Antenna array position error analysis method and system based on attitude adjustment

Abstract

The invention belongs to the related technical field of remote sensing technology, and discloses an antenna array position error analysis method and system based on attitude adjustment, and the method comprises the steps: respectively obtaining signals which are received by an antenna array in a first attitude and a second attitude and come from a same unknown signal source; for the received signal under each attitude, performing the following data processing: performing position correction on an array element, constructing a multi-signal classification function, and solving a maximum value point of the multi-signal classification function by taking a whole item containing an error correction parameter and an incident angle in the function as a decision quantity to obtain a value of the decision quantity; and based on values of decision quantities under two attitudes, an equation set is constructed by taking error correction parameters and incident angles as unknown quantities, and error analysis is realized by solving. According to the invention, accurate estimation of the array position error can be realized without introducing an auxiliary radiation source.

Description

Technical Field

[0001] The present invention belongs to the technical field related to remote sensing technology, and more specifically, relates to an antenna array position error analysis method based on attitude adjustment and a system thereof. Background Art

[0002] In the actual satellite-borne aperture microwave radiometer system, the system will produce random position offsets such as array arm misalignment and stretching during the launch process. This non-ideal offset is very fatal to the ASR working in the high-frequency band. Therefore, it is very necessary to correct the position error of the antenna array.

[0003] The existing solution is to set a controllable auxiliary source at the center of the antenna array field of view, and use the known information of the auxiliary radiation source to correct the antenna position error. However, according to radio management regulations, in order not to interfere with the normal operation of other satellites, it is not allowed to transmit auxiliary correction signals on the ground. This method cannot be implemented in a satellite environment.

[0004] Therefore, it is necessary to propose a method for realizing antenna array error analysis without adding auxiliary radiation sources. Summary of the invention

[0005] In view of the above defects or improvement needs of the prior art, the present invention provides an antenna array position error analysis method based on attitude adjustment and a system thereof, which aims to realize antenna array error analysis without adding auxiliary radiation sources.

[0006] To achieve the above object, the present invention provides an antenna array position error analysis method based on attitude adjustment, which comprises:

[0007] Respectively obtaining signals from the same unknown signal source received by the antenna array in a first posture and a second posture, and a difference in the signal incident angles between the two postures;

[0008] Data processing is performed for the received signals in each posture, the data processing comprising: constructing a multiple signal classification function of orthogonality between the steering vector and the noise subspace after introducing an error correction parameter to correct the position of the array element, taking the entire term including the error correction parameter and the incident angle in the function as the decision quantity, solving the maximum point of the multiple signal classification function, and obtaining the value of the decision quantity;

[0009] Based on the values ​​of the decision quantity under the first posture, the values ​​of the decision quantity under the second posture and the difference in the signal incident angle between the two postures, a group of equations is constructed with error correction parameters and the incident angle as unknown quantities, and the solution is used to realize error analysis.

[0010] Optionally, the maximum point of the multiple signal classification function is solved by a gradient descent criterion.

[0011] Optionally, the noise of the signal source is Gaussian white noise, and the signal-to-noise ratio is not less than 10 dB.

[0012] Optionally, the difference in incident angle between the second posture and the first posture ranges from 15° to 25°.

[0013] Optionally, the process of solving the noise subspace used in constructing the multiple signal classification function includes:

[0014] Discretize the current received signal into a snapshot data matrix X = [X1, X2…X i …X N ], X i =[x i (0),x i (1)…x i (M)] T The received signal of the i-th array element of the antenna array is discretized into a vector composed of M snapshot data, where N is the number of array elements and M is the number of discrete points;

[0015] Calculate the autocorrelation matrix of the corresponding received signal based on the current snapshot data matrix

[0016] By performing eigenvalue decomposition on the current autocorrelation matrix, the noise subspace vector is obtained.

[0017] Optionally, the introduced error correction parameter is an error coefficient, and the introduced error correction parameter is used to correct the position of the array element, including correcting the position of the i-th array element to d i (1+δ i ), d i is the ideal distance between the ith array element and the reference point, δ i is the error coefficient between the actual distance and the ideal distance, i=1,2,3,…,N, N is the number of array elements.

[0018] Optionally, the entire term including the error correction parameter and the incident angle in the function is used as the decision quantity, which is:

[0019] With d in the function i (1+δ i )sin(θ k ) as the decision quantity, where θ k is the incident angle of the received signal in the current posture.

