An interference polarization angle perception method

Abstract

The application discloses an interference polarization angle sensing method, comprising: establishing a polarization domain signal receiving model and determining a polarization domain receiving signal; the polarization domain receiving signal comprises multiple snap samples; determining a fitness function based on the polarization domain receiving signal; for each snap sample, searching for a global optimum by using the fitness function and a particle swarm algorithm, and taking the global optimum as a polarization angle sensing result; selecting polarization angle sensing results corresponding to a preset number of snap samples and calculating an average value to obtain a final polarization angle sensing result. The interference polarization angle sensing method provided by the application uses the particle swarm algorithm to seek an optimal solution according to iteration of particles, is not affected by real number polarization domain director vectors, can process multi-dimensional tasks, avoids the problem that polarization angle cannot be distinguished due to the same direction of signal space in the existing polarization sensitive array estimation polarization angle method, and effectively improves the radar polarization sensing capability.

Description

Technical Field

[0001] This invention belongs to the field of radar technology, specifically relating to a method for sensing interference polarization angle. Background Technology

[0002] In recent years, the electromagnetic environment faced by radar has become increasingly complex and severe. Therefore, how to mine and utilize the electromagnetic information of radar to improve its anti-jamming performance and survivability in the face of various types of interference, thus adapting to the complex and ever-changing electromagnetic environment, has become an urgent problem to be solved in the field of radar technology. With the deepening understanding and utilization of polarization information, polarization angle sensing technology has gradually become a hot topic in radar technology development.

[0003] When suppressing interference in the polarization domain, the first step is to obtain the polarization angle of the interference. The ability to obtain the polarization angle largely determines the effectiveness of interference suppression. In existing technologies, the polarization angle is mainly estimated using a polarization-sensitive array composed of spatial and polarization domain information. However, when two signals have the same spatial angle of arrival but different polarization states, they will superimpose to become one signal, and their polarization states will also superimpose to become another polarization state. For example, horizontal and vertical polarizations can superimpose to become a 45-degree oblique polarization state. In this case, estimation using a polarization-sensitive array is insufficient for differentiation. Summary of the Invention

[0004] To address the aforementioned problems in the existing technology, this invention provides a method for sensing interference polarization angle.

[0005] This invention provides a method for sensing interference polarization angle, comprising:

[0006] A polarization domain signal receiving model is established and the polarization domain received signal is determined; the polarization domain received signal includes multiple snapshot samples;

[0007] The fitness function is determined based on the received signal in the polarization domain.

[0008] For each of the aforementioned snapshot samples, the fitness function and particle swarm optimization algorithm are used to search for the global optimum, and the global optimum is used as the polarization angle sensing result;

[0009] Select a preset number of the polarization angle perception results corresponding to the snapshot samples and calculate the average value to obtain the final polarization angle perception result.

[0010] In one embodiment of the present invention, the polarization domain receives the following signal:

[0011] R = AS J +N

[0012] In the formula, noise N = [n1 n2 ... n L ] T n l The noise of the l-th linearly polarized antenna channel is represented by the polarization domain steering vector. θ k This represents the polarization angle of the k-th linearly polarized interference signal. S represents the polarization angle of the l-th linearly polarized antenna. J =[s j1 s j2 ... s jK ] T s jk This represents the k-th linearly polarized interference signal, and the received signal in the polarization domain is R = [r1 r2 ... r...]. L ] T r l Let k = 1, 2, ..., K, where K represents the number of linearly polarized interference signals, and l = 1, 2, ..., L, where L represents the number of linearly polarized antennas.

[0013] In one embodiment of the present invention, the number of linearly polarized interference signals K = 2, the number of linearly polarized antennas L = 4, and the fitness function is:

[0014]

[0015] In the formula, x1 and x4 are amplitude representation particles of the two linearly polarized interference signals, x2 and x5 are phase representation particles of the two linearly polarized interference signals, x3 and x6 are polarization angle representation particles of the two linearly polarized interference signals, and ||·||2 represents the L2 norm.

