An anti-interference method and related equipment based on transmit waveform and receive filter

Abstract

The application discloses an anti-interference method based on a transmitting waveform and a receiving filter and related equipment, and the method comprises the following steps: considering the signal peak value after pulse compression, reducing the output of the established autocorrelation peak sidelobe level model and the cross-correlation peak level model, constructing an optimization problem model to solve the transmitting waveform and the receiving filter, and realizing target detection in an interference environment according to the closed-form solution of the transmitting waveform and the receiving filter. The application constructs an autocorrelation model of the transmitting waveform and the receiving filter and a cross-correlation model of the interference signal and the receiving filter, optimizes the transmitting waveform and the receiving filter, reduces the autocorrelation peak sidelobe level of the transmitting waveform and the cross-correlation peak level of the interference signal, effectively suppresses the peak level caused by the interference signal in the pulse compression process, reduces the peak value of the interference signal after pulse compression, and improves the detection performance of the weak target.

Description

Technical Field

[0001] This invention belongs to the field of radar waveform design technology, specifically relating to an anti-interference method and related equipment based on transmitted waveforms and received filters. Background Technology

[0002] Regarding techniques for detecting weak targets and suppressing interference, many researchers have proposed technical solutions:

[0003] For example, the patent application with publication number CN106932761A, entitled "A Cognitive Constant Mode Waveform Design Method for Resisting Signal-Dependent Interference", discloses a cognitive constant mode waveform design method for resisting signal-dependent interference. By constructing a signal model and a signal autocorrelation model that are mutually correlated between the interference signal and the transmitted signal, considering constant mode constraints, and using an optimal minimization iterative search optimization algorithm for simplification and solution, signal-dependent interference is effectively suppressed, and the interference signal is suppressed.

[0004] This method can design the radar transmission waveform to effectively suppress signal-dependent interference. However, it only utilizes the degree of freedom of the transmission waveform and does not make full use of the degree of freedom of the receiving filter. Furthermore, when there is a weak target, the suppressed interference signal may mask the weak target, thereby reducing the detection performance of the weak target.

[0005] The patent application with publication number CN115267700A, entitled "Anti-interference method for intra-pulse segmented orthogonal-inter-pulse multidimensional agile waveform signals", discloses an anti-interference method for intra-pulse segmented orthogonal-inter-pulse multidimensional agile waveform signals. By designing intra-pulse segmented orthogonal-inter-pulse multidimensional agile waveform signals, radio frequency filtering and anti-intermittent sampling and repeater deception interference processing are performed on the total echo signal, thereby improving the radar's performance against complex repeater deception interference and target detection performance.

[0006] This method utilizes the degrees of freedom between pulses within the signal pulse to effectively suppress interference signals. However, this invention only suppresses interference signals and does not consider the impact of interference signal suppression on the target signal. When the RCS of the real target is small, the detection performance of the real target will be reduced after the interference is canceled. Summary of the Invention

[0007] The purpose of this invention is to provide an anti-interference method and related equipment based on the transmitted waveform and the received filter, so as to solve the technical problem that weak targets will be overwhelmed after radar interference suppression in the prior art.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] The anti-interference method based on the transmitted waveform and the received filter includes the following steps:

[0010] Establish the autocorrelation peak sidelobe level model and the cross-correlation peak level model;

[0011] To reduce the output values ​​of the autocorrelation peak sidelobe level model and the cross-correlation peak level model, an optimization problem model is established considering the signal peak value after pulse compression.

[0012] Solve the transmit waveform and receive filter for the optimization problem model;

[0013] Determine whether the obtained transmit waveform and receive filter meet the convergence condition. If so, output the corresponding closed-form solution of the transmit waveform and receive filter.

[0014] Furthermore, before establishing the autocorrelation peak sidelobe level model and the cross-correlation peak level model, the transmit waveform and receive filter parameters are initialized.

[0015] Furthermore, the autocorrelation peak sidelobe level model is as follows:

[0016]

[0017] Where APSL represents the autocorrelation peak sidelobe level model, r τ =h H T τ S, h represent the receive filter matrix, S represents the transmit waveform matrix, and T represents the transmit waveform matrix. τ Let be the time-shift matrix, where τ represents time, H represents the conjugate transpose, N represents the number of symbols in the pulse, and p is an undetermined coefficient.

