Parameter estimation and interference suppression method for smart interference

Abstract

The invention provides a smart interference parameter estimation and interference suppression method, and relates to the technical field of radar signal processing. Comprising the following steps: performing zero padding operation on a reference signal according to the length of each interference slice; determining a correlation result between each interference slice and the zero-filled reference signal according to the zero-filled reference signal, the sampling segment and the convolution kernel; determining an estimation parameter according to the length of the correlation result, the tail position of the correlation result and the length of each interference slice; determining a corresponding search range according to the estimation parameter, and reconstructing each new interference slice in the search range by using a least square method according to the value of each interference slice at all corresponding first sampling points and the values of the sampling fragment and the convolution kernel at all corresponding second sampling points; and determining an optimal interference suppression result by using the minimum mean square error according to the radar echo signal and each new interference slice in the search range. Effective suppression of smart interference is realized, and meanwhile, target information is reserved to a great extent.

Description

Technical Field

[0001] The present invention relates to the technical field of radar signal processing, and in particular to a parameter estimation and interference suppression method for smart jamming. Background Art

[0002] With the continuous development of radar countermeasures and countermeasures, the variety of interference types is also increasing. Traditional anti-interference methods struggle to effectively suppress the clever jamming currently encountered in radar operating environments, resulting in reduced radar performance and even the complete loss of target detection. Addressing clever jamming can effectively improve target detection and recognition capabilities, ensuring the stability of radar system performance. In complex electromagnetic interference environments, suppressing clever jamming while preserving target information to the greatest extent possible is a key challenge in improving radar performance.

[0003] Currently, smart jamming suppression is typically achieved by estimating interference parameters before applying suppression. However, target information in electromagnetic interference scenarios can be reduced by factors such as the environment and equipment. Existing interference suppression methods can result in significant loss of target information. Furthermore, the complex and changing working environment can lead to biased parameter estimation, resulting in poor suppression effectiveness. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide a method for parameter estimation and interference suppression of smart jamming, so as to solve the problem that the prior art may cause serious loss of target information and poor interference suppression effect.

[0005] To solve the above technical problems, the embodiments of the present invention provide the following technical solutions:

[0006] A first aspect of the present invention provides a method for parameter estimation and interference suppression of smart interference, the method comprising:

[0007] estimating the length of each interference slice in the radar echo signal, and performing a zero-padding operation on the reference signal in the radar echo signal according to the length of each interference slice to obtain a zero-padding reference signal;

[0008] Determine the correlation result between each interference slice and the zero-padded reference signal according to the zero-padded reference signal, the sampling segment and the convolution kernel;

[0009] determining an estimation parameter according to the length of the correlation result, the end position of the correlation result, and the length of each interference slice;

[0010] Determine the corresponding search range according to the estimated parameters, and reconstruct each new interference slice within the search range using the least squares method based on the values of each interference slice at all corresponding first sampling points, the sampling fragments, and the values of the convolution kernel at all corresponding second sampling points;

[0011] The optimal interference suppression result is determined based on the radar echo signal and each new interference slice within the search range using the minimum mean square error.

[0012] A second aspect of the present invention provides a smart interference parameter estimation and interference suppression device, the smart interference parameter estimation and interference suppression device comprising:

[0013] A zero-padding module is used to estimate the length of each interference slice in the radar echo signal and perform a zero-padding operation on the reference signal in the radar echo signal according to the length of each interference slice to obtain a zero-padding reference signal;

[0014] A correlation result determination module is used to determine the correlation result between each interference slice and the zero-padded reference signal based on the zero-padded reference signal, the sampling segment and the convolution kernel;

[0015] an estimation parameter determination module, configured to determine an estimation parameter based on the length of the correlation result, the end position of the correlation result, and the length of each interference slice;

[0016] A reconstruction module is used to determine a corresponding search range according to the estimated parameters, and reconstruct each new interference slice within the search range using a least squares method based on the values of each interference slice at all corresponding first sampling points, the sampling fragments, and the values of the convolution kernel at all corresponding second sampling points;

[0017] The determination module is used to determine the optimal interference suppression result according to the radar echo signal and each new interference slice within the search range by using the minimum mean square error.

