A sidelobe interference under subarray level adaptive beamforming method and device

Abstract

The application discloses a sidelobe interference under subarray level adaptive beam forming method and device, relates to the technical field, and includes: carrying out noise normalization processing on the subarray level output signal to obtain a normalized subarray level output signal, and calculating a normalized covariance matrix after diagonal loading; calculating the adaptive beam forming weight of the sum channel and the difference channel according to the normalized covariance matrix after diagonal loading; calculating the target matrix of the difference channel adaptive beam forming and the constraint matrix of the difference channel adaptive beam forming; calculating the difference channel adaptive beam forming weight according to the normalized covariance matrix after diagonal loading, the target matrix and the constraint matrix, and carrying out interference suppression on the received signal based on the adaptive beam forming weight of the sum channel and the difference channel to obtain the sum channel signal and the difference channel signal after interference suppression. The application can realize the adaptive suppression of sidelobe interference of the non-uniformly divided planar phased array radar system at the subarray level, and can realize the pattern shape preservation within a certain angle range.

Description

Technical Field

[0001] This application relates to the field of radar electronic countermeasures technology, specifically to a subarray-level adaptive beamforming method and apparatus under sidelobe interference. Background Technology

[0002] With the rapid development of electronic countermeasures technology, electronic interference poses a serious threat to the survivability and performance of radar systems under combat conditions. In phased array radar systems, signal processing techniques such as adaptive beamforming, adaptive sidelobe cancellation, and space-time two-dimensional signal processing are commonly used to suppress sidelobe interference. For radar systems on mobile platforms, due to hardware resource limitations, it is difficult to achieve array element-level signal reception. Typically, the array is divided into non-uniform subarrays, with analog phase shifters used for beamforming and output within each subarray. The number of receiving channels across the entire array is the same as the number of subarrays. In this case, sidelobe interference suppression needs to be performed at the subarray level.

[0003] The application of subarray-level adaptive beamforming is of great significance for improving the anti-jamming performance of radar systems on mobile platforms. However, this method can distort the radiation pattern, leading to angle measurement errors when using monopulse sum-difference beam angle measurement. These errors can even exceed the required angle measurement accuracy, failing to meet the practical requirements of radar systems. Summary of the Invention

[0004] This application provides a subarray-level adaptive beamforming method and apparatus under sidelobe interference, which can realize subarray-level adaptive suppression of sidelobe interference in a non-uniformly divided phased array radar system, while simultaneously achieving pattern conformal preservation within a certain angular range.

[0005] In a first aspect, embodiments of this application provide a subarray-level adaptive beamforming method under sidelobe interference, the subarray-level adaptive beamforming method under sidelobe interference includes: The subarray level output signal is noise normalized to obtain the normalized subarray level output signal, and the normalized covariance matrix after diagonal loading is calculated based on the normalized subarray level output signal. The adaptive beamforming weights of the channels are calculated based on the normalized covariance matrix after diagonal loading. The target matrix of the differential channel adaptive beamforming is calculated based on the radar radome compensation information, and the constraint matrix of the differential channel adaptive beamforming is calculated based on the adaptive beamforming weights of the sum channel and the array subarray level steering vector. The adaptive beamforming weights of the difference channel are calculated based on the normalized covariance matrix after diagonal loading, the target matrix, and the constraint matrix. The received signal is then processed based on the adaptive beamforming weights of the sum and difference channels to suppress interference, resulting in the interference-suppressed sum and difference channel signals.

[0006] In conjunction with the first aspect, in one embodiment, the noise normalization processing of the subarray-level output signal to obtain a normalized subarray-level output signal includes: Divide the radar system array into non-uniform sections Each subarray contains [number] subarrays. Each array element, The received signal of each subarray is represented as , , To represent the number of samples, the subarray output signal is as follows: ; According to the formula: Calculate the normalized matrix ,in, , This indicates the construction of a diagonal matrix. The amplitude weighting value of each array element; According to the formula: Calculate the normalized subarray level output signal .

