A Signal Doppler Tolerance and Sidelobe Optimization Method Based on Weighted Fuzzy Function Template

Abstract

This invention discloses a signal Doppler tolerance and sidelobe optimization method based on a weighted ambiguity function template. The method includes determining a discrete ambiguity function based on a two-dimensional ambiguity function of a continuous signal and a radar transmitted signal; obtaining matched filtering results for signals with different Doppler frequency shifts and determining the main lobe and sidelobe positions based on the Doppler frequency shift results; for the Doppler frequency shift case, establishing a cost function for Doppler tolerance optimization and a weighted cost function for joint optimization of Doppler tolerance and sidelobe based on the main lobe and sidelobe positions, respectively; setting the weight coefficients of the weighted cost function according to signal characteristic requirements to obtain the target cost function; and using simulation parameters for iterative updating to obtain the optimization result. This invention can improve signal Doppler tolerance while reducing signal sidelobes, significantly improving the practicality of random noise signals; and it allows for setting weight coefficients according to signal characteristic requirements to adjust the optimization ratio of signal Doppler tolerance and sidelobe, providing strong flexibility.

Description

Technical Field

[0001] This invention belongs to the field of low intercept radar waveform design, specifically involving a signal Doppler tolerance and sidelobe optimization method based on a weighted fuzzy function template. Background Technology

[0002] With the large-scale application of electronic science and technology in modern warfare and the further development of modern radar, ensuring the basic target detection and tracking functions of radar systems, improving their survivability in electronic warfare, and protecting them from enemy attacks have become increasingly important issues. While random noise signals inherently possess good low intercept performance, their thumbtack-shaped ambiguity function makes them Doppler sensitive. When Doppler frequency shift exists, matched filtering cannot achieve optimal results, leading to energy loss due to mismatch, thus resulting in excessively small Doppler tolerance in practical applications.

[0003] Current optimization techniques for random noise signals still have some problems. Specifically, using mismatch filters to optimize the sidelobes of a specified region of the noise signal can achieve very low sidelobes, but this only considers the sidelobes and does not take into account the low Doppler tolerance caused by the "thumbtack" ambiguity function. While methods based on high Doppler tolerance waveform design using ambiguity functions significantly improve the Doppler tolerance of the signal, the optimization direction is fixed, and this method can only optimize signals similar to linear frequency modulation. Furthermore, the optimization of the main and sidelobes of the ambiguity function cannot be separated, leading to consistent optimization results. Summary of the Invention

[0004] To address the aforementioned problems in the prior art, this invention provides a method, apparatus, electronic device, and storage medium for signal Doppler tolerance and sidelobe optimization based on a weighted fuzzy function template. The technical problem to be solved by this invention is achieved through the following technical solution:

[0005] In a first aspect, embodiments of the present invention provide a method for signal Doppler tolerance and sidelobe optimization based on a weighted fuzzy function template, the method comprising:

[0006] Based on the two-dimensional ambiguity function of the continuous signal and the radar transmitted signal, the discrete ambiguity function is determined;

[0007] Matched filtering is performed on signals with different Doppler frequency shifts according to the discrete fuzzy function to obtain the matched filtering results under the condition of Doppler frequency shift, and the positions of the main lobe and side lobe are obtained from them;

[0008] For cases with Doppler frequency shift, based on the main lobe position and side lobe position in the matched filtering results, a cost function for Doppler tolerance optimization and a weighted cost function for joint optimization of Doppler tolerance and side lobe are established respectively.

[0009] The weight coefficients in the weighted cost function for the joint optimization of Doppler tolerance and sidelobes are set according to the requirements of signal characteristics to adjust the optimization ratio of signal Doppler tolerance and sidelobes, thereby obtaining the target cost function. The target cost function is then updated and iterated using the obtained simulation parameters to obtain the corresponding optimization results.

[0010] Secondly, embodiments of the present invention provide a signal Doppler tolerance and sidelobe optimization device based on a weighted fuzzy function template, the device comprising:

[0011] The discrete fuzzy function determination module is used to determine the discrete fuzzy function based on the two-dimensional fuzzy function of the continuous signal and the radar transmission signal.

[0012] The module for obtaining matched filtering results when Doppler frequency shift exists is used to perform matched filtering on signals with different Doppler frequency shifts according to the discrete fuzzy function, obtain the matched filtering results when Doppler frequency shift exists, and obtain the positions of the main lobe and side lobe from them;

[0013] The cost function determination module is used to establish, based on the main lobe position and side lobe position in the matched filtering results, a cost function for Doppler tolerance optimization and a weighted cost function for joint optimization of Doppler tolerance and side lobe, respectively, for cases with Doppler frequency shift.