[0016] In one embodiment of the present invention, M = 6;

[0017] For each of the aforementioned snapshot samples, the step of searching for a global optimum using the fitness function and particle swarm optimization algorithm, and using the global optimum as the polarization angle sensing result, includes:

[0018] Q particles are randomly generated within a selected 6-dimensional search space, and initialization information for each particle is obtained through random initialization; the initialization information includes the initial velocity and initial position of each particle.

[0019] Set the initial position of each particle to the current individual's optimal pbest position. q Then, the initialization information of Q particles is substituted into the fitness function, and the initial position of the particle that makes the current fitness value the smallest is selected as the global optimal gbest of the population, and the minimum fitness value is selected as the current optimal fitness value of the population.

[0020] The initial velocity and initial position of each particle are used as the current velocity and current position, respectively, and the current velocity and current position are updated.

[0021] Substitute the updated velocity and position into the fitness function to obtain the updated fitness value; for each particle, if the updated fitness value is better than the current fitness value, then the updated position is taken as the new pbest. q ;

[0022] The minimum value of the updated fitness value is taken as the updated optimal fitness value of the population, and the updated optimal fitness value of the population is compared with the current optimal fitness value of the population; if the updated optimal fitness value of the population is better, the current position of the particle corresponding to the updated optimal fitness value of the population is taken as gbest.

[0023] When the number of iterations reaches the preset number, gbest will be used as the polarization angle perception result.

[0024] In one embodiment of the present invention, the current speed is updated according to the following formula:

[0025] v i,j (g+1)=wv i,j (g)+c1r1(pbest i,j (g)-x i,j (g))+c2r2(gbest j (g)-x i,j (g))

[0026] In the formula, v i,j (g) and x i,j (g) represents the current velocity and position of the i-th particle in the g-th iteration, respectively, and c1 represents the individual learning factor of the particle. i,j (g) represents the current individual optimality of the i-th particle in the j-th dimension search space at the g-th iteration, c2 represents the social learning factor of the particle, r1 and r2 are random numbers in [0,1], w represents the inertia weight, and v i,j (g+1) represents the updated velocity of the i-th particle, where j = 1, 2, ..., M.

[0027] In one embodiment of the present invention, the current position is updated according to the following formula:

[0028] x i,j (g+1)=x i,j (g)+v i,j (g+1)

[0029] In the formula, x i,j (g+1) represents the updated position of the i-th particle.

[0030] In one embodiment of the present invention,

[0031]

[0032] In the formula, w start w represents the maximum value of the inertia weight. end G represents the minimum value of the inertia weight. max Indicates the preset number of iterations.

[0033] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0034] This invention provides a method for sensing interference polarization angles. It establishes a polarization domain signal receiving model, then creates a fitness function to be optimized based on the received polarization domain signal, and finally uses a particle swarm optimization algorithm to estimate the polarization angle sensing result. Since the polarization domain steering vector in the received polarization domain signal is a real matrix, sparse reconstruction methods would lack complex domain information in the constructed dictionary, making it difficult to effectively reconstruct the polarization angle. This invention utilizes a particle swarm optimization algorithm to find the optimal solution through particle iteration, avoiding the influence of the real polarization domain steering vector, and can handle multi-dimensional tasks with high performance. This method avoids the problem in existing polarization-sensitive array methods for estimating polarization angles where the polarization angle cannot be distinguished due to signals having the same spatial orientation, effectively improving the radar's polarization sensing capability.

[0035] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0036] Figure 1 This is a flowchart of an interference polarization angle sensing method provided in an embodiment of the present invention;

[0037] Figure 2 This is a schematic diagram of an interference polarization angle sensing method provided in an embodiment of the present invention;

[0038] Figure 3 This is a schematic diagram of the root mean square error of the interference signal polarization angle sensing result provided in an embodiment of the present invention.

