at time k is related to the interference-to-signal ratio received by this radar at time k. The calculation formulas for the two are respectively

[0133]

[0134] where, P JeIndicates the effective received power of the radar for the interference signal, P Re Indicates the effective received power of the radar for the radar signal. Indicates the path transmission gain of the signal transmitted by radar n and received by it. Indicates the path transmission gain of the interference signal transmitted by jammer m reaching radar n. The calculation formulas for both are shown in Equation (5-6). Indicates the interference pattern adopted at time k. For radar n in the operating mode, the channel gain. The coefficient k0 = 1.38×10 -23 Indicates the Boltzmann constant, T t = 290K indicates the equivalent noise temperature of the radar receiver. C3, C4, and C6 are Gram-Charlier series coefficients, which are determined by the target fluctuation type.

[0135] The calculation methods of the process function φ(z) and the first process variable V in Equation (2) are shown in Equation (3-4).

[0136]

[0137]

[0138] Among them, Indicates the included angle between jammer m and the protected target relative to radar n at time k. λ R Indicates the radar operating wavelength, λ J Indicates the jammer operating wavelength. The first process coefficient Indicates a constant. Indicates the distance between radar n and the target. Indicates the distance between radar n and jammer m. Since the relative positions of the jammer, the target, and the radar are constantly changing, define as the interference direction mismatch factor for the interference signal to enter the radar receiver from the direction deviating from the maximum gain direction of the radar antenna. The calculation is as follows:

[0139]

[0140] Among them, the second process coefficient k1 = 0.05 is a constant.

[0141] Suppose the networked radar system adopts the rank-K information fusion criterion. When the number of radar nodes that simultaneously determine the discovery of the target in the network reaches the information fusion criterion threshold K, the networked radar system determines the discovery of the target. Then the fusion detection probability of the networked radar system is calculated as

[0142]

[0143] Among them, q represents the subscript index of the summation symbol ∑, dn represents the local decision made by radar n, taking values 0 or 1, d n = 1 indicates that the radar detects the presence of a target, d n = 0 indicates that the radar does not detect the target, represents the permutations and combinations of the sum of the decision results of all radars as the second process variable q, and the value of q is {K, K + 1, …, N}. d n is the decision made by one radar, and the sum of the decisions of all radars is q (i.e., ∑d n = q), and the permutations and combinations are represented as For example, there are 3 radars, K = 2, q can take 2 and 3. When q = 2, then it is represented as [1, 0, 1], [1, 1, 0], [0, 1, 1].

[0144] (12) Calculation of the tracking mean square error of the networked radar system

[0145] Select the three parameters of distance, azimuth angle, and elevation angle as the main measurement parameters of the radar. According to the signal-to-interference ratio, the measurement mean square errors of the three parameters are

[0146]

[0147] where c = 3.0×10 8 m / s represents the speed of light, and respectively represent the mean square errors of radar n measuring distance, azimuth angle, and elevation angle at time k. Thus, the measurement error vector of radar n for the target parameters at time k can be obtained Then, the covariance matrix of the single-radar measurement error vector is calculated as

[0148]

[0149] where P e,n (k) represents the covariance matrix of the radar n measurement error vector, E[*] represents the expectation calculation, and T represents the transpose of the matrix.

[0150] Suppose each radar in the network uses Kalman filtering to perform track association and tracking on the target. After integrating the filtering information of each radar node for the target, the fusion equation obtained is

[0151]

[0152] where x(k) represents the filtering information of the networked radar system after fusion, x n (k) represents the filtering information of radar n at time k, P e (k) represents the measurement error covariance matrix of the networked radar system at time k. According to Equation (11), the tracking mean square error P of the networked radar system at time k can be obtainedT k Calculated as

[0153]

[0154] Where tr[*] represents calculating the trace of a matrix.

[0155] (13) Calculation of radar interference recognition probability

[0156] First, calculate the radar's interference recognition probability for the electromagnetic radiation of the formation separately from the energy domain and the time domain. Excessively high radiation power in the energy domain will cause the radar to turn on the passive tracking mode to locate and identify the interference signal. Therefore, the interference power recognition coefficient of jammer m at time k is calculated as follows

[0157]

[0158] Where p s represents the safety power threshold. step(x) represents the step function.

[0159] Secondly, in the time domain, if a jammer is assigned to a radar node for a long time, it is also vulnerable to the passive tracking of that radar or other anti-jamming measures, increasing the risk of being successfully identified by the radar. To avoid this situation, the interference time recognition coefficient of jammer m at time k is calculated as follows

[0160]

[0161] Where represents the beam allocation variable at time l, and t s represents the safety time threshold.

