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

Abstract

The present invention belongs to the technical field related to remote sensing technology and discloses a method and system for analyzing antenna array position errors based on attitude adjustment. The method comprises: obtaining signals received by the antenna array in a first attitude and a second attitude from the same unknown signal source; performing the following data processing on the received signals in each attitude: constructing a multiple signal classification function after position correction of the array elements, using the terms in the function containing the error correction parameter and the incident angle as the decision quantity, solving the maximum point of the multiple signal classification function to obtain the value of the decision quantity; and constructing a system of equations based on the values ​​of the decision quantity in the two attitudes, using the error correction parameter and the incident angle as unknown quantities, and solving the equations to implement error analysis. The present invention can accurately estimate the array position error 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 actual satellite-borne aperture synthesis microwave radiometer systems, random positional offsets, such as array arm misalignment and stretching, occur during launch. This non-ideal offset is critical for ASRs operating in high-frequency bands. Therefore, correcting antenna array positional errors is essential.

[0003] Existing solutions primarily involve placing a controllable auxiliary source at the center of the antenna array's field of view and using known information from this auxiliary radiation source to correct antenna position errors. However, radio regulations prohibit transmitting auxiliary correction signals from the ground to avoid interfering with the normal operation of other satellites, making this approach impractical in a spaceborne environment.

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

[0005] In response to 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, the purpose of which is 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 includes:

[0007] respectively obtaining signals originating from the same unknown signal source and 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 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 that is orthogonal to the steering vector and the noise subspace, using the entire term containing the error correction parameter and the incident angle in the function as a decision quantity, solving for 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 set of equations is constructed with the error correction parameter 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 10dB.

[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 element of the antenna array is discretized into a vector consisting of M snapshot data, where N is the number of 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] The noise subspace vector is obtained by performing eigenvalue decomposition on the current autocorrelation matrix.

[0017] Optionally, 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 ), d i is the ideal distance between the i-th 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 variable, 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, configured to acquire signals originating from the same unknown signal source and 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;

[0022] a data processing unit configured to perform data processing on the received signals in each posture, the data processing comprising: constructing a multiple signal classification function that determines the orthogonality between the steering vector and the noise subspace after introducing an error correction parameter to correct the position of the array element, using the entire term containing the error correction parameter and the incident angle in the function as a decision quantity, solving for a maximum point of the multiple signal classification function, and obtaining a 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, the above technical solutions conceived by the present invention have the following beneficial effects compared with the prior art:

[0027] 1. The proposed method for analyzing antenna array position errors based on attitude adjustment requires only adjusting the attitude of the antenna array itself. Signals from the same source are received in two different states. This source can be any unknown source, as long as the antenna array can receive the signal it transmits. The received signals in the two attitudes are then processed separately. During data processing, a multiple signal classification function is constructed, which contains the position information of each antenna array element and the angle 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 elements. Since the signal source is unknown, the angle of the received signal is also unknown. The present invention uses a term containing the error correction parameter and the angle of incidence as the decision variable. By searching for spectral peaks, the value of the decision variable for each attitude is determined. Since the difference in the signal incident angle between the two antenna array attitudes is known, a system of equations is constructed by combining the value of the decision variable and the difference in the signal incident angle to solve the position error of each element. Through this method, the present invention achieves accurate estimation of array position error without introducing auxiliary radiation sources or 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 a method for analyzing antenna array position errors based on attitude adjustment in one embodiment of the present invention;

[0031] Figure 2 is a schematic diagram of an antenna array attitude 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 residual position error after correction in one embodiment;

[0033] Figure 4 is the probability distribution of the original position error and the 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 objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining 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 may be combined with each other as long as they do not conflict with each other.

[0036] Example 1

[0037] like Figure 1 FIG2 is a flowchart of a method for analyzing antenna array position errors based on attitude adjustment in one embodiment of the present invention, and the steps are described below.

[0038] Step S1: respectively obtaining signals originating from the same unknown signal source and 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] Among them, the posture of the antenna array can be adjusted, and the change in the signal incident angle caused by the adjustment is clear. Figure 2 FIG2 is a schematic diagram of an antenna array posture adjustment in an 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 and incident angle are unknown in the present invention. Therefore, the present invention does not require knowledge of the incident angle for each posture; it only requires understanding the change in the incident angle for two postures to perform error analysis. In one embodiment, when performing error analysis, a signal source with a high signal-to-noise ratio (SNR) of no less than 10 dB and Gaussian white noise is selected. This improves the accuracy of error calculation.

[0041] Step S2: Data processing is performed on the received signals in each posture. The data processing includes: introducing an error correction parameter to correct the position of the array element, constructing a multiple signal classification function with orthogonality between the steering vector and the noise subspace, taking the entire term containing 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.

[0042] During data processing, the present invention constructs and solves a multiple signal classification function. 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 eigendecomposition, 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, and thus realize 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 describes 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)], represents the vector composed of K original signal sources, whose 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, and 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 steering 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 error correction parameter introduced is the 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 array-corrected guidance vector can be written as:

[0055]

[0056] Where, δ i is the position error coefficient of the i-th 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 formula (4) can be estimated by the following formula

[0061]

[0062] Where X is the beat 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] Where 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 diagonal matrix consisting of the remaining Nr small eigenvalues.

