Moving ship SAR (Synthetic Aperture Radar) image simulation method, system, equipment and medium

Abstract

The invention discloses a moving ship SAR image simulation method, system and device and a medium, and relates to the field of SAR simulation, and the method comprises the steps: inputting randomly set radar parameters into a PO algorithm for calculation, and generating RCS sequence data of the radar parameters; training a full-connection network by using the RCS sequence data, inputting the to-be-interpolated radar parameters into the trained full-connection network for prediction, and outputting static RCS data of the to-be-interpolated radar parameters; sequentially carrying out pose simulation and coordinate system conversion on the obtained target ship motion parameters and SAR platform parameters to generate an equivalent sight attitude angle; performing two-dimensional sinc interpolation on the static RCS data by taking the equivalent sight attitude angle as an index to obtain dynamic RCS data; sAR image simulation is performed on the target ship by using the dynamic RCS data, so that the simulation efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of SAR imaging simulation, in particular to a moving ship SAR image simulation method, system, device and medium. BACKGROUND

[0002] As an active microwave remote sensing technology, synthetic aperture radar (SAR) plays an irreplaceable role in ocean environment monitoring, target detection and identification, etc. due to its all-weather and all-day imaging capability. Especially in ship target monitoring, SAR technology can overcome the limitations of traditional optical remote sensing methods and efficiently and reliably monitor ships in the open sea. Motion ship SAR image simulation can generate a large amount of controllable SAR data to provide data support for research and reduce research costs.

[0003] Motion ship SAR image simulation relies on electromagnetic scattering calculation, and the computing resources for electromagnetic scattering calculation are difficult to meet its needs, which leads to low precision and large error of existing motion ship SAR image simulation.

[0004] Therefore, how to accurately simulate SAR images has become a technical problem to be solved by those skilled in the art. SUMMARY

[0005] The present application provides a motion ship SAR image simulation method, system, device and medium to solve how to accurately predict RCS by training a twin model, while ensuring SAR simulation accuracy and improving simulation efficiency.

[0006] To solve the above technical problems, the present application embodiment provides a motion ship SAR image simulation method, comprising:

[0007] Inputting random radar parameters associated with the motion ship into the PO algorithm for calculation to generate RCS sequence data corresponding to the radar parameters;

[0008] Training a full connection network with the RCS sequence data, inputting the to-be-interpolated radar parameters into the trained full connection network for prediction, and outputting the static RCS data of the to-be-interpolated radar parameters;

[0009] Performing pose simulation and coordinate system conversion on the SAR platform parameters in sequence to obtain a first conversion result, performing pose simulation and coordinate system conversion on the target ship motion parameters in sequence to obtain a second conversion result, integrating the first conversion result and the second conversion result, and generating an equivalent line-of-sight attitude angle;

[0010] Using the equivalent line-of-sight attitude angle as an index, performing two-dimensional sinc interpolation on the static RCS data to obtain dynamic RCS data;

[0011] simulate a SAR image of a target ship by using the dynamic RCS data.

[0012] Further, before training the full connection network by using the RCS sequence data, the method further comprises:

[0013] reconstructing the RCS sequence data into training RCS sequence data and reference RCS sequence data corresponding to adjacent radar parameters.

[0014] Further, the training of the full connection network by using the RCS sequence data comprises:

[0015] inputting the training RCS sequence data into a preset full connection network for training;

[0016] In the training process, a loss between the reference RCS sequence data and a predicted value output by the full connection network is calculated, and a back propagation algorithm is used to gradually reduce the loss and update the weight;

[0017] iterating the training process until convergence.

[0018] Further, the generating of the equivalent line-of-sight attitude angle by sequentially performing pose simulation and coordinate system conversion on the obtained target ship motion parameters and SAR platform parameters comprises:

[0019] performing pose simulation on the SAR platform parameters to obtain first pose information of the SAR platform in a global coordinate system;

[0020] performing pose simulation on the target ship motion parameters to obtain second pose information of the target ship in the global coordinate system;

[0021] performing coordinate system conversion on the first pose information and the second pose information to map the equivalent line-of-sight attitude angle in a two-dimensional radar line-of-sight.

