Angle of arrival direction attack method and apparatus based on steering vector simulation

Abstract

The application discloses a direction-of-arrival angle direction attack method and device based on a steering vector simulation, and a method implementation thereof, and comprises the following steps: acquiring a steering vector dictionary set and a target position angle set; based on the steering vector dictionary set and the target position angle set, a target function and a constraint condition are established to simulate a target steering vector through a linear combination coefficient vector of a steering vector; the target function is solved to obtain a linear combination coefficient vector of the steering vector dictionary set; and based on the linear combination coefficient vector, a deployment direction of a signal source and a linear combination coefficient vector of a signal waveform are determined to deploy multiple directional signal sources pointing to a target receiver within a far-field range of the receiver. Coherent transmission signals are used in a linear combination manner to fake a detection spectral peak value at a preset target DOA, so that an error output result is generated by a detection algorithm, and thus an attack on mainstream DOA detection algorithms is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of angle of arrival estimation, and particularly relates to a direction of arrival attack method and device based on steering vector simulation, a computer device and a storage medium. BACKGROUND

[0002] Array signal processing has been a fundamental research area for the past few decades, and direction of arrival (DOA) estimation is one of the mainstream tasks in this widely researched area. In short, the goal of DOA estimation is to determine the angular direction of incident signals using a narrowband sensor array. These sensor arrays are usually assumed to be uniform linear arrays. So far, researchers have proposed many effective methods, among which Capon, MUSIC, ESPRIT and SPARROW are the most representative. Capon is a beamforming technique that aims to minimize the noise power while maintaining a fixed gain in the direction of interest. MUSIC is a subspace-based method that involves projecting the data into the noise subspace of the sample covariance matrix. ESPRIT utilizes the rotational invariance of steering vectors to perform singular value decomposition on the collected stacked samples. SPARROW is developed based on the measurement sample fitting problem induced by row sparsity, and adopts a coordinate descent scheme. However, all these algorithms may have degraded performance when the transmitted signals have coherence. The coherence of signals can cause the rank deficiency of the source covariance matrix, thereby causing the distortion of the noise subspace. This distortion can cause the spectral peaks of the DOA positions to be aliased. SUMMARY

[0003] Therefore, it is necessary to provide a direction of arrival attack method and device based on steering vector simulation, a computer device and a storage medium to solve at least one problem existing in the prior art.

[0004] In a first aspect, the present application is implemented by providing a direction of arrival attack method based on steering vector simulation, comprising:

[0005] Obtaining scene basic information and signal source deployment information, wherein the scene basic information includes a steering vector dictionary set, and the signal source deployment information includes a target position angle set;

[0006] Based on the steering vector dictionary set and the target position angle set, a target function and a constraint condition are established to simulate a target steering vector through a linear combination coefficient vector of steering vectors;

[0007] Solving the target function to obtain a linear combination coefficient vector of the steering vector dictionary set;

[0008] determine a deployment direction of the signal source and a linear combination coefficient vector of the signal waveforms based on the deployment direction of the signal source and the linear combination coefficient vector of the signal waveforms, so as to deploy a plurality of directional signal sources pointing to the target receiver within a far-field range of the target receiver.

[0009] In an embodiment, the constraint condition is that no signal source is deployed within a dangerous area to prevent being discovered by enemy reconnaissance equipment.

[0010] In an embodiment, the optimization target of the target function is that a distance between each steering vector in the target position angle set and a linear combination coefficient vector of a corresponding steering vector in the steering vector dictionary set is less than a preset threshold.

[0011] In an embodiment, the target function is expressed by the following formula:

[0012]

[0013] ||p||0≤K.

[0014] wherein a(θ T，i ) represents a steering vector of the azimuth angle θ T，i , max represents a maximum value, p is a linear combination coefficient vector of the steering vector dictionary, D A p represents a product vector of the steering vector dictionary matrix D A and the linear combination coefficient vector p, p n is an nth element of the vector p, and the constraint ||p||0≤K means that the total number of deployed signal sources is not more than K, and ||p||0 represents a number of non-zero elements in the vector p.