[0018] Furthermore, the cross-correlation peak level model is as follows:

[0019]

[0020] Wherein, CCPL represents the cross-correlation peak level model. h represents the receive filter matrix, S represents the transmit waveform matrix, and T represents the transmit waveform matrix. τ Let be the time-shift matrix, where τ represents time, H represents the conjugate transpose, N represents the number of symbols in the pulse, and p is an undetermined coefficient.

[0021] Furthermore, the optimization problem model is as follows:

[0022]

[0023] Where p1 is the optimization problem model, N represents the number of symbols in the pulse, and b max For the constrained pulse compression peak value, s(n) represents the nth symbol in the pulse, and f1(h,S) can be expressed as:

[0024] f1(h,S)=μ1APSL+μ2CCPL.

[0025] Furthermore, the specific process for solving the receiving filter for the optimization problem model is as follows:

[0026] Simplify and optimize the problem;

[0027] The transmitted waveform S and the received filter h are optimized using an alternating iterative method.

[0028] The transmitted waveform S and the received filter h optimized by the alternating iterative method are further optimized, and the optimization problem is formulated as L p Norm optimization problem;

[0029] L p The norm optimization problem is transformed into a linear term problem, and the corresponding closed-form solution is obtained.

[0030] Furthermore, the convergence condition is:

[0031]

[0032] An anti-interference system based on transmitted waveforms and received filters includes a model building module, a problem model construction module, a solution module, and an output module, wherein:

[0033] Model building module: used to build autocorrelation peak sidelobe level models and cross-correlation peak level models;

[0034] Problem Model Building Module: Used to establish an optimization problem model considering the signal peak value after pulse compression, while reducing the output values ​​of the autocorrelation peak sidelobe level model and the cross-correlation peak level model.

[0035] Solver module: Used to solve for the transmit waveform and receive filter for an optimization problem model;

[0036] The output module is used to determine whether the solved transmit waveform and receive filter meet the convergence condition. If so, it outputs the corresponding transmit waveform and receive filter values.

[0037] A terminal device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0038] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0039] Compared with the prior art, the present invention has the following beneficial technical effects:

[0040] This invention provides an anti-interference method based on transmitted waveforms and received filters. By considering the signal peak value after pulse compression and reducing the output of the established autocorrelation peak sidelobe level model and cross-correlation peak level model, an optimization problem model is constructed to solve for the transmitted waveform and received filters. Based on the closed-form solution of the transmitted waveform and received filters, weak target detection under interference environment is achieved. This invention optimizes the transmitted waveform and received filters by constructing an autocorrelation model of the transmitted waveform and received filters and a cross-correlation model of the interference signal received filters. By reducing the peak level of the transmitted waveform's autocorrelation peak sidelobe and the cross-correlation peak level of the interference signal, the peak level of the interference signal during pulse compression is effectively suppressed, reducing the submergence of the weak target signal by the peak value of the interference signal after pulse compression, and improving the detection performance of weak targets.

[0041] This invention formulates the optimization problem as a norm L by minimizing the APSL of the transmit waveform and the receive filter, as well as the CCPL of the interference waveform and the receive filter. p The optimization problem was solved by approximating and solving it using the MM algorithm, thus solving the resulting non-convex and non-smooth optimization problem.

[0042] Preferably, the system parameters are initialized before establishing the autocorrelation peak sidelobe level model and the cross-correlation peak level model to facilitate model construction.

[0043] Preferably, the established optimization problem model takes into account the peak value problem of pulse compression.

[0044] Preferably, the transmitted waveform S and the received filter h optimized by the alternating iterative method are further optimized, thus the optimization problem is expressed as L p The norm optimization problem further transforms the quadratic term problem into a linear term problem, simplifying the solution process. Attached Figure Description

[0045] Figure 1 This is a flowchart of an anti-interference method based on transmitted waveform and received filter in an embodiment of the present invention.

[0046] Figure 2 This is a detailed flowchart of the anti-interference method based on the transmitted waveform and the received filter in an embodiment of the present invention.