[0018] Compared to the prior art, the parameter estimation and interference suppression method for smart jamming provided by the present invention estimates the length of each interference slice in the radar echo signal, and performs a zero-padding operation on the reference signal in the radar echo signal based on the length of each interference slice to obtain a zero-padding reference signal; determines the correlation result of each interference slice with the zero-padding reference signal based on the zero-padding reference signal, the sampling segment, and the convolution kernel; determines the estimated parameters based on the length of the correlation result, the end position of the correlation result, and the length of each interference slice; determines the corresponding search range based on the estimated parameters, and reconstructs each new interference slice within the search range using the least squares method based on the values of each interference slice at all corresponding first sampling points, the sampling segment, and the convolution kernel at all corresponding second sampling points; and determines the optimal interference suppression result based on the radar echo signal and each new interference slice within the search range using the minimum mean square error. In this way, the corresponding search range can be determined based on the estimated parameters, and the minimum mean square error can be used to search for the optimal cancellation result within the determined search range to obtain the optimal interference suppression result, thereby effectively suppressing smart jamming while greatly preserving target information and improving radar performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0020] Figure 1 The flowchart of the parameter estimation and interference suppression method of smart jamming is schematically shown;

[0021] Figure 2 The following diagram schematically shows the echo signal and pulse pressure results of the radar in a jamming scenario where the smart jamming sampling is turned on and off;

[0022] Figure 3 Schematically shows the correlation results of interference slices and reference signals obtained according to the method of the present invention in a smart interference sampling one-to-one interference environment;

[0023] Figure 4 Schematically illustrates the interference suppression results obtained by the method of the present invention in a one-to-one interference environment with smart jamming under parameter estimation error;

[0024] Figure 5 Schematically shows the relationship between the sampling segment start position estimation error, the sampling segment length estimation error and the signal-to-interference ratio after interference suppression at the same interference-to-noise ratio and signal-to-interference ratio;

[0025] Figure 6Schematically shows the interference suppression result obtained by the method of the present invention in an interference environment where smart jamming is used in a one-to-one manner;

[0026] Figure 7 The structure of the smart jammer parameter estimation and interference suppression device is schematically shown. DETAILED DESCRIPTION

[0027] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0028] It should be noted that, unless otherwise specified, the technical or scientific terms used in the present invention should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0029] The method in the embodiment of the present invention is described in detail below.

[0030] Figure 1 The flowchart of the parameter estimation and interference suppression method for smart interference in the embodiment of the present invention is schematically shown. Figure 1 As shown, the parameter estimation and interference suppression method of smart jamming may include:

[0031] S101 , estimating the length of each interference slice in the radar echo signal, and performing a zero-padding operation on a reference signal in the radar echo signal according to the length of each interference slice to obtain a zero-padding reference signal.

[0032] The specific interference form of the smart interference in the present invention is convolution noise interference.

[0033] Based on the characteristic that the smart jamming signal is much stronger than the noise and target signal, the interference slices in the radar echo can be separated and the length of each interference slice can be estimated. The length of each interference slice is recorded as L, and the zero-padding length of the zero-padding operation is the length of each interference slice; L zeros are padded before the first point of the reference signal to complete the zero-padding operation.

[0034] S102: Determine a correlation result between each interference slice and the zero-padded reference signal according to the zero-padded reference signal, the sampling segment, and the convolution kernel.

[0035] Each interference slice is represented by S j , the convolution kernel is represented by N j , the sampling segment is represented as T j, the reference signal after zero padding is S; the interference fragment can be represented by the convolution kernel and the sampling fragment convolution, and the convolution process is defined as

[0036]

[0037] Define corr(·) as the correlation function, and the correlation result S of each interference slice and the reference signal after zero padding is corr It can be expressed as:

[0038]

[0039] Because the cross-correlation of two signals is equal to the conjugate inverse of one signal convolved with the other, that is Where f(·) is defined as the conjugate inversion of the signal, and A and B are any two signals. Since the convolution of two signals followed by the conjugate inversion is equivalent to the convolution of the two signals followed by the conjugate inversion, that is, Then, the correlation result between each interference slice and the reference signal after zero padding can be expressed as: Re-expressed as: Finally, according to the commutative law of convolution, the expression of the correlation result between each interference slice and the reference signal after zero padding can be converted into:

[0040]

[0041] Among them, S corr is the correlation result, corr(·) is the correlation function, is the convolution operation, S is the reference signal after zero padding, S j For each interference slice, T j is the sampling segment corresponding to each interference slice, N j is the convolution kernel corresponding to each interference slice, and f(·) is the conjugate inversion.