[0162] Correlate the interference power recognition coefficient and the interference time recognition coefficient to obtain the interference recognition probability of the networked radar system for jammer m as

[0163] step 3. Design of objective function and constraint conditions

[0164] To achieve the collaborative optimization of maximizing interference effectiveness and minimizing interference recognition risk, a weighted summation optimization objective function F is established and calculated as follows

[0165]

[0166] Where w m represents the importance coefficient of jammer m, with coefficients k2 = 0.9, k3 = 4, k4 = 1, c1 = 0.6, and c2 = 0.4.

[0167] Due to the limitations of interference resources and jammer capabilities in practical applications, the following constraints are added in the design:

[0168] (1) Limitation of the jammer beam capability. At the current moment, the jammer can only interfere with a limited number of radar nodes simultaneously, which is expressed as

[0169]

[0170] where P represents the maximum number of radars that the jammer can interfere with simultaneously. In a specific embodiment of the present invention, P = 2 is set.

[0171] (2) Limitation of interference target allocation. The number of jammers allocated to each radar node at the current moment is also limited, which is expressed as

[0172]

[0173] where Q represents the maximum number of jammers allocated to each radar node. In a specific embodiment of the present invention, Q = 1 is set.

[0174] (3) Limitation of decision variable coupling. The jammer can only select the interference pattern after transmitting the interference beam, which is expressed as

[0175]

[0176] where ⊙ represents the exclusive NOR operation.

[0177] Step 3) Implementation of the dynamic adaptive solver;

[0178] The solver adopts a serial structure and consists of two parts. The first part is the dynamic adjustment module, and the second part is the intelligent optimization module for interference strategies. The specific implementation steps are as follows:

[0179] STEP 1. Dynamic adjustment module

[0180] The dynamic adjustment module predicts the radar operating state in the radar networking system by real-time monitoring of the radar transmission signal parameters and dynamically adjusts the objective function. The specific steps are as follows:

[0181] (31) Observe the radar signal phase in real time and construct the radar signal observation sequence where represents the radar signal phase observed at time k;

[0182] (32) Update the radar state belief probability using the Bayesian rule, and the calculation method is

[0183]

[0184] where, b(sT ) represents the belief probability that the radar was in state s at the previous moment T , and b′(s T ) represents the updated belief probability, and T(s T , s′ T ) represents the transition probability that the radar changes from state s T to state s′ T . represents the probability of observing T when the radar state is s′ . S represents the set of radar states. The state transition probability T(s T , s′ T ) and the observation probability are statistically obtained by using the Expectation-Maximization algorithm (EM) on the reconnaissance radar signal intelligence data accumulated in the early stage. The EM algorithm is well-known to those skilled in the art and will not be elaborated here.

[0185] (33) Select the state with the maximum belief probability b(s T ) at the current moment as the estimated state of the radar.

[0186] (34) When the interference recognition probability of jammer m reaches the upper limit of 1 at time k, trigger the update of the target function weight w m :

[0187]

[0188] where M a is a very large positive real number. In a specific embodiment of the present invention, M a is set to 1000.

[0189] STEP 2. Interference Strategy Intelligent Optimization Module

[0190] This module designs a real-number coding strategy to handle mixed variables (binary beam allocation variables + discrete interference pattern selection variables), and then uses the embedded swarm intelligence optimization algorithm (such as genetic algorithm, particle swarm algorithm, etc.) to complete the online solution of the interference resource scheduling strategy. The specific steps are as follows:

[0191] (41) Randomly initialize U k as an M×P-dimensional real vector , which is the initial beam allocation scheme encoded at time k, and initialize V k as an M-dimensional real vector , which is the initial interference pattern selection scheme encoded at time k. The element x in the vector i and the element y in the vector iObtained by using formulas (22-23) respectively.

[0192] x i = x L +(x U - x L )·rand(22)

[0193] y i = y L +(y U - y L )·rand(23)

[0194] Wherein, x U and x L are the upper and lower limits of x i respectively. In a specific embodiment of the present invention, x U is set to 1, and x L is set to -1. y U and y L are the upper and lower limits of y i respectively. In a specific embodiment of the present invention, y U is set to 3, and y L is set to 0. ran d represents a random number uniformly distributed within the range [0,1], and let the iteration number t = 0.