[0015] In an embodiment, the solving of the target function comprises:

[0016] An augmented Lagrangian function of the target function is obtained by introducing a residual intermediate variable of a target azimuth angle, a residual modulus value of the target azimuth angle, and a global residual modulus value;

[0017] The augmented Lagrangian function is iteratively solved by block-wise minimization of the augmented Lagrangian function and a dual variable ascending operation.

[0018] In an embodiment, the iterative solving of the augmented Lagrangian function comprises:

[0019] Step a: initializing the augmented Lagrangian function to determine initial parameter values;

[0020] Step b: calculating a square of a maximum singular value of the steering vector dictionary set;

[0021] Step c: for any target position, calculating an initial residual pre-update variable;

[0022] Step d: for any target position, updating a residual intermediate variable of target azimuth and a residual modulus value based on the initial residual pre-update variable;

[0023] Step e: for any target position, calculating a secondary residual pre-update variable based on the updated residual intermediate variable of target azimuth;

[0024] Step f: calculating a signal source selection intermediate variable based on the secondary residual pre-update variable and square of maximum singular value;

[0025] Step g: calculating a non-dangerous region subset variable of the signal source selection intermediate variable, and constructing a maximum modulus value index set based on the non-dangerous region subset variable;

[0026] Step h: updating the linear combination coefficient vector based on the maximum modulus value index set;

[0027] Step i: updating a global residual modulus value;

[0028] Step j: for any target position, updating a Lagrange dual variable;

[0029] repeating the above steps b-j when the iteration number is less than a preset threshold, and outputting the current linear combination coefficient vector when the iteration number is equal to / equal to the preset threshold.

[0030] In an embodiment, the augmented Lagrange function is represented by the following formula:

[0031]

[0032] wherein Re represents taking the real part of a complex number, the superscript H represents conjugate transpose, ∑ represents summation, ρ is an augmented parameter, and dual variables λ and μ are introduced in the augmented Lagrange function. i and μ i , λ i are dual variables related to constraints z i = a(θ T，i )-D A p, and μ i is a dual variable related to constraint t i = t.

[0033] In a second aspect, a direction of arrival attack device based on a steering vector simulation is provided, comprising:

[0034] An information acquisition unit is configured to acquire scene basic information and signal source deployment information, wherein the scene basic information comprises a set of steering vector dictionaries, and the signal source deployment information comprises a set of target position angles;

[0035] A target function construction unit is configured to establish a target function and a constraint condition based on the set of steering vector dictionaries and the set of target position angles, so as to simulate a target steering vector by a linear combination coefficient vector of steering vectors;

[0036] A target function solving unit is configured to solve the target function to obtain a linear combination coefficient vector of the set of steering vector dictionaries;

[0037] A signal source deployment unit is configured to determine a deployment direction of a signal source and a linear combination coefficient vector of a signal waveform based on the linear combination coefficient vector, so as to deploy a plurality of directional signal sources pointing to a target receiver in a far-field range of the receiver based on the deployment direction of the signal source and the linear combination coefficient vector of the signal waveform.

[0038] In a third aspect, a computer device is provided, which comprises a memory, a processor, and computer readable instructions stored in the memory and running on the processor, and the processor executes the computer readable instructions to implement the above-mentioned method for angle of arrival direction attack based on steering vector simulation.

[0039] In a fourth aspect, a readable storage medium is provided, which stores computer readable instructions, and the computer readable instructions are executed by a processor to implement the above-mentioned method for angle of arrival direction attack based on steering vector simulation.