[0020] The present invention also provides an antenna array position error analysis system based on attitude adjustment, which specifically includes:

[0021] A data acquisition unit, used to acquire signals from the same unknown signal source received by the antenna array in a first posture and a second posture, and a difference in the signal incident angle between the two postures;

[0022] A data processing unit is used to perform data processing on the received signals in each posture, wherein the data processing includes: constructing a multiple signal classification function of orthogonality between the steering vector and the noise subspace after introducing an error correction parameter to correct the position of the array element, taking the entire term including the error correction parameter and the incident angle in the function as a decision quantity, solving the maximum point of the multiple signal classification function, and obtaining the value of the decision quantity;

[0023] The error solving unit is used to construct a set of equations based on the value of the decision quantity under the first posture, the value of the decision quantity under the second posture and the difference in the signal incident angle between the two postures, with the error correction parameter and the incident angle as unknown quantities, and solve to realize error analysis.

[0024] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0025] The present invention also provides a computer program product, comprising a computer program or instructions, wherein when the computer program or instructions are executed by a processor, the steps of any of the above methods are implemented.

[0026] In general, compared with the prior art, the above technical solutions conceived by the present invention mainly have the following beneficial effects:

[0027] 1. The antenna array position error analysis method based on attitude adjustment proposed in the present invention only needs to adjust the attitude of the antenna array itself, and receive signals from the same signal source in two different states. The signal source can be any unknown signal source, as long as the antenna array can receive the signal it transmits. Then, the received signals in the two attitudes are processed separately. During the data processing, a multiple signal classification function is constructed. The function has the position information of each element of the antenna array and the angle information of the received signal. When constructing the multiple signal classification function, the present invention introduces an error correction parameter to correct the position information of the element. Since the signal source is unknown, the angle of the received signal is unknown. The present invention uses the term containing the error correction parameter and the incident angle as the decision quantity, and determines the value of the decision quantity in each attitude by searching the spectrum peak. Since the difference in the signal incident angle between the two attitudes of the antenna array is known, the value of the decision quantity and the difference in the signal incident angle are combined to construct an equation group, and the position error of each element can be solved. Through the above method, the present invention can achieve accurate estimation of the array position error without introducing an auxiliary radiation source and increasing the complexity of the mechanical system.

[0028] 2. Optionally, the gradient descent criterion can be used to quickly find the extreme points, speed up the calculation, and improve the solution accuracy.

[0029] 3. Optionally, receiving signals from a signal source with a high signal-to-noise ratio can reduce the impact of noise and help improve the accuracy of error analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a flowchart of the steps of an antenna array position error analysis method based on attitude adjustment in one embodiment of the present invention;

[0031] Figure 2 is a schematic diagram of the antenna array posture adjustment in one embodiment of the present invention;

[0032] Figure 3 is a comparison diagram of the original position error of each array element and the corresponding corrected residual position error in one embodiment;

[0033] Figure 4 is a probability distribution of an original position error and a residual position error after correction in one embodiment;

[0034] Figure 5 is the distribution of the residual mean square error before and after correction for the six experiments. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0036] Example 1

[0037] like Figure 1 The figure shows a flow chart of the steps of an antenna array position error analysis method based on attitude adjustment in one embodiment of the present invention, and the steps are introduced below.

[0038] Step S1: respectively obtaining signals from the same unknown signal source received by the antenna array in a first posture and a second posture, and a difference in the signal incident angle between the two postures.

[0039] The attitude of the antenna array can be adjusted, and the change in the signal incident angle caused by the adjustment is clear. Figure 2 FIG. 1 is a schematic diagram of the posture adjustment of the antenna array in one embodiment of the present invention. The antenna array has N array elements, S k(t) is the kth signal source, n(t) is noise, and when the antenna array rotates clockwise by Δθ, its signal incident angle also increases by Δθ. In one embodiment, the difference in incident angle between the second posture and the first posture ranges from 15° to 25°.

[0040] It should be noted that the signal source of the present invention is unknown, and its incident angle is also unknown. The present invention does not need to know the incident angle in each posture, but only needs to know the change of the incident angle in two postures to perform error analysis. In one embodiment, when performing error analysis, a signal source with a high signal-to-noise ratio is selected, the signal-to-noise ratio is not less than 10dB, and the noise is Gaussian white noise, so that the error calculation accuracy can be improved.

