High-resolution spaceborne SAR (Synthetic Aperture Radar) ship target imaging processing method under complex sea condition

Abstract

The invention provides a high-resolution spaceborne SAR ship target imaging processing method under a complex sea condition, and relates to the technical field of imaging processing, and the method comprises the steps: enabling a system to flexibly adjust an accumulated energy ratio threshold according to a real-time signal condition through a dynamic adjustment fine tuning index strategy, introducing a phase compensation function as a primary function, and adding a time window parameter, thereby achieving the high-resolution spaceborne SAR ship target imaging processing. The method is more suitable for the instability of strong scattering point echoes under high sea conditions, and the robustness of the algorithm is further improved; the steps of parameter estimation, residual error updating and component extraction are carried out in a frequency domain, so that the search dimension is reduced, and the operation speed is improved; the parameterized time-frequency distribution construction method effectively reduces the influence of cross terms and side lobes; the system can estimate and extract multiple PPS signal components in parallel, and further improves the search efficiency and global optimal capability of time-frequency decomposition.

Description

Technical Field

[0001] The invention relates to the technical field of imaging processing, and in particular to a high-resolution satellite-borne SAR ship target imaging processing method under complex sea conditions. Background Art

[0002] When the ship target is large in size or is imaged in high-resolution spaceborne SAR, especially in complex sea conditions, the target movement is extremely complex, the Doppler frequency of the original echo signal is time-varying, and the Doppler phase of different scattering points of the target will change greatly with azimuth and time. The various existing self-focusing methods will not be able to obtain good imaging effects. From the perspective of echo signals, such movement leads to the introduction of high-order Doppler phases in the spaceborne SAR radar echo, which can be expressed in the form of polynomial phase signals (PPS). In the case of high resolution and large-size targets, a spaceborne SAR echo pulse often contains echo components of several scattering centers. At this time, the echo signal is called a multi-component polynomial phase signal (mc-PPS); At the same time, as an improved maximum likelihood method, the key process of the Adaptive Joint Time-Frequency (AJTF) method is to find the optimal parameters in the solution space, which is essentially a multi-dimensional optimization problem with different extreme value solutions; however, in the time-frequency decomposition of multi-component polynomial phase signals, since the extreme value solution is not completely the true value solution corresponding to the PPS signal components of different intensities, it is easy to fall into the local minimum. In terms of algorithm efficiency, it is necessary to resolve the contradiction between parameter search speed and global convergence, which makes the decomposition optimization problem more complicated. This non-convex optimal problem requires a lot of calculations, which limits the algorithm in practical applications. Therefore, it is necessary to apply the optimization algorithm to the AJTF decomposition process to speed up the processing; In the prior art, the publication number is CN106772373A, and the name is a SAR imaging method for arbitrary ground moving targets, which mainly solves the problem of defocusing of moving targets caused by range migration in the prior art, including: 1) Construct a positive side-view radar echo signal model; 2) After performing range-direction pulse compression on the target echo signal, transform it into the range-frequency domain; 3) construct a reference function from the range-frequency domain expression of the pulse compression signal; 4) Taking the complex conjugate of the reference function, multiply it by the range frequency domain expression of the slow target pulse compression signal, and then Fourier transform the result in the azimuth direction to obtain the two-dimensional frequency domain of range and azimuth; 5) Performing a range-direction inverse Fourier transform on the two-dimensional frequency domain to obtain the target signal amplitude spectrum; There is no need to estimate the parameters of the target. Good imaging effect can be achieved by only using fast Fourier transform without interpolation operation, and it can be used for tracking and identifying any unknown moving target.

[0003] In the prior art, the publication number is CN115236626A, and the name is multi-channel radar sea clutter suppression method and system based on time-frequency analysis. A multi-channel radar sea clutter suppression method and system based on time-frequency analysis is also disclosed, including: Step S1: Calculate the coherence time of sea clutter using the sea state series; Step S2: transform the sea clutter data into the time-frequency domain by using the coherence time window of the sea clutter; Step S3: Suppressing sea clutter in the time-frequency domain by using a subspace projection method based on a time-domain sliding window; Step S4: restore the suppressed clutter data to the post-Doppler domain and then perform the second stage of suppression; It can realize effective compensation for multi-channel radar sea clutter and fully consider the temporal decorrelation characteristics of sea clutter, so as to achieve more robust suppression of sea clutter in the space-time-frequency domain.