[0040] The method, device, computer device and storage medium based on the direction vector simulation attack method of the angle of arrival direction, the method comprises the following steps: acquiring scene basic information and signal source deployment information, the scene basic information comprises a direction vector dictionary set, and the signal source deployment information comprises a target position angle set; based on the direction vector dictionary set and the target position angle set, a target function and a constraint condition are established to simulate a target direction vector through a linear combination coefficient vector of a direction vector; the target function is solved to obtain a linear combination coefficient vector of the direction vector dictionary set; based on the linear combination coefficient vector, the deployment direction of the signal source and the linear combination coefficient vector of the signal waveform are determined, and multiple directional signal sources pointing to the receiver are deployed in the far field range of the target receiver based on the deployment direction of the signal source and the linear combination coefficient vector of the signal waveform. In the embodiment of the application, a coherent transmission signal is used to simulate a detection spectrum peak value in a preset target DOA in a linear combination manner, so as to cause the detection algorithm to produce an incorrect output result, thereby realizing the attack on the mainstream DOA detection algorithm. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the description of the embodiments of the present application will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0042] Figure 1 is a flowchart of the direction vector simulation attack method of the angle of arrival direction based on the embodiment of the present application;

[0043] Figure 2 is an application environment diagram of signal source deployment in an embodiment of the present application;

[0044] Figure 3 is a structure diagram of the direction vector simulation attack device based on the angle of arrival direction in an embodiment of the present application;

[0045] Figure 4 is a schematic diagram of a computer device in an embodiment of the present application. DETAILED DESCRIPTION

[0046] The technical solutions of the embodiments of the present application will be described clearly and completely in the following description of the embodiments of the present application with reference to the drawings. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0047] In an embodiment, as shown in Figure 1 a direction-of-arrival attack method based on steering vector simulation is provided, comprising the following steps:

[0048] In step S110, scene basic information and signal source deployment information are acquired, the scene basic information comprising a steering vector dictionary set, and the signal source deployment information comprising a target position angle set;

[0049] The scene basic information can specifically comprise a steering vector length M, and the steering vector dictionary set D A , wherein is a steering vector under different angles, corresponding to each integer angle value (arranged in ascending order) between -90° and 89°, N D represents the dictionary capacity, and the value is equal to 180, for For any angle θ in the set, the steering vector a(θ) = [1, exp(jπsinθ), …, exp(jπ(M-1)sinθ)] T exp represents an exponential function with the natural constant e as the base, j represents an imaginary unit, π is a circular constant, sin is a sine function, and the superscript T represents a transposition operation of a vector or a matrix.

[0050] The signal source deployment information can specifically comprise a target position angle set Θ T = {θ T,1 , K, θ T,I}, which is denoted as I is the total number of target positions, K is the upper limit of the total number of signal sources to be deployed, and the index set of dangerous positions where the signal source nodes are not suitable to be deployed Generally, must satisfy

[0051] In step S120, based on the steering vector dictionary set and the target position angle set, a target function and a constraint condition are established to simulate a target steering vector through a linear combination coefficient vector of steering vectors;

[0052] In the embodiments of the present application, in the far field range of the target receiver, multiple directional signal sources pointing to the receiver can be deployed to interfere with the detection of the enemy receiver. These signal sources can be located within the range of -90° to 90° of the arrival angle of the target receiver, and the distance to the receiver is equal, which can be arbitrarily specified. The direction-of-arrival detection attack is realized by simultaneously transmitting the same signal wave pattern from the signal sources, and the signal wave pattern can be arbitrarily specified. The deployment direction of the signal sources and the linear combination coefficient vector of the signal wave pattern are obtained through the linear combination coefficient vector p of the steering vector dictionary set.

[0053] Therefore, based on the guide vector dictionary set and the target position angle set, with the target position angle set Θ T The guiding vector a(θ) in T，i All of them must be combined with the linear combination coefficient vector D of the guide vector dictionary set. A p is made as close as possible, i.e., the difference between the two is calculated and the distance value is kept as close to 0 as possible, so as to achieve the purpose of using a linear combination of guide vectors to simulate the target guide vector and construct the objective function and constraints.

[0054] The objective function can be specifically expressed by the following formula:

[0055]

[0056] Where, a(θ) T，i ) represents the azimuth angle θ T，i The guiding vector, max represents taking the maximum value, p is the linear combination coefficient vector of the guiding vector dictionary, D A p represents the guide vector dictionary matrix D A The product vector of the linear combination coefficient vector p, p n Let p be the nth element of vector p. The constraint ||p||0≤K means that the total number of deployed signal sources does not exceed K. ||p||0 represents the number of non-zero elements in the vector p.