[0039] Figure 4 This is a schematic diagram of the interference signal polarization angle sensing result provided in this embodiment of the invention after averaging every 4 snapshots;

[0040] Figure 5 This is a schematic diagram of the interference signal polarization angle sensing result provided in this embodiment of the invention after averaging every 16 snapshots;

[0041] Figure 6 This is a schematic diagram showing the average processing of the interference signal polarization angle sensing result provided in this embodiment of the invention after taking 64 snapshots. Detailed Implementation

[0042] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0043] Figure 1 This is a flowchart of an interference polarization angle sensing method provided in an embodiment of the present invention. Figure 1 As shown, this embodiment of the invention provides a method for sensing interference polarization angle, including:

[0044] S1. Establish a polarization domain signal receiving model and determine the polarization domain received signal; the polarization domain received signal includes multiple snapshot samples;

[0045] S2. Determine the fitness function based on the received signal in the polarization domain;

[0046] S3. For each of the snapshot samples, the fitness function and particle swarm optimization algorithm are used to search for the global optimum, and the global optimum is used as the polarization angle sensing result.

[0047] S4. Select a preset number of polarization angle perception results corresponding to the snapshot samples and calculate the average value to obtain the final polarization angle perception result.

[0048] It should be noted that when using the above-mentioned method provided by this invention to sense the polarization angle of the interference signal, the following conditions must be met: (1) Far-field assumption: The interference signal is located in the far-field region of the antenna, that is, when the wavefront of the interference signal reaches the antenna, it can be considered as a plane wave. (2) Fixed polarization assumption: The electromagnetic wave corresponding to the interference signal is a fully polarized wave with a fixed polarization form, and the polarization mode of the interference signal is linear polarization, that is, the polarization phase difference η = 0 or η = π. (3) Array element assumption: The polarization isolation between array element channels is ideal, that is, there is no electromagnetic coupling between array elements, and the complete reception of the signals of their respective polarization channels can be achieved. For example, a horizontally polarized antenna can achieve complete reception of the horizontally polarized components. (4) Propagation medium assumption: The propagation medium is isotropic, lossless, uniform, linear and non-dispersive, so as to ensure that the electromagnetic wave is not distorted or attenuated in the medium, and satisfies the principle of linear superposition. (5) Noise assumption: The noise inside each array element channel is independent and the noise mean is 0 and the variance is σ. 2 Noise and interference signals are also independent of each other, i.e., E{n(t1)j H (t2)}=0.

[0049] In step S1 above, the polarization domain receives the following signal:

[0050] R = AS J +N

[0051] In the formula, noise N = [n1 n2 ... n L ] T n l The noise of the l-th linearly polarized antenna channel is represented by the polarization domain steering vector. θ k This represents the polarization angle of the k-th linearly polarized interference signal. S represents the polarization angle of the l-th linearly polarized antenna. J =[s j1 s j2 ... s jK ] T s jk This represents the k-th linearly polarized interference signal, and the received signal in the polarization domain is R = [r1 r2 ... r...]. L ] T r l Let k = 1, 2, ..., K, where K represents the number of linearly polarized interference signals, and l = 1, 2, ..., L, where L represents the number of linearly polarized antennas.

[0052] Specifically, the k-th linear polarization interference signal is denoted as s. ji The polarization angle of the k-th linearly polarized interference signal is denoted as θ. k The polarization state of a linearly polarized interference signal can be represented using the Jones vector as follows:

[0053] Furthermore, the polarization angle of the l-th linearly polarized antenna is denoted as... The polarization state of a linearly polarized receiving antenna can be represented using the Jones vector as follows: At this point, the interference signal received by the l-th linearly polarized antenna can be expressed as:

[0054]

[0055] In the above formula, k = 1, 2, ..., K, where K represents the number of linearly polarized interference signals. When there are K interference signals and L linearly polarized receiving antennas, let...

[0056]

[0057] S J =[s j1 s j2 ... s jK ] T

[0058]

[0059] The interference signal received by the L linearly polarized antennas is:

[0060] R = [r1 r2 ... r] L ] T =AS J

[0061] Adding noise to this, the polarization domain received signal by L linearly polarized antennas is:

[0062] R = [r1 r2 ... r] N ] T =AS J +N

[0063] Wherein, the noise of the linearly polarized antenna channel is N = [n1 n2 ... n L ] T n1, n2, ... n L These are the mean values ​​received by each polarization channel. The noise is Gaussian white noise with a mean of 0 and a variance of 1. They are independent of each other and independent of the interference signal.