[0042] From this, we can see that the correlation between each interference slice and the zero-padded reference signal can be considered as first correlating the sample segment with the zero-padded reference signal, and then convolving it with the conjugate-inverted convolution kernel. The correlation between the sample segment and the zero-padded reference signal can be approximated as an impulse function, so the correlation result can be considered as convolving the impulse function with the conjugate-inverted convolution kernel. Therefore, the length of the final correlation result is approximately the same as the convolution kernel length. Furthermore, because the convolution kernel is conjugate-inverted, the end of the final correlation result should be near the beginning of the sample segment.

[0043] Before calculating the correlation between each interference slice and the zero-padded reference signal, the reference signal is padded with zeros equal to the interference slice length. This ensures that the inverse-conjugated convolution kernel appears intact in the final correlation result, allowing the kernel length to be estimated. Therefore, when estimating the starting position of the sampling segment, the end position of the obtained correlation result (also known as the end position of the correlation segment) is subtracted from the interference slice length. Finally, the obtained interference slice length parameters, convolution kernel length parameters, and sampling segment starting position parameters are combined with the zero-padded reference signal to obtain the complete signal sampling segment.

[0044] S103: Determine an estimation parameter according to the length of the correlation result, the end position of the correlation result, and the length of each interference slice.

[0045] The estimated parameters include the length of the convolution kernel and the starting position of the sampling segment.

[0046] Specifically, the estimation parameters are determined according to the length of the correlation result, the end position of the correlation result, and the length of each interference slice, including:

[0047] Step A1: Determine the length of the correlation result as the length of the convolution kernel.

[0048] Since the reference signal is padded with zero, the obtained reference signal after zero padding is a complete segment. By calculating the correlation result between each interference slice and the reference signal after zero padding, S corr The length of is the convolution kernel length, and the end position of the correlation result is the starting position of the sampling segment plus the zero-padding length.

[0049] Step A2: The difference between the end position of the correlation result and the length of each interference slice is determined as the starting position of the sampling segment.

[0050] Record S corr The length of the convolution kernel is M; and record S corr The end position of the sampling segment is n, that is, the starting position P of the sampling segment is nL, and the length of the sampling segment is L-M+1.

[0051] S104. Determine a corresponding search range based on the estimated parameters, and reconstruct new interference slices within the search range using the least squares method based on the values of each interference slice at all corresponding first sampling points, the sampling fragments, and the values of the convolution kernel at all corresponding second sampling points.

[0052] The search range includes the length search range of the convolution kernel and the starting position search range of the sampling segment.

[0053] When there are errors in the estimated parameters, it is necessary to set the search range of the starting position of the sampling segment and the search range of the convolution kernel length.

[0054] Specifically, the corresponding search range is determined according to the estimated parameters, including:

[0055] Step B1: The difference between the length of the convolution kernel and the first search length point number of the convolution kernel corresponding vector is used as the upper boundary of the convolution kernel length search range, and the sum of the length of the convolution kernel and the first search length point number is determined as the lower boundary of the convolution kernel length search range to determine the convolution kernel length search range.

[0056] The number of points in the first search length of the convolution kernel corresponding vector is Δm, the upper boundary of the convolution kernel length search range is M-Δm, and the lower boundary of the convolution kernel length search range is M+Δm. Then the convolution kernel length search range is [M-Δm,M+Δm].

[0057] Step B2: Use the difference between the starting position of the sampling segment and the corresponding second search length points as the upper boundary of the starting position search range of the sampling segment, and use the sum of the starting position of the sampling segment and the second search length points as the lower boundary of the starting position search range of the sampling segment to determine the starting position search range of the sampling segment.

[0058] The number of points in the second search length corresponding to the starting position of the sampling segment is Δp. The upper boundary of the search range for the starting position of the sampling segment is P-Δp, and the lower boundary of the search range for the starting position of the sampling segment is P+Δp. The search range for the starting position of the sampling segment is [P-Δp, P+Δp]. Similarly, the length of the sampling segment changes with the change of the convolution kernel length search and the starting position search of the sampling segment. The range of the sampling segment length change is [L-M+Δm+1,LM-Δm+1].