[0057] Among them, constraints This means that signal sources must not be deployed in dangerous areas to prevent them from being detected by enemy reconnaissance equipment.

[0058] In step S130, the objective function is solved to obtain the linear combination coefficient vector of the guide vector dictionary set;

[0059] Solving the objective function includes:

[0060] By introducing the intermediate residual variable of the target azimuth angle, the residual magnitude of the target azimuth angle, and the global residual magnitude, the augmented Lagrangian function of the objective function is obtained.

[0061] The augmented Lagrangian function is solved iteratively by minimizing the block-based augmented Lagrangian function and performing an ascent operation on the dual variable.

[0062] Optionally, to facilitate solving the objective function, auxiliary variables can be introduced to transform the objective function, as shown below:

[0063]

[0064] subject to z i = a(θ T，i )- D A p, i = 1,..., I

[0065] t i = t, i = 1,..., I

[0066] ||z i ||2≤ t i , i = 1,..., I

[0067]

[0068] ||p||0≤ K.

[0069] where z i is the residual intermediate variable of the ith target azimuth angle, t i represents the residual modulus value of the ith target azimuth angle, and t represents the global residual modulus value. Then, the augmented Lagrangian function of the above transformed function can be written, and by following the algorithmic framework of the alternating direction multiplier method, by block-wise minimization of the augmented Lagrangian function and dual variable ascent operation, an iterative algorithm for solving the optimization problem can be derived.

[0070] where the augmented Lagrangian function of the above optimization problem can be represented by the following formula:

[0071]

[0072] where Re represents taking the real part of a complex number, the superscript H represents conjugate transpose, ∑ represents summation, and ρ is an augmented parameter. The dual variables λ i and μ i are introduced in the augmented Lagrangian function, λ i is the dual variable related to the constraint z i = a(θ T，i )- D A p, and μ i is the dual variable related to the constraint t i = t.

[0073] Further, the iterative solving of the augmented Lagrangian function comprises:

[0074] Step a: initializing the augmented Lagrangian function to determine the initial parameter values;

[0075] Step b: calculating the square of the maximum singular value of the steering vector dictionary set;

[0076] Step c: for any target position, calculating the initial residual pre-update variable;

[0077] Step d: for any target position, update the residual intermediate variable of target azimuth and the residual modulus value based on the initial residual pre-update variable;

[0078] Step e: for any target position, calculate the secondary residual pre-update variable based on the updated residual intermediate variable of target azimuth;

[0079] Step f: calculate the signal source selection intermediate variable based on the secondary residual pre-update variable and the square of the maximum singular value;

[0080] Step g: calculate the non-dangerous region subset variable of the signal source selection intermediate variable, and construct the maximum modulus value index set based on the non-dangerous region subset variable;

[0081] Step h: update the linear combination coefficient vector based on the maximum modulus value index set;

[0082] Step i: update the global residual modulus value;

[0083] Step j: for any target position, update the Lagrange dual variable;

[0084] When the number of iterations is less than a preset threshold, repeat the above steps b-j, and when the number of iterations is equal to / equal to the preset threshold, output the current linear combination coefficient vector.

[0085] Optionally, the augmented Lagrange function can be initialized to obtain initial parameter values, for example, initial iteration number k=0, random initialization of variables t, p, {z i}, {t i}, {λ i}, {μ i}, and the augmented parameter ρ is initialized to 0.01.

[0086] Then, the steering vector dictionary set D A can be calculated by the following formula: max

[0087]

[0088] For any target position i of the signal source, its initial primary residual pre-update variable b i is calculated as follows:

[0089] b i = a(θ T,i )-D A p.

[0090] For any target position i of the signal source, the residual intermediate variable of target azimuth and the residual modulus value are updated as follows:​

[0091] When (z i , t i ) is updated to (0, 0), when (z i , t i ) is updated to (z i , t i ) is updated to

[0092] For any target position i, a secondary residual pre-update variable c i is calculated, which can be calculated by the following formula:

[0093] c i = a(θ T，i )-z i ;

[0094] wherein z i is a residual intermediate variable of the i-th target azimuth angle, and a(θ T，i ) represents a steering vector of the azimuth angle θ T，i .