[0041] Step S2: Data processing is performed on the received signals in each posture respectively. The data processing includes: introducing error correction parameters to correct the position of the array elements, constructing a multiple signal classification function with orthogonality between the steering vector and the noise subspace, taking the terms including the error correction parameters and the incident angle in the function as a whole as the decision quantity, solving the maximum point of the multiple signal classification function, and obtaining the value of the decision quantity.

[0042] During data processing, the present invention constructs a multiple signal classification function and solves it. The multiple signal classification (MUSIC) algorithm is a spatial spectrum estimation algorithm. Its core idea is to use the covariance matrix of the received data to perform feature decomposition, separate the signal subspace and the noise subspace, and use the orthogonality of the signal direction vector and the noise subspace to construct a spatial scanning spectrum, perform a full-domain search for spectrum peaks, thereby realizing signal parameter estimation.

[0043] Therefore, before constructing the multiple signal classification function, it is necessary to calculate the noise subspace of the received signal. The following introduces the calculation method of the noise subspace.

[0044] The first step is to discretize the current received signal into a snapshot data matrix X = [X1, X2…X i …X N ], X i =[x i (0),x i (1)…x i (M)] T The received signal of the i-th element of the antenna array is discretized into a vector composed of M snapshot data.

[0045] Specifically, in practical applications, the channel discretely samples the signal to obtain the sampled signal, so the received signal can be discretized into a snapshot data matrix X = [X1, X2…X i …X N ], where X i =[x i (0),xi (1)…x i (M)] T is the sampling data of the signal received by the antenna with serial number i in the array, and the number of sampling points is M.

[0046] The second step is to calculate the autocorrelation matrix of the corresponding received signal based on the current snapshot data matrix.

[0047] Specifically, the receiving model of the antenna array is:

[0048] X(t)=A(θ)S(t)+N(t) (1)

[0049] Where X(t) = [x1(t), x2(t), …, x N (t)] T is the signal received by the antenna array, x i (t) is the received signal of the ith array element, the number of array elements is N, S(t) = [s1(t), s2(t), …, s K (t)], which represents the vector composed of K original signal sources, and its incident angle is θ = [θ1, θ2, ···, θ K ], the set of steering vectors is A(θ)=[α(θ1),α(θ2)…α(θ K )],α(θ k ) is the steering vector of the kth signal source, N(t)=[n1,n2,…,n N ] represents the noise vector in the antenna array receiving signal, the noise has a mean of 0 and a variance of σ 2 Gaussian distribution of I, σ 2 is the power of the noise signal.

[0050] In the ideal case with no unknown errors, the ideal guidance vector α(θ k ) is expressed as:

[0051]

[0052] Where λ is the wavelength of the signal wave; d i is the distance between the ith array element and the origin of the coordinate axis.

[0053] If the geometric error of the array element is considered, the error correction parameter d can be introduced i Make a correction and substitute the corrected distance into the guidance vector.

[0054] For example, the introduced error correction parameter is an error coefficient, and the error correction parameter is introduced to correct the position of the array element, including correcting the position of the i-th array element to d i (1+δ i ), δ iis the error coefficient between the actual distance and the ideal distance. The guidance vector after array correction can be written as:

[0055]

[0056] In the formula, δ i is the position error coefficient of the ith array element.

[0057] The autocorrelation matrix R is obtained from X(t) in formula (1):

[0058] R=E[X(t)·X(t) H ] (4)

[0059] Where R is the autocorrelation matrix of the signal source.

[0060] Assuming that the time average under stationary conditions can be approximated as the statistical average, the autocorrelation matrix R in equation (4) can be estimated by the following equation:

[0061]

[0062] Where X is the shot data matrix obtained in the first step, X H is the transpose of X, and M is the number of sampling points.

[0063] The third step is to obtain the noise subspace vector by performing eigenvalue decomposition on the current autocorrelation matrix.