[0195] (42) Substitute the beam allocation scheme into formula (16), and use the embedded swarm intelligence algorithm (such as genetic algorithm, particle swarm algorithm) to solve the single-variable model formula (24) only for the interference pattern selection variable

[0196]

[0197] Wherein, F1 represents the objective function of model (24).

[0198] (43) Assign the solution result of (42) to for updating, that is Then substitute into formula (16), and use the embedded swarm intelligence algorithm (such as genetic algorithm, particle swarm algorithm) to solve the single-variable model formula (25) only for the interference beam allocation variable .

[0199]

[0200] Wherein, F2 represents the objective function of model (25), and P represents the maximum number of radars that the jammer can jam simultaneously.

[0201] ​(44) Assign the solution result of formula (25) to perform an update, that is

[0202] (45) Determine whether the iteration number t reaches the maximum iteration number Max_T. If it reaches, enter step 4). If not, let the iteration number t = t + 1, and return to step (42).

[0203] Step 4) Result output;

[0204] For the solution result finally obtained in step 3) and perform decoding to obtain the optimized beam allocation scheme U k and the interference pattern selection scheme V k . The decoding methods of the two decision variables are as follows:

[0205] (51) Decoding of interference beam allocation variables;

[0206] As shown in the appendix Figure 2 shown, sort the elements in the vector in ascending order to obtain the sorted vector c sort = [c1, c2,..., c M×P , The number of elements of × P is M N , take the first N elements to form a new vector [c1, c2,..., c n , then the jammer number of the interfering radar n is (c % M) + 1. % is the modulo symbol. Taking M = 3, N = 4, P = 2, as an example, first sort in ascending order according to the element size to obtain the sorted vector c sort = [2, 6, 1, 5, 3, 4], then take the first N elements to form a new vector [2, 6, 1, 5], and then take the remainder of each element in this vector with respect to M and add 1 to get the decoding result as [3, 1, 2, 3]. Then the interference beam allocation scheme is: the first jammer allocates interference beams to radar 2, the second jammer allocates interference beams to radar 3, and the third jammer allocates interference beams to radar 1 and radar 4.

[0207] (52) Decoding of interference pattern selection variables

[0208] As shown in the appendix Figure 3 shown, round up the elements in the vector to obtain the interference pattern number selected by the jammer m. Taking For example, calculate the smallest positive integer not less than each element in the vector, and the decoding result is [3, 2, 1, 2]. Then the interference pattern selection scheme is as follows: the first jammer selects pattern 3, the second jammer selects pattern 2, the third jammer selects pattern 1, and the fourth jammer selects pattern 2.

[0209] The present invention realizes a method for managing and controlling interference resources for accompanying cooperative support that can embed swarm intelligence optimization algorithms. By constructing a dynamic multi-objective interference resource management and control model, combining real-number vector coding, swarm intelligence optimization algorithms, and an online adjustment mechanism, it can help decision-makers quickly provide accurate interference resource allocation strategies while maintaining a low interference recognition risk. When dealing with the detection and tracking of a networked radar system in a complex electromagnetic environment, it has stronger adaptability and higher resource utilization rate than traditional management and control methods.