[0022] Further, the simulating of the SAR image of the target ship by using the dynamic RCS data comprises:

[0023] performing convolution operation on the dynamic RCS data and a preset SAR transfer function to obtain a SAR echo signal;

[0024] performing SAR imaging on the SAR echo signal by using a frequency modulation scaling algorithm.

[0025] Further, the full connection network comprises three full connection layers, and each full connection layer is configured with a number of neurons.

[0026] Further, the full connection network is integrated with an L2 loss function and a constraint loss function.

[0027] Another embodiment of the present application provides a moving ship SAR image simulation system, comprising:

[0028] An initial RCS calculation module is configured to input random radar parameters associated with the moving ship into a PO algorithm for calculation, and generate RCS sequence data corresponding to the radar parameters;

[0029] A static RCS prediction module is configured to train a full connection network with the RCS sequence data, input an interpolated radar parameter into the trained full connection network for prediction, and output static RCS data of the interpolated radar parameter;

[0030] A posture equivalent module is configured to sequentially perform pose simulation and coordinate system conversion on SAR platform parameters to obtain a first conversion result, sequentially perform pose simulation and coordinate system conversion on target ship motion parameters to obtain a second conversion result, and integrate the first conversion result and the second conversion result to generate an equivalent line-of-sight attitude angle;

[0031] A dynamic RCS generation module is configured to use the equivalent line-of-sight attitude angle as an index to perform two-dimensional sinc interpolation on the static RCS data to obtain dynamic RCS data;

[0032] A simulation imaging module is configured to use the dynamic RCS data to perform SAR image simulation on the target ship.

[0033] Still another embodiment of the present application provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the moving ship SAR image simulation method as described above when executing the computer program.

[0034] Still another embodiment of the present application provides a computer readable storage medium, which stores a computer program, wherein a device in which the computer readable storage medium is located implements the moving ship SAR image simulation method as described above when executing the computer program.

[0035] Compared with the prior art, the embodiment of the present application has at least one of the following advantages:

[0036] The present application trains a twin full connection network with RCS data randomly generated by a PO algorithm, can accurately predict the static RCS value of the azimuth angle, and greatly reduces the calculation amount; the posture equivalent method is introduced to map the target three-dimensional motion into a two-dimensional radar line-of-sight angle change and generate dynamic RCS through sinc interpolation, avoiding the calculation redundancy of adjusting the three-dimensional model posture at each time; finally, the full-link SAR simulation process is integrated, the dynamic RCS is convolved with the system function for imaging, which ensures the physical precision of electromagnetic scattering, and significantly improves the efficiency of moving ship SAR simulation. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 is a motion ship SAR image simulation method flow chart in one of the embodiments of the present application;

[0038] Figure 2 is a full connection network structure schematic diagram in one of the embodiments of the present application;

[0039] Figure 3 is a posture equivalent process schematic diagram in one of the embodiments of the present application;

[0040] Figure 4 is a motion ship SAR image simulation system structure schematic diagram in one of the embodiments of the present application;

[0041] Figure 5 is a structure block diagram of a preferred embodiment of a computer device provided by the present application. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. The purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0043] In the description of the present application, the terms "first", "second", "third" and the like are only used for description purpose, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second", "third" and the like can explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more.

[0044] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. The terms "vertical," "horizontal," "left," "right," "upper," "lower," and similar expressions used herein are for illustrative purposes only and do not indicate or imply that the device or component referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0045] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0046] One embodiment of the present invention provides a method for simulating SAR images of moving ships. For details, please refer to [link / reference]. Figure 1 , Figure 1 The diagram shown illustrates a flowchart of a SAR image simulation method for moving ships according to one embodiment of the present invention, including the following steps:

[0047] S1. Input the random radar parameters associated with the moving vessel into the PO algorithm for calculation, and generate the RCS (radar cross section) sequence data corresponding to the radar parameters.