[0064] Specifically, the eigenvalue decomposition of the autocorrelation matrix R is shown in formula (6):

[0065] R=UΛU T (6)

[0066] From formula (1) (4), we can get:

[0067] R=AR s A H +σ 2 I (7)

[0068] In the formula, R s =E[S(t)·S(t) H ], the autocorrelation matrix R can be divided into blocks by equations (6) and (7), and since the eigenvalue of the signal subspace is greater than the eigenvalue of the noise subspace, the gradient of the eigenvalue can be used to divide the signal subspace and the noise subspace

[0069]

[0070] Among them, U s is the signal subspace vector, U n is the noise subspace vector. Sis a diagonal matrix consisting of r large eigenvalues, Λ n The remaining Nr small eigenvalues ​​form a diagonal matrix.

[0071] From this, we can get the noise subspace vector U n , combined with the corrected array element position, the construction of the multiple signal classification function can be realized. The constructed function contains two unknown quantities: the error correction parameter and the incident angle.

[0072] For example, correct the position of each array element to d i (1+δ i ), the multiple signal classification function is expressed as follows:

[0073]

[0074] The guidance vector obtained after position correction is expressed as

[0075]

[0076] It can be seen that the multiple signal classification function contains two unknown quantities, the incident angle and the error correction parameter. When processing data under each posture, the item containing these two unknown quantities can be used as a decision quantity for decision making, and the decision quantity can be solved through the peak search mechanism.

[0077] For example, you can use d i (1+δ i )sin(θ) term as the decision quantity, where δ i is the error correction parameter, and θ is the incident angle at the current posture.

[0078] When processing the data of the received signal of the first posture, it is assumed that the incident angle θ k =ρ, the decision quantity can be

[0079] ξ i =d i (1+δ i )sin(ρ) (10)

[0080] Substituting it into formula (3), the guide vector Transformed into:

[0081]

[0082] The multiple signal classification function is transformed into:

[0083]

[0084] When processing the data of the received signal of the second posture, the incident angle is the change in the incident angle after the antenna is rotated compared to the first posture, which can be determined according to the rotation angle of the antenna. At this time, the decision quantity can be made as:

[0085]

[0086] Substituting it into formula (3), the guide vector Transformed into:

[0087]

[0088] The multiple signal classification function is transformed into:

[0089]

[0090] The multiple signal classification function under each posture is solved to find the maximum value, and the maximum value point is the value of the decision quantity.

[0091] Specifically, the objective function (12) is solved to obtain the decision quantities ξ1,ξ2...ξ N The value of; Solve the objective function (15) to obtain the decision quantities η1, η2...η N The value of .

[0092] Specifically, when solving the objective function, the gradient descent criterion can be used to solve it. Take solving (12) as an example:

[0093] For the objective function, initialize the parameters And set the learning rate μ and the stop iteration condition threshold ε, the parameter update method is

[0094]

[0095] in k is the number of iterations; when

[0096] |P M [ξ(k+1)]-P M [ξ(k)]|<ε

[0097] Stop iteration.

[0098] Step S3: Based on the value of the decision quantity under the first posture, the value of the decision quantity under the second posture and the difference in the signal incident angle between the two postures, a set of equations is constructed with the error correction parameter and the incident angle as unknown quantities, and the solution is solved to realize error analysis.

[0099] In step S2, after obtaining the values ​​of the decision quantity under the first posture and the values ​​of the decision quantity under the second posture, a set of equations can be constructed to solve the error correction parameters and the incident angle of the signal source.

[0100] For example, the decision quantities ξ1,ξ2...ξ in the first posture N The value of the decision quantity η1,η2...η under the second posture N The values ​​of are obtained, and for any array element i, the equation system can be established

[0101]

[0102] In this system of equations, the error coefficient δ i The incident angle ρ in the first posture is an unknown quantity. By solving the equation group, the value of the unknown quantity can be obtained. The corrected position can be expressed as:

[0103]

[0104] In a specific embodiment, the present solution may also be implemented according to the following steps:

[0105] A1: Discretize the current received signal into a snapshot data matrix X = [X1, X2…X i …X N ];

[0106] A2: Calculate the autocorrelation matrix of the corresponding received signal according to the current snapshot data matrix;

[0107] A3: Obtain the noise subspace vector by performing eigenvalue decomposition on the current autocorrelation matrix;

[0108] A4: Correct the distance between the i-th array element and the preset reference point to d i +δ i , d i is the ideal distance between the i-th array element and the preset reference point, δ i is the error between the actual distance and the ideal distance, i = 1, 2, 3, ..., N; construct a multiple signal classification function of orthogonality between the guide vector and the noise subspace according to the noise subspace vector and the corrected distance;