Claims

1. A method for managing and controlling interference resources for team-based collaborative support that can embed swarm intelligence optimization algorithms, characterized in that, The specific steps are as follows: Step 1) Input parameter classification; The input parameters are divided according to the characteristic layer of the networked radar system, the formation ability layer, and the algorithm configuration layer, specifically as follows: A. Characteristic layer of the networked radar system: Basic parameters: number of radars N, radar operating wavelength λ R , pulse width τ R , bandwidth Δf R ; Detection characteristic parameters: radar transmit power P R , radar antenna gain G R , non-coherent integration pulse number n p , detection threshold V T , noise factor F n , transmission loss L R , false alarm probability P fa , azimuth main lobe width , elevation main lobe width Information fusion parameter: Information fusion criterion threshold K; B. Formation ability layer: Jamming resource parameters: number of jammers M, transmit power of jammers P J , operating wavelength of jammers λ J , antenna gain of jammers G J , optional jamming pattern set {0, 1, 2, ..., I}, where I represents the number of jamming patterns; Platform characteristic parameters: radar cross section σ of the protected target, operating frequency f of the jammer J , mismatch loss L of the jammer J ; Hardware constraint parameter: Maximum number of transmitting beams P of the jammer; C. Algorithm configuration layer: Maximum number of iterations Max_T, population size N p ; Step 2) Construction of the interference resource management and control optimization model; step1. Definition of decision variables; (01) Beam allocation variable: Construct a binary interference beam allocation matrix Among them, the matrix element is used to represent the beam allocation variable at time k; indicates that jammer m allocates an interference beam to radar n at time k, indicates that jammer m does not allocate an interference beam to radar n at time k; (02) Jamming pattern selection variable: Define a discrete jamming pattern selection vector Element in the vector Indicates the jamming pattern variable selected by jammer m at time k; step 2. Design of optimization indicators; (11) Calculation of the fusion detection probability of the networked radar system; The detection probability of a single radar n at time k under interference conditions is related to the interference-to-signal ratio received by the radar at time k and their calculation formulas are respectively Among them, P Je represents the effective received power of the radar for the interference signal, and P Re represents the effective received power of the radar for the radar signal; represents the path transmission gain of the signal transmitted by radar n being received by it, represents the path transmission gain of the interference signal transmitted by jammer m reaching radar n. The calculation formulas for both are shown in Equation (5-6), represents the interference pattern adopted at time k for the channel gain of radar n operating in the working mode. The coefficient k0 = 1.38×10 -23 represents the Boltzmann constant, and T t = 290K represents the equivalent noise temperature of the radar receiver. C3, C4, and C6 are the Gram-Charlier series coefficients, which are determined by the target fluctuation type. The calculation methods of the process function φ(z) and the first process variable V in Equation (2) are shown in Equation (3-4); Among them, represents the included angle between the jammer m and the protected target relative to the radar n at the k-th moment; λ R represents the operating wavelength of the radar, λ J represents the operating wavelength of the jammer, and the first process coefficient represents a constant, represents the distance between the radar n and the target, represents the distance between the radar n and the jammer m. Since the relative positions of the jammer, the target, and the radar are constantly changing, define as the interference direction mismatch factor for the interference signal to enter the radar receiver from the direction deviating from the maximum gain direction of the radar antenna, and the calculation is as follows: Among them, the second process coefficient k1 = 0.05 is a constant; Assume that the networked radar system adopts the rank-K information fusion criterion. When the number of radar nodes that simultaneously determine the discovery of a target in the network reaches the information fusion criterion threshold K, the networked radar system determines the discovery of a target. Then the calculation of the fusion detection probability of the networked radar system is where d n represents the local decision made by radar n, taking values 0 or 1. d n = 1 indicates that the radar detects the presence of a target, and d n = 0 indicates that the radar does not detect the target; represents the permutations and combinations of the sum of the decision results of all radars as the second process variable q; (12) Calculation of the tracking mean square error of the networked radar system Select the three parameters of distance, azimuth angle, and elevation angle as the main measurement parameters of the radar. According to the interference-to-signal ratio, the measurement mean square errors of the three parameters are where c = 3.0×10 8 m / s represents the speed of light, and respectively represent the mean square errors of the range, azimuth angle, and elevation angle measured by radar n at time k; from this, the measurement error vector of the target parameters by radar n at time k can be obtained Then the covariance matrix of the single - radar measurement error vector is calculated as where P e,n (k) represents the covariance matrix of the radar n measurement error vector, E[*] represents the expectation calculation, and T represents the transpose of the matrix; Assume that each radar in the network uses Kalman filtering to perform track association and tracking on the target. The fusion equation obtained after integrating the filtering information of each radar node on the target is where \(x(k)\) represents the filtering information of the integrated radar network system after fusion, and \(x\) n (k) represents the filtering information of radar \(n\) at time \(k\), and \(P\) e (k) represents the measurement error covariance matrix of the integrated radar network system at time \(k\); according to Equation (11), the tracking mean square error of the integrated radar network system at time \(k\) can be calculated as Among them, tr[*] represents calculating the trace of the matrix; (13) Calculation of the radar interference recognition probability Interference power recognition coefficient of