[0014] The simulation solution module is used to set the weight coefficients in the weighted cost function for the joint optimization of Doppler tolerance and sidelobes according to the requirements of signal characteristics, so as to adjust the optimization ratio of signal Doppler tolerance and sidelobes, obtain the target cost function, and use the obtained simulation parameters to update and iteratively solve the target cost function to obtain the corresponding optimization results.

[0015] Thirdly, embodiments of the present invention provide an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0016] The memory is used to store computer programs;

[0017] When the processor executes the program stored in the memory, it implements the steps of the signal Doppler tolerance and sidelobe optimization method based on the weighted fuzzy function template provided in the embodiments of the present invention.

[0018] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements the steps of the signal Doppler tolerance and sidelobe optimization method based on a weighted fuzzy function template provided in embodiments of the present invention.

[0019] The beneficial effects of this invention are:

[0020] In the solution provided by this invention, firstly, a discrete ambiguity function is determined based on the two-dimensional ambiguity function of the continuous signal and the radar transmitted signal; secondly, matched filtering is performed on signals with different Doppler frequency shifts based on the discrete ambiguity function to obtain the matched filtering result under the condition of Doppler frequency shift, and the positions of the main lobe and side lobe are obtained from it; then, for the case of Doppler frequency shift, based on the main lobe position and side lobe position in the matched filtering result, a cost function for Doppler tolerance optimization and a weighted cost function for joint optimization of Doppler tolerance and sidelobe are established respectively; finally, according to the requirements of signal characteristics, the weight coefficients in the weighted cost function for joint optimization of Doppler tolerance and sidelobe are set to adjust the optimization ratio of signal Doppler tolerance and sidelobe, to obtain the target cost function, and the target cost function is updated and iteratively solved using the obtained simulation parameters to obtain the corresponding optimization result. As can be seen, to address the problem of excessively small Doppler tolerance in the "thumbtack-shaped" ambiguity function of random noise signals, this invention addresses the issue by jointly optimizing the Doppler tolerance and sidelobes, or by optimizing them separately. This approach can improve the Doppler tolerance while reducing the sidelobes, significantly enhancing the practicality of random noise signals. Furthermore, this invention allows for greater flexibility in adjusting the optimization weights of the Doppler tolerance and sidelobes using a weighted cost function that can be set with different weighting coefficients based on the specific signal characteristics required.

[0021] Furthermore, embodiments of the present invention can arbitrarily change the slope of the ambiguity function of the signal according to the designed template, which can improve the practicality of the signal. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating a signal Doppler tolerance and sidelobe optimization method based on a weighted fuzzy function template provided in an embodiment of the present invention.

[0023] Figures 2(a) to 2(c) This is a characteristic diagram of the signal before optimization in the simulation experiment of this invention;

[0024] Figures 3(a) to 3(d) This is a characteristic diagram of the signal after optimization in the simulation experiment of this invention;

[0025] Figures 4(a) to 4(d)This is a diagram showing the optimized result of changing the slope of the ambiguity function in the simulation experiment signal of this invention;

[0026] Figures 5(a) to 5(f) This is an optimization result diagram with the Doppler tolerance and sidelobe optimization weight coefficient λ = 0.5 in the simulation experiment of this invention;

[0027] Figures 6(a) to 6(f) This is a diagram showing the optimization results of the Doppler tolerance and sidelobe optimization weight coefficient λ=1 in the simulation experiment of this invention;

[0028] Figure 7 This is a schematic diagram of a signal Doppler tolerance and sidelobe optimization device based on a weighted fuzzy function template provided in an embodiment of the present invention;

[0029] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0031] To address the issue of Doppler sensitivity in random noise signals, current random noise optimization techniques can only optimize either the Doppler tolerance or one of the sidelobes of the autocorrelation function, and cannot separate the two for individual or joint optimization. In response to this problem, this invention provides a method, apparatus, electronic device, and storage medium for optimizing signal Doppler tolerance and sidelobes based on a weighted fuzzy function template.

[0032] It should be noted that the execution entity of the signal Doppler tolerance and sidelobe optimization method based on a weighted fuzzy function template provided in this embodiment of the invention can be a signal Doppler tolerance and sidelobe optimization device based on a weighted fuzzy function template, which can run in an electronic device. This electronic device can be a server or a terminal device, but is not limited to these.