[0095] Then, a signal source selection intermediate variable d is calculated, which can be calculated by the following formula:

[0096]

[0097] wherein c i represents the secondary residual pre-update variable, l max represents the square of the maximum singular value, represents the conjugate transpose of the D A matrix, ρ represents an augmented parameter, λ i represents a dual variable, p is a linear combination coefficient vector of a steering vector dictionary, and D A p represents a product vector of the steering vector dictionary matrix D A and the linear combination coefficient vector p.

[0098] A non-dangerous region subset variable of the signal source selection intermediate variable d is calculated which can be calculated by the following formula:

[0099]

[0100] wherein | represents the modulus of each element of a vector.

[0101] Then, a maximum modulus value index set is constructed represents a vector The Kth largest element value in the array.

[0102] Based on this signal source, select the non-dangerous area subset variable d. The linear combination coefficient vector p can be updated, specifically as follows:

[0103]

[0104] Then, the global residual modulus t can be updated, which can be calculated using the following formula:

[0105]

[0106] For any i, update the Lagrange dual variable λ. i and μ i :

[0107] Update λ i For λ i +ρ(z i -(a(θ T，i )-D A p)), update μ i For μ i +ρ(tt i ).

[0108] At this point, if the number of iterations k is less than the preset threshold, for example, 100, then the number of iterations is incremented by 1, i.e., k = k + 1, and the process returns to step b and restarts step bj. If the number of iterations k is greater than or equal to the preset threshold, for example, 100, then the currently updated linear combination coefficient vector p is output.

[0109] In step S140, based on the linear combination coefficient vector, the deployment orientation of the signal source and the linear combination coefficient vector of the signal waveform are determined, so as to deploy multiple directional signal sources pointing towards the receiver in the far field range of the target receiver based on the deployment orientation of the signal source and the linear combination coefficient vector of the signal waveform.

[0110] Optionally, after obtaining the linear combination coefficient vector p, if the magnitude of each element in the linear combination coefficient vector p is less than or equal to 0.001, it indicates that no signal source is deployed at that angular azimuth; otherwise, a signal source is deployed at that angular azimuth, and the corresponding linear combination coefficient vector for that signal source is the corresponding element in vector p. It should be noted that in practical engineering, the linear combination coefficient vector will be implemented using a post-amplifier and phase shifter.

[0111] like Figure 2 As shown, a scenario diagram for signal source deployment based on linear combination coefficient vectors is provided, with M=10 and Θ T= {60° ± 3°}, N0 = {n | θ n -60° |≤ 10°, θ n the value of the integer}, K = 10, after the above steps S110-S140, the signal source is deployed according to the linear combination coefficient vector obtained by solving. In the figure, Receiver represents the enemy receiver, Transmit Sources represents the deployed signal sources, Signal Propagation represents the signal propagation direction, and the gray shaded area is the dangerous area. It can be seen from the figure that the signal source can be deployed around the enemy receiver, and the distance between each signal source and the enemy receiver is the same. At the same time, the deployment position of the signal source avoids the dangerous area, and can avoid being discovered by the enemy reconnaissance equipment. Therefore, it meets the optimization target of the objective function and the constraint condition, and uses the coherent transmission signal to realize the target-oriented vector simulation, forms an angle of arrival direction attack technology based on the oriented vector simulation, and achieves the purpose of interfering with the detection of the enemy receiver. Figure 2 It can be seen that the signal source can be deployed around the enemy receiver, and the distance between each signal source and the enemy receiver is the same. At the same time, the deployment position of the signal source avoids the dangerous area, and can avoid being discovered by the enemy reconnaissance equipment. Therefore, it meets the optimization target of the objective function and the constraint condition, and uses the coherent transmission signal to realize the target-oriented vector simulation, forms an angle of arrival direction attack technology based on the oriented vector simulation, and achieves the purpose of interfering with the detection of the enemy receiver.