jammer m at time k is calculated as follows where p s represents the safety power threshold; step(x) represents the step function; Interference time recognition coefficient of jammer m at time k The calculation is as follows Among them, represents the beam allocation variable at time l, t s represents the safety time threshold; Identify the interference power recognition coefficient and the interference time recognition coefficient Perform information association to obtain the interference recognition probability of the networking radar system for jammer m as step 3. Design of the objective function and constraint conditions Establish an optimization objective function F of weighted summation, and the calculation is as follows where, w m represents the important coefficient of jammer m, with coefficient k2 = 0.9, k3 = 4, k4 = 1, c1 = 0.6, and c2 = 0.4; Add the following constraints: (1) Limitation of the jammer beam ability; At the current moment, the jammer can only interfere with a limited number of radar nodes simultaneously, expressed as Among them, P represents the maximum number of radars that the jammer can interfere with simultaneously; (2) Limitation of interference target allocation; The number of jammers allocated to each radar node at the current moment is also limited, expressed as Among them, Q represents the maximum number of jammers allocated to each radar node; (3) Limitation of decision variable coupling; The jammer can only select the interference pattern after transmitting the interference beam, expressed as Among them, ⊙ represents the exclusive NOR operation; Step 3) Implementation of the dynamic adaptive solver; The solver adopts a serial structure and consists of two parts. The first part is the dynamic adjustment module, and the second part is the intelligent optimization module for interference strategies. The specific implementation steps are as follows: STEP 1. Dynamic adjustment module The specific steps are as follows: (31) Observe the phase of the radar signal in real time and construct an observation sequence of the radar signal where represents the phase of the radar signal observed at time k; (32) Update the radar state belief probability using the Bayesian rule, and the calculation method is Among them, b(s T ) represents the belief probability that the radar was in state s at the previous moment T , b′(s T ) represents the updated belief probability, T(s T , s′ T ) represents the transition probability that the radar transitions from state s T to state s′ T ; represents the probability of observing T when the radar state is s′ , S represents the set of radar states; the state transition probability T(s T , s′ T ) and the observation probability are statistically obtained by using the Expectation-Maximization algorithm EM on the reconnaissance radar signal intelligence data accumulated in the early stage; (33) Select the state with the highest belief probability b(s T ) at the current moment as the estimated state of the radar; When the interference recognition probability of jammer m at time k reaches the upper limit of 1, the weight w of the objective function is triggered m Update: where M a is an extremely large positive real number; STEP 2. Intelligent optimization module for interference strategies The specific steps are as follows: (41) Initialize U k randomly as an M×P-dimensional real vector which is the initial beam allocation scheme after encoding at time k, and initialize V k as an M-dimensional real vector which is the initial interference pattern selection scheme after encoding at time k; the elements x in the vector i and the elements y in the vector i are obtained by using formulas (22 - 23) respectively; x i = x L + (x U - x L ) · rand(22) y i = y L + (y U - y L ) · rand(23) where x U and x L are the upper and lower limits of x i respectively, and y U and y L are the upper and lower limits of y i respectively; rand represents a random number uniformly distributed within the range [0, 1], and let the iteration number t = 0; (42) Substitute the beam allocation scheme into formula (16), and use the embedded swarm intelligence algorithm to solve the single-variable model formula (24) only about the interference pattern selection variable Among them, F1 represents the objective function of model (24); (43) Assign the solution result of (42) to perform an update, that is then substitute it into formula (16), and use the embedded swarm intelligence algorithm to solve the single-variable model formula (25) only for the interference beam allocation variable ; Among them, F2 represents the objective function of model (25); (44) Assign the solution result of formula (25) to perform an update, that is (45) Determine whether the iteration number t reaches the maximum iteration number Max_T. If it reaches, enter step 4). If it does not reach, let the iteration number t = t + 1, and return to step (42); Step 4) Result output; For the result obtained in step 3) and decode them to obtain the optimized beam allocation scheme U k and the interference pattern selection scheme V k ; The decoding methods for the two decision variables are as follows: (51) Decoding of the interference beam allocation variable; For the vector sort the element sizes in ascending order to obtain the sorted vector c sort = [c1, c2,..., c M×P , take the first N elements to form a new vector [c1, c2,..., c N , then the jammer number of the interfering radar n is (c n % M) + 1; % is the modulo symbol; (52) Decoding of the interference pattern selection variable Round up the elements in the vector to obtain the interference pattern number selected by jammer m.

2. The method for managing and controlling interference resources for team-based collaborative support that can embed swarm intelligence optimization algorithms according to claim 1, characterized in that In step 1), set I = 3, indicating that the interference pattern is not selected, selecting noise suppression interference, selecting range gate pull-off interference, selecting smart noise interference.

3. The method for controlling interference resources for on-team collaborative support that can embed a swarm intelligence optimization algorithm according to claim 1, wherein In step 2) step3, set P = 2 and Q = 1.

4. The method for managing and controlling interference resources for team-based collaborative support that can embed swarm intelligence optimization algorithms according to claim 1, characterized in that In (34) of STEP1 in step 3), set M a = 1000.

5. The method for controlling interference resources of team-based collaborative support embedded with a swarm intelligence optimization algorithm according to claim 1, characterized in that, In (41) of STEP 2 in step 3), set x U = 1, x L = -1; set y U = 3, y L = 0.

Images