[0059] For each interference slice The convolution process can be regarded as the process of summing the linear equations, and the linear equation is TN j =S j , S j is the vector form of each interference slice, T is the vector group of sampling segments, N j is the convolution kernel vector.

[0060] Reconstructing each new interference slice within the search range using the least squares method according to the values of each interference slice at all corresponding first sampling points, the sampling fragments, and the values of the convolution kernel at all corresponding second sampling points, including:

[0061] Step C1: constructing a vector form of each interference slice according to the values of each interference slice at all corresponding first sampling points.

[0062] The vector form of each interference slice is expressed as:

[0063]

[0064] Among them, S j is the vector form of each interference slice, S j (1) is the value of the first sampling point of each interference slice, S j (L) is the value of the Lth first sampling point of each interference slice.

[0065] Step C2: regroup the sampled segments within the search range to construct a vector group of the sampled segments.

[0066] The vector group of sampling segments is a vector group in which the vectors at the diagonal positions are sampling segments corresponding to the interference slices, and all other vectors except the vectors at the diagonal positions are 0 vectors.

[0067] The expression of the vector group of sampling fragments is:

[0068]

[0069] Among them, T is the vector group of sampling segments, T j is the sampling segment corresponding to each interference slice.

[0070] Step C3: Construct a convolution kernel vector according to the values of the convolution kernel at all corresponding second sampling points.

[0071] The expression of the convolution kernel vector is:

[0072]

[0073] Among them, N j is the convolution kernel vector, N j (1) is the value of the convolution kernel corresponding to each interference slice at the first second sampling point, N j (M) is the value of the convolution kernel corresponding to each interference slice at the Mth second sampling point.

[0074] Step C4: using the least squares method, determine the optimization function according to the vector form of each interference slice, the vector group of the sampling segment and the convolution kernel vector.

[0075] The optimization objective of the optimization function is to minimize the difference between the vector form of each interference slice and the product of the vector group of the sampling segment and the convolution kernel vector, and the solution parameter of the optimization function is the convolution kernel vector.

[0076] Specifically, the expression of the optimization function is:

[0077] min||S j-TN j ||2;

[0078] Among them, the solution parameter of the optimization function is the convolution kernel vector, N j =(T H T) -1 T H S j H , S j is the vector form of each interference slice, T is the vector group of sampling segments, N j is the convolution kernel vector, T H is the conjugate transpose of the vector group of the sampled segments, S j H is the conjugate transpose of the vector form of each interference slice.

[0079] The solution parameter of the optimization function, that is, the least squares solution of the optimization function, is the convolution kernel vector N j =(T H T) -1 T H S j H .

[0080] Step C5: Reconstruct each new interference slice within the search range according to the vector group of the sampling segments and the solution parameters of the optimization function.

[0081] The expressions of each new interference slice, i.e., the reconstructed fragment, within the search range are:

[0082] S j_refactor =TN j ;

[0083] Among them, S j_refactor For each new interference slice.

[0084] The search range in the present invention is a two-dimensional search range, which includes a search range for the length of the convolution kernel and a search range for the starting position of the sampling segment.

[0085] S105 , using minimum mean square error, to determine an optimal interference suppression result according to the radar echo signal and each new interference slice within the search range.

[0086] Specifically, the minimum mean square error is used to determine the optimal interference suppression result based on the radar echo signal and each new interference slice within the search range, including:

[0087] Step D1: Use the minimum mean square error to cancel the radar echo signal and each new interference slice within the search range to obtain multiple cancellation results.

[0088] Step D2: Determine the minimum square norm corresponding to the multiple cancellation results as the optimal interference suppression result.

[0089] Specifically, the expression of the optimal interference suppression result is:

[0090] S′=min||(S r -S j_refactor ) k ||2:

[0091] Among them, S′ is the optimal interference suppression result, S r is the radar echo signal, S j_refactor is each new interference slice, ||·||2 is the two-norm, and k is the search range.

[0092] In order to verify the effectiveness of the smart jamming parameter estimation and interference suppression method of the present invention, the embodiment of the present invention also conducted a simulation experiment as follows:

[0093] Simulation conditions: The simulation experiment of the present invention uses an AMD Ryzen 9 7945HX CPU @ 2.50 GHz, a 64-bit Windows operating system, and MATLAB (R 2024a) as the simulation software.