[0007] In conjunction with the first aspect, in one implementation, calculating the diagonally loaded normalized covariance matrix based on the normalized subarray level output signal includes: Calculate the normalized covariance matrix based on the normalized subarray output signal; Based on the normalized covariance matrix, the diagonally loaded normalized covariance matrix is ​​calculated using a linear shrinkage method.

[0008] In conjunction with the first aspect, in one implementation, the step of calculating the normalized covariance matrix based on the normalized subarray level output signal includes: According to the formula: Calculate the normalized covariance matrix .

[0009] In conjunction with the first aspect, in one implementation, the step of calculating the diagonally loaded normalized covariance matrix using a linear shrinkage method based on the normalized covariance matrix includes: According to the formula: Calculate parameters ,in, Denotes the Frobenius norm; According to the formula: Calculate parameters ,in, Represents the trace of a matrix; According to the formula: Calculate parameters ,in, Represents the identity matrix; According to the formula: Calculate parameters ; According to the formula: Calculate the normalized covariance matrix after diagonal loading. .

[0010] In conjunction with the first aspect, in one implementation, the calculation of the adaptive beamforming weights of the channels based on the diagonally loaded normalized covariance matrix includes: According to the formula: Calculation and adaptive beamforming weights for channels ,in, It is a scalar constant. .

[0011] In conjunction with the first aspect, in one implementation, the step of calculating the target matrix for differential channel adaptive beamforming based on radar radome compensation information includes: The beam direction is to be adjusted The radome compensation table for the transmitted signal includes an azimuth radome compensation table and an elevation radome compensation table. The radome compensation table includes the azimuth scanning angle. Pitch scanning angle Angles corresponding to the azimuth radome compensation table Difference and ratio And the angle corresponding to the elevation radome compensation table Difference and ratio ; According to the formula: By fitting the azimuth radome compensation table data, the independent variables are obtained. coefficient and independent variables coefficient ; According to the formula: By fitting the azimuth radome compensation table data, the independent variables are obtained. coefficient and independent variables coefficient ; According to the formula: Calculate the azimuth difference channel adaptive beamforming target matrix ; According to the formula: Calculate the target matrix of the pitch difference channel adaptive beamforming; in, The azimuth width of the main lobe. The pitch is the width of the main lobe.

[0012] In conjunction with the first aspect, in one implementation, the step of calculating the constraint matrix for differential channel adaptive beamforming based on the adaptive beamforming weights of the channels and the array subarray-level steering vectors includes: According to the formula: Calculate the constraint matrix for adaptive beamforming of the difference channel. ; in, for The array subarray steering vectors corresponding to different downward beam pointing angles.

[0013] In conjunction with the first aspect, in one implementation, the step of calculating the difference channel adaptive beamforming weights based on the diagonally loaded normalized covariance matrix, the target matrix, and the constraint matrix includes: According to the formula: Calculate the azimuth difference channel adaptive beamforming weights ; According to the formula: Calculate the adaptive beamforming weights for the pitch difference channel. .

[0014] Secondly, embodiments of this application provide a subarray-level adaptive beamforming device under sidelobe interference, the subarray-level adaptive beamforming device under sidelobe interference comprising: The normalization processing module is used to perform noise normalization processing on the subarray level output signal to obtain the normalized subarray level output signal, and calculate the normalized covariance matrix after diagonal loading based on the normalized subarray level output signal. The weight calculation module calculates the weights of the channel based on the normalized covariance matrix after diagonal loading and the adaptive beamforming of the channel. The weight calculation module also calculates the target matrix of the differential channel adaptive beamforming based on the radar radome compensation information, and calculates the constraint matrix of the differential channel adaptive beamforming based on the adaptive beamforming weight of the sum channel and the array subarray level steering vector. The weight calculation module also calculates the differential channel adaptive beamforming weights based on the diagonally loaded normalized covariance matrix, the target matrix, and the constraint matrix. The interference suppression module performs interference suppression on the received signal based on adaptive beamforming weighting of the sum and difference channels, resulting in interference-suppressed sum and difference channel signals.