[0004] Therefore, accurate estimation of mc-PPS signal is the key to realize imaging of complex moving targets by spaceborne SAR. Existing deficiencies: How to design an optimization algorithm that can further improve the search speed and global optimal search capability in time-frequency decomposition, speed up the processing speed, and on this basis propose a parameterized time-frequency distribution construction method to reduce the impact of cross terms and side lobes; How to adapt to signal environments of different complexity; whether in high-noise or low-noise environments, the system can adjust the predetermined threshold of the cumulative energy ratio according to current conditions, thereby optimizing the signal processing effect; How to design particle swarm combination partitioning and information interaction sharing methods based on the co-evolutionary particle swarm optimization (PSO) algorithm used in the AJTF algorithm to address the conflicting constraints between the amount of computation and global convergence, estimate and extract multiple PPS signal components in parallel, and further improve the search speed and global optimal search capability in time-frequency decomposition is a direction that needs to be improved at present; The above information disclosed in the above Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The purpose of the present invention is to provide a high-resolution satellite-borne SAR ship target imaging processing method under complex sea conditions to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: The high-resolution spaceborne SAR ship target imaging processing method under complex sea conditions is improved on the basis of the AJTF algorithm to obtain an adaptive joint time-frequency imaging processing method. The following adaptive joint time-frequency imaging processing method is referred to as FDE-AJTF. The specific steps include: Step S1: Obtain the SAR echo signal data of the moving ship target in complex sea conditions, and define it as an mc-PPS signal output. The PPS signal component of a single strong scattering point is defined as ; Step S2: Constructing the phase compensation basis function of the echo signal The PPS signal components are conjugated, and the length and overlap of the time window parameters are adjusted to match the time-frequency characteristics of the target echo signal of the ship in high sea conditions. Step S3: Using phase compensation basis function The polynomial phase error is corrected to determine the polynomial phase error of the SAR echo signal in SAR imaging, and the conjugate inner product of the adjusted time window parameter and the SAR echo signal and the phase compensation basis function is used as the objective function; Multiply the SAR echo signal with the phase compensation basis function to obtain the compensation signal , for the compensation signal The result is transformed into the spectrum domain through Fourier transform to obtain the compensated spectrum in the image ; Step S4: Based on the signal corrected in step S3, the objective function of the adaptive joint time-frequency processing is defined as: ; in, Indicates the parameter item that needs to be estimated. After imaging processing, according to the maximum value of the spectrum The peak frequency point is determined by the location , thus obtaining the parameters , and then complete the search for all remaining parameters, Indicated in the spectrum The frequency point corresponding to the maximum value , Indicates the PPS signal component Phase compensation basis functions The product of is Fourier transformed; Indicates that the first parameter of the estimate is the frequency point ; Will The energy in the main lobe area of ​​the signal spectrum peak is used as the signal component strength, while the energy in the area outside the main lobe of the sinc function is used as the residual signal; Through the optimal estimation algorithm, the estimated value of the PPS signal at each order component is obtained. The estimated value is defined as , the calculation formula is as follows: ; in, for The intensity component of the spectral maximum, is the optimal estimation result of each order component of the PPS signal, represents the polynomial order, and The change in time t is represented by t raised to the power of n, and j represents the imaginary unit; Step S5: based on the objective function result optimized in step S4, extract the PPS signal components of each scattering point, calculate the signal residual, and then perform the same processing on the signal residual, iteratively extract new scattering point PPS signal components and estimate phase parameters until the residual reaches a predetermined threshold set according to previous data analysis or prior knowledge; In each iteration, the statistical features of signal-to-noise ratio, scattering point density and signal change rate are extracted, and after the corresponding data set is constructed, the average difference change rate of these features is calculated; Obtain the average difference change rate corresponding to the signal-to-noise ratio, scattering point density and signal change rate, analyze and process them, and construct a threshold fine-tuning index, which is used to provide a dynamic adjustment strategy for the predetermined threshold; The residual signal is further processed in the frequency domain, the frequency domain residual signal is inversely transformed to the time domain, and conjugate multiplied with the phase compensation function to obtain the time domain residual signal ; Set the PPS signal corresponding to each order component The threshold value is , when the PPS signal component When it is lower than the threshold value, the subsequent PPS signal components will be lower than the effective components and the iterative search will stop; Step S6: Use the co-evolutionary PSO algorithm to optimize the result of step S5; Step S7: The FDE-AJTF algorithm and the co-evolutionary PSO algorithm are simulated and verified with actual data, and the imaging quality is evaluated under different complex sea conditions using a ship target model with complex motion and real high-resolution satellite-borne SAR data.

[0007] Compared with the prior art, the beneficial effects of the present invention are as follows: through the dynamic adjustment strategy of the fine-tuning index, the system can flexibly adjust the cumulative energy ratio threshold according to the real-time signal conditions; it can ensure the robustness and processing accuracy of the system under different signal conditions; using the phase compensation function as the basis function and adding the time window parameter is more in line with the actual situation of the instability of the strong scattering point echo under high sea conditions, thereby improving the robustness of the algorithm; The steps of parameter estimation, residual update and component extraction are carried out in the frequency domain, and the search dimension is reduced, which improves the operation speed. On this basis, a parameterized time-frequency distribution construction method is proposed to reduce the influence of cross terms and side lobes. Finally, simulation and real data are used to verify that the image quality of ships under complex motion conditions can be significantly improved after processing with the FDE-AJTF algorithm. The boundaries and structures of ships can be clearly displayed, and the geometric characteristics, scattering characteristics and structural characteristics of ships can be obtained normally, effectively restoring the application capability of spaceborne SAR images in the ocean. It is able to estimate and extract multiple PPS signal components in parallel, further improving the search speed and global optimal search capability in time-frequency decomposition; the processing results of ship scattering point target models and real high-resolution satellite-borne SAR data show that the image quality of complex moving ships is significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 It is a schematic diagram of the overall method flow of the present invention; Figure 2 It is the flow chart of the PSO optimization algorithm of the present invention; Figure 3 It is the process diagram of the collaborative evolution PSO algorithm of the present invention; Figure 4 The original result of the satellite-borne SAR complex motion ship imaging of the present invention; Figure 5 The results of the collaborative evolution PSO algorithm are obtained using the real data of the present invention; Figure 6 The imaging processing result of the simulated moving point target of the present invention; Figure 7 The FDE-AJTF compensation process of the present invention adopts the collaborative evolution PSO algorithm to process the result. DETAILED DESCRIPTION