[0048] The Physical Optical (PO) algorithm is commonly used for electromagnetic scattering calculations in SAR simulations. In this embodiment, the randomly set radar parameters include azimuth, elevation, and frequency. For example, random parameters can be automatically generated by writing a corresponding program or script, such as a script written in Python. In the field of maritime observation, the set azimuth value can range from 0° to 360°, the elevation angle from 0.1° to 30°, and the frequency from 4 to 12 GHz. Within this range, for example, 100 sets of random radar parameters are generated, and then the PO algorithm is called to calculate the RCS.

[0049] Specifically, the randomly set radar parameters can be: azimuth angle phi, frequency freq, and elevation angle theta as inputs to the PO algorithm, and then the PO algorithm outputs: the RCS values ​​corresponding to the azimuth angle phi and elevation angle theta at a specific frequency freq, i.e., (Re[E theta ], Im[E theta ], Re[E phi ], Im[E phi The complete RCS sequence data structure is represented as: (phi, freq, theta, Re[E]). theta ], Im[E theta ], Re[E phi ], Im[E phi ]).

[0050] S2. Train a fully connected network using RCS sequence data, input the radar parameters to be interpolated into the trained fully connected network for prediction, and output the static RCS data of the radar parameters to be interpolated.

[0051] Before inputting the fully connected network for training, the RCS sequence data needs to be preprocessed. In this embodiment, the RCS sequence data is reconstructed into RCS sequence data to be trained and reference RCS sequence data corresponding to adjacent radar parameters. Specifically, this embodiment reconstructs the RCS sequence data into three forms of data, for example: Form 1 is the sequence data corresponding to the previous azimuth angle: (phi j-1 ,freq,theta, Form 2 is the sequence data corresponding to the next azimuth angle: (phi) j+1 ,freq,theta, Forms 1 and 2 represent the RCS sequence data to be trained corresponding to adjacent radar parameters. Form 3 represents the true RCS value corresponding to the current target azimuth angle, which is used as reference RCS sequence data in this embodiment for error comparison with the predicted value output by the fully connected network training process.

[0052] Furthermore, the RCS sequence data to be trained is input into a pre-defined fully connected network for training. Specifically, please refer to the structure of the fully connected network in this embodiment. Figure 2 As shown, the fully connected network comprises three fully connected layers, each with a certain number of neurons. In a preferred embodiment, the first layer has two groups of neurons, each with 10 neurons; the second layer has 10 neurons; and the third layer has 4 neurons. The input to the neurons in the second layer is the sum of the outputs of the first two groups of neurons. Preferably, the activation function configured for each neuron is ReLU.

[0053] During the training of the fully connected network, half of the total amount of reconstructed RCS sequence data is used as the training set, and the other half as the test set. Figure 2 Input 1 (sequence data of form 1) and input 2 (sequence data of form 2) are fed forward to obtain the output of the fully connected network. Then, the loss between the reference RCS sequence data and the predicted output value of the fully connected network is calculated. The loss is gradually reduced and the weights are updated using the backpropagation algorithm. Specifically, in a preferred embodiment, the fully connected network integrates an L2 loss function and a constraint loss function, as follows:

[0054] L = L² + 10L s

[0055] Where L2 represents the L2 loss function, L s This represents the constraint loss function.

[0056] The constraint loss function is used to characterize the symbolic constraint loss and is expressed by the following formula:

[0057]

[0058] in, This indicates calculating the mean. y and These represent the reference RCS value and the predicted RCS value, respectively. This represents the sign function.

[0059] Finally, the training process is iterated, alternating between feedforward and backpropagation, until convergence. For example, the preferred training process uses the Adam optimization algorithm with a learning rate of 0.00003 and a batch size of 50, achieving convergence after 30 epochs.

