Arrival angle direction attack method and device based on steering vector simulation

Abstract

The invention discloses an arrival angle direction attack method and device based on steering vector simulation, and the method comprises the steps: obtaining a steering vector dictionary set and a target position angle set; based on the steering vector dictionary set and the target position angle set, establishing a target function and a constraint condition to simulate a target steering vector through a linear combination coefficient vector of the steering vector; solving the target function to obtain a linear combination coefficient vector of the steering vector dictionary set; and based on the linear combination coefficient vector, determining the deployment orientation of the signal source and the linear combination coefficient vector of the signal waveform so as to deploy a plurality of directional signal sources pointing to the receiver in the far field range of the target receiver. A detection spectrum peak value is forged at a preset target DOA by using a coherent transmission signal in a linear combination manner, so that a wrong output result is generated by a detection algorithm, and the attack on a mainstream DOA detection algorithm is realized.

Description

Technical Field

[0001] The present application relates to the technical field of angle of arrival estimation, and in particular to an angle of arrival direction attack method, device, computer equipment and storage medium based on steering vector simulation. Background Art

[0002] Array signal processing has been a fundamental research area for the past few decades, and angle of arrival (DOA) estimation is one of the mainstream tasks in this broad research field. In short, the goal of DOA estimation is to determine the angular direction of the incident signal 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 pointing direction of interest. MUSIC is a subspace-based method that involves projecting the data into the noise feature space of the sample covariance matrix. ESPRIT uses the rotational invariance of the steering vector 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 degrade in performance when the transmitted signal is coherent. The coherence of the signal leads to the loss of rank of the source covariance matrix, which causes distortion of the noise subspace. This distortion may cause aliasing of the spectrum peaks at the DOA location. Summary of the invention

[0003] Based on this, it is necessary to provide an arrival angle direction attack method, device, computer equipment and storage medium based on steering vector simulation to address the above technical problems, so as to solve at least one problem existing in the above-mentioned prior art.

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

[0005] Acquire basic scene information and signal source deployment information, wherein the basic scene 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, an objective function and constraint conditions are established to simulate the target steering vector through a linear combination coefficient vector of the steering vector;

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

[0008] 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 to the receiver within 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.

[0009] In one embodiment, the constraint condition is that the signal source shall not be deployed in the dangerous area to prevent being discovered by enemy reconnaissance equipment.

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

[0011] In one embodiment, the objective function is expressed by the following formula:

[0012]

[0013] ||p||0≤K.

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

[0015] In one embodiment, solving the objective function includes:

[0016] By introducing the residual intermediate variable of the target azimuth angle, the residual modulus value of the target azimuth angle and the global residual modulus value, an augmented Lagrangian function of the objective function is obtained;

[0017] The augmented Lagrangian function is iteratively solved by minimizing the augmented Lagrangian function in blocks and performing dual variable raising operations.

[0018] In one embodiment, the iteratively solving the augmented Lagrangian function includes:

[0019] Step a: Initializing the augmented Lagrangian function and determining initial parameter values;

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

[0021] Step c: For any target position, calculate the initial residual pre-update variable;

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

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

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

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

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

[0027] Step i: Update the global residual modulus value;

[0028] Step j: For any target position, update the Lagrangian dual variables;

[0029] When the number of iterations is less than a preset threshold, the above step bj is repeated until the number of iterations is equal to / equal to the preset threshold, and the current linear combination coefficient vector is output.

[0030] In one embodiment, the augmented Lagrangian function is expressed by the following formula:

[0031]

[0032] Where Re means taking the real part of a complex number, the superscript H means conjugate transpose, ∑ means summation, ρ is the augmented parameter, and the dual variable λ is introduced in the augmented Lagrangian function. i and μ i ,λ i is about the constraint z i =a(θ T，i )-D A The dual variable of p, μ i It is about the constraint t i = the dual variable of t.

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

[0034] An information acquisition unit, used to acquire basic scene information and signal source deployment information, wherein the basic scene information includes a steering vector dictionary set, and the signal source deployment information includes a target position angle set;

[0035] An objective function construction unit, configured to establish an objective function and constraint conditions 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 the steering vector;

[0036] An objective function solving unit, used for solving the objective function to obtain a linear combination coefficient vector of the steering vector dictionary set;

[0037] A 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 to the receiver within 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.