[0015] The beneficial effects of the technical solutions provided in this application include at least the following: The subarray-level adaptive beamforming method under sidelobe interference in this application involves: normalizing the noise of the subarray-level output signal to obtain a normalized subarray-level output signal; calculating the diagonally loaded normalized covariance matrix based on the normalized subarray-level output signal; calculating the adaptive beamforming weights of the sum channel based on the diagonally loaded normalized covariance matrix; calculating the target matrix of the differential channel adaptive beamforming based on the radar radome compensation information; calculating the constraint matrix of the differential channel adaptive beamforming based on the adaptive beamforming weights of the sum channel and the array subarray-level steering vector; calculating the differential channel adaptive beamforming weights based on the diagonally loaded normalized covariance matrix, the target matrix, and the constraint matrix; and processing the received signal based on the adaptive beamforming weights of the sum and differential channels to suppress interference, thereby obtaining the interference-suppressed sum and differential channel signals.

[0016] In other words, this application employs a weighted approach to the subarray signals, using adaptive beamforming weighting to process the received signals and suppress interference, thus performing adaptive beamforming. The resulting interference-suppressed sum and difference channel signals are then fed into a subsequent signal processing module to complete the subsequent signal processing flow. This solves the problem of inaccurate angle measurement caused by pattern distortion in conventional subarray-level adaptive beamforming methods. Furthermore, this application requires no additional radar hardware; it can be implemented simply by adding corresponding functions to the signal processing module. It is easy to implement, has strong engineering applicability, and is beneficial for radar systems to combat sidelobe interference. Attached Figure Description

[0017] Figure 1 This is a flowchart of an embodiment of the subarray-level adaptive beamforming method under sidelobe interference in this application; Figure 2 This is a schematic diagram of the signal processing flow of this application; Figure 3 This is a schematic diagram illustrating the relative relationship between the beam pointing angle and the radar array in this application; Figure 4 This is a structural block diagram of an embodiment of the subarray-level adaptive beamforming device under sidelobe interference of this application. Detailed Implementation

[0018] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0020] In a first aspect, embodiments of this application provide a subarray-level adaptive beamforming method under sidelobe interference.

[0021] In one embodiment, reference is made to Figure 1 As shown, Figure 1 This is a flowchart of an embodiment of the subarray-level adaptive beamforming method under sidelobe interference according to this application. Figure 1 As shown, the subarray-level adaptive beamforming method under sidelobe interference includes: S1. Perform noise normalization processing on the subarray level output signal to obtain the normalized subarray level output signal, and calculate the normalized covariance matrix after diagonal loading based on the normalized subarray level output signal. S2. Calculate the adaptive beamforming weights of the channels based on the normalized covariance matrix after diagonal loading; S3. Calculate the target matrix of the differential channel adaptive beamforming based on the radar radome compensation information, and calculate the constraint matrix of the differential channel adaptive beamforming based on the adaptive beamforming weights of the sum channel and the array subarray level steering vector. S4. Calculate the adaptive beamforming weights of the difference channel based on the normalized covariance matrix, target matrix, and constraint matrix after diagonal loading. Process the received signal based on the adaptive beamforming weights of the sum and difference channels to suppress interference and obtain the interference-suppressed sum and difference channel signals.

[0022] In this embodiment, the subarray signals are weighted and the received signals are processed using adaptive beamforming weights to suppress interference. The resulting interference-suppressed sum and difference channel signals are then sent to the subsequent signal processing module. This enables the subarray-level adaptive suppression of sidelobe interference in a non-uniformly divided phased array radar system on a mobile platform, while simultaneously maintaining the radiation pattern within a certain angular range.

[0023] See Figure 2 As shown, this is a schematic diagram of the signal processing flow involved in the above steps. The following section will combine... Figure 2 Further explanation of the above steps: Step 1: Receive the echo signal. The beam direction of the transmitted signal is... ( The beam azimuth angle, (beam pitch angle) Figure 3 This diagram illustrates the relative relationship between the beam pointing angle and the radar array. The radar system array is non-uniformly divided into... Each subarray contains [number] subarrays. Each array element, The received signal of each subarray is represented as , , The number of samples is [number]. Interference signals enter the antenna array from the sidelobes. The signal output from the subarray stage is represented as [symbol]. .