[0060] After obtaining the trained fully connected network model for predicting RCS values, it is applied to new radar parameter data to be interpolated. For example, to obtain the RCS value corresponding to a certain azimuth angle, the sequence data corresponding to the adjacent upper / lower azimuth angles are input into the trained fully connected network. The output of the fully connected network is the predicted RCS value for that azimuth angle; this value is the static RCS value. For instance, the azimuth angle to be interpolated, phi... n Adjacent PO data (i.e., phi) n-1 and phi n+1 The corresponding RCS value is input into the trained fully connected network to obtain the azimuth angle phi to be interpolated. n Static RCS value: ( ).

[0061] S3-S4: Perform pose simulation and coordinate system transformation on the SAR platform parameters in sequence to obtain the first transformation result. Perform pose simulation and coordinate system transformation on the target ship motion parameters in sequence to obtain the second transformation result. Integrate the first and second transformation results to generate the equivalent line-of-sight attitude angle. Using the equivalent line-of-sight attitude angle as an index, perform two-dimensional sinc interpolation on the static RCS data to obtain the dynamic RCS data.

[0062] The prediction results of a fully connected network are static values. Since SAR simulations involve moving ships, there is relative motion between the radar and the ship target, causing continuous changes in radar parameters such as azimuth and elevation angles, resulting in fluctuations in the RCS value. Therefore, dynamic RCS values ​​are needed. To improve simulation efficiency, this embodiment combines an attitude equivalence method to generate dynamic RCS data. Please refer to [reference needed] for details. Figure 3 As shown, Figure 3 The diagram shown is a schematic representation of the attitude equivalence process in one embodiment of the present invention.

[0063] Specifically, the target ship motion parameters include the ship's target navigation parameters and three-dimensional rotation parameters, such as the ship's position, speed, and three-dimensional rotation angle. SAR platform parameters include platform track and antenna attitude data. The platform track includes the platform's latitude and longitude position and velocity vector, while the antenna attitude includes the pointing angle and beamwidth. For example, the acquired ship position could be (longitude 121.5°, latitude 31.2°, altitude 0m), speed could be 5.14m / s, and heading could be 60°; the platform's latitude and longitude position could be (longitude 121.3°, latitude 31.5°), and speed could be 150m / s.

[0064] The poses of the target ship's motion parameters and the SAR platform parameters are simulated in a global coordinate system. Specifically, a global coordinate system is defined, and the pose of the SAR platform parameters is simulated to obtain the first pose information of the SAR platform in the global coordinate system; the pose of the target ship's motion parameters is simulated to obtain the second pose information of the target ship in the global coordinate system.

[0065] Next, the first pose information and the second pose information are transformed into coordinate systems, resulting in the first transformation result corresponding to the first pose information and the second transformation result corresponding to the second pose information. In other words, both pose information are mapped to equivalent line-of-sight attitude angles under a two-dimensional radar line of sight. It should be understood that this embodiment determines the two-dimensional radar line-of-sight attitude angles in the target body coordinate system by transforming the pose information through a series of coordinate system transformations.

[0066] Combination Figure 3It can be seen that, next, the equivalent line-of-sight attitude angle will be used as an index to perform two-dimensional sinc interpolation on the static RCS data to obtain dynamic RCS data reflecting the motion characteristics of the target ship. For example, in some embodiments of the present invention, the static RCS data exists in a densely gridded form. The equivalent line-of-sight attitude angle is used as a query index to locate the nearest neighbor grid point in this densely gridded data. For example, if the equivalent line-of-sight attitude angle is: azimuth angle... If the pitch angle θ = 5.3°, then locate the nearest grid point in the database: θ = 5°, will ( The dynamic RCS data is output after sinc interpolation (θ = 5°).

[0067] S5. Use dynamic RCS data to perform SAR image simulation of the target ship.