[0009] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0010] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0011] Embodiment 1: See also Figures 1 to 7 , the present invention provides a technical solution: The high-resolution spaceborne SAR ship target imaging processing method under complex sea conditions includes the following specific steps: The classic AJTF method is improved to obtain the adaptive joint time-frequency (Frequency Domain Extraction for Adaptive Joint Time-Frequency) imaging processing method. The following adaptive joint time-frequency imaging processing method is referred to as FDE-AJTF, which specifically includes: Step S1: Obtain the SAR echo signal data of the moving ship target in complex sea conditions, and define it as an mc-PPS signal output. The PPS signal component of a single strong scattering point is defined as ; Step S2: Constructing the phase compensation basis function of the echo signal The PPS signal components are conjugated, and the length and overlap of the time window parameters are adjusted to match the time-frequency characteristics of the target echo signal of the ship in high sea conditions. Step S3: Using phase compensation basis function The polynomial phase error is corrected to determine the polynomial phase error of the SAR echo signal in SAR imaging, and the conjugate inner product of the adjusted time window parameter and the SAR echo signal and the phase compensation basis function is used as the objective function; Multiply the SAR echo signal with the phase compensation basis function to obtain the compensation signal , for the compensation signal The result is transformed into the spectrum domain through Fourier transform to obtain the compensated spectrum in the image ; Step S4: Based on the signal corrected in step S3, the objective function of the adaptive joint time-frequency processing is defined as: ; in, Indicates the parameter item that needs to be estimated. After imaging processing, according to the maximum value of the spectrum The peak frequency point is determined by the location , thus obtaining the parameters , then complete the search for all remaining parameters, N is the total number of parameters; Indicated in the spectrum The frequency point corresponding to the maximum value , Indicates the PPS signal component Phase compensation basis functions The product of is Fourier transformed; Indicates that the first parameter of the estimate is the frequency point ; p represents the index mark of the PPT signal, c represents the index mark after compensation; FT represents Fourier transform; Will The energy in the main lobe area of ​​the signal spectrum peak is used as the signal component strength, while the energy in the area outside the main lobe of the sinc function is used as the residual signal; Through the optimal estimation algorithm, the estimated value of the PPS signal at each order component is obtained. The estimated value is defined as , the calculation formula is as follows: ; in, for The intensity component of the spectral maximum, is the optimal estimation result of each order component of the PPS signal, represents the polynomial order, and The change in time t is represented by t raised to the power of n, and j represents the imaginary unit; Step S5: based on the objective function result optimized in step S4, extract the PPS signal components of each scattering point, calculate the signal residual, and then perform the same processing on the signal residual, iteratively extract new scattering point PPS signal components and estimate phase parameters until the residual reaches a predetermined threshold set according to previous data analysis or prior knowledge; In each iteration, the statistical features of signal-to-noise ratio, scattering point density and signal change rate are extracted, and after the corresponding data set is constructed, the average difference change rate of these features is calculated; The residual signal is further processed in the frequency domain, the frequency domain residual signal is inversely transformed to the time domain, and conjugate multiplied with the phase compensation function to obtain the time domain residual signal ; Set PPS signal component The threshold value is , when the PPS signal component When it is lower than the threshold value, the subsequent PPS signal components will be lower than the effective components and the iterative search will stop; Step S6: Use the co-evolutionary PSO algorithm to optimize the output result of step S5, specifically including: a) Take the PPS signal component as the input initial particle swarm and define the particle number set as ,and Indicated in The first particle Particles; b) Set the initial fitness function and calculate the fitness of the m′th particle in the subgroup P; The particle position is defined as the position of the particle parameter set in the multidimensional solution space. The parameters include the start time, end time and polynomial coefficients of the PPS signal component and the second order or higher. c) Divide the input signal into three groups, The best group The suboptimal group is a random group, and the initial number of iterations is set to ; FDE-AJTF time-frequency decomposition compensates by searching for second-order and higher phase parameters, and extracts the maximum value of the spectrum of the phase compensation signal as the fitness evaluation function. The position of the peak is essentially the position of the azimuth scattering center point corresponding to the component signal; therefore, the position of the scattering center point is taken as the optimal group particle field, and is used as a distinction between the optimal group and the suboptimal group. d) Update the position of the particle group and divide the random group into two subgroups by random division; that is, , and merged with the optimal group and the suboptimal group into two new mixed groups respectively; among them, represents the random group in the g-th iteration, There are two corresponding subgroups; it can improve the randomness of the entire population and enable information exchange between the three subgroups; e) Update the fitness and the local best particle position of each group; f) According to the optimal fitness of the optimal subgroup and the suboptimal subgroup, the optimal population is replaced. If the maximum value of the local optimal fitness of the individuals in the suboptimal subgroup is greater than the maximum value of the optimal population, the two subgroups exchange roles; h) Determine whether the algorithm meets the stopping conditions, including the maximum number of iterations and the convergence of the global fitness results. The convergence is represented by the fitness ratio of the residual signals of the suboptimal solution before and after extraction. If the maximum number of iterations and the fitness ratio conditions are met, execute step i), otherwise, execute step e); i) Output the global optimal parameters of the optimal group and the suboptimal group to complete the algorithm estimation; Step S7: The FDE-AJTF algorithm and the co-evolutionary PSO algorithm are simulated and verified with actual data, and the imaging quality is evaluated under different complex sea conditions using a ship target model with complex motion and real high-resolution satellite-borne SAR data.

[0012] Further explanation: the SAR echo signal data of the moving ship target in complex sea conditions is obtained, and the time window length and overlap are adjusted to match the time-frequency characteristics of the high sea condition echo signal, and the conjugate inner product of the adjusted time window parameter, the SAR echo signal and the basis function is used as the objective function, specifically including: Data acquisition: Use a spaceborne synthetic aperture radar (SAR) system to detect moving ship targets, especially under complex sea conditions, such as high waves and strong winds in this embodiment, to obtain their echo signal data; since complex sea conditions may cause the signal to contain a variety of noise and unstable scattering point characteristics, it is necessary to pre-process the data; Initial time window settings: According to the sea conditions and target characteristics, an initial time window length and overlap are first set; the time window length is used to define the time period for signal analysis, while the overlap determines the proportion of overlapping areas between adjacent time windows; the initial time window length is usually estimated based on the frequency range of the signal, and the overlap is initially set to 50%, that is, 50% of the area of ​​adjacent windows is overlapped; Adaptive time window adjustment: Apply short-time Fourier transform (STFT) to the acquired SAR echo signal to obtain its time-frequency distribution diagram; by analyzing the signal characteristics on the time-frequency diagram, especially the changes in instantaneous bandwidth and center frequency, if it is found that the signal changes dramatically within a certain time window, it means that under high sea conditions, the scattering point echoes in the signal have greater instability; at this time, dynamically adjust the time window length and overlap to adapt to these characteristics of the signal; for example, if the bandwidth of the signal fluctuates greatly within a certain time window, the time window length will automatically increase to capture more stable signal characteristics, and the overlap will be reduced to avoid unnecessary computational redundancy; through multiple adjustments, the time window parameters are gradually optimized and finally reach a state that best matches the signal characteristics; Objective function construction and optimization: After each time window adjustment, the matching degree between the adjusted time window parameters and the SAR echo signal is calculated; this matching degree is evaluated by constructing an objective function, that is, the degree of conjugate interaction between the time window parameters and the signal and the preset basis function; during the optimization process, the value of the objective function will be continuously maximized, and finally the optimal time window length and overlap will be selected to achieve the best signal processing effect; Data output and preparation: Finally, the optimized and adjusted time window parameters are applied to SAR echo signal processing and basis function matching is performed; the processed signal will be output for subsequent phase error correction and frequency domain processing steps; this step ensures that the time-frequency characteristics of the signal are adapted to the dynamic environment under complex sea conditions, providing a more accurate and clear signal foundation; Further explanation: after receiving the SAR echo signal, the PPS signal component of each scattering point is extracted, and the signal residual is calculated. Then, the signal residual is processed in the same way, and the new scattering point PPS signal component and the estimated phase parameter are iteratively extracted until the residual reaches a predetermined threshold value set according to previous data analysis or prior knowledge, specifically including: A deterministic method based on cumulative energy ratio is selected to set a predetermined threshold for the residual error, and the energy of the current PPS signal component is calculated in each iteration. and accumulated energy , defining the cumulative energy ratio The calculation formula is: ; in, represents the energy of the PPS signal component extracted at the kth iteration step, represents the energy of the PPS signal component extracted at the i-th iteration; Represents the total energy of the target signal, which is defined as the sum of the energies of all extracted PPS signal components; Setting a predetermined threshold , that is, when When , the iteration stops; at this time, the energy of the residual signal does not exceed 1% of the target signal, ensuring that the residual mainly contains noise and invalid components; the specific steps are as follows: SAR echo signal received After that, the initial PPS signal components of each scattering point are obtained through preliminary processing. ; calculate Energy , and calculate the initial residual signal Energy ; In the kth iteration, extract the kth PPS signal component , calculate its energy And update the cumulative energy ratio ; Update the residual signal , and calculate its energy ; when or When it is lower than 1% of the total signal energy, the iteration stops; at this time, only weak noise and invalid components are retained in the residual signal.