[0094] Simulation experiment content: The simulation experiment conditions and experimental parameters of the present invention are set as the radar is in a working scenario with smart interference, and the convolution noise interference adopts a one-to-one transmission working mode.

[0095] Figure 2 The following diagram schematically shows the echo signal and pulse pressure results of the radar in the interference scenario of smart jamming sampling. Figure 2 As shown, Figure 2 (a) in the figure is the time domain diagram of the radar echo signal, the horizontal axis is the sampling point, the vertical axis is the amplitude, the blue line is the radar echo signal, and the red line is the target signal. Figure 2 (b) shows a time-frequency plot of the radar echo signal. The horizontal axis shows time in microseconds, and the vertical axis shows frequency in megahertz. The radar echo signal includes the target signal, noise, and a smart jammer. The echo signal is a linear frequency modulation signal with a bandwidth of [-30 MHz, 30 MHz], a pulse width of 10 μs, and a sampling rate of 125 MHz. The smart jammer operates in a sampling mode with a sampling duration of 2 μs and a convolution kernel length of 1 μs. The sampling segments start at the 1st and 625th points of the reference signal, respectively, and the convolution kernel length is 125 samples. Figure 2 (c) is the result of pulse compression of the radar echo signal. The horizontal axis is the distance unit and the vertical axis is the amplitude. The interference signal forms a group of false targets with amplitudes far exceeding the target after pulse compression, while the target echo is a point target obtained by pulse compression and is submerged in the interference area.

[0096] Figure 3 Schematically shows the interference slice and reference signal correlation results obtained according to the method of the present invention in a smart interference sampling one-to-one interference environment, see Figure 3 As shown in FIG, the length of the segment exceeding the threshold is the length of the convolution kernel, the threshold can be 0.15, and the end position minus the interference slice length is the starting position of the sampling segment. Figure 3 (a) in the figure is the result of slice interference 1. The estimated length of the convolution kernel is 126 sampling points, and the starting position of the sampling segment is the second point of the reference signal. Figure 3 (b) in the figure is the result of slice interference 2. The estimated length of the convolution kernel is 127 sampling points, and the starting position of the sampling slice is the 623rd point of the reference signal.

[0097] Figure 4 The interference suppression results obtained by the method of the present invention are schematically shown for the interference environment of smart jamming under parameter estimation error. Figure 4 As shown, the horizontal axis is the distance unit, the vertical axis is the amplitude, the convolution kernel length is misestimated by 5 sampling points, and the starting position of the sampling segment deviates from the interference suppression result under 3 sampling points.

[0098] Figure 5 The relationship between the sampling segment start position estimation error, the sampling segment length estimation error and the signal-to-interference ratio after interference suppression is schematically shown at the same interference-to-noise ratio and signal-to-interference ratio. Figure 5 As shown in the figure, the horizontal axis is the estimation error and the vertical axis is the signal-to-interference ratio. This figure shows the relationship between the sampling segment length estimation error, the sampling segment starting position estimation error, and the signal-to-interference ratio after interference suppression when the interference-to-noise ratio is 15dB and the signal-to-noise ratio is -10dB. The more accurate the parameter estimation, the higher the signal-to-interference ratio after interference suppression, and the better the interference suppression effect.

[0099] Figure 6 Schematically shows the interference suppression result obtained by the method of the present invention in the interference environment of smart interference sampling. Figure 6 As shown, the horizontal axis is the distance unit and the vertical axis is the amplitude. The interference suppression effect is obvious and the target information is retained to a great extent.

[0100] Based on the above Figure 1As can be seen from the implementation method, the embodiment of the present invention estimates the length of each interference slice in the radar echo signal and, based on the length of each interference slice, performs a zero-padding operation on the reference signal in the radar echo signal to obtain a zero-padding reference signal; determines the correlation result between each interference slice and the zero-padding reference signal based on the zero-padding reference signal, the sampling segment, and the convolution kernel; determines the estimation parameters based on the length of the correlation result, the end position of the correlation result, and the length of each interference slice; determines the corresponding search range based on the estimated parameters, and reconstructs each new interference slice within the search range using the least squares method based on the values of each interference slice at all corresponding first sampling points, the sampling segment, and the values of the convolution kernel at all corresponding second sampling points; and determines the optimal interference suppression result based on the radar echo signal and each new interference slice within the search range using the minimum mean square error. In this way, the corresponding search range can be determined based on the estimated parameters, and the minimum mean square error can be used to search for the optimal cancellation result within the determined search range to obtain the optimal interference suppression result, thereby effectively suppressing smart interference while greatly preserving target information and improving radar performance.