[0033] In a first aspect, embodiments of the present invention provide a signal Doppler tolerance and sidelobe optimization method based on a weighted ambiguity function template, such as... Figure 1 As shown, the method may include the following steps:

[0034] S1. Determine the discrete ambiguity function based on the two-dimensional ambiguity function of the continuous signal and the radar transmission signal;

[0035] In one optional implementation, S1 may include the following steps:

[0036] S11, the expression for the two-dimensional fuzzy function of the continuous signal is:

[0037]

[0038] Where s(t) represents a continuous signal; * represents the conjugate operation; t represents the time variable; τ represents the echo delay; f d Indicates Doppler frequency shift; j represents the imaginary unit;

[0039] S12, the expression for obtaining the radar transmitted signal is:

[0040]

[0041] Where N is the number of code elements; The modulation sequence to be designed; Energy constraints Represents a rectangular pulse of integer shape; t p To divide the total pulse width T of the transmitted signal evenly according to the number of symbols N, the pulse width of each symbol is T = Nt. p T is abbreviated as time width, t p Simply put, it's the time width per symbol.

[0042] S13, Substitute the expression for the radar transmitted signal into the expression for the two-dimensional ambiguity function of the continuous signal to obtain the expression for the ambiguity function as follows:

[0043]

[0044] Among them, f d Indicates Doppler frequency shift, x * (m) is for the purpose of interacting with x * (n) Distinguish between representations using different variable parameters; p m (t-τ) is for the purpose of interacting with p n (t) Distinguish between representations using different variable parameters

[0045] S14, the expression of the fuzzy function is subjected to time delay discretization and Doppler discretization to obtain the discretized fuzzy function;

[0046] The delay discretization process is expressed as: τ = kt p k = -N+1, ... 0, ... N-1;

[0047] The Doppler discretization process is expressed as: f d =p / Nt p p is an integer;

[0048] The expression for the discretized fuzzy function is:

[0049]

[0050] Where sinc(x) = sin(x) / x;

[0051] S15, based on the condition that the Doppler frequency shift is much smaller than the signal bandwidth, the expression of the discretized fuzzy function is rewritten to obtain the expression of the discrete fuzzy function as follows:

[0052]

[0053] In cases where the Doppler frequency shift is much smaller than the signal bandwidth, |p| << N.

[0054] As those skilled in the art will understand, the Doppler frequency shift is generally much smaller than the signal bandwidth, so |p| << N is usually taken. In this case, we have

[0055] S2, perform matched filtering on signals with different Doppler frequency shifts according to the discrete fuzzy function to obtain the matched filtering results under the condition of Doppler frequency shift, and obtain the positions of the main lobe and side lobe from them;

[0056] In one optional implementation, S2 may include the following steps:

[0057] S21, the modulation sequence is written in column vector form as: x = [x(1), x(2), ... x(n)] T ;

[0058] S22, the matched filter can be written in matrix form as follows:

[0059]

[0060] S23, the matched filtering result is:

[0061]

[0062] in,

[0063] The autocorrelation sequence of the modulation sequence is represented by the expression for the autocorrelation sequence:

[0064]

[0065] S24, when the modulation sequence x = [x(1), x(2), ... x(n)]T When the Doppler frequency shift is different, the modulation sequence matrix X is determined, the matched filtering result under the condition of Doppler frequency shift is obtained according to the modulation sequence matrix X, and the main lobe and side lobe positions are obtained from the matched filtering result under the condition of Doppler frequency shift.

[0066] In the matched filtering results of different Doppler frequency shifts in the modulation sequence, the positions of the main lobe and the side lobe can be freely selected. That is to say, under the condition of Doppler frequency shift, any position in the matched filter can be designed as the main lobe, and the other positions are the side lobes.

[0067] For ease of understanding, this embodiment of the invention uses the various different Leyne frequency shifts p=0, p=1, p=2 as examples to illustrate step S24, which may specifically include the following steps:

[0068] S241, when the modulation sequence x = [x(1), x(2), ... x(n)] T When the Doppler frequency shift is p=0, p=1, p=2, the modulation sequence matrix X is obtained as follows:

[0069]

[0070] Wherein, the column corresponding to the subscript fd0 represents the sequence when the Doppler frequency shift p = 0; the column corresponding to the subscript fd1 represents the sequence when the Doppler frequency shift p = 1, denoted as follows: The column corresponding to the subscript fd2 represents the sequence when the Doppler frequency shift p=2, denoted as: P represents the Doppler frequency shift number, and P=3 represents three types of Doppler frequency shifts;

[0071] S242, the matched filtering result under the condition of Doppler frequency shift is determined as follows:

[0072]

[0073] S243, determine that in the matched filtering result under the condition of Doppler frequency shift, R0(0) at the zero Doppler position is the main lobe position of the matched filter, and the rest are the side lobes.

[0074] S3. For cases with Doppler frequency shift, based on the main lobe position and side lobe position in the matched filtering results, establish a cost function for Doppler tolerance optimization and a weighted cost function for joint optimization of Doppler tolerance and side lobe, respectively.

[0075] S3 is used to model the fuzzy function optimization model.