[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 from this 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 term 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 rotates compared to the first posture, which can be determined according to the antenna rotation angle. In this case, the decision quantity can be:

[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 with the goal of finding the maximum value, and the maximum value point is the value of the decision quantifier.

[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) and 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 stopping 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 used to implement 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 second attitude decision quantity η1,η2...η N The values ​​of are obtained, and for any array element i, the equation group 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 equations, the unknown quantity can be obtained. The corrected position can be expressed as:

[0103]

[0104] In a specific embodiment, this 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 based on 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; a multiple signal classification function with orthogonality between the guidance vector and the noise subspace is constructed based on the noise subspace vector and the corrected distance;

[0109] A5: d i (1+δ i )sin(ρ) is used as the decision unknown, and the maximum point of the metric function is solved to obtain the value of the decision unknown 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 received 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 is constructed for each array element, and the corrected position of each array element i is obtained by solving it.

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

[0114] A linear array has N array elements and a minimum element spacing of 0.5λ. Generally, under far-field narrowband conditions, the antenna receives the signal and samples it to obtain a snapshot data matrix

[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, its 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, 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 the target signal-to-noise ratio is 10dB, the noise is Gaussian white noise, the number of snapshots is 512, and the angle of incidence is unknown. If the antenna array is rotated 10° so that the angle of incidence in the two postures deviates by 10°, the array position after solving using the proposed solution is:

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

[0135] like Figure 3 The figure shows the original position error of each array element and the corresponding residual position error after correction in one embodiment, as shown in FIG. Figure 4 The figure 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 for six experiments. The mean square error between the array position after correction 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, configured to acquire signals originating from the same unknown signal source and 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;

[0139] a data processing unit for performing 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 that is orthogonal to the steering vector and the noise subspace, using the entire term containing the error correction parameter and the incident angle in the function as a decision quantity, solving for 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. For specific details, please refer to 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, which implements the steps of the above method when the computer program is 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, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (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 device.

[0145] Example 4

[0146] An embodiment of the present invention provides a computer program product or 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 executes the computer instructions, causing the computer device to perform the steps of the method of the above embodiment of the present invention.

[0147] The technical features of the above-described embodiments may be combined in any manner. To simplify the description, not all possible combinations of the technical features in the above-described 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 the phrases "in one embodiment," "for example," "and another example," etc., of the present invention are intended to illustrate the present invention and are not intended to limit the present invention.

[0148] The above-described embodiments merely illustrate several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, and all such variations and improvements fall within the scope of protection of the present invention.

Claims

1. A method for analyzing antenna array position errors based on attitude adjustment, characterized in that: include: respectively obtaining signals originating from the same unknown signal source and 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 on the received signals in each posture, and the data processing includes: introducing an error correction parameter to correct the position of the array element, constructing a multiple signal classification function with orthogonality between the steering vector and the noise subspace, taking the entire term containing 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; wherein the introduced error correction parameter is an error coefficient, and the introduction of the error correction parameter to correct the position of the array element includes correcting the position of the i-th array element to , is the ideal distance between the ith array element and the reference point, is the error coefficient between the actual distance and the ideal distance, i=1,2,3,……,N, N is the number of array elements; the overall term containing the error correction parameter and the incident angle in the function is used as the decision quantity, which is: As the decision quantity, is the incident angle of the received signal in the current posture; 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 set of equations is constructed with the error correction parameter 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 according to 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 according to 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 according to 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 according to claim 1, characterized in that: The process of solving the noise subspace used in constructing the multiple signal classification function includes: Discrete the current received signal into a snapshot data matrix , To set the antenna array The received signal of each array element is discretized into a vector composed of M snapshot data. is the number of array elements, is a discrete number of points; Calculate the autocorrelation matrix of the corresponding received signal based on the current snapshot data matrix ; The noise subspace vector is obtained by performing eigenvalue decomposition on the current autocorrelation matrix.

6. An antenna array position error analysis system based on attitude adjustment, characterized in that: include: a data acquisition unit, configured to acquire signals originating from the same unknown signal source and 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; The data processing unit is used to perform data processing on the received signals in each posture respectively, and the data processing includes: introducing an error correction parameter to correct the position of the array element, constructing a multiple signal classification function with orthogonality between the steering vector and the noise subspace, taking the terms 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; wherein the introduced error correction parameter is an error coefficient, and the introduction of the error correction parameter to correct the position of the array element includes correcting the position of the i-th array element to , is the ideal distance between the ith array element and the reference point, is the error coefficient between the actual distance and the ideal distance, i=1,2,3,……,N, N is the number of array elements; the overall term containing the error correction parameter and the incident angle in the function is used as the decision quantity, which is: As the decision quantity, is the incident angle of the received signal in the current posture; 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.

7. 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 5 are implemented.

8. 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 5 are implemented.

Images