[0064] The above analysis shows that the expression for the interference signal received by the l-th linearly polarized antenna is related to the form of the interference signal, the number of interference signals, the polarization angle of the interference signal, and the polarization angle of the linearly polarized receiving antenna. When the polarization angle of the interference signal is equal to the polarization angle of the l-th linearly polarized receiving antenna, the linearly polarized antenna can achieve optimal reception, also known as optimal matching.

[0065] In this embodiment, the number of linearly polarized interference signals K = 2, the number of linearly polarized antennas L = 4, and the fitness function is:

[0066]

[0067] In the formula, x1 and x4 are the amplitude representation particles of the two linearly polarized interference signals, x2 and x5 are the phase representation particles of the two linearly polarized interference signals, x3 and x6 are the polarization angle representation particles of the two linearly polarized interference signals, and ||·||2 represents the L2 norm.

[0068] In this embodiment, x1, x2, x3, x4, x5, and x6 can be preset random values. The fitness function represents the L2 norm of the difference between the polarization domain received signal composed of six-dimensional particle parameters and the real signal model. The smaller the L2 norm, the closer it is to the actual value.

[0069] In step S3 above, M = 6;

[0070] For each of the aforementioned snapshot samples, the step of searching for a global optimum using the fitness function and particle swarm optimization algorithm, and using the global optimum as the polarization angle sensing result, includes:

[0071] S301. Randomly generate Q particles in the selected 6-dimensional search space, and obtain the initialization information of each particle through random initialization; the initialization information includes the initial velocity and initial position of each particle.

[0072] S302. Set the initial position of each particle to the current individual's optimal position (pbest). q Then, the initialization information of Q particles is substituted into the fitness function, and the initial position of the particle that makes the current fitness value the smallest is selected as the global optimal gbest of the population, and the minimum fitness value is selected as the current optimal fitness value of the population.

[0073] S303. Take the initial velocity and initial position of each particle as the current velocity and current position respectively, and update the current velocity and current position;

[0074] S304. Substitute the updated velocity and updated position into the fitness function to obtain the updated fitness value; for each particle, if the updated fitness value is better than the current fitness value, then the updated position is taken as the new pbest. q ;

[0075] S305. Take the minimum value of the updated fitness value as the updated optimal fitness value of the population, and compare the updated optimal fitness value of the population with the current optimal fitness value of the population; if the updated optimal fitness value of the population is better, then take the current position of the particle corresponding to the updated optimal fitness value of the population as gbest.

[0076] S306. When the number of iterations reaches the preset number, gbest will be used as the polarization angle perception result.

[0077] Specifically, during the initialization phase, the initial velocity and initial position of the q-th particle are defined as x, ... q =(x q,1 ,x q,2 ,...,x q,M ), v q =(v q,1 ,v q,2 ,...,v q,M The initial position of each particle is set to its individual optimal position (pbest). q Then, the initial velocities and initial positions of these Q particles are substituted into the fitness function, and the particle that minimizes the fitness value is selected as the optimal particle of the entire population, and its corresponding initial position is the global optimum gbest.

[0078] Optionally, after setting the initial velocity and initial position of each particle as the current velocity and current position, the current velocity and current position information of the particles are updated according to the following formula, and the velocity is limited so that it cannot exceed the maximum flight speed V of the particle. max :

[0079] v i,j (g+1)=wv i,j (g)+c1r1(pbest i,j (g)-x i,j (g))+c2r2(gbest j (g)-x i,j (g))

[0080] x i,j (g+1)=x i,j (g)+v i,j (g+1).

[0081] In the formula, v i,j (g) and x i,j (g) represents the current velocity and position of the i-th particle in the g-th iteration, respectively, and c1 represents the individual learning factor of the particle. i,j (g) represents the current individual optimality of the i-th particle in the j-th dimension search space at the g-th iteration, c2 represents the social learning factor of the particle, r1 and r2 are random numbers in [0,1], w represents the inertia weight, and v i,j (g+1) represents the updated velocity of the i-th particle, where j = 1, 2, ..., M.

[0082] For example, this embodiment adopts a linearly decreasing inertia weight strategy, and the formula for the linearly decreasing inertia weight strategy is as follows:

[0083]

[0084] In the formula, w start w represents the maximum value of the inertia weight. end G represents the minimum value of the inertia weight. max This represents the preset number of iterations, where w start The value is 0.9, w end The value is 0.3, where g is the current iteration number, and G is the value of 0.3. max Indicates the preset number of iterations.