[0038] In a third aspect, a computer device is provided, comprising a memory, a processor, and computer-readable instructions stored in the memory and running on the processor, wherein when the processor executes the computer-readable instructions, the arrival angle direction attack method based on the steering vector simulation as described above is implemented.

[0039] In a fourth aspect, a readable storage medium is provided, on which computer-readable instructions are stored, and when the computer-readable instructions are executed by a processor, the arrival angle direction attack method based on the steering vector simulation as described above is implemented.

[0040] The above-mentioned arrival angle direction attack method, device, computer equipment and storage medium based on steering vector simulation, the method implementation includes: obtaining basic scene information and signal source deployment information, the basic scene information includes a steering vector dictionary set, and the signal source deployment information includes a target position angle set; based on the steering vector dictionary set and the target position angle set, establish an objective function and constraints to simulate the target steering vector through the linear combination coefficient vector of the steering vector; solve the objective function to obtain the linear combination coefficient vector of the steering vector dictionary set; based on the linear combination coefficient vector, determine the deployment direction of the signal source and the linear combination coefficient vector of the signal waveform, so as to deploy multiple directional signal sources pointing to the receiver within 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, a coherent transmission signal is used to forge a detection spectrum peak at a preset target DOA in a linear combination manner, causing the detection algorithm to produce an erroneous output result, thereby realizing an attack on the mainstream DOA detection algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0042] Figure 1 It is a flow chart of an arrival angle direction attack method based on steering vector simulation in one embodiment of the present application;

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

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

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

[0046] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0047] In one embodiment, if Figure 1 As shown, a method for attacking the angle of arrival direction based on the simulation of the steering vector is provided, comprising the following steps:

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

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

[0050] The signal source deployment information may specifically include a target position angle set θ T ={θ T,1 ,K,θ T,I}, abbreviated as I is the total number of target locations, the upper limit of the total number of signal sources to be deployed K, and the index set of dangerous locations where signal source nodes should not be deployed Generally, Must meet

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

[0052] In an embodiment of the present application, multiple directional signal sources pointing to the receiver can be deployed within the far field of the target receiver to achieve the purpose of interfering with the detection of the enemy receiver. These signal sources can be located within the arrival angle range of -90° to 90° of the target receiver, and the distances to the receiver are equal, and the distance values ​​can be arbitrarily specified. The arrival angle direction detection attack is achieved by the signal sources simultaneously emitting the same signal waveform, and the signal waveform can be arbitrarily specified. The linear combination coefficient vector of the deployment azimuth of the signal source and the signal waveform is obtained by the linear combination coefficient vector p of the steering vector dictionary set.

[0053] Therefore, based on the steering vector dictionary set and the target position angle set, the target position angle set θ T The steering vector a(θ T，i ), all of which must be combined with the linear combination coefficient vector D of the steering vector dictionary set A p is as close as possible, that is, the difference between the two is taken, and the distance value is calculated, and the distance value is as close to 0 as possible, so as to achieve the purpose of using the linear combination of the guidance vector to simulate the target guidance vector to construct the objective function and constraint conditions.

[0054] Among them, the objective function can be specifically expressed by the following formula:

[0055]

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

[0057] Among them, the 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 a linear combination coefficient vector of the steering vector dictionary set;

[0059] Among them, solving the objective function includes:

[0060] By introducing the residual intermediate variable of the target azimuth angle, the residual modulus value of the target azimuth angle and the global residual modulus value, an augmented Lagrangian function of the objective function is obtained;

[0061] The augmented Lagrangian function is iteratively solved by minimizing the augmented Lagrangian function in blocks and performing dual variable raising operations.

[0062] Optionally, in order to facilitate solving the objective function, auxiliary variables may 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] Among them, z i is the residual intermediate variable of the azimuth of the i-th target, t i Represents the residual modulus of the azimuth of the i-th target, and t represents the global residual modulus. Then, the augmented Lagrangian function of the above deformed function can be written. According to the framework of the alternating direction multiplier method algorithm, the iterative algorithm for solving the optimization problem can be derived by minimizing the augmented Lagrangian function in blocks and the dual variable raising operation.