[0013] Further explanation: in each iteration, the statistical features of signal-to-noise ratio, scattering point density and signal change rate are extracted, and after the corresponding data set is constructed, the average difference change rate of these features is calculated, including: The signal-to-noise ratio, scattering point density, and signal change rate are marked as SNR, SD, and SVR respectively; Set the number of iterations to , where k is the total number of iterations and i represents the index of the i-th iteration; In the i-th iteration, the signal-to-noise ratio is calculated as follows: ; in, is the signal energy at the i-th iteration; is the noise energy at the i-th iteration; The change in SNR difference between adjacent iterations is calculated as: ; Calculate all Average of the difference changes: ; in, is the average value of the signal-to-noise ratio difference change, is the change in SNR difference between adjacent iterations; In the i-th iteration, the scatter point density is calculated as follows: ; in, is the number of scattering points at the i-th iteration, and As is the fixed area covered by the signal; The SD difference between adjacent iterations is calculated as: ; Calculate all Average of the difference changes: ; in, is the average value of the density difference of the scattering points, is the change in the difference in scattering point density between adjacent iterations; In the i-th iteration, the signal change rate is calculated as follows: ; in, is the signal characteristic change at the i-th iteration; It is a fixed time interval; The SVR difference change between adjacent iteration steps is calculated as: ; Then, calculate all Average of the difference changes: ; in, is the average value of the difference in the signal change rate, It is the difference in the signal change rate between adjacent iterations.

[0014] Further explanation: the average difference change rate corresponding to the signal-to-noise ratio, the scattering point density and the signal change rate is obtained, and analyzed and processed to construct a threshold fine-tuning index, which is used to provide a dynamic adjustment strategy for the predetermined threshold, specifically including: Define the fine-tuning index as , the calculation formula is as follows: ; in, is the dynamically adjusted judgment value, e is the base of the natural logarithm, To prevent the constant from dividing by zero, in this embodiment, , The specific value is determined by the expert group through experimental data. is the time series normalization coefficient, is the amplitude of the PPS signal component at time t, is the total observation time, is the variance of the signal amplitude; According to previous data analysis or prior knowledge, determine , and The benchmark values ​​are , and ; like , and If the difference between any two values ​​in the table and the corresponding reference values ​​is more than 10%, The following calibration is performed; the 10% difference can be adjusted based on the expert group system through experimental demonstration and expert group analysis, and is not limited to the 10% value; ; in, ; is the fine-tuning index after calibration, Determined by the expert group through experimental data; Limited fine-tuning index The valid value range is , within this range, It means that signal processing allows for a larger noise component and is suitable for complex signal processing environments; Indicates that signal processing requires more stringent noise suppression and is suitable for high-quality signal processing scenarios; 1.1) Signal processing in complex environments: In complex sea conditions, the signal processing system needs to cope with the violent fluctuations of the ocean surface, multipath effects, wind and wave interference, and other electromagnetic noise sources, such as the influence of communication signals from other ships. In this environment, a certain degree of noise is allowed to exist, and the processing method focuses more on ensuring the detection and preliminary imaging of the target rather than the highest precision imaging quality. Quantitative criteria: The signal-to-noise ratio (SNR) is in the range of 10-20dB, a lower SNR is allowed, and the processing method is more tolerant to noise; Resolution requirements: The resolution requirements are relatively low, at the level of 5-10 meters, and the focus is on detecting the target rather than fine imaging; Image quality: the image may be noisy and blurry, but it is sufficient to identify large objects and obtain their rough location; 1.2) High-quality signal processing scenario: In relatively calm or standard sea conditions, the signal processing system pursues high-resolution and high-precision imaging, and strictly suppresses noise; this scenario requires high signal quality in order to clearly distinguish the details of the ship target, and is suitable for applications that require precise target recognition and classification; Quantitative standard: signal-to-noise ratio (SNR) above 20-30dB, a higher SNR is required to ensure accurate target imaging; Resolution requirements: high resolution, below 1 meter in the sub-meter level, to enable clear imaging and identification of target details; Image quality: clear images without obvious noise, able to accurately distinguish and identify different types of targets; when Value in range, the signal processing process allows more noise components; this means that the predetermined threshold The cumulative energy ratio can be reduced to between 95% and 99% for use in complex signal processing environments; The adjusted cumulative energy ratio threshold is , and is calculated by the following formula: ; Example: When hour, ; In this case, the cumulative energy ratio is required to be 79.2%, which allows a larger proportion of noise to remain, and is suitable for complex signal processing environments; If during signal processing, , then the cumulative energy ratio ; In this case, the signal processing will tolerate more noise and is used in scenarios with lower signal quality or higher noise components; when The value is range, the signal processing requires more stringent noise suppression; in this case, the predetermined threshold The cumulative energy ratio can be increased to between 100% and 149% to ensure higher signal quality, which is suitable for high-quality signal processing scenarios; Adjusted cumulative energy ratio threshold Calculated by the following formula: ; Example: When hour, ;In this case, the noise is strictly suppressed; If during signal processing, , then the cumulative energy ratio ; At this time, the signal processing will strictly suppress the noise to ensure that the signal residual is extremely low, which is suitable for high-quality signal processing scenarios; By fine-tuning the index Dynamic adjustment strategy, the system can flexibly adjust the cumulative energy ratio threshold according to real-time signal conditions ; Can ensure the robustness and processing accuracy of the system under different signal conditions.