[0047] Figure 3 To design the autocorrelation function plot of the transmitted waveform and the received filter using the method of the present invention.

[0048] Figure 4 To design the cross-correlation function graph between the interference signal and the receiving filter using the method of this invention.

[0049] Figure 5The image shows the pulse compression results when using the method of the present invention in the presence of one strong target, two weak targets, and one interference. Detailed Implementation

[0050] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0051] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0052] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0053] The present invention will now be described in further detail with reference to the accompanying drawings:

[0054] like Figure 1 As shown, the anti-interference method based on the transmitted waveform and the received filter includes the following steps:

[0055] Step 1: Establish the autocorrelation peak sidelobe level model and the cross-correlation peak level model;

[0056] Specifically, before establishing the two models mentioned above, the transmitted waveform and receiver filtering parameters are initialized, and the autocorrelation peak sidelobe level (APSL) and cross-correlation peak level (CCPL) models are established respectively. The APSL and CCPL models have a significant impact on the detectability of weak targets, and they can improve the performance of target detection and interference suppression, respectively.

[0057] The APSL and CCPL models can be defined as follows:

[0058]

[0059] Where APSL represents the autocorrelation peak sidelobe level model, r τ =h H T τ S, h represent the receive filter matrix, S represents the transmit waveform matrix, and T represents the transmit waveform matrix. τ Let be the time-shift matrix, where τ represents time, H represents the conjugate transpose, N represents the number of symbols in the pulse, and p is an undetermined coefficient.

[0060]

[0061] Wherein, CCPL represents the cross-correlation peak level model. h represents the receive filter matrix, S represents the transmit waveform matrix, and T represents the transmit waveform matrix. τ Let be the time-shift matrix, where τ represents time, H represents the conjugate transpose, N represents the number of symbols in the pulse, and p is an undetermined coefficient.

[0062] Step 2: Considering the signal peak value after pulse compression, and establish an optimization problem model while reducing the output values ​​of the autocorrelation peak sidelobe level model and the cross-correlation peak level model;

[0063] Specifically, the peak sidelobe level of the autocorrelation function between the transmitted waveform and the receiving filter, and the peak level of the cross-correlation function between the interference waveform and the receiving filter are reduced using the APSL and CCPL models in step one. To this end, we establish the following optimization problem model by reducing APSL and CCPL, considering the signal peak value after pulse compression;

[0064]

[0065] Where p1 is the optimization problem model, N represents the number of symbols in the pulse, and b max For the constrained pulse compression peak value, s(n) represents the nth symbol in the pulse, and f1(h,S) can be expressed as:

[0066] f1(h,S)=μ1APSL+μ2CCPL.

[0067] Step 3: Solve for the transmitted waveform and the received filter based on the optimization problem model.

[0068] The specific process for solving the receiving filter in the optimization problem model is as follows:

[0069] Simplify and optimize the problem;

[0070] Specifically, we first simplify the optimization problem f1(h,S):

[0071]

[0072] Due to the function Since it is monotonically increasing, f1(h,S) can be expressed as:

[0073]

[0074] The transmitted waveform S and the received filter h are optimized using an alternating iterative method.

[0075] After simplifying f1(h,S), we use the overlapping iteration method to optimize the transmitted waveform S and the received filter h. The optimization problem can be rewritten as:

[0076]

[0077] Among them, Ω h ={h∈R|h H h=N},Ω s ={s∈R||s(n)|=1 / N, n=1,…,N}.

[0078] The transmitted waveform S and the received filter h optimized by the alternating iterative method are further optimized, and the optimization problem is formulated as L p Norm optimization problem;

[0079] This is divided into two categories: optimizing the receive filter and optimizing the transmit waveform. When optimizing the receive filter, the optimization problem simplifies to:

[0080]

[0081] sth H h = N

[0082] When optimizing the transmit waveform, the optimization problem simplifies to:

[0083]

[0084] st|s(n)|=1 / N,n=1,…,N

[0085] in, ψ h , Represented as:

[0086]

[0087] L p The norm optimization problem is transformed into a linear term problem, and the corresponding closed-form solution is obtained:

[0088] The above will L p The detailed process of transforming the norm optimization problem into a linear term problem is as follows:

[0089] Using Theorem 1, L pThe norm optimization problem is transformed into a quadratic polynomial problem;

[0090] According to Theorem 2, the quadratic terms in the quadratic polynomial problem can be transformed into linear terms.