[0070] Among them, the augmented Lagrangian function of the above optimization problem can be expressed by the following formula:

[0071]

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

[0073] Furthermore, the iterative solution of the augmented Lagrangian function includes:

[0074] Step a: Initializing the augmented Lagrangian function and determining initial parameter values;

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

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

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

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

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

[0080] Step g: calculating the non-dangerous area subset variables of the signal source selection intermediate variables, and constructing a maximum modulus index set based on the non-dangerous area subset variables;

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

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

[0083] Step j: For any target position, update the Lagrangian dual variables;

[0084] When the number of iterations is less than a preset threshold, the above step bj is repeated until the number of iterations is equal to / equal to the preset threshold, and the current linear combination coefficient vector is output.

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

[0086] Then, the steering vector dictionary set D can be calculated by the following formula: A The square of the largest singular value l max :

[0087]

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

[0089] b i =a(θ T,i )-D A p.

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

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

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

[0093] c i =a(θ T，i )-z i ;

[0094] Among them, z i is the residual intermediate variable of the azimuth of the i-th target, a(θ T，i ) represents the azimuth angle θ T，i The guiding vector.

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

[0096]

[0097] Among them, c i represents the secondary residual pre-update variable, l max represents the square of the largest singular value, Indicates D A The conjugate transpose of the matrix, ρ represents the augmentation parameter, λ i represents the dual variable, p is the linear combination coefficient vector of the guided vector dictionary, D A p represents the steering vector dictionary matrix D A Product vector with the linear combination coefficient vector p.

[0098] Calculate the non-dangerous area subset variable of the signal source selection intermediate variable d The specific calculation can be obtained through the following formula:

[0099]

[0100] Among them, | means taking the modulus of each element of the vector.

[0101] Then, construct the maximum modulus index set Representation vector The K-th largest element value in .

[0102] Based on this signal source, a subset variable of the non-dangerous area of ​​the intermediate variable d is selected. The linear combination coefficient vector p can be updated, which can be specifically expressed as:

[0103]

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

[0105]

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

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

[0108] At this time, if the number of iterations k is less than the preset threshold, for example, 100, the number of iterations is increased by 1, that is, k=k+1, and returns to step b, and restarts step bj. If the number of iterations k is greater than / equal to the preset threshold, for example, 100, 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 to the receiver within 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 modulus of the elements in the linear combination coefficient vector p is less than or equal to 0.001, it means that there is no signal source deployed at the angle, otherwise, a signal source is deployed at the angle, and the linear combination coefficient vector corresponding to the signal source is the corresponding element in the vector p. It should be noted that the linear combination coefficient vector will be realized by a post-placed power amplifier and phase shifter in engineering practice.

[0111] like Figure 2 As shown, a scene graph for signal source deployment based on linear combination coefficient vector is provided, with M = 10, Θ T= {60°±3°}, N0 = {n|θ n -60°|≤10°, θ n The value of is an integer}, K = 10, after executing the above steps S110 to 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 source, Signal Propagation represents the signal propagation direction, and the gray shaded area is the dangerous area. 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 goal of the objective function and the constraints, and utilizes the coherent transmission signal to realize the target guidance vector simulation, forming an arrival angle direction attack technology based on guidance vector simulation, thereby achieving the purpose of interfering with the enemy receiver detection.

[0112] In an embodiment of the present application, a method for attacking the angle of arrival direction based on steering vector simulation is provided, including: obtaining basic scene information and signal source deployment information, the basic scene information includes a steering vector dictionary set, and the signal source deployment information includes a target position angle set; based on the steering vector dictionary set and the target position angle set, an objective function and constraints are established to simulate the target steering vector through the linear combination coefficient vector of the steering vector; the objective function is solved to obtain the linear combination coefficient vector of the steering vector dictionary set; based on the linear combination coefficient vector, the deployment azimuth 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 to the receiver within the far field range of the target receiver based on the deployment azimuth of the signal source and the linear combination coefficient vector of the signal waveform. In an embodiment of the present application, a coherent transmission signal is used to forge a detection spectrum peak at a preset target DOA in a linear combination manner, causing the detection algorithm to produce an erroneous output result, thereby realizing an attack on the mainstream DOA detection algorithm.