[0068] This step involves performing the final SAR simulation imaging operation using dynamic RCS data. Specifically, the dynamic RCS data is convolved with a preset SAR transfer function to obtain the SAR echo signal; this SAR transfer function is the SAR system function.

[0069] Then, the frequency modulation scaling algorithm (CS algorithm) is used to perform SAR imaging processing on the SAR echo signal, and finally outputs the SAR image of the moving ship.

[0070] In summary, this embodiment enables the fully connected network to effectively learn the contextual relationships of RCS data of adjacent radar parameters by branching the generated RCS sequence data into input, thereby improving prediction accuracy while reducing computational load. Furthermore, by mapping ship motion parameters and platform parameters to changes in the two-dimensional radar line-of-sight angle through coordinate system transformation, and using sinc interpolation to extend the static RCS into a dynamic RCS, it can reasonably capture the dynamic changes of moving ship parameters. Finally, the dynamic RCS is used to perform SAR simulation of the target ship, generating a high-fidelity SAR image of the moving ship.

[0071] One embodiment of the present invention provides a SAR image simulation system for moving ships. For details, please refer to [link / reference]. Figure 4 , Figure 4 The diagram shown illustrates the structure of a SAR image simulation system for moving ships according to one embodiment of the present invention, comprising:

[0072] The initial RCS calculation module M1 is used to input random radar parameters associated with the moving vessel into the PO algorithm for calculation, and generate RCS sequence data corresponding to the radar parameters; the radar parameters include azimuth angle, elevation angle and frequency;

[0073] The static RCS prediction module M2 is used to train a fully connected network with the RCS sequence data, input the radar parameters to be interpolated into the trained fully connected network for prediction, and output the static RCS data of the radar parameters to be interpolated.

[0074] The attitude equivalence module M3 is used to perform pose simulation and coordinate system transformation on the SAR platform parameters in sequence to obtain the first transformation result, and to perform pose simulation and coordinate system transformation on the target ship motion parameters in sequence to obtain the second transformation result. The first transformation result and the second transformation result are integrated to generate the equivalent line-of-sight attitude angle.

[0075] The dynamic RCS generation module M4 is used to perform two-dimensional sinc interpolation on the static RCS data using the equivalent line-of-sight attitude angle as an index to obtain dynamic RCS data.

[0076] The simulation imaging module M5 is used to perform SAR image simulation of the target ship using the dynamic RCS data.

[0077] like Figure 5 As shown, this embodiment of the invention also provides a computer device. Figure 5 This is a structural block diagram of a preferred embodiment of a computer device provided by the present invention. The computer device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the method described above.

[0078] Preferably, the computer program can be divided into one or more modules / units (such as computer program 1, computer program 2, ...), and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the computer device.

[0079] The processor can be a central processing unit (CPU), or other general-purpose processors, 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, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor can be any conventional processor. The processor is the control center of the terminal device, connecting various parts of the terminal device through various interfaces and lines.

[0080] The memory mainly includes a program storage area and a data storage area. The program storage area can store the operating system, applications required for at least one function, etc., while the data storage area can store related data, etc. Furthermore, the memory can be a high-speed random access memory, or a non-volatile memory, such as a plug-in hard drive, a SmartMedia Card (SMC), a Secure Digital (SD) card, and a Flash Card, or other volatile solid-state storage devices.

[0081] It should be noted that the aforementioned terminal devices may include, but are not limited to, processors and memory, as will be understood by those skilled in the art. Figure 5 The structural block diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown, or combine certain components, or use different components. Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium may be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0082] Accordingly, embodiments of the present invention provide a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the steps in the method of the above embodiments, for example... Figure 1 Steps S1 to S5 as described above.

[0083] The technical features and effects of the SAR image simulation system for moving ships proposed in this embodiment of the invention are the same as those of the SAR image simulation method for moving ships proposed in this embodiment of the invention, and will not be repeated here.