[0112] In the embodiment of the present application, a method for angle of arrival direction attack based on oriented vector simulation is provided, which comprises: obtaining scene basic information and signal source deployment information, wherein the scene basic information comprises a dictionary set of oriented vectors, and the signal source deployment information comprises a target position angle set; based on the dictionary set of oriented vectors and the target position angle set, an objective function and a constraint condition are established to simulate a target oriented vector through a linear combination coefficient vector of the oriented vector; the objective function is solved to obtain a linear combination coefficient vector of the dictionary set of oriented vectors; based on the linear combination coefficient vector, a deployment direction of the signal source and a linear combination coefficient vector of the signal waveform are determined, so that multiple directional signal sources pointing to the receiver are deployed in the far field range of the target receiver based on the deployment direction of the signal source and the linear combination coefficient vector of the signal waveform. In the embodiment of the present application, the coherent transmission signal is used to fake a detection spectral peak value at a preset target DOA in a linear combination manner, so as to cause the detection algorithm to produce an incorrect output result, thereby realizing the attack on the mainstream DOA detection algorithm.

[0113] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0114] In an embodiment, a device for angle of arrival direction attack based on oriented vector simulation is provided, which corresponds to the method for angle of arrival direction attack based on oriented vector simulation in the above embodiment. As shown in Figure 3As shown, the arrival angle direction attack device based on the steering vector simulation includes an information acquisition unit 10, a target function construction unit 20, a target function solving unit 30, and a signal source deployment unit 40. The functions of each module are described in detail as follows:

[0115] The information acquisition unit 10 is configured to acquire scene basic information and signal source deployment information, wherein the scene basic information includes a steering vector dictionary set, and the signal source deployment information includes a target position angle set;

[0116] The target function construction unit 20 is configured to establish a target function and a constraint condition based on the steering vector dictionary set and the target position angle set, so as to simulate a target steering vector through a linear combination coefficient vector of steering vectors;

[0117] The target function solving unit 30 is configured to solve the target function to obtain a linear combination coefficient vector of the steering vector dictionary set;

[0118] The signal source deployment unit 40 is configured to determine a deployment direction of a signal source and a linear combination coefficient vector of a signal waveform based on the linear combination coefficient vector, so as to deploy multiple directional signal sources pointing to a target receiver in a far field range of the receiver based on the deployment direction of the signal source and the linear combination coefficient vector of the signal waveform.

[0119] In an embodiment of the present application, the constraint condition is that no signal source can be deployed in a dangerous area to prevent being discovered by enemy reconnaissance equipment.

[0120] In an embodiment of the present application, an optimization target of the target function is that a similarity between each steering vector in the target position angle set and a linear combination coefficient vector of steering vectors in the steering vector dictionary set is greater than a preset threshold.

[0121] In an embodiment of the present application, the target function is represented by the following formula:

[0122]

[0123]

[0124] ||p||0≤K.

[0125] Wherein, a(θ T，i ) represents a steering vector of an azimuth angle θ T，i , max represents a maximum value, p is a linear combination coefficient vector of a steering vector dictionary, D A p represents a product vector of a steering vector dictionary matrix D A and a linear combination coefficient vector p, and p nFor the n-th element of the vector p, the constraint ||p||0≤K means that the total number of deployed signal sources is no more than K, and ||p||0 represents the number of non-zero elements in the statistical vector p.

[0126] In an embodiment of the present application, the target function solving unit 30 is further configured to:

[0127] An augmented Lagrangian function of the target function is obtained by introducing a residual intermediate variable of a target azimuth angle, a residual modulus value of the target azimuth angle, and a global residual modulus value.

[0128] The augmented Lagrangian function is iteratively solved by block-wise minimizing the augmented Lagrangian function and a dual variable ascending operation.