[0091] Theorem 1 is as follows:

[0092] Define x∈[0,t], p≥2, when x0∈[0,t), h(x)=x p It can be represented as: x p ≈ax 2 +bx+c

[0093] in, c is a constant.

[0094] Theorem 2 is:

[0095] Let L be an n×n Hermitian matrix, and M be another n×n Hermitian matrix. If M ≥ L, then for any x 0, the quadratic term x H Lx can be represented as

[0096]

[0097] Taking the solution of the receiving filter as an example, in order to effectively solve L p Norm problem, L p The problem is transformed into a quadratic polynomial problem:

[0098] therefore, It can be rewritten as:

[0099]

[0100] Similarly It can also be rewritten as:

[0101]

[0102] Substitute it into the optimization problem:

[0103]

[0104] sth H h = N

[0105] And because

[0106]

[0107] The above problem can be expressed as:

[0108]

[0109] sth H h = N

[0110] in,

[0111] By transforming the quadratic terms in the above optimization problem of the receiver filter h into linear terms, the optimization problem can be simplified to:

[0112]

[0113] sth H h = N

[0114] in,

[0115] Based on the simplified optimization problem described above, the closed-form solution to the optimization problem of the receiver filter h can be obtained as follows:

[0116]

[0117] Among them W (l) It can be represented as:

[0118]

[0119] When solving for the optimal transmitted waveform S, the simplified formula for the optimization problem is as follows:

[0120]

[0121] st|s(n)|=1 / N,n=1,…,N

[0122] in, ψ h , Represented as:

[0123]

[0124]

[0125] By solving the above formula, the closed-form solution to the problem of optimizing the transmission waveform can be obtained as follows:

[0126]

[0127] Step 4: Determine whether the solved transmit waveform and receive filter satisfy the convergence condition. If so, output the corresponding transmit waveform and receive filter values:

[0128] Specifically, such as Figure 2As shown, after solving the closed-form solutions of the transmitted waveform S and the receiving filter h through the above steps, it is determined whether the corresponding closed-form solutions of the transmitted waveform S and the receiving filter h satisfy the convergence condition. If yes, the corresponding closed-form solutions of the transmitted waveform S and the receiving filter h are output to realize weak target detection under interference environment; if not, step three is repeated to continue iteratively solving the closed-form solutions of the transmitted waveform S and the receiving filter h until the corresponding closed-form solutions satisfy the convergence condition, wherein the convergence condition is specifically:

[0129]

[0130] Figure 3 To design the autocorrelation function graph of the transmit waveform and the receive filter using the method of this invention, the symbol and receive filter lengths are 256, derived from... Figure 3 It can be seen that after optimizing the transmit and receive filters, the sidelobe peaks of the autocorrelation function are more uniform and there are no local peaks, which is beneficial for the detection of weak targets.

[0131] Figure 4 To design the cross-correlation function graph of the interference signal and the receiving filter using the method of this invention, the symbol and receiving filter lengths are both 256. Figure 4 It can be seen that after optimizing the transmit and receive filters, the peak level of the interference cross-correlation does not have a significant spike. Therefore, after interference suppression, it will not affect the weak target detection results.

[0132] Figure 5 To illustrate the pulse compression result diagram using the method of this invention when there is one strong target, two weak targets, and one interference, the symbol and receiver filter lengths are 256. Figure 5 It can be seen that after optimizing the transmit and receive filters, weak targets are not overwhelmed by the cross-correlation peaks of interference, indicating that this method can effectively improve the detection performance of weak targets after interference suppression.

[0133] This invention also provides an optimization solution system for radar waveforms, including a model building module, a problem model construction module, a solution module, and an output module, wherein:

[0134] Model building module: used to build autocorrelation peak sidelobe level models and cross-correlation peak level models;

[0135] Problem Model Building Module: Used to establish an optimization problem model considering the signal peak value after pulse compression, while reducing the output values ​​of the autocorrelation peak sidelobe level model and the cross-correlation peak level model.