[0024] Step 2: Perform noise normalization on the subarray output signal. This includes the following sub-steps: Step 21: Calculate the normalized matrix Normalized matrix for:

[0025]

[0026] in, This indicates the construction of a diagonal matrix. The amplitude is weighted for each array element.

[0027] Step 22, normalize the subarray level output signal as follows: .

[0028] Step 3: Calculate the normalized covariance matrix. The normalized covariance matrix is... .

[0029] Step 4: Calculate the normalized covariance matrix after diagonal loading using the linear shrinkage method. This includes the following sub-steps: Step 41: Calculate parameters Parameters for:

[0030] in, This represents the Frobenius norm.

[0031] Step 42: Calculate parameters Parameters for:

[0032] in, Represents the trace of a matrix.

[0033] Step 43: Calculate parameters Parameters for:

[0034] in, Represents the identity matrix.

[0035] Step 44: Calculate parameters Parameters for:

[0036] Step 45: Calculate the normalized covariance matrix after diagonal loading. The normalized covariance matrix after diagonal loading. for:

[0037] Step 5: Calculate and channel adaptive beamforming weights And channel adaptive beamforming weights for:

[0038]

[0039] in, It is a scalar constant.

[0040] Step 6: Calculate the differential channel adaptive beamforming constraint matrix and target matrix. This includes the following sub-steps: Step 61, Retrieve The corresponding azimuth and elevation radome compensation tables (radome compensation information) are provided. The radome compensation table consists of three dimensions; the first dimension represents the azimuth scanning angle. The second dimension represents the pitch scan angle. The third dimension is for each angle. Corresponding difference and ratio (Corresponding azimuth radome compensation table) or (Corresponding to the elevation radome compensation table). The azimuth main lobe width is... The pitch-to-main-lobe width is .

[0041] Step 62: Azimuth Radome Compensation Table - Binary Linear Fitting. The binary linear fitting model is defined as follows:

[0042] in, To fit the model output, , and These are the parameters to be estimated. as independent variable coefficient, as independent variable The coefficients were obtained by fitting the data from the azimuth radome compensation table. and .

[0043] Step 63: Pitch-to-radome compensation table - binary linear fitting. The binary linear fitting model is defined as follows:

[0044] in, To fit the model output, , and These are the parameters to be estimated. as independent variable coefficient, as independent variable The coefficients were obtained by fitting the data from the elevation radome compensation table. and .

[0045] Step 64: Calculate the differential channel adaptive beamforming target matrix. and Azimuth difference channel adaptive beamforming target matrix for:

[0046] Pitch difference channel adaptive beamforming target matrix for:

[0047] Step 65: Calculate the differential channel adaptive beamforming constraint matrix. Differential channel adaptive beamforming constraint matrix for:

[0048] in, for The array subarray steering vectors corresponding to different downward beam pointing angles, their magnitudes are: A column vector of n elements.

[0049] Step 7: Calculate the adaptive beamforming weights for the azimuth and elevation difference channels. Azimuth difference channel adaptive beamforming weights. Pitch difference channel adaptive beamforming weights They are respectively:

[0050]

[0051] Step 8: Perform adaptive beamforming to obtain the interference-suppressed signal. The subarray signals are weighted to obtain the interference-suppressed sum channel signal. Azimuth difference channel signal Pitch difference channel signal They are respectively

[0052]

[0053]

[0054] Step 9: Send the interference-suppressed sum and difference channel signals to the subsequent signal processing module to complete the subsequent signal processing flow.