[0129] In an embodiment of the present application, the target function solving unit 30 is further configured to:

[0130] Step a: initializing the augmented Lagrangian function to determine initial parameter values;

[0131] Step b: calculating the square of the maximum singular value of the steering vector dictionary set;

[0132] Step c: calculating an initial residual pre-update variable for any target position;

[0133] Step d: updating the residual intermediate variable and the residual modulus value of the target azimuth angle for any target position;

[0134] Step e: calculating a secondary residual pre-update variable for any target position;

[0135] Step f: calculating a signal source selection intermediate variable;

[0136] Step g: calculating a non-dangerous region subset variable of the signal source selection intermediate variable, and constructing a maximum modulus value index set based on the non-dangerous region subset variable;

[0137] Step h: updating the linear combination coefficient vector based on the maximum modulus value index set;

[0138] Step i: updating the global residual modulus value;

[0139] Step j: updating the Lagrange dual variable for any target position;

[0140] When the number of iterations is less than a preset threshold, repeating the above steps b-j, and when the number of iterations is equal to / equal to the preset threshold, outputting the current linear combination coefficient vector.

[0141] In an embodiment of the present application, the augmented Lagrangian function is represented by the following formula:

[0142]

[0143] wherein, Re denotes taking the real part of a complex number, the superscript H denotes conjugate transpose, ∑ denotes summation, p is an augmented parameter, and λ is a dual variable introduced in the augmented Lagrangian function i , μ i , λ i is a dual variable with respect to the constraint z i = a(θ T，i )-D A p, μ i is a dual variable with respect to the constraint t i = t.

[0144] In the embodiments of the present application, a coherent transmission signal is used to forge a detection spectral peak at a preset target DOA in a linear combination manner, so as to cause the detection algorithm to produce an incorrect output result, thereby achieving an attack on mainstream DOA detection algorithms.

[0145] Specific limitations of the DOA direction attack device based on steering vector simulation can be referred to the limitations of the DOA direction attack method based on steering vector simulation in the foregoing, which will not be described herein. Each module in the above DOA direction attack device based on steering vector simulation can be realized by software, hardware, and combinations thereof, in whole or in part. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above modules.

[0146] In an embodiment, a computer device is provided, which can be a terminal device, and an internal structure diagram thereof can be as shown in Figure 4 The computer device includes a processor, a memory, and a network interface connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a readable storage medium. The readable storage medium stores computer readable instructions. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer readable instructions are executed by the processor to implement a DOA direction attack method based on steering vector simulation. The readable storage medium provided in the embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.

[0147] In the embodiments of the present application, a computer device is provided, which includes a memory, a processor, and computer readable instructions stored in the memory and executable on the processor. When the processor executes the computer readable instructions, the steps of the DOA direction attack method based on steering vector simulation are implemented.

[0148] In the application embodiment, a readable storage medium is provided, and the readable storage medium stores computer readable instructions. The computer readable instructions are executed by a processor to implement the steps of the above-mentioned guided vector simulation-based angle of arrival direction attack method.

[0149] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by computer readable instructions instructing related hardware. The computer readable instructions can be stored in a non-volatile readable storage medium or a volatile readable storage medium. When the computer readable instructions are executed, the processes of the above-mentioned embodiments can be included. Any reference to memory, storage, database or other medium used in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM) and memory bus dynamic RAM (RDRAM) and the like.

[0150] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of functional units and modules is exemplified. In actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the above-mentioned functions.

[0151] The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, but not to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalent ones. Such modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for attacking the direction of arrival based on guide vector simulation, characterized in that, The method includes: Acquire basic scene information and signal source deployment information. The basic scene information includes a guide vector dictionary set, and the signal source deployment information includes a target position angle set. Based on the set of guide vector dictionaries and the set of target position angles, an objective function and constraints are established to simulate the target guide vector through a linear combination coefficient vector of guide vectors; Solving the objective function yields the linear combination coefficient vector of the guide vector dictionary set; Based on the linear combination coefficient vector, the deployment orientation of the signal source and the linear combination coefficient vector of the signal waveform are determined, so that multiple directional signal sources pointing towards the target receiver are deployed in the far field range of the target receiver based on the deployment orientation of the signal source and the linear combination coefficient vector of the signal waveform.

2. The arrival angle direction attack method based on guide vector simulation as described in claim 1, characterized in that, The constraint is that signal sources must not be deployed in dangerous areas to prevent detection by enemy reconnaissance equipment.