[0076] In the discrete fuzzy function, each Doppler offset frequency shift element is: Each time shift unit (i.e., an R(·)) is t p.

[0077] For ease of understanding, this embodiment of the invention uses an example where the slope of the ambiguity function is equal to 1, that is, when the Doppler frequency shift p = 1, R... fd1 (1), when p=2, R fd2 (2) The position is the main lobe, and the above cost function is established for the case of Doppler frequency shift.

[0078] Specifically,

[0079] For cases with Doppler frequency shift, establishing a cost function for Doppler tolerance optimization based on the main lobe position in the matched filtering results may include the following steps:

[0080] Step a1: Extract the main lobe from each Doppler shift unit in the matched filtering result under the Doppler shift condition, and represent it as follows:

[0081]

[0082] in, R H The sequence x = [x(1), x(2), ..., x(n)] represents the sequence. T Matched filtering results with Doppler frequency shift; Λx represents the sequence x=[x(1),x(2)...x(n)] T The matched filtering result; The conjugate of the main lobe position of the autocorrelation function when the Doppler frequency shift p = 0; This represents the conjugate of the main lobe position of the autocorrelation function when the Doppler frequency shift p = 1; This represents the conjugate of the main lobe position of the autocorrelation function when the Doppler frequency shift p = 2; row(·) is an operation that extracts a specific value from each row of the matrix, and the processing method is as follows:

[0083]

[0084] Step a2, for the case of Doppler frequency shift, the cost function for Doppler tolerance optimization is determined as follows:

[0085]

[0086] in,

[0087]

[0088]

[0089] The objective of optimization is to indicate the existence of Doppler frequencies.

[0090] The value of the time-shifted main lobe is equal to the value of the main lobe at zero Doppler time; st.|x(n)|=1 is a constraint condition.

[0091] It is understandable that by substituting the relevant simulation parameters into the cost function for Doppler tolerance optimization, the corresponding optimization results can be obtained, thus achieving separate optimization of the Doppler tolerance.

[0092] For cases with Doppler frequency shift, establishing a weighted cost function for joint optimization of Doppler tolerance and sidelobes based on the sidelobe positions in the matched filtering results may include the following steps:

[0093] Step b1, based on the definition of sidelobes in the matched filtering result under the Doppler frequency shift condition, the expression for the sidelobe energy is obtained as follows:

[0094]

[0095] Where O is a single-column vector.

[0096] E S0 =|R0(-N+1)| 2 +...+|R0(-1)| 2 +|R0(1)| 2 +...+|R0(N-1)| 2 ;

[0097] E S1 =|R fd1 (-N+1)| 2 +...+|R fd1 (0)| 2 +|R fd1 (2)| 2 +...+|R fd1 (N-1)| 2 ;

[0098] E S2 =|R fd2 (-N+1)| 2 +...+|R fd2 (1)| 2 +|R fd2 (3)| 2 +...+|R fd2 (N-1)| 2 ;

[0099] row s (·) represents row processing of the matrix, and the processing method is as follows:

[0100]

[0101] Step b2, for the case of Doppler frequency shift, determines the weighted cost function for joint optimization of Doppler tolerance and sidelobes as follows:

[0102]

[0103] Among them, E0, diag(E D Please refer to the previous text for the meaning of ); st.|x(n)|=1 is a constraint condition, λ is the weight coefficient of Doppler tolerance and sidelobe optimization, and the value range is [0,1]. When λ=0, it means that Doppler tolerance is optimized alone.

[0104] It is understandable that by substituting the relevant simulation parameters into the weighted cost function for the joint optimization of Doppler tolerance and sidelobe, the corresponding optimization results can be obtained, thus achieving the joint optimization of Doppler tolerance and sidelobe.

[0105] S4. Based on the signal characteristics, set the weight coefficients in the weighted cost function for joint optimization of Doppler tolerance and sidelobes to adjust the optimization weights of Doppler tolerance and sidelobes, obtaining the target cost function. Use the acquired simulation parameters to update and iteratively solve the target cost function to obtain the corresponding optimization result. This embodiment of the invention can determine the target cost function based on whether the optimization requirement is to optimize Doppler tolerance alone or to jointly optimize Doppler tolerance and sidelobes. This can be achieved by setting the weight coefficients λ in the weighted cost function for joint optimization of Doppler tolerance and sidelobes. Furthermore, the weighted cost function for joint optimization of Doppler tolerance and sidelobes can be set with different λ values ​​to adjust the optimization weights of Doppler tolerance and sidelobes depending on the signal characteristics.

[0106] In one optional implementation, the step of updating and iteratively solving the target cost function using the acquired simulation parameters to obtain the corresponding optimization result includes:

[0107] Based on the obtained simulation parameters, the target cost function is updated and iterated using a genetic algorithm until the iteration stops, and the corresponding optimization results are obtained.