[0055] In summary, the subarray-level adaptive beamforming method under sidelobe interference in this application involves: normalizing the noise of the subarray-level output signal to obtain a normalized subarray-level output signal; calculating the diagonally loaded normalized covariance matrix based on the normalized subarray-level output signal; calculating the adaptive beamforming weights of the sum channel based on the diagonally loaded normalized covariance matrix; calculating the target matrix for the differential channel adaptive beamforming based on the radar radome compensation information; calculating the constraint matrix for the differential channel adaptive beamforming based on the adaptive beamforming weights of the sum channel and the array subarray-level steering vector; calculating the differential channel adaptive beamforming weights based on the diagonally loaded normalized covariance matrix, the target matrix, and the constraint matrix; and processing the received signal based on the adaptive beamforming weights of the sum and differential channels to suppress interference, thereby obtaining the interference-suppressed sum and differential channel signals.

[0056] In other words, this application employs a weighted approach to the subarray signals, using adaptive beamforming weighting to process the received signals and suppress interference, thus performing adaptive beamforming. The resulting interference-suppressed sum and difference channel signals are then fed into a subsequent signal processing module to complete the subsequent signal processing flow. This solves the problem of inaccurate angle measurement caused by pattern distortion in conventional subarray-level adaptive beamforming methods. Furthermore, this application requires no additional radar hardware; it can be implemented simply by adding corresponding functions to the signal processing module. It is easy to implement, has strong engineering applicability, and is beneficial for radar systems to combat sidelobe interference.

[0057] Secondly, embodiments of this application provide a subarray-level adaptive beamforming device under sidelobe interference.

[0058] In one embodiment, reference is made to Figure 4 As shown, Figure 4 This is a structural block diagram of an embodiment of the subarray-level adaptive beamforming device under sidelobe interference according to this application. Figure 4 As shown, the subarray-level adaptive beamforming device under sidelobe interference includes: a normalization processing module, a weight calculation module, and an interference suppression module.

[0059] The normalization processing module is used to perform noise normalization processing on the subarray level output signal to obtain the normalized subarray level output signal, and calculate the normalized covariance matrix after diagonal loading based on the normalized subarray level output signal. The weight calculation module calculates the weights of the channel based on the normalized covariance matrix after diagonal loading and the adaptive beamforming of the channel. The weight calculation module also calculates the target matrix of the differential channel adaptive beamforming based on the radar radome compensation information, and calculates the constraint matrix of the differential channel adaptive beamforming based on the adaptive beamforming weight of the sum channel and the array subarray level steering vector. The weight calculation module also calculates the differential channel adaptive beamforming weights based on the diagonally loaded normalized covariance matrix, the target matrix, and the constraint matrix. The interference suppression module performs interference suppression on the received signal based on adaptive beamforming weighting of the sum and difference channels, resulting in interference-suppressed sum and difference channel signals.

[0060] Further, in one embodiment, the normalization processing module performs noise normalization processing on the subarray-level output signal to obtain a normalized subarray-level output signal, including: Divide the radar system array into non-uniform sections Each subarray contains [number] subarrays. Each array element, The received signal of each subarray is represented as , , To represent the number of samples, the subarray output signal is as follows: ; According to the formula: Calculate the normalized matrix ,in, , This indicates the construction of a diagonal matrix. The amplitude weighting value of each array element; According to the formula: Calculate the normalized subarray level output signal .

[0061] Further, in one embodiment, the normalization processing module calculates the diagonally loaded normalized covariance matrix based on the normalized subarray level output signal, including: Calculate the normalized covariance matrix based on the normalized subarray output signal; Based on the normalized covariance matrix, the diagonally loaded normalized covariance matrix is ​​calculated using a linear shrinkage method.

[0062] Further, in one embodiment, the normalization processing module calculates the normalized covariance matrix based on the normalized subarray level output signal, including: According to the formula: Calculate the normalized covariance matrix .

[0063] Further, in one embodiment, the normalization processing module calculates the diagonally loaded normalized covariance matrix based on the normalized covariance matrix using a linear shrinkage method, including: According to the formula: Calculate parameters ,in, Denotes the Frobenius norm; According to the formula: Calculate parameters ,in, Represents the trace of a matrix; According to the formula: Calculate parameters ,in, Represents the identity matrix; According to the formula: Calculate parameters ; According to the formula: Calculate the normalized covariance matrix after diagonal loading. .

