Radar waveform optimization method, system and device and storage medium

Abstract

The invention provides a radar waveform optimization method, system and device and a storage medium, and belongs to the field of radar waveform design, and the method comprises the steps: randomly generating a multi-phase coding waveform according to the waveform length, carrying out the matching calculation, introducing a waveform optimization coefficient, and constructing an optimization objective function representing a peak side lobe, an integral side lobe and a to-be-constrained frequency spectrum; and performing iterative optimization on the optimization objective function by calculating a gradient value and a dynamic stepping coefficient to obtain an optimized waveform. The method can adapt to a complex electromagnetic environment, has good sidelobe performance, and improves the target detection performance and anti-interference capability of the radar.

Description

Technical Field

[0001] This invention belongs to the field of radar waveform design technology, specifically relating to a radar waveform optimization method, system, device, and storage medium. Background Technology

[0002] In radar systems, waveform design is crucial for the accuracy and precision of target detection. Good waveform characteristics also mean better parameter estimation capabilities and stronger anti-interference capabilities when facing different working environments and detection scenarios. In recent years, MIMO radar has become a new research hotspot in radar signal processing, and waveform design is one of the important aspects of MIMO radar research. Its key objective is to suppress the correlation level of the waveform used by the radar, to a certain extent avoiding the triggering of false alarms or the flooding of weak targets in nearby range cells by the autocorrelation sidelobes of the waveform, while also avoiding mutual interference between different waveforms and enabling the receiving antenna to distinguish the echo signals of different waveforms. However, with the rapid development of electronic information technology, the demand and usage of electronic devices such as radar, communication, and navigation have increased significantly. In modern life, wireless communication networks such as 5G, Wi-Fi, and Bluetooth are widely used; satellite navigation systems such as GPS and BeiDou provide convenience for global positioning; and in the military field, radar, as a key detection tool, plays an important role in target tracking, battlefield environment early warning, and meteorological monitoring. The large-scale deployment and use of these devices has resulted in a dense overlap of various electromagnetic signals in space, forming an unprecedentedly complex electromagnetic environment. For radar systems, the complex electromagnetic environment means a wide variety and number of interferences, as well as serious overlap with the operating frequency bands of other systems, which greatly restricts their performance.

[0003] In an increasingly complex electromagnetic spectrum environment, radar systems need to constrain their transmitted waveforms to specific electromagnetic frequencies in order to share spectrum resources with other electronic systems. Therefore, spectral constraints are imposed on the waveforms. Designing radar waveforms to resist co-channel interference signals in complex electromagnetic environments is crucial for minimizing radar detection performance loss.

[0004] In summary, under complex electromagnetic environments, radar waveform design must consider the influence of co-channel interference signals in addition to the waveform sidelobe characteristics. Current waveform optimization algorithms either target peak sidelobes, integral sidelobes, or other factors such as echo signal-to-noise ratio, without taking into account the problem of co-channel interference under complex electromagnetic environments. Summary of the Invention

[0005] To address the issue of co-channel interference in radar systems operating under complex electromagnetic environments, this invention provides a radar waveform optimization method, system, device, and storage medium.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A radar waveform optimization method specifically includes the following steps: Based on the waveform length requirement, a polyphase coded waveform is randomly generated; a matching calculation is performed on the polyphase coded waveform; and the peak sidelobe, integral sidelobe, and unconstrained spectrum of the initial polyphase coded waveform are calculated based on the matching result. Waveform optimization coefficients are introduced to construct an optimization objective function that characterizes the peak sidelobe, integral sidelobe, and the constrained spectrum; The optimization objective function is iteratively solved using a method based on gradient descent and dynamic adjustment of the step coefficient to obtain the optimized waveform. Specifically, this iterative solution involves: calculating the gradient value of the optimization objective function using gradient descent; substituting the gradient value into randomly generated step coefficients to calculate the objective function value corresponding to each coefficient; selecting the optimal step coefficient that minimizes the objective function value as the step coefficient for the next iteration; calculating the norm of the gradient value in the current iteration and comparing it with a set threshold; if it is less than the set threshold, the iteration process is terminated, and the optimized waveform is obtained.

[0007] Preferably, when calculating the norm of the gradient value of the new waveform data and comparing it with a set threshold, if it is greater than the set threshold, the joint optimization objective function is iteratively optimized again.

[0008] Preferably, the optimization objective function is: ; in, Matching results for the waveform; , and For waveform optimization coefficients; represents the frequency domain energy of the waveform; N represents the waveform length. It is the phase of the waveform.

[0009] Preferably, the objective function value corresponding to each coefficient is calculated using the following formula: ; in, This is called the dynamic step coefficient. It is the phase of the waveform. This represents the gradient value of the current waveform.

[0010] Preferably, the polyphase coded waveform is subjected to matching calculation, and the specific calculation formula is as follows: ; in, , For 0 to The initial phase of the waveform can take any value between these ranges; .

[0011] The present invention also provides a radar waveform optimization system, specifically comprising: The initial module is used to randomly generate a polyphase coded waveform based on the waveform length requirement; perform matching calculations on the polyphase coded waveform; and calculate the peak sidelobes, integral sidelobes, and unconstrained spectrum of the initial polyphase coded waveform based on the matching results.