3. The method for attacking the direction of arrival angle by simulating the guide vector as described in claim 1, characterized in that, The objective of the objective function is that the distance between each guide vector in the target position angle set and the linear combination coefficient vector of the corresponding guide vector in the guide vector dictionary set is less than a preset threshold.

4. The method for attacking the angle of arrival direction using guided vector simulation as described in claim 1, characterized in that, The objective function is expressed by the following formula: in, Indicates azimuth angle The guide vector, This indicates taking the maximum value, where p is the linear combination coefficient vector of the guiding vector dictionary. Dictionary matrix representing the guide vector The product vector of the linear combination coefficient vector p. For vectors The nth element, constraint This means that the total number of deployed signal sources does not exceed K. This represents the number of non-zero elements in the statistical vector p.

5. The method for attacking the direction of arrival angle by simulating the guide vector as described in claim 1, characterized in that, Solving the objective function includes: By introducing the intermediate residual variable of the target azimuth angle, the residual magnitude of the target azimuth angle, and the global residual magnitude, the augmented Lagrangian function of the objective function is obtained. The augmented Lagrangian function is solved iteratively by minimizing the augmented Lagrangian function in blocks and by performing an ascent operation on the dual variable.

6. The method for attacking the direction of arrival angle by simulating the guide vector as described in claim 5, characterized in that, The iterative solution of the augmented Lagrangian function includes: Step a: Initialize the augmented Lagrangian function and determine the initial parameter values; Step b: Calculate the square of the largest singular value of the guide vector dictionary set; Step c: For any target location, calculate the initial residual pre-update variables; Step d: For any target location, based on the initial residual pre-update variable, update the residual intermediate variable of the target azimuth and the residual magnitude; Step e: For any target location, calculate the secondary residual pre-update variable based on the intermediate residual variable of the updated target azimuth angle; Step f: Calculate the intermediate variable for signal source selection based on the secondary residual pre-update variable and the square of the maximum singular value; Step g: Calculate the non-dangerous region subset variables of the intermediate variables for signal source selection, and construct a maximum modulus index set based on the non-dangerous region subset variables; Step h: Update the linear combination coefficient vector based on the maximum modulus index set; Step i: Update the global residual modulus; Step j: For any target location, update the Lagrange dual variable; When the number of iterations is less than a preset threshold, repeat the above steps bj until the number of iterations is greater than or equal to the preset threshold, and then output the current linear combination coefficient vector.

7. The method for attacking the direction of arrival angle by simulating the guide vector as described in claim 5, characterized in that, The augmented Lagrange function is expressed by the following formula: in, This indicates taking the real part of a complex number, and the superscript H indicates the conjugate transpose. To express summation, To augment the parameters, a dual variable is introduced into the augmented Lagrangian function. as well as , It's about constraints. dual variables, It's about constraints. The dual variable.

8. An attack device based on the angle of arrival direction simulated by a guide vector, characterized in that, The device includes: The information acquisition unit is used to acquire basic scene information and signal source deployment information. The basic scene information includes a guide vector dictionary set, and the signal source deployment information includes a target position angle set. The objective function construction unit is used to establish an objective function and constraints based on the guide vector dictionary set and the target position angle set, so as to simulate the target guide vector through the linear combination coefficient vector of the guide vector; The objective function solving unit is used to solve the objective function to obtain the linear combination coefficient vector of the guide vector dictionary set; The signal source deployment unit is used to determine the deployment orientation of the signal source and the linear combination coefficient vector of the signal waveform based on the linear combination coefficient vector, so as to deploy multiple directional signal sources pointing towards the receiver in the far field range of the target receiver based on the deployment orientation of the signal source and the linear combination coefficient vector of the signal waveform.

9. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and running on the processor, characterized in that, When the processor executes the computer-readable instructions, it implements the angle-of-arrival attack method based on guide vector simulation as described in any one of claims 1 to 7.

10. A readable storage medium having computer-readable instructions stored thereon, characterized in that, When the computer-readable instructions are executed by a processor, they implement the angle-of-arrival attack method based on guide vector simulation as described in any one of claims 1 to 7.

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