[0064] Further, in one embodiment, the weight calculation module calculates the adaptive beamforming weights of the channels based on the diagonally loaded normalized covariance matrix, including: According to the formula: Calculation and adaptive beamforming weights for channels ,in, It is a scalar constant. .

[0065] Further, in one embodiment, the weight calculation module calculates the target matrix for differential channel adaptive beamforming based on radar radome compensation information, including: The beam direction is to be adjusted The radome compensation table for the transmitted signal includes an azimuth radome compensation table and an elevation radome compensation table. The radome compensation table includes the azimuth scanning angle. Pitch scanning angle Angles corresponding to the azimuth radome compensation table Difference and ratio And the angle corresponding to the elevation radome compensation table Difference and ratio ; According to the formula: By fitting the azimuth radome compensation table data, the independent variables are obtained. coefficient and independent variables coefficient ; According to the formula: By fitting the azimuth radome compensation table data, the independent variables are obtained. coefficient and independent variables coefficient ; According to the formula: Calculate the azimuth difference channel adaptive beamforming target matrix ; According to the formula: Calculate the target matrix of the pitch difference channel adaptive beamforming; in, The azimuth width of the main lobe. The pitch is the width of the main lobe.

[0066] Further, in one embodiment, the weight calculation module calculates the constraint matrix of the differential channel adaptive beamforming based on the adaptive beamforming weights of the channels and the array subarray-level steering vectors, including: According to the formula: Calculate the constraint matrix for adaptive beamforming of the difference channel. ; in, for The array subarray steering vectors corresponding to different downward beam pointing angles.

[0067] Further, in one embodiment, the weight calculation module calculates the difference channel adaptive beamforming weights based on the diagonally loaded normalized covariance matrix, the target matrix, and the constraint matrix, including: According to the formula: Calculate the azimuth difference channel adaptive beamforming weights ; According to the formula: Calculate the adaptive beamforming weights for the pitch difference channel. .

[0068] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0069] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0070] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0071] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0072] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0073] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0074] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A subarray-level adaptive beamforming method under sidelobe interference, characterized in that, The subarray-level adaptive beamforming method under sidelobe interference includes: The subarray level output signal is noise normalized to obtain the normalized subarray level output signal, and the normalized covariance matrix after diagonal loading is calculated based on the normalized subarray level output signal. The adaptive beamforming weights of the channels are calculated based on the normalized covariance matrix after diagonal loading. The target matrix of the differential channel adaptive beamforming is calculated based on the radar radome compensation information, and the constraint matrix of the differential channel adaptive beamforming is calculated based on the adaptive beamforming weights of the sum channel and the array subarray level steering vector. The adaptive beamforming weights of the difference channel are calculated based on the normalized covariance matrix after diagonal loading, the target matrix, and the constraint matrix. The received signal is then processed based on the adaptive beamforming weights of the sum and difference channels to suppress interference, resulting in the interference-suppressed sum and difference channel signals. The noise normalization process performed on the subarray-level output signal to obtain the normalized subarray-level output signal includes: Divide the radar system array into uneven sections Each subarray contains [number] subarrays. Each array element, The received signal of each subarray is represented as , , To represent the number of samples, the subarray output signal is as follows: ; According to the formula: Calculate the normalized matrix ,in, , This indicates the construction of a diagonal matrix. The amplitude weighting value of each array element; According to the formula: Calculate the normalized subarray level output signal .

2. The subarray-level adaptive beamforming method under sidelobe interference as described in claim 1, characterized in that, The step of calculating the diagonally loaded normalized covariance matrix based on the normalized subarray output signal includes: Calculate the normalized covariance matrix based on the normalized subarray output signal; Based on the normalized covariance matrix, the diagonally loaded normalized covariance matrix is ​​calculated using a linear shrinkage method.