[0012] The objective function module is used to introduce waveform optimization coefficients to construct an optimization objective function that characterizes the peak sidelobe, integral sidelobe, and the constrained spectrum.

[0013] The optimization module is used to iteratively solve the objective function using a gradient descent-based method with dynamic adjustment of the step coefficient to obtain the optimized waveform. Specifically, the iterative solution of the objective function using gradient descent and dynamic adjustment of the step coefficient involves: calculating the gradient value of the objective function using gradient descent; substituting the gradient value into randomly generated step coefficients to calculate the objective function value corresponding to each coefficient; selecting the optimal step coefficient that minimizes the objective function value as the step coefficient for the next iteration; calculating the norm of the gradient value of the current iteration and comparing it with a set threshold; if it is less than the set threshold, the iteration process is terminated, and the optimized waveform is obtained.

[0014] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps described in the radar waveform optimization method.

[0015] The present invention also provides a computer-readable storage medium storing a computer program that, when loaded by a processor, can execute the steps described in the radar waveform optimization method.

[0016] The radar waveform optimization method provided by this invention has the following beneficial effects: This invention simultaneously considers the integral sidelobes, peak sidelobes, and spectral anti-interference characteristics of the waveform to construct the objective function, comprehensively taking into account the impact of interference signals on radar waveforms under complex environments. Waveform optimization coefficients are introduced to iteratively solve the optimized objective function, taking into account both the time-domain sidelobe characteristics and frequency-domain anti-interference characteristics of the waveform. The sidelobe and frequency-domain characteristics are dynamically adjusted through gradient descent and step coefficients for iterative solving, resulting in an optimized waveform that mitigates co-channel interference and improves radar anti-interference performance. Attached Figure Description

[0017] To more clearly illustrate the embodiments and design schemes of the present invention, the accompanying drawings required for this embodiment will be briefly described below. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a radar waveform optimization method according to an embodiment of the present invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0020] Example This invention provides a radar waveform optimization method, such as... Figure 1 As shown, the specific steps include: Step 1: Determine the waveform length N based on the radar system, and randomly generate the initial polyphase coded waveform. ,in , For 0 to The initial phase of the waveform can take any value between these two values.

[0021] Step 2: Obtain the matching result of the initial polyphase coded waveform based on the matched filtering principle of radar waveforms. , , It's a time delay.

[0022] Step 3: Calculate the sidelobe performance results of the initial polyphase coded waveform based on the results of Step 2, including peak sidelobe (PSL) and integral sidelobe (ISL).

[0023] Peak sidelobes are ,and This represents 2N to 2 non-periodic autocorrelation sidelobes of the waveform. The main lobe value. The integral side lobe result is... .

[0024] Step 4: Calculate the constrained spectrum of the waveform according to the frequency domain anti-interference requirements.

[0025] The frequency domain energy expression of the waveform is as follows: ; in, For frequency domain steering vectors, and These represent the spectral ranges to be constrained. Indicates the inner product.

[0026] Further analysis yielded ,here .

[0027] Step 5: Select appropriate waveform optimization coefficients based on the radar system constraints. , and The objective function for waveform optimization is: ; The specific constraints during optimization are as follows: , It is the phase of the waveform.

[0028] Step 6: Iteratively solve the objective function using a gradient descent-based method with dynamic adjustment of the search step coefficient. Based on the objective function obtained in Step 5, the peak sidelobes are... Integral sidelobe and spectral constraints The optimization was analyzed.

[0029] ; Where M is the norm value. Solving for the gradient expression during the iterative process of the objective function yields:

[0030] .

[0031] Step 7: The iteration begins, specifically calculated using the following formula: ; in, This is called the dynamic step coefficient. Calculate the coefficients for 10 random numbers between [0,1]. The target function value of the corresponding waveform, select one of them. Make the current Corresponding waveform The optimal objective function The minimum value, i.e., the optimization direction, is the calculation direction for the next iteration.

[0032] Step 8: Calculate the gradient value of the current waveform. The norm value, and the threshold Compare the results; if the value is greater than the threshold, proceed to step 5 to continue the iteration; if the value is less than the threshold... Exit the current iteration process and obtain the optimized waveform. .

[0033] Step 9: Analyze the performance of the optimized waveform after radar matched filtering. Calculate the peak sidelobes of the optimized waveform. Integral sidelobes and frequency domain energy .

[0034] The sidelobe performance and frequency domain characteristics of the optimized waveform parameters were compared and analyzed with those of the initial waveform parameters. The optimized waveform, when used for MIMO radar target detection, exhibits superior target detection performance and resistance to co-channel interference compared to the initial waveform.

[0035] The present invention also provides a radar waveform optimization system, specifically comprising: The initial module is used to randomly generate polyphase coded waveforms based on waveform length requirements; perform matching calculations on the polyphase coded waveforms; and calculate the peak sidelobes, integral sidelobes, and unconstrained spectrum of the initial polyphase coded waveforms based on the matching results.