[0113] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0114] In one embodiment, an arrival angle direction attack device based on steering vector simulation is provided, and the arrival angle direction attack device based on steering vector simulation corresponds one-to-one to the arrival angle direction attack method based on steering vector simulation in the above embodiment. Figure 3As shown, the arrival angle direction attack device based on steering vector simulation includes an information acquisition unit 10, an objective function construction unit 20, an objective function solution unit 30 and a signal source deployment unit 40. Each functional module is described in detail as follows:

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

[0116] An objective function construction unit 20, for establishing an objective function and constraint conditions 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 the steering vector;

[0117] An objective function solving unit 30, used to solve the objective function to obtain a linear combination coefficient vector of the steering vector dictionary set;

[0118] The signal source deployment unit 40 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 to the receiver within 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.

[0119] In one embodiment of the present application, the constraint condition is that the signal source must not be deployed in the dangerous area to prevent being discovered by enemy reconnaissance equipment.

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

[0121] In one embodiment of the present application, the objective function is expressed by the following formula:

[0122]

[0123]

[0124] ||p||0≤K.

[0125] Among them, a(θ T，i ) represents the azimuth angle θ T，i The steering vector, max means taking the maximum value, p is the linear combination coefficient vector of the steering vector dictionary, D A p represents the steering vector dictionary matrix D A The product vector with the linear combination coefficient vector p, p nis the nth element of vector p. The constraint ||p||0≤K means that the total number of deployed signal sources does not exceed K, and ||p||0 represents the number of non-zero elements in the statistical vector p.

[0126] In one embodiment of the present application, the objective function solving unit 30 is further used to:

[0127] By introducing the residual intermediate variable of the target azimuth angle, the residual modulus value of the target azimuth angle and the global residual modulus value, an augmented Lagrangian function of the objective function is obtained;

[0128] The augmented Lagrangian function is iteratively solved by minimizing the augmented Lagrangian function in blocks and performing dual variable raising operations.

[0129] In one embodiment of the present application, the objective function solving unit 30 is further used to:

[0130] Step a: Initializing the augmented Lagrangian function and determining initial parameter values;

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

[0132] Step c: For any target position, calculate the initial residual pre-update variable;

[0133] Step d: For any target position, update the residual intermediate variable and residual modulus value of the target azimuth;

[0134] Step e: For any target position, calculate the secondary residual pre-update variable;

[0135] Step f: Calculate the signal source and select the intermediate variable;

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

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

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

[0139] Step j: For any target position, update the Lagrangian dual variables;

[0140] When the number of iterations is less than a preset threshold, the above step bj is repeated until the number of iterations is equal to / equal to the preset threshold, and the current linear combination coefficient vector is output.

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

[0142]

[0143] Where Re means taking the real part of a complex number, the superscript H means conjugate transpose, ∑ means summation, ρ is the augmented parameter, and the dual variable λ is introduced in the augmented Lagrangian function. i , μ i ,λ i is about the constraint z i =a(θ T，i )-D A The dual variable of p, μ i It is about the constraint t i = the dual variable of t.

[0144] In the embodiment of the present application, coherent transmission signals are utilized to forge a detection spectrum peak at a preset target DOA in a linear combination manner, causing the detection algorithm to produce an erroneous output result, thereby achieving an attack on the mainstream DOA detection algorithm.

[0145] For the specific definition of the arrival angle direction attack device based on steering vector simulation, please refer to the definition of the arrival angle direction attack method based on steering vector simulation above, which will not be repeated here. Each module in the above-mentioned arrival angle direction attack device based on steering vector simulation can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0146] In one embodiment, a computer device is provided. The computer device may be a terminal device, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the computer device is used 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 used to communicate with an external terminal through a network connection. When the computer-readable instructions are executed by the processor, an arrival angle direction attack method based on steering vector simulation is implemented. The readable storage medium provided in this embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.

[0147] In an embodiment of the present application, a computer device is provided, including 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 arrival angle direction attack method based on guidance vector simulation as described above are implemented.

[0148] In an embodiment of the application, a readable storage medium is provided, wherein the readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the arrival angle direction attack method based on steering vector simulation as described above are implemented.

[0149] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through computer-readable instructions, and 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, they may include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), 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), etc.