[0015] The above experimental data analysis is as follows: This embodiment aims to verify a fine-tuning index based The advantages of the dynamic threshold adjustment method in complex signal processing; the experiment dynamically adjusts the predetermined threshold by measuring and calculating the signal-to-noise ratio (SNR), scattering point density (SD), and signal change rate (SVR) under different signal environments. , to optimize the effect of signal processing; Signal acquisition: Three signal sources of different complexity were selected, namely "signal source A, signal source D" (low noise environment), "signal source B, signal source E" (medium noise environment), and "signal source C, signal source F" (high noise environment); the frequency range of each signal source was between 1-10kHz, and the sampling rate was 100kHz; Initial parameter setting: Setting the baseline signal-to-noise ratio , scatter point density , signal change rate ; The application of the fine-tuning index formula is as follows: ; Calculate the fine-tuning index ;in , , the variance of the signal amplitude Calculated from actual signal data; Signal processing: Through the preset signal processing algorithm, the signal energy is extracted for each iteration, and the PPS signal component energy of each iteration is calculated. and total energy ; Then, calculate the cumulative energy ratio ,when When , stop the iteration and record the energy of the residual signal; Dynamic adjustment: Based on the measured , and The average rate of change of the difference, applying the fine-tuning index Predetermined threshold To make dynamic adjustments, the specific operations are as follows: when , and When the difference between any two values ​​and the reference value is more than 10%, calculate the fine-tuning index after calibration , and use the calibrated fine-tuning index to adjust the cumulative energy ratio threshold ; Calculate the corresponding processing of different signal sources Value, and record the cumulative energy ratio threshold after each processing ; Data recording and analysis: Each experiment records the average difference change rate of signal-to-noise ratio (SNR), scattering point density (SD), signal change rate (SVR) and the final Compare and analyze the experimental results of different signal sources to verify the fine-tuning index Improvement of signal processing effect; The experimental data record table is shown in Table 1 below: Table 1 Verification fine-tuning index Improvements to signal processing:

[0016] Data Analysis: For signal sources A and D, the signal-to-noise ratio is The difference from the benchmark value is less than 10%, resulting in a fine-tuning index The value of is low, indicating that the cumulative energy is higher than the threshold It can be adjusted to a range below 100% to meet more complex environmental requirements; For signal sources C and F, the noise is high and the signal-to-noise ratio Differences from the benchmark value exceeding 10% lead to fine-tuning the index Increase to above 1; at this time, the cumulative energy ratio threshold Adjusted to above 100% for tight noise suppression to ensure high quality signal processing; In the fine-tuning index calculation formula, Based on the signal-to-noise ratio , scatter point density and signal change rate The multiple nonlinear functions of the signal comprehensively consider multiple characteristic parameters to ensure the rationality and accuracy of the dynamic adjustment strategy; In the table, for signal sources C and F, Higher (3.5 and 3.4dB respectively), and is also correspondingly higher, resulting in Reaching 1.15 and 1.12, further calibration yields is 1.18 and 1.14; this directly affects the cumulative energy ratio threshold The rise of the two indexes reached 116.82% and 113.86% respectively; this indicates that the present invention can automatically improve the strictness of signal processing in the face of a high noise environment through dynamic adjustment of the fine-tuning index, thereby ensuring high-quality signal output; In contrast, the signal sources A and D lower (2.8 and 2.9 dB respectively), corresponding to and is also lower, which ultimately leads to They are 94.06% and 96.11% respectively; this dynamic adjustment strategy shows that in a low-noise environment, the system can reduce the cumulative energy ratio threshold, allowing more noise components to exist, thereby adapting to complex signal environments; In the dynamic adjustment strategy of the present invention, each parameter in the formula , and There is a close numerical correlation between ; for example: The increase in the fine-tuning index will directly increase ,because Multiply The value of plays an amplifying role in the exponential part of the formula; when When the increase is 10%, assuming other conditions remain unchanged, The changes will lead to The increase is between 5% and 15%, depending on the overall noise level of the signal; The increase in fine-tuning index The impact of is also significant; The existence of the function, Each percentage point increase in the index will double the change, further amplifying and increase in As part of the denominator, its decrease will increase Therefore, the reduction of the signal change rate indicates that the stability of the signal is enhanced, which will increase the tolerance to noise and thus reduce , which enables the system to have stronger noise suppression capability in high-stability signal processing; For example, in signal source C, When , and after calibration is 1.18; this change directly leads to the cumulative energy ratio threshold It rises to 116.82%, 17.82% higher than the standard 99%; this shows that the system can strictly control the signal quality by increasing the threshold in a high-noise environment to ensure the reliability of the output signal; From the analysis of the tables and formulas, it can be seen that the present invention has shown obvious innovation and advantages in the following aspects: Dynamic adaptability: , and Real-time monitoring and calculation, fine-tuning index Dynamic adjustment enables the system to adapt to signal environments of different complexity; whether in high-noise or low-noise environments, the system can adjust the cumulative energy ratio threshold according to current conditions , thereby optimizing the signal processing effect; Precise control: Compared with the traditional fixed threshold processing method, the present invention achieves precise control of the signal processing threshold by calculating and calibrating the fine-tuning index; the data in the table show that this method performs well in dealing with different signal environments and can find the best balance between noise control and signal fidelity; Enhanced robustness: By comparing the processing results of different signal sources, the present invention demonstrates higher robustness; whether in an environment with drastic signal changes or in a stable signal, the system can ensure the quality of the final processing result by adjusting the fine-tuning index; Further explanation: The calculation formula is as follows: ; Where A is the intensity value of the ship SAR echo signal; represents the standard rectangular window function, T is the time window width; is the phase constant term; It is a first-order linear term at time t, representing the actual position of the scattering center in the SAR image; It is an n-order linear term of time t, which is related to the complex motion of the target. It is the motion error phase that affects motion compensation and imaging focus, and needs to be decomposed and compensated; represents the polynomial order, j represents the imaginary unit, indicating that the signal has a complex phase; Phase compensation basis functions The calculation formula is: ; in, Indicates the time window, u and U are the center and width of the window; setting the time window can accurately fit the time of the real component, and select cosine window or rectangular window as needed; Since the synthetic aperture time of high-resolution spaceborne SAR is relatively long, and the strong reflection center of the ship changes with the rotation of the imaging angle and position, there will be occlusion or certain fluctuations in the intensity value; the time is not completely consistent with the entire synthetic aperture time, which will bring uncertain errors to the subsequent parameter estimation and component extraction; The calculation formula is as follows: ; in, is a complex constant representing the amplitude and initial phase of the signal; describes the phase component that varies with time; The calculation formula is as follows: ; in, represents Fourier transform; It represents the result of SAR secondary imaging, which is the frequency domain of the compensated signal; The maximum value in appears at the peak position and is represented by the sinc function, that is, , and the peak position represents the spatial position of the reflection point; Will The energy in the main lobe area of ​​the signal spectrum peak is used as the signal component strength, and the energy in the area outside the main lobe of the sinc function is used as the residual signal, which specifically includes: Set its frequency domain characteristics as follows: ; in, Indicates the peak frequency point The left and right neighborhood range is ;By expanding the scope of the neighborhood, the robustness of the algorithm is improved; The main lobe area refers to the area around the peak frequency of the spectrum. A symmetric interval ,in Indicates the width of the interval; The value of is set to 0 in the main lobe area, indicating that the PPS signal component in this frequency range is "shielded" or "removed"; Outside the main lobe area, i.e. "others", Keep the original spectrum Consistency; The calculation formula is as follows: ; in, represents inverse Fourier transform; Based on the estimated results The conjugate of the phase compensation function; other components can be gradually extracted from the residual function, and finally all PPS signal components are obtained. The linear addition of all PPS signal components is the mc-PPS signal; at the same time, in order to improve the efficiency during the extraction process, the PPS signal components are tested for validity by setting a threshold value. According to the FDE-AJTF time-frequency decomposition objective function analysis, the PPS signal components are extracted one by one according to the signal strength; The calculation formula is as follows; ; Where M represents the number of PPS signal components in the echo. represents the mth PPS signal component, represents the optimal estimate of each PPS signal component, and is the residual after extracting M PPS signal components.