[0101] Based on the same inventive concept, as an implementation of the above-mentioned smart jamming parameter estimation and interference suppression method, an embodiment of the present invention further provides a smart jamming parameter estimation and interference suppression device. Figure 7 This is a structural diagram of the smart jamming parameter estimation and interference suppression device in an embodiment of the present invention, see Figure 7 As shown, the smart jammer parameter estimation and interference suppression device may include:

[0102] The zero-padding module 701 is used to estimate the length of each interference slice in the radar echo signal and perform a zero-padding operation on the reference signal in the radar echo signal according to the length of each interference slice to obtain a zero-padding reference signal;

[0103] A correlation result determination module 702 is configured to determine a correlation result between each interference slice and the zero-padded reference signal based on the zero-padded reference signal, the sampling segment, and the convolution kernel;

[0104] An estimation parameter determination module 703 is configured to determine an estimation parameter based on the length of the correlation result, the end position of the correlation result, and the length of each interference slice;

[0105] A reconstruction module 704 is configured to determine a corresponding search range based on the estimated parameters, and reconstruct each new interference slice within the search range using a least squares method based on the values of each interference slice at all corresponding first sampling points, the sampling segments, and the values of the convolution kernel at all corresponding second sampling points;

[0106] The determination module 705 is configured to determine an optimal interference suppression result based on the radar echo signal and each new interference slice within the search range by using a minimum mean square error.

[0107] In the correlation result determination module 702, the correlation result between each interference slice and the zero-padded reference signal is expressed as follows:

[0108]

[0109] Among them, S corr is the correlation result, corr(·) is the correlation function, is the convolution operation, S is the reference signal after zero padding, S j For each interference slice, T j is the sampling segment corresponding to each interference slice, N j is the convolution kernel corresponding to each interference slice, and f(·) is the conjugate inversion.

[0110] The estimation parameter determination module 703 is specifically configured to determine the length of the correlation result as the length of the convolution kernel; and determine the difference between the end position of the correlation result and the length of each interference slice as the starting position of the sampling segment.

[0111] The reconstruction module 704 determines the corresponding search range according to the estimated parameters, including: taking the difference between the length of the convolution kernel and the first search length points of the convolution kernel corresponding vector as the upper boundary of the convolution kernel length search range, and determining the sum of the length of the convolution kernel and the first search length points as the lower boundary of the convolution kernel length search range, so as to determine the length search range of the convolution kernel; taking the difference between the starting position of the sampling segment and the corresponding second search length points as the upper boundary of the starting position search range of the sampling segment, and taking the starting position of the sampling segment and the sum of the second search length points as the lower boundary of the starting position search range of the sampling segment, so as to determine the starting position search range of the sampling segment; the search range includes the convolution kernel length search range and the starting position search range of the sampling segment.

[0112] The reconstruction module 704 reconstructs each new interference slice within the search range using the least squares method based on the values of each interference slice at all corresponding first sampling points, the sampling segments, and the values of the convolution kernel at all corresponding second sampling points. The reconstruction module 704 includes: constructing a vector form of each interference slice based on the values of each interference slice at all corresponding first sampling points; recombining the sampling segments within the search range to construct a vector group of sampling segments, where the vectors of the sampling segments are sampling segments corresponding to each interference slice, and all other vectors except the diagonal position vectors are zero vectors; constructing a convolution kernel vector based on the values of the convolution kernel at all corresponding second sampling points; determining an optimization function using the least squares method based on the vector form of each interference slice, the vector group of sampling segments, and the convolution kernel vector, where the optimization objective of the optimization function is the minimum value of the difference between the vector form of each interference slice and the product of the vector group of sampling segments and the convolution kernel vector, and the solution parameter of the optimization function is the convolution kernel vector; and reconstructing each new interference slice within the search range based on the vector group of sampling segments and the solution parameter of the optimization function.