[0109] A5: d i (1+δ i )sin(ρ) is used as the decision unknown quantity, and the maximum point of the metric function is solved to obtain the value of the decision unknown quantity corresponding to N array elements, where ρ is the incident angle of the current received signal;

[0110] A6: Rotate the antenna array to adjust the incident angle to Acquire a reception signal received by the antenna array in a second posture and discretize it into a snapshot data matrix;

[0111] A7: Execute A2 to A5 again;

[0112] A8: Based on the values ​​of the unknown quantities of the N array elements in the first posture and the unknown quantities of the N array elements in the second posture, the error δ i With the incident angle as unknown variables, a set of linear equations of two variables for each array element is constructed, and the corrected position of each array element i is obtained by solving it.

[0113] The following is a verification with a specific example.

[0114] A linear array, the number of array elements is N, and the minimum element spacing is 0.5λ. Generally, under far-field narrowband conditions, the antenna receives the signal and obtains a snapshot data matrix after sampling

[0115] X=[X1,X2…X i …X N ]

[0116] Under the assumed stationary condition, find the autocorrelation matrix

[0117]

[0118] The autocorrelation matrix can be decomposed into

[0119]

[0120] U s is the characteristic vector of the signal, ∧ s is the corresponding eigenvalue, U n is the eigenvector of the noise space, ∧ n is the corresponding eigenvalue, and since the noise subspace is orthogonal to the signal’s guidance vector, the objective optimization function is as follows

[0121]

[0122] By traversing (ξ1,ξ2...ξ N ), detect P M (ξ1,ξ2...ξ N ) peak value, we can find the extreme point

[0123]

[0124] With the normal direction of the antenna array as the axis, rotate the antenna array along the plane where it is located by an angle Its objective optimization function is

[0125]

[0126] Find the extreme point

[0127] According to the two extreme points sought, we can solve d i (1+δ i )

[0128]

[0129] For a non-uniform antenna array with N=8 elements, the ideal array position is:

[0130] {0,0.5λ,3λ,7λ,12λ,19λ,31λ,56λ}

[0131] The array position error follows a normal distribution with a mean of 0 and a standard deviation of 0.2λ. The actual array position is:

[0132] {0,0.5409λ,3.0100λ,7.0781λ,11.7755λ,18.8924λ,30.8248λ,56.1532λ}

[0133] Assume that the target signal noise ratio is 10dB, the noise is Gaussian white noise, the number of snapshots is 512, and the incident angle is unknown. When the antenna array rotates 10° so that the incident angles in the two postures deviate by 10°, the array position after solving using the solution proposed by the present invention is:

[0134] {0,0.5723λ,3.0323λ,7.1216λ,11.7975λ,18.9195λ,30.8579λ,56.1777λ}

[0135] like Figure 3 FIG. 4 shows the original position error of each array element and the corresponding residual position error after correction in one embodiment. Figure 4 FIG. 1 shows the probability distribution of the original position error and the residual position error after correction in one embodiment. Figure 5 The figure shows the distribution of the residual mean square error before and after correction of six experiments. The mean square error between the corrected array position and the actual position is 7.8% of the mean square error before correction. It can be seen that after the error analysis and correction of the present invention, the position error is greatly reduced.

[0136] Example 2

[0137] The present invention also relates to an antenna array position error analysis system based on attitude adjustment, which comprises:

[0138] A data acquisition unit, used to acquire signals from the same unknown signal source received by the antenna array in a first posture and a second posture, and a difference in the signal incident angle between the two postures;

[0139] A data processing unit is used to perform data processing on the received signals in each posture, the data processing comprising: introducing an error correction parameter to correct the position of the array element, constructing a multiple signal classification function of orthogonality between the steering vector and the noise subspace, taking the entire term including the error correction parameter and the incident angle in the function as a decision quantity, solving the maximum point of the multiple signal classification function, and obtaining the value of the decision quantity;

[0140] The error solving unit is used to construct a set of equations based on the value of the decision quantity under the first posture, the value of the decision quantity under the second posture and the difference in the signal incident angle between the two postures, with the error correction parameter and the incident angle as unknown quantities, and solve to realize error analysis.