[0017] To further illustrate, in view of the essence that the suboptimal solution extracted by the algorithm during the time-frequency decomposition of the mc-PPS signal is also the true solution, the following co-evolutionary PSO algorithm is designed. The components of the mc-PPS signal are independent of each other, so the extraction of each PPS signal component will not affect other components; the co-evolutionary PSO algorithm sets different permissions for each subgroup and adds a random group; co-evolution means that the excellent individuals found in the search are migrated between different subgroups as shared information to guide the progress of evolution, thereby significantly improving the global convergence efficiency of the algorithm; the co-evolutionary PSO algorithm combines the ability of PSO to explore search spaces with different priorities, and the population is divided into optimal group, suboptimal group and random group, each subgroup represents a subspace in the solution space, and also represents a problem Solution; The optimal group includes particles with the global optimal position, and the suboptimal group is artificially prohibited from using the neighborhood of the optimal group for iteration, which enhances the global optimal ability of the optimal group; The suboptimal group only limits the search area in the suboptimal solution, but retains the probability of particle position update for subgroup promotion; If after a certain iteration, the particles in the suboptimal group have the best fitness of all individuals in the world, the suboptimal group can be upgraded to the optimal group, which means that the problem of premature stagnation of particles has been solved; The random group has no best particles and is updated together with other groups, which improves the randomness of the entire population and the information exchange between groups; When the optimal group obtains the local optimal solution, the suboptimal group retains the ability to search for another optimal solution outside the area, so this method can extract multiple components at the same time, effectively improving the global convergence speed; The PPS signal component is used as the input initial particle swarm, including: the PPS signal component is the azimuth position of the scattering center extracted from the residual function, and other order components are gradually extracted from the residual function, and finally the PPS signal components of all particles are obtained; The fitness calculation formula is as follows: ; in, is the Fourier transform, is the residual signal to be processed, is the phase compensation function, and The parameters are related to is the fitness function, which is used to evaluate the performance of each particle in the solution space; Definition The parameter set calculation formula for each particle is as follows; ; in, Indicates The parameter set of a particle is a particle in the particle swarm optimization algorithm. is the high-order component parameter of the mc-PPS signal, and are the start time and end time of the time window respectively; due to the complex movement of the ship and the occlusion under high-resolution conditions, the reflection point signal will change, and some signals will appear or disappear within the synthetic aperture time; is the total number of particles, which mainly depends on the SAR image resolution and the size of the target; is the polynomial order of the input signal phase; Update the fitness and the local best particle position of each group, specifically including: according to the fitness function, find the global optimal and suboptimal particle fitness in their respective ranges, and update the parameters and scattering center position of the optimal particle; at the same time, limit the suboptimal group to search and update near the scattering center area; in the g-th generation update, The local and global optimal fitness values ​​of each particle are calculated as follows: ; in, Indicates that at the gth generation, The local optimal fitness value of a particle, Represents all position parameter records of the particle from the first iteration to the g iteration, Indicates the global optimal fitness value of the g-th generation in the global scope; Definition The positions of the particles at the current local and global optimal solutions are as follows: ; in, is the local optimal solution position of the current particle in the g-th generation iteration; is the position of the global optimal solution in the g-th iteration; The fittest survive according to the local fitness of the new mixed group of the random group. If the maximum fitness value of the local individual particles of the random subgroup is greater than the minimum fitness value of the local particles in the optimal group, the two particle individuals in the new mixed group of the random group are exchanged to perform the survival of the fittest. The same process is also applied to another mixed group of the random group. The expression for two subgroups to exchange roles is as follows: ; in, is the maximum value of the local optimal fitness of the suboptimal subgroup individuals, is the maximum value of the local optimal fitness of the individuals in the optimal population, symbol Indicates individual exchange groups; and are the particle numbers with the largest local optimal fitness in the two subgroups of the optimal group and the suboptimal group, namely: ; Iteration from , the three subgroups after the individuals and groups are updated in the previous steps As the Three subgroups corresponding to the generations; Determine whether the algorithm meets the stopping conditions, including the maximum number of iterations and the convergence of the global fitness results, including: The maximum number of iterations is set to 300, and the fitness ratio is set to 0.99. Since the co-evolutionary PSO algorithm can output multiple component solutions at the same time, if there is only one valid component left in the signal, the second component parameter is invalid and validity judgment is required; according to the parameter extraction process, if the suboptimal solution is a real component parameter, it is independent of the optimal solution, and the extraction of each component is not affected by the extraction of the other component; the fitness ratio as the basis for convergence judgment is defined as follows: ; in, represents the fitness of the suboptimal solution during the iteration process, is the fitness of the suboptimal solution and the residual signal after optimal component extraction, is the ratio of the fitness of the suboptimal solution before and after the optimal component extraction, is the judgment threshold, Indicates that it is effective. Indicates invalid, and the value of this embodiment is 0.99; according to the characteristics of the mc-PPS signal, when the suboptimal solution is not the true solution, its fitness will be affected by the extraction of the optimal component, so the ratio is low, and when the suboptimal solution is the true solution, the change in its fitness is small; therefore, by discriminating the parameters of the suboptimal solution, the invalid components can be excluded more accurately.