[0113] In the reconstruction module 704, the vector form of each interference slice is expressed as:

[0114]

[0115] Among them, S j is the vector form of each interference slice, S j (1) is the value of the first sampling point of each interference slice, S j (L) is the value of the Lth first sampling point of each interference slice;

[0116] The expression of the vector group of sampling fragments is:

[0117]

[0118] Among them, T is the vector group of sampling segments, T j is the sampling segment corresponding to each interference slice;

[0119] The expression of the convolution kernel vector is:

[0120]

[0121] Among them, N j is the convolution kernel vector, N j (1) is the value of the convolution kernel corresponding to each interference slice at the first second sampling point, N j (M) is the value of the convolution kernel corresponding to each interference slice at the Mth second sampling point.

[0122] Reconstructing module 704, the expression of the optimization function is:

[0123] min||S j -TN j ||2;

[0124] Among them, the solution parameter of the optimization function is the convolution kernel vector, N j =(T H T) -1 T H S j H , S j is the vector form of each interference slice, T is the vector group of sampling segments, N j is the convolution kernel vector, T H is the conjugate transpose of the vector group of the sampled segments, S j H is the conjugate transpose of the vector form of each interference slice.

[0125] The determination module 705 is specifically configured to cancel the radar echo signal and each new interference slice within the search range using the minimum mean square error to obtain multiple cancellation results; and determine the minimum square norm corresponding to the multiple cancellation results as the optimal interference suppression result.

[0126] In the determination module 705, the expression of the optimal interference suppression result is:

[0127] S′=min||(S r -S j_refactor ) k ||2:

[0128] Among them, S′ is the optimal interference suppression result, S r is the radar echo signal, S j_refactor is each new interference slice, ||·||2 is the two-norm, and k is the search range.

[0129] It should be noted that the above description of the embodiment of the apparatus for parameter estimation and interference suppression for smart interference is similar to the description of the embodiment of the method for parameter estimation and interference suppression for smart interference, and has similar beneficial effects as the embodiment of the method for parameter estimation and interference suppression for smart interference. For technical details not disclosed in the embodiment of the apparatus for parameter estimation and interference suppression for smart interference according to the present invention, please refer to the description of the embodiment of the method for parameter estimation and interference suppression for smart interference according to the present invention.

[0130] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for parameter estimation and interference suppression of smart jamming, characterized in that: include: estimating the length of each interference slice in the radar echo signal, and performing a zero-padding operation on a reference signal in the radar echo signal according to the length of each interference slice to obtain a zero-padding reference signal; Determining a correlation result between each interference slice and the zero-padded reference signal according to the zero-padded reference signal, the sampling segment, and the convolution kernel; determining an estimation parameter according to the length of the correlation result, the end position of the correlation result, and the length of each interference slice; Determine a corresponding search range according to the estimated parameters, and reconstruct each new interference slice within the search range using a least squares method according to the values of each interference slice at all corresponding first sampling points and the values of the sampling segment and the convolution kernel at all corresponding second sampling points; An optimal interference suppression result is determined according to the radar echo signal and each new interference slice within the search range by utilizing a minimum mean square error.

2. The method for smart jamming parameter estimation and interference suppression according to claim 1, wherein: The expression of the correlation result between each interference slice and the zero-padded reference signal is: Among them, S corr is the correlation result, corr(·) is the correlation function, is the convolution operation, S is the reference signal after zero padding, S j For each interference slice, T j is the sampling segment corresponding to each interference slice, N j is the convolution kernel corresponding to each interference slice, and f(·) is the conjugate inversion.

3. The method for smart jamming parameter estimation and interference suppression according to claim 1, wherein: The estimated parameters include the length of the convolution kernel and the starting position of the sampling segment, and determining the estimated parameters according to the length of the correlation result, the end position of the correlation result, and the length of each interference slice includes: Determining the length of the correlation result as the length of the convolution kernel; The difference between the end position of the correlation result and the length of each interference slice is determined as the starting position of the sampling segment.

4. The method for smart jamming parameter estimation and interference suppression according to claim 3, wherein: The search range includes a convolution kernel length search range and a sampling segment starting position search range, and determining the corresponding search range according to the estimated parameters includes: The difference between the length of the convolution kernel and the first search length point number of the convolution kernel corresponding vector is used as the upper boundary of the convolution kernel length search range, and the sum of the length of the convolution kernel and the first search length point number is determined as the lower boundary of the convolution kernel length search range, so as to determine the convolution kernel length search range; The difference between the starting position of the sampling segment and the corresponding second search length point number is used as the upper boundary of the starting position search range of the sampling segment, and the sum of the starting position of the sampling segment and the second search length point number is used as the lower boundary of the starting position search range of the sampling segment to determine the starting position search range of the sampling segment.