[0108] In the above process, the value of the objective cost function is updated iteratively to make it smaller and smaller. For example, after 150 iterations, the iteration stopping condition is determined. At this time, the corresponding optimization result can be obtained, and the signal Doppler tolerance and sidelobe before and after optimization can be compared and analyzed to judge the optimization effect.

[0109] The simulation parameters include the number of symbols N, the Doppler frequency shift P, the time width T, and the time width t per symbol. p Each Doppler frequency shift unit f d =1 / Ntp Each time-shift unit R(·). Of course, in different optimization processes of the embodiments of the present invention, the set fuzzy function slope can also be changed, so the set fuzzy function slope can also be included in the range of simulation parameters.

[0110] In the solution provided by this embodiment of the invention, firstly, a discrete ambiguity function is determined based on the two-dimensional ambiguity function of the continuous signal and the radar transmitted signal; secondly, matched filtering is performed on signals with different Doppler frequency shifts based on the discrete ambiguity function to obtain the matched filtering result under the condition of Doppler frequency shift, and the positions of the main lobe and side lobe are obtained from it; then, for the case of Doppler frequency shift, based on the main lobe position and side lobe position in the matched filtering result, a cost function for Doppler tolerance optimization and a weighted cost function for joint optimization of Doppler tolerance and sidelobe are established respectively; finally, according to the requirements of signal characteristics, the weight coefficients in the weighted cost function for joint optimization of Doppler tolerance and sidelobe are set to adjust the optimization ratio of signal Doppler tolerance and sidelobe, to obtain the target cost function, and the target cost function is updated and iteratively solved using the obtained simulation parameters to obtain the corresponding optimization result. As can be seen, to address the problem of excessively small Doppler tolerance in the "thumbtack-shaped" ambiguity function of random noise signals, this invention addresses the issue by jointly optimizing the Doppler tolerance and sidelobes, or by optimizing them separately. This approach can improve the Doppler tolerance while reducing the sidelobes, significantly enhancing the practicality of random noise signals. Furthermore, this invention allows for greater flexibility in adjusting the optimization weights of the Doppler tolerance and sidelobes using a weighted cost function that can be set with different weighting coefficients based on the specific signal characteristics required.

[0111] Furthermore, embodiments of the present invention can arbitrarily change the slope of the ambiguity function of the signal according to the designed template, which can improve the practicality of the signal.

[0112] To facilitate understanding of the effects of the embodiments of the present invention, simulation experiments are used for illustration below.

[0113] (I) Simulation Parameter Settings

[0114] Please refer to Table 1 for the specific simulation parameters.

[0115] Table 1 Simulation Parameters

[0116]

[0117] (II) Simulation Content

[0118] ① Simulation 1 Doppler tolerance optimization

[0119] Under the simulation parameters shown in Table 1, the cost function for Doppler tolerance optimization obtained by solving S3 using a genetic algorithm is analyzed and compared with the peak sidelobe ratio and integral sidelobe ratio parameters of the signal pulse compression map before and after optimization.

[0120] First, the slope of the ambiguity function is set to: for a Doppler frequency shift of one unit, a delay offset of one unit is applied, i.e., the slope of the ambiguity function is 1. Based on this, optimization is performed, and the results are shown below. Figures 2(a) to 2(c) This is the signal characteristic before optimization. Figures 3(a) to 3(d) These are the optimized signal characteristics. Figures 2(a) and 2(b) are two-dimensional and three-dimensional ambiguity function graphs, respectively. Figure 2(c) is the plane showing the maximum value of the ambiguity function. It can be seen that the ambiguity function of the random noise signal before optimization is "thumbtack-shaped" and has a very low Doppler tolerance. Figures 3(a) and 3(b) are two-dimensional and three-dimensional ambiguity function graphs, respectively. The part within the red box in the figures is the optimized part. Figure 3(c) shows the change curve of the cost function during the iteration process. Figure 3(d) is the plane showing the maximum value of the ambiguity function. It can be seen that the ambiguity function of the optimized random noise signal becomes "knife-edge-shaped" similar to that of the linear frequency modulated signal, but the slope of the ambiguity function is not the same, and the Doppler tolerance of the signal is significantly improved.

[0121] By changing the slope of the ambiguity function, i.e., optimizing the Doppler frequency shift by one cell and the delay offset by two cells, the result is as follows: Figures 4(a) to 4(d) As shown, it can be seen that the Doppler tolerance is significantly improved after both optimizations, and the signal ambiguity function is also transformed into a "knife-edge" shape similar to that of a linear frequency modulated signal.