[0018] Further explanation: In summary, in view of the image problems of sub-meter satellite-borne SAR under high sea conditions, the phase compensation function is used as the basis function, and the time window parameter is added, which is more in line with the actual situation of unstable echoes of strong scattering points under high sea conditions and improves the robustness of the algorithm; the steps of parameter estimation, residual update and component extraction are carried out in the frequency domain, and the search dimension is reduced, which improves the operation speed; on this basis, a parameterized time-frequency distribution construction method is proposed to reduce the influence of cross terms and side lobes; finally, simulation and real data are used to verify that after processing with the FDE-AJTF algorithm, the image quality of ships under complex motion conditions can be significantly improved, the boundaries and structures of ships can be clearly displayed, and the geometric characteristics, scattering characteristics and structural characteristics of ships can be obtained normally, effectively restoring the application ability of satellite-borne SAR images in the ocean; Aiming at the conflicting constraints between the computational complexity and global convergence of the FDE-AJTF algorithm, a co-evolutionary particle swarm optimization (PSO) algorithm is proposed and applied to the high-resolution spaceborne SAR compensation processing; Aiming at the conflict of the FDE-AJTF algorithm due to the non-convex optimal calculation in the multi-dimensional solution space, this application proposes a co-evolutionary PSO algorithm based on the comparison of genetic algorithm and PSO algorithm, designs a particle swarm combination partitioning and information interaction sharing method, which can estimate and extract multiple PPS signal components in parallel, further improving the search speed and global optimal search capability in time-frequency decomposition; The processing results of the ship scattering point target model and the real high-resolution spaceborne SAR data show that the image quality of complex moving ships is significantly improved, and the overall processing speed is increased by more than 40%. The imaging results verify the efficiency and robustness of the co-evolutionary PSO algorithm; In order to verify the processing ability of the co-evolutionary particle swarm algorithm for high-resolution spaceborne SAR ship target data, a spaceborne SAR spatial geometric imaging simulation model is established; nine points on the edge of the hull are simulated for signal simulation, and the position of each point is [00, 1000, –1000, 5050, 500, 50–50, –5050, –500, –50–50], in meters, and the complex motion sea state of the simulated ocean background is level 5; the co-evolutionary PSO algorithm is evaluated from three aspects: point target simulation visualization effect, point target quality and processing speed; the specific imaging parameters and position information are as follows; Table 2 Main simulation parameters

[0019] In order to evaluate the algorithm's performance in processing actual data, real spaceborne SAR data was used for experimental processing. The data was imaged in sliding beam mode with a spatial resolution of 0.5 meters and the data product format was SLC data. Figure 4 For satellite-borne SAR ship images with complex motion, it can be found that in the original imaging results, due to the complex motion of the sea surface, the three-dimensional motion caused by the waves causes the hull to be seriously out of focus, and the ship target image is seriously divergent in azimuth, and there is no way to distinguish the structure and appearance characteristics of the ship. Even if this imaging result is interpreted by manual targets or based on deep learning algorithms, it will not be possible to obtain the typical parameters of the ship, and it is completely incapable of marine surveillance applications such as classification and recognition. After processing with the FDE-AJTF method optimized by the co-evolutionary PSO, the various phase components of the ship can be accurately extracted to obtain the scattering center information; the image maintains the basic outline of the ship and geometric information such as length and width, while the dense area of ​​scattering centers also reflects the scattering structure characteristics of the ship. It can be clearly seen from the image that there is a facility structure in the middle of the hull; and under the same HP820 workstation hardware configuration conditions, the time consumption of the co-evolutionary PSO algorithm is reduced by 42% compared with the standard PSO algorithm; the imaging results verify the efficiency and robustness of the co-evolutionary PSO algorithm; The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0020] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0021] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0022] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. The high-resolution spaceborne SAR ship target imaging processing method under complex sea conditions is improved on the basis of the AJTF algorithm to obtain an adaptive joint time-frequency imaging processing method. The following adaptive joint time-frequency imaging processing method is referred to as FDE-AJTF, which is characterized by: The specific steps include: Step S1: Obtain the SAR echo signal data of the moving ship target in complex sea conditions, and define it as an mc-PPS signal output. The PPS signal component of a single strong scattering point is defined as ; Step S2: Constructing the phase compensation basis function of the echo signal , conjugate the PPS signal component, and adjust the length and overlap of the time window parameters to match the time-frequency characteristics of the ship target echo signal in high sea conditions; Step S3: Using phase compensation basis function The polynomial phase error is corrected to determine the polynomial phase error of the SAR echo signal in SAR imaging, and the adjusted time window parameters are combined with the SAR echo signal and the phase compensation basis function. The conjugate inner product of is taken as the objective function; The SAR echo signal is combined with the phase compensation basis function Multiply to get the compensation signal , for the compensation signal The result is transformed into the spectrum domain through Fourier transform to obtain the compensated spectrum in the image ; Step S4: Based on the signal corrected in step S3, the objective function of the adaptive joint time-frequency processing is defined as: ； in, Indicates the parameter item that needs to be estimated. After imaging processing, according to the maximum value of the spectrum The peak frequency point is determined by the location , thus obtaining the parameters , and then complete the search for all remaining parameters, Indicated in the spectrum The frequency point corresponding to the maximum value , Indicates the PPS signal component Phase compensation basis functions The product of is Fourier transformed; Indicates that the first parameter of the estimate is the frequency point ; Will The energy in the main lobe area of ​​the signal spectrum peak is used as the signal component strength, while the energy in the area outside the main lobe of the sinc function is used as the residual signal; Through the optimal estimation algorithm, the estimated value of the PPS signal at each order component is obtained. The estimated value is defined as , the calculation formula is as follows: ; in, for The intensity component of the spectral maximum, is the optimal estimation result of each order component of the PPS signal, represents the polynomial order, and The change in time t is represented by t raised to the power of n, and j represents the imaginary unit; Step S5: based on the objective function result optimized in step S4, extract the PPS signal components of each scattering point, calculate the signal residual, and then perform the same processing on the signal residual, iteratively extract new scattering point PPS signal components and estimate phase parameters until the residual reaches a predetermined threshold set according to previous data analysis or prior knowledge; In each iteration, the statistical features of signal-to-noise ratio, scattering point density and signal change rate are extracted, and after the corresponding data set is constructed, the average difference change rate of these features is calculated; Obtain the average difference change rate corresponding to the signal-to-noise ratio, scattering point density and signal change rate, analyze and process them, and construct a threshold fine-tuning index, which is used to provide a dynamic adjustment strategy for the predetermined threshold; The residual signal is further processed in the frequency domain, the frequency domain residual signal is inversely transformed to the time domain, and conjugate multiplied with the phase compensation function to obtain the time domain residual signal ; Set PPS signal component The threshold value is , when the PPS signal component When it is lower than the threshold value, the subsequent PPS signal components will be lower than the effective components and the iterative search will stop; Step S6: Use the co-evolutionary PSO algorithm to optimize the output result of step S5; Step S7: The FDE-AJTF algorithm and the co-evolutionary PSO algorithm are simulated and verified with actual data, and the imaging quality is evaluated under different complex sea conditions using a ship target model with complex motion and real high-resolution satellite-borne SAR data.