[0136] Solver module: Used to solve for the transmit waveform and receive filter for an optimization problem model;

[0137] The output module is used to determine whether the solved transmit waveform and receive filter meet the convergence condition. If so, it outputs the corresponding transmit waveform and receive filter values.

[0138] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0139] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0140] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0141] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0142] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims of the invention.

Claims

1. An anti-jamming method based on transmit waveform and receive filter, characterized in that, The method comprises the following steps: establishing an autocorrelation peak sidelobe level model and a cross-correlation peak level model; in the case of reducing the output values of the autocorrelation peak sidelobe level model and the cross-correlation peak level model, considering the peak value of the pulse compressed signal, establishing an optimization problem model; solving the transmit waveform and the receive filter according to the optimization problem model; judging whether the solved transmit waveform and the receive filter meet a convergence condition, and if yes, outputting the closed-form solution of the corresponding transmit waveform and the receive filter; the specific process of solving the receive filter according to the optimization problem model comprises: simplifying the optimization problem; optimizing the transmit waveform and the receive filter by using an alternating iteration method; Continue to optimize the transmit waveform and receive filter optimized by the alternating iteration method, and express the optimization problem as a norm optimization problem; The The norm optimization problem is converted into a linear term problem, and the corresponding closed-form solution is obtained.

2. The transmit waveform and receive filter based anti-jamming method of claim 1, wherein, before the autocorrelation peak sidelobe level model and the cross-correlation peak level model are established, initializing the parameters of the transmit waveform and the receive filter.

3. The transmit waveform and receive filter based anti-jamming method of claim 1, wherein, The autocorrelation peak sidelobe level model is: wherein, APSL represents an autocorrelation peak side lobe level model, , represents a receive filter matrix, represents a transmit waveform matrix, is a time shift matrix, represents time, represents a conjugate transpose, represents a number of symbols of a pulse, is a coefficient to be determined.

4. The transmit waveform and receive filter based anti-jamming method of claim 1, wherein, The cross-correlation peak level model is: where CCPL denotes a cross-correlation peak level model, , denotes a receive filter matrix, denotes a transmit waveform matrix, is a time shift matrix, denotes time, denotes a conjugate transpose, denotes a number of symbols of a pulse, is an undetermined coefficient.

5. The transmit waveform and receive filter based anti-jamming method of claim 1, wherein, The optimization problem model is: wherein, for an optimization problem model, denotes the number of symbols of a pulse, is a constraint pulse compression peak, denotes the n-th symbol in a pulse, is denoted as: wherein represents a receive filter matrix, represents a transmit waveform matrix, APSL represents an autocorrelation peak side lobe level model, and CCPL represents a cross-correlation peak level model.

6. The transmit waveform and receive filter based anti-jamming method of claim 1, wherein, The convergence condition is: wherein denotes the receive filter matrix, denotes the transmit waveform matrix.

7. An anti-jamming system based on a transmit waveform and a receive filter, characterized in that, The method comprises a model establishing module, a problem model constructing module, a solving module and an output module, wherein: The model establishing module is used for establishing the autocorrelation peak sidelobe level model and the cross-correlation peak level model; The problem model constructing module is used for, in the case of reducing the output values of the autocorrelation peak sidelobe level model and the cross-correlation peak level model, considering the peak value of the pulse compressed signal, establishing an optimization problem model The solving module is used for solving the transmit waveform and the receive filter according to the optimization problem model; The output module is used for judging whether the solved transmit waveform and the receive filter meet a convergence condition, and if yes, outputting the closed-form solution of the corresponding transmit waveform and the receive filter; the specific process of solving the receive filter according to the optimization problem model comprises: simplifying the optimization problem; optimizing the transmit waveform and the receive filter by using an alternating iteration method; Continue to optimize the transmit waveform and receive filter optimized by the alternating iteration method, and express the optimization problem as a norm optimization problem; The The norm optimization problem is converted into a linear term problem, and the corresponding closed-form solution is obtained.

8. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor implements the computer program when the computer program is executed by the processor to realize the method of claim 1 6. The step of the method of any one of the preceding claims.

9. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8. The computer program, which is executed by a processor, implements the method as claimed in claim 1 6. The step of the method of any one of the preceding claims.

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