[0122] ② Simulation 2: Joint optimization of Doppler tolerance and sidelobe

[0123] In embodiment S3 of this invention, the weighted cost function for the joint optimization of Doppler tolerance and sidelobe can separate the optimization of the main lobe, i.e., Doppler tolerance, from that of the sidelobe. The following section describes the joint optimization of the Doppler tolerance and sidelobe of the fuzzy function.

[0124] Under the simulation parameters shown in Table 1, the Doppler tolerance and sidelobe optimization weighting coefficients λ = 0.5 were first set for optimization, and the results are as follows: Figures 5(a) to 5(f) As shown in Figure 5, the optimized random noise signal ambiguity function also becomes a "knife-edge" shape similar to that of the linear frequency modulated signal, and the Doppler tolerance of the signal is significantly improved. Figure 5(e) shows the pulse compression plot of the signal before optimization, where the peak sidelobe ratio is -14.58dB and the integral sidelobe ratio is -9.40dB; Figure 5(f) shows the pulse compression plot of the optimized signal, where the peak sidelobe ratio is -18.51dB and the integral sidelobe ratio is -14.33dB. It can be seen that the sidelobes of the signal are significantly reduced after optimization.

[0125] After changing the weight coefficient λ = 1 and performing another optimization, the result is as follows: Figures 6(a) to 6(f) As shown, Figures 6(a) to 6(f) Figure 6(e) shows the signal characteristic curve after optimization when λ = 1. It can be seen that the Doppler tolerance of the optimized random noise signal is also significantly improved. Figure 6(f) shows the pulse compression plot of the signal before optimization, where the peak-to-sidelobe ratio is -16.58 dB and the integral-to-sidelobe ratio is -12.15 dB; Figure 6(f) shows the pulse compression plot of the optimized signal, where the peak-to-sidelobe ratio is -27.66 dB and the integral-to-sidelobe ratio is -22.31 dB. Comparing the results of the two optimizations, it can be seen that when the weight λ increases, the effect of sidelobe optimization becomes more significant, further reducing sidelobes while maintaining Doppler tolerance optimization.

[0126] This invention enables joint optimization of Doppler tolerance and sidelobes in random noise signals. Furthermore, it separates the optimization of these two aspects by changing the weighting coefficients of the Doppler tolerance and sidelobe optimizations, thus better controlling the cost function and improving the performance of both. For example, in the simulation experiment, when the weighting coefficient is set to 0.5, the resulting ambiguity function becomes "knife-edge shaped," significantly improving the Doppler tolerance. Before optimization, the peak sidelobe ratio was -14.58 dB and the integral sidelobe ratio was -9.40 dB; after optimization, the peak sidelobe ratio was -18.51 dB and the integral sidelobe ratio was -14.33 dB, demonstrating a significant reduction in sidelobes. The above analysis concludes that the joint optimization improves Doppler tolerance and reduces sidelobes overall, proving the practicality of the method presented in this invention.

[0127] Secondly, corresponding to the above method embodiments, this invention also provides a signal Doppler tolerance and sidelobe optimization device based on a weighted ambiguity function template, such as... Figure 7 As shown, the device includes:

[0128] The discrete fuzzy function determination module 701 is used to determine the discrete fuzzy function based on the two-dimensional fuzzy function of the continuous signal and the radar transmission signal.

[0129] The matched filtering result acquisition module 702 with Doppler frequency shift is used to perform matched filtering on signals with different Doppler frequency shifts according to the discrete fuzzy function, to obtain the matched filtering result with Doppler frequency shift, and to obtain the main lobe and side lobe positions from it;

[0130] The cost function determination module 703 is used to establish, based on the main lobe position and side lobe position in the matched filtering result, a cost function for Doppler tolerance optimization and a weighted cost function for joint optimization of Doppler tolerance and side lobe, respectively, for the case of Doppler frequency shift.

[0131] The simulation solution module 704 is used to set the weight coefficients in the weighted cost function for the joint optimization of Doppler tolerance and sidelobe according to the requirements of signal characteristics, so as to adjust the optimization ratio of signal Doppler tolerance and sidelobe, obtain the target cost function, and use the obtained simulation parameters to update and iteratively solve the target cost function to obtain the corresponding optimization result.

[0132] For details on the specific processing procedures of each module of the device, please refer to the relevant content in the first section, which will not be repeated here.

[0133] Thirdly, embodiments of the present invention also provide an electronic device, such as... Figure 8 As shown, it includes a processor 801, a communication interface 802, a memory 803, and a communication bus 804, wherein the processor 801, the communication interface 802, and the memory 803 communicate with each other through the communication bus 804.

[0134] The memory is used to store computer programs;

[0135] When the processor executes the program stored in the memory, it implements any of the steps of signal Doppler tolerance and sidelobe optimization based on a weighted fuzzy function template provided in the first aspect of the present invention.