[0150] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0151] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An arrival angle direction attack method based on steering vector simulation, characterized in that: The method comprises: Acquire basic scene information and signal source deployment information, wherein the basic scene information includes a steering vector dictionary set, and the signal source deployment information includes a target position angle set; Based on the steering vector dictionary set and the target position angle set, an objective function and constraint conditions are established to simulate the target steering vector through a linear combination coefficient vector of the steering vector; Solving the objective function to obtain a linear combination coefficient vector of the steering 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 as to deploy multiple directional signal sources pointing to the receiver within 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 steering vector simulation according to claim 1, characterized in that: The constraint condition is that signal sources must not be deployed in dangerous areas to prevent being discovered by enemy reconnaissance equipment.

3. The arrival angle direction attack method of the steering vector simulation according to claim 1, characterized in that: The optimization goal of the objective function is that the distance between each steering vector in the target position angle set and the linear combination coefficient vector of the corresponding steering vector in the steering vector dictionary set must be less than a preset threshold.

4. The arrival angle direction attack method of the steering vector simulation according to claim 1, characterized in that: The objective function is expressed by the following formula: ||p||0≤K. Among them, a(θ T，i ) represents the azimuth angle θ T，i The steering vector, max means taking the maximum value, p is the linear combination coefficient vector of the steering vector dictionary, D A p represents the steering vector dictionary matrix D A The product vector with the linear combination coefficient vector p, p n is the nth element of vector p. The constraint ||p||0≤K means that the total number of deployed signal sources does not exceed K, and ||p||0 represents the number of non-zero elements in the statistical vector p.

5. The arrival angle direction attack method of the steering vector simulation according to claim 1, characterized in that: The solving the objective function comprises: By introducing the residual intermediate variable of the target azimuth angle, the residual modulus value of the target azimuth angle and the global residual modulus value, an augmented Lagrangian function of the objective function is obtained; The augmented Lagrangian function is iteratively solved by minimizing the augmented Lagrangian function in blocks and performing dual variable raising operations.

6. The arrival angle direction attack method of the steering vector simulation according to claim 5, characterized in that: The iterative solution of the augmented Lagrangian function comprises: Step a: Initializing the augmented Lagrangian function and determining initial parameter values; Step b: Calculate the square of the maximum singular value of the steering vector dictionary set; Step c: For any target position, calculate the initial residual pre-update variable; Step d: for any target position, based on the initial residual pre-updated variable, update the residual intermediate variable and the residual modulus value of the target azimuth; Step e: for any target position, calculating a secondary residual pre-updated variable based on the updated residual intermediate variable of the target azimuth; Step f: calculating the signal source selection intermediate variable based on the secondary residual pre-updated variable and the square of the maximum singular value; Step g: calculating the non-dangerous area subset variables of the signal source selection intermediate variables, and constructing a maximum modulus index set based on the non-dangerous area subset variables; Step h: updating the linear combination coefficient vector based on the maximum modulus index set; Step i: Update the global residual modulus value; Step j: For any target position, update the Lagrangian dual variables; When the number of iterations is less than a preset threshold, the above step bj is repeated until the number of iterations is equal to / equal to the preset threshold, and the current linear combination coefficient vector is output.

7. The arrival angle direction attack method of the steering vector simulation according to claim 5, characterized in that: The augmented Lagrangian function is expressed by the following formula: Where Re means taking the real part of a complex number, the superscript H means conjugate transpose, ∑ means summation, ρ is the augmented parameter, and the dual variable λ is introduced in the augmented Lagrangian function. i and μ i ,λ i It is about constraint Z i =a(θ T，i )-D A The dual variable of p, μ i It is about the constraint t i = the dual variable of t.

8. An arrival angle direction attack device based on steering vector simulation, characterized in that: The device comprises: An information acquisition unit, used to acquire basic scene information and signal source deployment information, wherein the basic scene information includes a steering vector dictionary set, and the signal source deployment information includes a target position angle set; An objective function construction unit, configured to establish an objective function and constraint conditions 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 the steering vector; An objective function solving unit, used for solving the objective function to obtain a linear combination coefficient vector of the steering vector dictionary set; A 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 to the receiver within 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 executed on the processor, characterized in that: When the processor executes the computer-readable instructions, the arrival angle direction attack method based on steering vector simulation as described in any one of claims 1 to 7 is implemented.

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

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