2. The high-resolution spaceborne SAR ship target imaging processing method under complex sea conditions according to claim 1 is characterized by: Extract the PPS signal components of each scattering point and calculate the signal residual. Then perform the same processing on the signal residual, iteratively extract new scattering point PPS signal components and estimate phase parameters until the residual reaches a predetermined threshold set based on previous data analysis or prior knowledge, specifically including: A deterministic method based on cumulative energy ratio is selected to set a predetermined threshold for the residual error, and the energy of the current PPS signal component is calculated in each iteration. and accumulated energy , defining the cumulative energy ratio The calculation formula is: ； in, represents the energy of the PPS signal component extracted at the kth iteration step, represents the energy of the PPS signal component extracted at the i-th iteration; Represents the total energy of the target signal, which is specifically defined as the sum of the energies of all extracted PPS signal components; Set a predetermined threshold , that is, when When , the iteration stops; at this time, the energy of the residual signal does not exceed 1% of the target signal to ensure that the residual mainly contains noise and invalid components.

3. The high-resolution spaceborne SAR ship target imaging processing method under complex sea conditions according to claim 2 is characterized by: In each iteration, the statistical features of signal-to-noise ratio, scattering point density and signal change rate are extracted, and after the corresponding data set is constructed, the average difference change rate of these features is calculated, including: The signal-to-noise ratio, scattering point density, and signal change rate are marked as SNR, SD, and SVR respectively; Set the number of iterations to , where k is the total number of iterations and i represents the index of the i-th iteration; In the i-th iteration, the signal-to-noise ratio is calculated as follows: ； in, is the signal energy at the i-th iteration; is the noise energy at the i-th iteration; The change in SNR difference between adjacent iterations is calculated as: ； Calculate all Average of the difference changes: ； in, is the average value of the signal-to-noise ratio difference change, is the change in SNR difference between adjacent iterations; In the i-th iteration, the scatter point density is calculated as follows: ； in, is the number of scattering points at the i-th iteration, and As is the fixed area covered by the signal; The SD difference between adjacent iterations is calculated as: ； Calculate all Average of the difference changes: ； in, is the average value of the density difference of the scattering points, is the change in the difference in scattering point density between adjacent iterations; In the i-th iteration, the signal change rate is calculated as follows: ； in, is the signal characteristic change at the i-th iteration; It is a fixed time interval; The SVR difference change between adjacent iteration steps is calculated as: ； Calculate all Average of the difference changes: ； in, is the average value of the difference in the signal change rate, It is the difference in the signal change rate between adjacent iterations.

4. The high-resolution spaceborne SAR ship target imaging processing method under complex sea conditions according to claim 3 is characterized by: The average difference change rate corresponding to the signal-to-noise ratio, scattering point density and signal change rate is obtained, and analyzed and processed to construct a threshold fine-tuning index. The fine-tuning index is used to provide a dynamic adjustment strategy for the predetermined threshold, including: Define the fine-tuning index as , the calculation formula is as follows: ； in, is the dynamically adjusted judgment value, e is the base of the natural logarithm, To prevent division by zero, is the time series normalization coefficient, is the amplitude of the PPS signal component at time t, is the total observation time, is the variance of the signal amplitude; According to previous data analysis or prior knowledge, determine , and The benchmark values ​​are , and ; like , and If the difference between any two values ​​in the table and the corresponding reference values ​​is more than 10%, Perform the following calibration; ； in, ; is the fine-tuning index after calibration, and limits the fine-tuning index The valid value range is , within this range, It means that signal processing allows for a larger noise component and is suitable for complex signal processing environments; Indicates that signal processing requires more stringent noise suppression and is suitable for high-quality signal processing scenarios; when Value in In the range, the signal processing process allows a standard amount of noise components to be used in complex signal processing environments. The adjusted cumulative energy ratio threshold is , and is calculated by the following formula: ； when Value in When the signal processing process requires more stringent noise suppression, it is used in high-quality signal processing scenarios, and the adjusted cumulative energy ratio threshold pass Formula calculation.

5. The high-resolution spaceborne SAR ship target imaging processing method under complex sea conditions according to claim 4 is characterized in that: Will The energy in the main lobe area of ​​the signal spectrum peak is taken as the signal component strength, and The energy outside the main lobe of the function is taken as the residual signal, which includes: Set the frequency domain characteristics as follows: ； in, Indicates the peak frequency point The left and right neighborhood range is , T is the time window width; The main lobe area refers to the area around the peak frequency of the spectrum. A symmetric interval ,in Indicates the width of the interval; The value of is set to 0 in the main lobe area, indicating that the PPS signal component in this frequency range is "shielded" or "removed"; others means outside the main lobe area, Keep the original spectrum Consistent.

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