5. The method for smart jamming parameter estimation and interference suppression according to claim 1, wherein: The reconstructing each new interference slice within the search range by using a least squares method according to the values of each interference slice at all corresponding first sampling points, the sampling segment, and the values of the convolution kernel at all corresponding second sampling points, includes: Constructing a vector form of each interference slice according to the values of each interference slice at all corresponding first sampling points; Recombining the sampling segments within the search range to construct a vector group of the sampling segments, where the vectors at diagonal positions are sampling segments corresponding to the interference slices, and all other vectors except the diagonal position vectors are 0 vectors; Constructing a convolution kernel vector according to the values of the convolution kernel at all corresponding second sampling points; Determine an optimization function using the least squares method based on the vector form of each interference slice, the vector group of the sampling segments, and the convolution kernel vector, wherein the optimization objective of the optimization function is the minimum value of the difference between the vector form of each interference slice and the product of the vector group of the sampling segments and the convolution kernel vector, and the solution parameter of the optimization function is the convolution kernel vector; Reconstructing each new interference slice within the search range according to the vector group of the sampling segments and the solution parameters of the optimization function.

6. The method for smart jamming parameter estimation and interference suppression according to claim 5, characterized in that: The vector form of each interference slice is expressed as follows: Among them, S j is the vector form of each interference slice, S j (1) is the value of the first sampling point of each interference slice, S j (L) is the value of the Lth first sampling point of each interference slice; The expression of the vector group of the sampling fragments is: Where T is the vector group of the sampling segments, T j are sampling segments corresponding to the interference slices; The expression of the convolution kernel vector is: Among them, N j is the convolution kernel vector, N j (1) is the value of the convolution kernel corresponding to each interference slice at the first second sampling point, N j (M) is the value of the convolution kernel corresponding to each interference slice at the Mth second sampling point.

7. The method for smart jamming parameter estimation and interference suppression according to claim 6, wherein: The expression of the optimization function is: my||S j -TN j ||2; The solution parameter of the optimization function is the convolution kernel vector, N j =(T H T) -1 T H S j H , S j is the vector form of each interference slice, T is the vector group of the sampling segment, N j is the convolution kernel vector, T H is the conjugate transpose of the vector group of the sampled segments, S j H is the conjugate transpose of the vector form of each interference slice.

8. The method for smart jamming parameter estimation and interference suppression according to claim 7, wherein: The utilizing minimum mean square error to determine an optimal interference suppression result according to the radar echo signal and each new interference slice within the search range includes: canceling the radar echo signal and each new interference slice within the search range using the minimum mean square error to obtain a plurality of cancellation results; The minimum square norm corresponding to the multiple cancellation results is determined as the optimal interference suppression result.

9. The method for smart jamming parameter estimation and interference suppression according to claim 8, characterized in that: The expression of the optimal interference suppression result is: S′=min||(S r -S j_refactor ) k ||2: Among them, S′ is the optimal interference suppression result, S r is the radar echo signal, S j_refactor are the new interference slices, ||·||2 is the two-norm, and k is the search range.

10. A smart jammer parameter estimation and interference suppression device, characterized in that: include: a zero-padding module, configured to estimate the length of each interference slice in the radar echo signal, and perform a zero-padding operation on the reference signal in the radar echo signal according to the length of each interference slice to obtain a zero-padding reference signal; a correlation result determination module, configured to determine a correlation result between each interference slice and the zero-padded reference signal according to the zero-padded reference signal, the sampling segment, and the convolution kernel; an estimation parameter determination module, configured to determine an estimation parameter according to the length of the correlation result, the end position of the correlation result, and the length of each interference slice; a reconstruction module, configured to determine a corresponding search range according to the estimated parameters, and reconstruct each new interference slice within the search range using a least squares method based on the values of each interference slice at all corresponding first sampling points and the values of the sampling segment and the convolution kernel at all corresponding second sampling points; The determination module is used to determine an optimal interference suppression result according to the radar echo signal and each new interference slice within the search range by using a minimum mean square error.

Images