[0036] The objective function module is used to introduce waveform optimization coefficients to construct an optimization objective function that characterizes the peak sidelobe, integral sidelobe, and the constrained spectrum.

[0037] The optimization module is used to iteratively solve the objective function using a gradient descent-based method with dynamic adjustment of the step coefficient to obtain the optimized waveform. Specifically, the iterative solution of the objective function using gradient descent and dynamic adjustment of the step coefficient involves: calculating the gradient value of the objective function using gradient descent; substituting the gradient value into randomly generated step coefficients to calculate the objective function value corresponding to each coefficient; selecting the optimal step coefficient that minimizes the objective function value as the step coefficient for the next iteration; calculating the norm of the gradient value of the current iteration and comparing it with a set threshold; if it is less than the set threshold, the iteration process is terminated, and the optimized waveform is obtained.

[0038] The modules in the aforementioned radar waveform optimization system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0039] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps in an embodiment of a radar waveform optimization method. Specific implementation methods can be found in the method embodiments, and will not be repeated here.

[0040] Furthermore, the present invention also provides a non-transitory computer-readable storage medium containing instructions, on which a computer program is stored. For example, a memory containing instructions that can be executed by a processor of a computer device to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc. When the computer program is executed by the processor, it can implement the steps in an embodiment of a radar waveform optimization method. Specific implementation methods can be found in the method embodiments, which will not be repeated here.

[0041] Those skilled in the art will understand that embodiments of the present invention can provide 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.

[0042] 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, as well as 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0043] 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.

[0044] 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 1The steps of the function specified in one or more boxes.

[0045] It should be noted that the specific embodiments described above enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although the present invention has been described in detail in this specification and embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are covered within the protection scope of the present invention patent. No reference numerals in the claims should be construed as limiting the scope of the claims. Any simple variations or equivalent substitutions of technical solutions that can be readily obtained by those skilled in the art within the scope of the technology disclosed in the present invention are within the protection scope of the present invention.

Claims

1. A radar waveform optimization method, characterized in that, Includes the following steps: Based on the waveform length requirement, a polyphase coded waveform is randomly generated; a matching calculation is performed on the polyphase coded waveform; and the peak sidelobe, integral sidelobe, and unconstrained spectrum of the initial polyphase coded waveform are calculated based on the matching result. Waveform optimization coefficients are introduced to construct an optimization objective function that characterizes the peak sidelobe, integral sidelobe, and the constrained spectrum; The optimization objective function is iteratively solved using a method based on gradient descent and dynamic adjustment of the step coefficient to obtain the optimized waveform. Specifically, this iterative solution involves: calculating the gradient value of the optimization objective function using gradient descent; substituting the gradient value into randomly generated step coefficients to calculate the objective function value corresponding to each coefficient; selecting the optimal step coefficient that minimizes the objective function value as the step coefficient for the next iteration; calculating the norm of the gradient value in the current iteration and comparing it with a set threshold; if it is less than the set threshold, the iteration process is terminated, and the optimized waveform is obtained.

2. The radar waveform optimization method according to claim 1, characterized in that, When calculating the norm of the gradient value of the new waveform data and comparing it with a set threshold, if it is greater than the set threshold, the joint optimization objective function is iteratively optimized again.

3. The radar waveform optimization method according to claim 1, characterized in that, The specific optimization objective function is as follows: ； in, Matching results for the waveform; , and For waveform optimization coefficients; The frequency domain energy of the waveform is denoted by ; N is the waveform length. It is the phase of the waveform.

4. The radar waveform optimization method according to claim 1, characterized in that, The objective function value corresponding to each coefficient is calculated using the following formula: ； in, This is called the dynamic step coefficient. It is the phase of the waveform. This represents the gradient value of the current waveform.

5. The radar waveform optimization method according to claim 1, characterized in that, The matching calculation is performed on the polyphase coded waveform, and the specific calculation formula is as follows: ； in, , For 0 to The initial phase of the waveform can take any value between these ranges; .

6. A radar waveform optimization system, characterized in that, include: The initial module is used to randomly generate polyphase coded waveforms based on waveform length requirements; Perform matching calculations on the polyphase coded waveform; Calculate the peak sidelobe, integral sidelobe, and unconstrained spectrum of the initial polyphase coded waveform based on the matching results; The objective function module is used to introduce waveform optimization coefficients to construct an optimization objective function that characterizes the peak sidelobe, integral sidelobe, and the constrained spectrum; The optimization module is used to iteratively solve the objective function using a gradient descent-based method with dynamic adjustment of the step coefficient to obtain the optimized waveform. Specifically, the iterative solution of the objective function using gradient descent and dynamic adjustment of the step coefficient involves: calculating the gradient value of the objective function using gradient descent; substituting the gradient value into randomly generated step coefficients to calculate the objective function value corresponding to each coefficient; selecting the optimal step coefficient that minimizes the objective function value as the step coefficient for the next iteration; calculating the norm of the gradient value of the current iteration and comparing it with a set threshold; if it is less than the set threshold, the iteration process is terminated, and the optimized waveform is obtained.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is loaded by the processor, it is able to perform the steps of the method according to any one of claims 1 to 5.

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