[0136] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0137] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0138] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0139] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0140] The method provided in this invention can be applied to electronic devices. Specifically, the electronic device can be a desktop computer, a portable computer, a smart mobile terminal, a server, etc. No limitation is made herein; any electronic device that can implement this invention falls within the protection scope of this invention.

[0141] Fourthly, corresponding to the signal Doppler tolerance and sidelobe optimization method based on weighted fuzzy function template provided in the first aspect, this embodiment of the invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the steps of any of the signal Doppler tolerance and sidelobe optimization methods based on weighted fuzzy function template provided in the first aspect of this invention.

[0142] For the embodiments of the device / electronic device / storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and relevant parts can be referred to in the description of the method embodiments.

[0143] It should be noted that the device, electronic device, and storage medium in the embodiments of the present invention are respectively devices, electronic devices, and storage media that apply the above-mentioned signal Doppler tolerance and sidelobe optimization method based on weighted fuzzy function template. Therefore, all embodiments of the above-mentioned signal Doppler tolerance and sidelobe optimization method based on weighted fuzzy function template are applicable to the device, electronic device, and storage medium, and can achieve the same or similar beneficial effects.

[0144] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A method for signal Doppler tolerance and sidelobe optimization based on a weighted fuzzy function template, characterized in that, include: Based on the two-dimensional ambiguity function of the continuous signal and the radar transmitted signal, the discrete ambiguity function is determined; Matched filtering is performed on signals with different Doppler frequency shifts according to the discrete fuzzy function to obtain the matched filtering results under the condition of Doppler frequency shift, and the positions of the main lobe and side lobe are obtained from them; For cases with Doppler frequency shift, based on the main lobe position and side lobe position in the matched filtering results, a cost function for Doppler tolerance optimization and a weighted cost function for joint optimization of Doppler tolerance and side lobe are established respectively. According to the requirements of signal characteristics, the weight coefficients in the weighted cost function for the joint optimization of Doppler tolerance and sidelobe are set to adjust the optimization ratio of signal Doppler tolerance and sidelobe, so as to obtain the target cost function. The target cost function is updated and iterated using the obtained simulation parameters to obtain the corresponding optimization results. For cases with Doppler frequency shift, a cost function for Doppler tolerance optimization is established based on the main lobe position in the matched filtering results, including: The main lobe of each Doppler shift cell in the matched filtering result with Doppler shift is extracted and represented as: ； , , ; Represents a sequence Matched filtering results with Doppler frequency shift; Represents a sequence The matched filtering result; Indicates Doppler frequency shift The conjugate of the main lobe position of the time-autocorrelation function; Indicates Doppler frequency shift The conjugate of the main lobe position of the time-autocorrelation function; Indicates Doppler frequency shift The conjugate of the main lobe position of the time-autocorrelation function; To extract a specific value from each row of a matrix, the processing method is as follows: ； For the case of Doppler frequency shift, the cost function for Doppler tolerance optimization is determined as follows: ； ； ; The optimization objective is to indicate that the value of the main lobe is equal to the value of the main lobe at the zero Doppler moment when there is a Doppler frequency shift. These are constraints; Specifically, for cases with Doppler frequency shift, a weighted cost function for joint optimization of Doppler tolerance and sidelobes is established based on the sidelobe positions in the matched filtering results, including: Based on the definition of sidelobes in matched filtering results with Doppler frequency shift, the expression for sidelobe energy is obtained as follows: ； It is a column vector consisting of only one column. ； ； ； To process the rows of the matrix, the processing method is as follows: ； For cases involving Doppler frequency shift, the weighted cost function for joint optimization of Doppler tolerance and sidelobes is determined as follows: ； The weighting coefficients for Doppler tolerance and sidelobe optimization, when When, it indicates that the optimization is performed separately for the Doppler tolerance.

2. The signal Doppler tolerance and sidelobe optimization method based on weighted fuzzy function template according to claim 1, characterized in that, The step of determining the discrete ambiguity function based on the two-dimensional ambiguity function of the continuous signal and the radar transmission signal includes: The expression for the two-dimensional fuzzy function of a continuous signal is: ； in, Indicates a continuous signal; * indicates conjugate operation; Represents a time variable; Indicates echo delay; Indicates Doppler frequency shift; Represents the imaginary unit; The expression for obtaining the radar transmitted signal is: ； Where N is the number of code elements; The modulation sequence to be designed; Energy constraints ; , represents a shaped rectangular pulse; To make the total pulse width of the transmitted signal The pulse width of each symbol is obtained by dividing the number of symbols N equally. , Abbreviated as time bandwidth Simply put, it's the time width per symbol. Substituting the expression for the radar transmitted signal into the expression for the two-dimensional ambiguity function of the continuous signal, we obtain the expression for the ambiguity function as follows: ； in, Indicates Doppler frequency shift, In order to be with Distinguish between representations using different variable parameters; In order to be with Distinguish between representations using different variable parameters; The expression of the fuzzy function is subjected to time-delay discretization and Doppler discretization to obtain a discretized fuzzy function; wherein, the time-delay discretization is expressed as: The Doppler discretization process is expressed as follows: , For integers; the expression for the discretized fuzzy function is: ； in, ; ; Since the Doppler frequency shift is much smaller than the signal bandwidth, the expression of the discretized fuzzy function is rewritten to obtain the expression of the discrete fuzzy function as follows: ； In cases where the Doppler frequency shift is much smaller than the signal bandwidth, , , .