[0085] Furthermore, the updated fitness value is calculated using the updated velocity and updated position; for each particle, if the updated fitness value is less than the current fitness value, the updated position of the particle is taken as the new pbest. q Otherwise, no changes will be made.

[0086] The process of finding the optimal individual can be expressed by the following formula:

[0087] vector pbest q (g)=(pbest q,1 (g),pbest q,2 (g),...,pbest q,M (g) represents the current position of particle q, which has the best fitness value found from initialization to the g-th iteration; that is, the individual optimal position. After each iteration, the individual optimal position of each particle needs to be updated according to the following formula:

[0088]

[0089] Here, f(·) represents the objective function corresponding to the current position of the particle.

[0090] In step S305, the updated optimal fitness value of the population is compared with the current optimal fitness value of the population. If the updated optimal fitness value of the population is better, the current position of the particle corresponding to the updated optimal fitness value of the population is updated to gbest; otherwise, no change is made.

[0091] Determine whether the termination condition is met. The termination condition can be whether the maximum number of iterations has been reached, whether the preset minimum error has been met, or whether a sufficiently good fitness value has been obtained. If any of the above three conditions are met, the optimization process ends; otherwise, return to step S303 to perform the step of updating the current velocity and current position.

[0092] Since the fitness function constructed in step S2 is a six-dimensional variable optimization problem, in this embodiment the population size can be set to 10000, the preset number of iterations can be set to 100, and the individual learning factor and social learning factor can both be set to 2.

[0093] In step S4 above, the results of sensing the polarization angles of multiple interference signals can be averaged using multiple snapshots. Specifically, the polarization angle of the linearly polarized interference signal is denoted as θ, and the polarization angle sensing result estimated using the particle swarm optimization algorithm is denoted as θ. est The polarization angle estimation error is expressed as θ. error =θ-θ est Considering the randomness of the particle swarm optimization algorithm, the estimated polarization angle error after PSO angle measurement should conform to a Gaussian distribution after averaging via snapshot.

[0094] If a random variable x follows a probability distribution model with location parameter μ and scale parameter σ, then its probability density function can be expressed as:

[0095]

[0096] This random variable is also called a Gaussian random variable, and its distribution is a Gaussian distribution, denoted as X ~ N(μ, σ). 2 ), where μ represents the mean and σ represents the standard deviation. The mean μ indicates its location, and the standard deviation σ indicates its distribution amplitude.

[0097] The y1, y2, ..., y M Since Y is an independent observation, the expected value and variance of each observation are consistent, and the variance is set as D(Y). If the average is taken for every n numbers, the average value is... The variance is:

[0098]

[0099] The above formula means that for a set of data that follows a Gaussian distribution and is statistically independent, if the average of every n numbers is taken, the variance becomes 1 / n of the original variance.

[0100] It should be understood that the Gaussian distribution follows the "68-95-99.7 rule," which means that under a Gaussian distribution model, 68% of the data deviates from the mean by one standard deviation, 95% deviates from the mean by two standard deviations, and 99.7% deviates from the mean by three standard deviations. The probability distribution function represents the probability that a random variable falls within any given range. The probability distribution function of the Gaussian distribution is shown below:

[0101]

[0102] Finally, the polarization angle perception result after averaging multiple snapshots is recorded as the final polarization angle perception result.

[0103] The following simulation experiment further illustrates the interference polarization angle sensing method provided by this invention.

[0104] Firstly, we verified the impact of the number of receiving antennas and the interference-to-noise ratio on the polarization sensing performance.

[0105] Experimental conditions: A linearly polarized interference signal is incident on the antenna array. The number of receiving antennas is set to 2, 3 and 4 respectively. The interference-to-noise ratio varies between 10-60dB. The polarization angle is 45° and the number of snapshots is 500.