[0084] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for simulating SAR images of moving ships, characterized in that, include: Random radar parameters associated with the moving vessel are input into the PO algorithm for calculation, generating RCS sequence data corresponding to the radar parameters; A fully connected network is trained using the RCS sequence data. The radar parameters to be interpolated are input into the trained fully connected network for prediction, and the static RCS data of the radar parameters to be interpolated is output. The SAR platform parameters are sequentially subjected to pose simulation and coordinate system transformation to obtain the first transformation result. The target ship motion parameters are sequentially subjected to pose simulation and coordinate system transformation to obtain the second transformation result. The first and second transformation results are integrated to generate the equivalent line-of-sight attitude angle. Using the equivalent line-of-sight attitude angle as an index, the static RCS data is interpolated in two dimensions using sinc to obtain dynamic RCS data. The dynamic RCS data is used to perform SAR image simulation of the target ship.

2. The SAR image simulation method for moving ships as described in claim 1, characterized in that, Before training the fully connected network with the RCS sequence data, the method further includes: The RCS sequence data is reconstructed into training RCS sequence data and reference RCS sequence data corresponding to adjacent radar parameters.

3. The SAR image simulation method for moving ships as described in claim 2, characterized in that, The training of the fully connected network using the RCS sequence data includes: The RCS sequence data to be trained is input into a preset fully connected network for training; During training, the loss between the reference RCS sequence data and the predicted output value of the fully connected network is calculated, and the backpropagation algorithm is used to gradually reduce the loss and update the weights. The training process continues iteratively until convergence.

4. The SAR image simulation method for moving ships as described in claim 1, characterized in that, The process of generating the equivalent line-of-sight attitude angle includes: Pose simulation was performed on the SAR platform parameters to obtain the first pose information of the SAR platform in the global coordinate system; The motion parameters of the target ship are simulated to obtain the second pose information of the target ship in the global coordinate system. The first pose information is transformed into a coordinate system and mapped to obtain a first transformation result; the second pose information is transformed into a coordinate system and mapped to obtain a second transformation result. By integrating the first and second transformation results, the equivalent line-of-sight attitude angle under the two-dimensional radar line of sight is generated.

5. The SAR image simulation method for moving ships as described in claim 1, characterized in that, The step of using the dynamic RCS data to perform SAR image simulation of the target ship includes: The dynamic RCS data is convolved with a preset SAR transfer function to obtain the SAR echo signal. The SAR echo signal was subjected to SAR imaging using a frequency modulation scaling algorithm.

6. The SAR image simulation method for moving ships as described in claim 1, characterized in that, The fully connected network consists of three fully connected layers, each with a number of neurons.

7. The SAR image simulation method for moving ships as described in claim 1, characterized in that, The fully connected network integrates an L2 loss function and a constraint loss function.

8. A SAR image simulation system for moving ships, characterized in that, include: The initial RCS calculation module is used to input random radar parameters associated with the moving vessel into the PO algorithm for calculation, and generate RCS sequence data corresponding to the radar parameters; The static RCS prediction module is used to train a fully connected network with the RCS sequence data, input the radar parameters to be interpolated into the trained fully connected network for prediction, and output the static RCS data of the radar parameters to be interpolated. The attitude equivalence module is used to perform pose simulation and coordinate system transformation on the SAR platform parameters in sequence to obtain the first transformation result, and to perform pose simulation and coordinate system transformation on the target ship motion parameters in sequence to obtain the second transformation result. The first transformation result and the second transformation result are integrated to generate the equivalent line-of-sight attitude angle. The dynamic RCS generation module is used to perform two-dimensional sinc interpolation on the static RCS data using the equivalent line-of-sight attitude angle as an index to obtain dynamic RCS data. The simulation imaging module is used to perform SAR image simulation of the target ship using the dynamic RCS data.

9. A computer device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the SAR image simulation method for moving ships as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the SAR image simulation method for moving ships as described in any one of claims 1 to 7.

Images