3. The signal Doppler tolerance and sidelobe optimization method based on weighted fuzzy function template according to claim 2, characterized in that, Matched filtering is performed on signals with different Doppler frequency shifts based on the discrete fuzzy function to obtain the matched filtering results under the condition of Doppler frequency shift, and the positions of the main lobe and side lobes are obtained from them, including: The modulation sequence can be written in column vector form as follows: ; The matched filter can be written in matrix form as follows: ； The matched filtering result is as follows: ； in, The autocorrelation sequence of the modulation sequence is represented by the expression for the autocorrelation sequence: ; When the modulation sequence Determine the modulation sequence matrix when the Doppler frequency shift is different. According to the modulation sequence matrix The matched filtering result with Doppler frequency shift is obtained, and the positions of the main lobe and side lobe are obtained from the matched filtering result with Doppler frequency shift.

4. The signal Doppler tolerance and sidelobe optimization method based on weighted fuzzy function template according to claim 3, characterized in that, When multiple different Doppler frequency shifts are When the modulation sequence Determine the modulation sequence matrix when the Doppler frequency shift is different. According to the modulation sequence matrix Obtain the matched filtering result with Doppler frequency shift, and derive the main lobe and side lobe positions from the matched filtering result with Doppler frequency shift, including: When the modulation sequence Doppler frequency shift is At that time, the modulation sequence matrix is ​​obtained. for: ； Among them, subscript The corresponding column represents the Doppler frequency shift. Time sequence; subscript The corresponding column represents the Doppler frequency shift. The sequence of time is represented as ; Subscript The corresponding column represents the Doppler frequency shift. The sequence of time is represented as ; Represents the Doppler frequency shift number. Indicates three types of Doppler frequency shifts; The matched filtering result under the condition of Doppler frequency shift is determined as follows: ； In the matched filtering results under the condition of Doppler frequency shift, the zero Doppler position is determined. The position of the main lobe is for matched filtering; the rest are the side lobes.

5. The signal Doppler tolerance and sidelobe optimization method based on weighted fuzzy function template according to any one of claims 1 to 4, characterized in that, The step of updating and iteratively solving the target cost function using the acquired simulation parameters to obtain the corresponding optimization results includes: Based on the obtained simulation parameters, a genetic algorithm is used to iteratively update and solve the objective cost function until the iteration stops, yielding the corresponding optimization result. The simulation parameters include the number of symbols N and the Doppler frequency shift. Time width Width per symbol Each Doppler frequency shift unit Each time shift unit .

6. A signal Doppler tolerance and sidelobe optimization device based on a weighted fuzzy function template, characterized in that, The signal Doppler tolerance and sidelobe optimization method based on a weighted fuzzy function template as described in any one of claims 1-5, wherein the signal Doppler tolerance and sidelobe optimization device based on a weighted fuzzy function template comprises: The discrete fuzzy function determination module is used to determine the discrete fuzzy function based on the two-dimensional fuzzy function of the continuous signal and the radar transmission signal. The module for obtaining matched filtering results when Doppler frequency shift exists is used to perform matched filtering on signals with different Doppler frequency shifts according to the discrete fuzzy function, obtain the matched filtering results when Doppler frequency shift exists, and obtain the positions of the main lobe and side lobe from them; The cost function determination module is used to establish, based on the main lobe position and side lobe position in the matched filtering results, a cost function for Doppler tolerance optimization and a weighted cost function for joint optimization of Doppler tolerance and side lobe, respectively, for cases with Doppler frequency shift. The simulation solution module is used to set the weight coefficients in the weighted cost function for the joint optimization of Doppler tolerance and sidelobes according to the requirements of signal characteristics, so as to adjust the optimization ratio of signal Doppler tolerance and sidelobes, obtain the target cost function, and use the obtained simulation parameters to update and iteratively solve the target cost function to obtain the corresponding optimization results.

7. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; The memory is used to store computer programs; When the processor executes the program stored in the memory, it implements the steps of the method described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method described in any one of claims 1-5.

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