[0106] Figure 3 This is a schematic diagram of the root mean square error of the polarization angle sensing result provided in an embodiment of the present invention. After angle measurement using the particle swarm optimization algorithm, as shown... Figure 3As shown, the higher the interference noise ratio (IRR), the smaller the root mean square error (RMSE) of the polarization angle sensing result. When the INR is greater than or equal to 50 dB, the RMSE of the polarization angle sensing result is close to 0. Furthermore, when the INR is less than 40 dB, the number of receiving channels is greater, and the RMSE of the polarization angle sensing result is also smaller. When the INR is greater than or equal to 40 dB, the number of receiving channels has little effect on the estimated RMSE of the polarization angle sensing result.

[0107] Secondly, we verified the impact of averaging multiple snapshots on polarization sensing performance.

[0108] Experimental conditions: Two linearly polarized interference signals were incident on the antenna array. Four receiving antennas were used: a horizontally polarized antenna, a 30° linearly polarized antenna, a 60° linearly polarized antenna, and a vertically polarized antenna. One interference signal had an interference ratio (IRR) of 50 dB and a polarization angle of 90°, while the other interference signal had a fixed IIR of 10 dB and a polarization angle of 0°. The error in the polarization angle sensing result of the interference signal with an IIR of 50 dB obtained after angle measurement using the particle swarm optimization algorithm was averaged using multiple snapshots.

[0109] Simulation 1: Assuming there are two interferences in the polarization domain signal receiving model, the error of the polarization angle sensing result of the interference signal with an interference ratio of 50dB obtained after angle measurement by particle swarm optimization algorithm is averaged every 4 snapshots.

[0110] Simulation 2: Assuming there are two interferences in the polarization domain signal receiving model, the error of the polarization angle sensing result of the interference signal with an interference ratio of 50dB obtained after angle measurement by particle swarm optimization algorithm is averaged every 16 snapshots.

[0111] Simulation 3 assumes that there are two interferences in the polarization domain signal receiving model. The error of the polarization angle sensing result of the interference signal with an interference ratio of 50dB obtained by the particle swarm algorithm angle measurement is averaged every 64 snapshots.

[0112] Figure 4 This is a schematic diagram illustrating the averaging process of the interference signal polarization angle sensing results provided in this embodiment of the invention, which is performed using four snapshots. Figure 5 This is a schematic diagram illustrating the averaging process of the interference signal polarization angle sensing results provided in this embodiment of the invention, which is calculated by taking the average of every 16 snapshots. Figure 6 This is a schematic diagram illustrating the averaging process of the interference signal polarization angle sensing results provided in this embodiment of the invention, taken from every 64 snapshots. The root mean square error of the polarization angle sensing results is shown in Table 1.

[0113] Table 1

[0114] Data processing methods Root mean square error Single snapshot processing 0.3855 Average of every 4 snapshots. 0.1922 Average of 16 snapshots were processed. 0.0974 Average of 64 snapshots were processed. 0.0500

[0115] from Figure 4-6 It can be seen that the distribution of the error histogram of the polarization angle sensing result after averaging the snapshots conforms to a Gaussian distribution. Furthermore, as the number of snapshots for averaging increases, the error of the polarization angle sensing result gradually decreases. Table 1 shows that as the number of snapshots for averaging increases from 1 to 4 to 16 to 64, the root mean square error of the estimated polarization angle decreases by a factor of 2, conforming to a Gaussian distribution and meeting the expected conclusions.

[0116] As can be seen from the above embodiments, the beneficial effects of the present invention are as follows:

[0117] This invention provides a method for sensing interference polarization angles. It establishes a polarization domain signal receiving model, then creates a fitness function to be optimized based on the received polarization domain signal, and finally uses a particle swarm optimization algorithm to estimate the polarization angle sensing result. Since the polarization domain steering vector in the received polarization domain signal is a real matrix, sparse reconstruction methods would lack complex domain information in the constructed dictionary, making it difficult to effectively reconstruct the polarization angle. This invention utilizes a particle swarm optimization algorithm to find the optimal solution through particle iteration, avoiding the influence of the real polarization domain steering vector, and can handle multi-dimensional tasks with high performance. This method avoids the problem in existing polarization-sensitive array methods for estimating polarization angles where the polarization angle cannot be distinguished due to signals having the same spatial orientation, effectively improving the radar's polarization sensing capability.