[0141] It can be understood that the above units are used to execute the corresponding steps in Example 1. The specific details can be found in the introduction of Example 1 and will not be repeated here.

[0142] Example 3

[0143] The present invention also relates to a computer-readable storage medium having a computer program stored thereon, and the computer program implements the steps of the above method when executed by a processor.

[0144] Specifically, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, an internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0145] Example 4

[0146] The embodiment of the present invention provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps of the method of the above embodiment of the present invention.

[0147] The technical features of the above-mentioned embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification. It should be noted that "in one embodiment", "for example", "for example", etc. of the present invention are intended to illustrate the present invention, rather than to limit the present invention.

[0148] The above-mentioned embodiments only express several implementation methods of the present invention, and the description is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent application. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A method for analyzing antenna array position error based on attitude adjustment, characterized in that: include: Respectively obtaining signals from the same unknown signal source received by the antenna array in a first posture and a second posture, and a difference in the signal incident angles between the two postures; Data processing is performed for the received signals in each posture, the data processing comprising: constructing a multiple signal classification function of orthogonality between the steering vector and the noise subspace after introducing an error correction parameter to correct the position of the array element, taking the entire term including the error correction parameter and the incident angle in the function as the decision quantity, solving the maximum point of the multiple signal classification function, and obtaining the value of the decision quantity; Based on the values ​​of the decision quantity under the first posture, the values ​​of the decision quantity under the second posture and the difference in the signal incident angle between the two postures, a group of equations is constructed with error correction parameters and the incident angle as unknown quantities, and the solution is used to realize error analysis.

2. The antenna array position error analysis method based on attitude adjustment as claimed in claim 1, characterized in that: The maximum point of the multiple signal classification function is solved by the gradient descent criterion.

3. The antenna array position error analysis method based on attitude adjustment as claimed in claim 1, characterized in that: The noise of the signal source is Gaussian white noise, and the signal-to-noise ratio is not less than 10dB.

4. The antenna array position error analysis method based on attitude adjustment as claimed in claim 1, characterized in that: The difference in incident angle between the second posture and the first posture ranges from 15° to 25°.

5. The antenna array position error analysis method based on attitude adjustment as claimed in claim 1, characterized in that: The process of solving the noise subspace used in constructing the multiple signal classification function includes: Discretize the current received signal into a snapshot data matrix X = [X1, X2…X i …X N ], X i =[x i (0),x i (1)…x i (M)] T The received signal of the i-th array element of the antenna array is discretized into a vector composed of M snapshot data, where N is the number of array elements and M is the number of discrete points; Calculate the autocorrelation matrix of the corresponding received signal based on the current snapshot data matrix By performing eigenvalue decomposition on the current autocorrelation matrix, the noise subspace vector is obtained.

6. The antenna array position error analysis method based on attitude adjustment as claimed in claim 1, characterized in that: The introduced error correction parameter is an error coefficient. The error correction parameter is introduced to correct the position of the array element, including correcting the position of the i-th array element to d i (1+δ i ), d i is the ideal distance between the ith array element and the reference point, δ i is the error coefficient between the actual distance and the ideal distance, i=1,2,3,…,N, N is the number of array elements.

7. The antenna array position error analysis method based on attitude adjustment as claimed in claim 6, characterized in that: The overall decision amount is the term including the error correction parameter and the incident angle in the function: With d in the function i (1+δ i )sin(θ k ) as the decision quantity, where θ k is the incident angle of the received signal in the current posture.

8. An antenna array position error analysis system based on attitude adjustment, characterized in that: include: A data acquisition unit, used to acquire signals from the same unknown signal source received by the antenna array in a first posture and a second posture, and a difference in the signal incident angle between the two postures; A data processing unit is used to perform data processing on the received signals in each posture, wherein the data processing includes: constructing a multiple signal classification function of orthogonality between the steering vector and the noise subspace after introducing an error correction parameter to correct the position of the array element, taking the entire term including the error correction parameter and the incident angle in the function as a decision quantity, solving the maximum point of the multiple signal classification function, and obtaining the value of the decision quantity; The error solving unit is used to construct a set of equations based on the value of the decision quantity under the first posture, the value of the decision quantity under the second posture and the difference in the signal incident angle between the two postures, with the error correction parameter and the incident angle as unknown quantities, and solve to realize error analysis.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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