3. The subarray-level adaptive beamforming method under sidelobe interference as described in claim 2, characterized in that, The calculation of the normalized covariance matrix based on the normalized subarray output signal includes: According to the formula: Calculate the normalized covariance matrix .

4. The subarray-level adaptive beamforming method under sidelobe interference as described in claim 3, characterized in that, The step of calculating the diagonally loaded normalized covariance matrix using a linear shrinkage method based on the normalized covariance matrix includes: According to the formula: Calculate parameters ,in, Denotes the Frobenius norm; According to the formula: Calculate parameters ,in, Represents the trace of a matrix; According to the formula: Calculate parameters ,in, Represents the identity matrix; According to the formula: Calculate parameters ; According to the formula: Calculate the normalized covariance matrix after diagonal loading. .

5. The subarray-level adaptive beamforming method under sidelobe interference as described in claim 4, characterized in that, The calculation of adaptive beamforming weights for the channels based on the normalized covariance matrix after diagonal loading includes: According to the formula: Calculation and adaptive beamforming weights for channels ,in, It is a scalar constant. .

6. The subarray-level adaptive beamforming method under sidelobe interference as described in claim 5, characterized in that, The calculation of the target matrix for differential channel adaptive beamforming based on radar radome compensation information includes: The beam direction is to be adjusted The radome compensation table for the transmitted signal includes an azimuth radome compensation table and an elevation radome compensation table. The radome compensation table includes the azimuth scanning angle. Pitch scanning angle Angles corresponding to the azimuth radome compensation table Difference and ratio And the angle corresponding to the elevation radome compensation table Difference and ratio ; According to the formula: By fitting the azimuth radome compensation table data, the independent variables are obtained. coefficient and independent variables coefficient ; According to the formula: By fitting the azimuth radome compensation table data, the independent variables are obtained. coefficient and independent variables coefficient ; According to the formula: Calculate the azimuth difference channel adaptive beamforming target matrix ; According to the formula: Calculate the target matrix of the pitch difference channel adaptive beamforming; in, The azimuth width of the main lobe. The pitch is the width of the main lobe.

7. The subarray-level adaptive beamforming method under sidelobe interference as described in claim 6, characterized in that, The calculation of the constraint matrix for differential channel adaptive beamforming based on the adaptive beamforming weights of the channels and the array subarray-level steering vectors includes: According to the formula: Calculate the constraint matrix for differential channel adaptive beamforming. ; in, for The array subarray steering vectors corresponding to different downward beam pointing angles.

8. The subarray-level adaptive beamforming method under sidelobe interference as described in claim 7, characterized in that, The step of calculating the differential channel adaptive beamforming weights based on the diagonally loaded normalized covariance matrix, target matrix, and constraint matrix includes: According to the formula: Calculate the azimuth difference channel adaptive beamforming weights ; According to the formula: Calculate the adaptive beamforming weights for the pitch difference channel. .

9. A subarray-level adaptive beamforming apparatus for sidelobe interference, implementing the subarray-level adaptive beamforming method for sidelobe interference as described in any one of claims 1 to 8, characterized in that, The subarray-level adaptive beamforming device under sidelobe interference includes: The normalization processing module is used to perform noise normalization processing on the subarray level output signal to obtain the normalized subarray level output signal, and calculate the normalized covariance matrix after diagonal loading based on the normalized subarray level output signal. The weight calculation module calculates the weights of the channel based on the normalized covariance matrix after diagonal loading and the adaptive beamforming of the channel. The weight calculation module also calculates the target matrix of the differential channel adaptive beamforming based on the radar radome compensation information, and calculates the constraint matrix of the differential channel adaptive beamforming based on the adaptive beamforming weight of the sum channel and the array subarray level steering vector. The weight calculation module also calculates the differential channel adaptive beamforming weights based on the diagonally loaded normalized covariance matrix, the target matrix, and the constraint matrix. The interference suppression module performs interference suppression on the received signal based on adaptive beamforming weighting of the sum and difference channels, resulting in interference-suppressed sum and difference channel signals.

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