[0118] In the description of this invention, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. In addition, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0119] Although this application has been described herein in conjunction with various embodiments, other variations of the disclosed embodiments can be understood and implemented by those skilled in the art in carrying out the claimed application by reviewing the accompanying drawings, the disclosure, and the appended claims.

[0120] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for sensing interference polarization angle, characterized in that, Satisfy the following conditions: Far-field assumption: the interference signal is located in the far-field region of the antenna; Polarization fixed assumption: the electromagnetic wave corresponding to the interference signal is a completely polarized wave with fixed polarization form of the interference signal, and the polarization mode of the interference signal is linear polarization, and the polarization phase difference ; Array element assumption: there is no electromagnetic coupling between the array elements, and the signals of the respective polarization channels can be completely received; Propagation medium assumption: the propagation medium is isotropic, lossless, uniform, linear, and non-dispersive; Noise assumption: the internal noise of each array element channel is independent and the mean value of the noise is 0 and the variance is , and the noise and interference signals are also independent of each other; The method comprises: establishing a polarization domain signal receiving model and determining a polarization domain receiving signal; the polarization domain receiving signal comprises a plurality of snapshot samples; determining a fitness function based on the polarization domain receiving signal; for each snapshot sample, searching for a global optimum using the fitness function and a particle swarm algorithm, and taking the global optimum as a polarization angle perception result; selecting polarization angle perception results corresponding to a preset number of snapshot samples and calculating an average value to obtain a final polarization angle perception result; the number of linearly polarized jamming signals the number of linearly polarized antennas the fitness function is In the formula, , The amplitudes of the two linearly polarized interference signals represent the particles, respectively. , These are the phase representation particles of the two linearly polarized interference signals, respectively. , The polarization angles of the two linearly polarized interference signals represent the particles, respectively. Represents the L2 norm, , , and They represent the first The polarization angle of a linearly polarized antenna.

2. The method of claim 1, wherein, the polarization domain receiving signal is: where noise , represents the noise of the th linear polarized antenna channel, the polarization domain steering vector , represents the polarization angle of the th linear polarized interference signal, represents the polarization angle of the th linear polarized antenna, , represents the th linear polarized interference signal, the polarization domain received signal , represents the polarization domain received signal received by the th linear polarized antenna, , represents the number of linear polarized interference signals, , represents the number of linear polarized antennas.

3. The method of claim 2, wherein the polarization angle of interference is determined by: Dimension of search space ; Randomly generating a particle in a selected 6-dimensional search space and obtaining initial information of each particle through random initialization; the initial information includes initial speed and initial position of each particle; Set the initial position of each particle as the current individual optimum After that, Substitute the initial information of the particle into the fitness function, and select the initial position of the particle that makes the current fitness value minimum as the global optimum of the population , and take the minimum fitness value as the current population optimum fitness value taking the initial speed and the initial position of each particle as the current speed and the current position respectively, and updating the current speed and the current position; substituting the updated speed and the updated position into the fitness function to obtain an updated fitness value; For each of the particles, if the updated fitness value is better than the current fitness value, the updated position is taken as the new ; The minimum value of the updated fitness values is taken as an updated population optimal fitness value, and the updated population optimal fitness value is compared with the current population optimal fitness value; if the updated population optimal fitness value is better, the current position of the particle corresponding to the updated population optimal fitness value is taken as the current population optimal position ; When the number of iterations reaches the preset number of times, the iteration is stopped, and the current result is output as the polarization angle sensing result. as a polarization angle sensing result.

4. The method of claim 3, wherein, updating the current speed according to the following formula: wherein and denote the current velocity and the current position of the i-th particle at the j-th iteration, respectively, denote the individual learning factor of the particle, denote the current individual optimum of the i-th particle in the j-th iteration in the d-th dimension of the search space, denote the social learning factor of the particle, j , is a random number, denotes the inertia weight, denotes the updated velocity of the i-th particle, = 1, 2, …, j M .​​​​​​​ 5. The method of claim 4, wherein, updating the current position according to the following formula: In the formula is the first updated position of the particle.

6. The interference polarization angle perception method according to claim 4, characterized in that, In the formula, represents the maximum value of the inertia weight, represents the minimum value of the inertia weight, represents the preset number of iterations.

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