High-Resolution Spaceborne SAR Ship Target Imaging Processing Method under Complex Sea Conditions

Abstract

The present invention provides a method for imaging processing of ship targets by high-resolution spaceborne SAR under complex sea conditions, which relates to the technical field of imaging processing. By dynamically adjusting the fine-tuning index strategy, the system can flexibly adjust the cumulative energy ratio threshold according to real-time signal conditions, introduce the phase compensation function as the basis function and increase the time window parameter, making it more adaptable to the instability of strong scatterer echoes under high sea conditions, and further improving the robustness of the algorithm; the parameter estimation, residual update and component extraction steps are carried out in the frequency domain, reducing the search dimension and improving the operation speed; the parametric time-frequency distribution construction method effectively reduces the influence of cross terms and sidelobes; the system can parallelly estimate and extract multi-PPS signal components, further improving the search efficiency and global optimal ability of time-frequency decomposition.

Description

Technical Field

[0001] The present invention relates to the field of imaging processing technology, and particularly to a method for imaging processing of high-resolution spaceborne SAR ship targets under complex sea conditions. Background Art

[0002] When the size of the ship target is large or in high-resolution spaceborne SAR imaging, especially under complex sea conditions, the movement of the target is extremely complex. The Doppler frequency of the original echo signal has time-varying characteristics, and the Doppler phases of different scattering points of the target will change greatly with the azimuth time. Existing autofocus methods will not be able to obtain good imaging effects. Analyzing from the perspective of the echo signal, such movement introduces high-order Doppler phases into the spaceborne SAR radar echo, which can be expressed in the form of a Polynomial Phase Signal (PPS). In the case of high resolution and large-size targets, the echo pulse of a spaceborne SAR often contains echo components of several scattering centers. At this time, the echo signal is called a Multicomponent Polynomial Phase Signal (mc-PPS);

[0003] At the same time, as an improved maximum likelihood method, the Adaptive Joint Time-Frequency (AJTF) method has a key process of finding optimal parameters in the solution space, which is essentially a multi-dimensional optimization problem with different extreme solutions; however, in the time-frequency decomposition processing of multi-component polynomial phase signals, since the extreme solutions are not exactly the true value solutions corresponding to PPS signal components of different intensities, it is easy to fall into local maximum values. In terms of algorithm efficiency, it is necessary to solve the contradiction between the parameter search speed and global convergence, which makes the decomposition optimization problem more complex. This non-convex optimal problem requires a large amount of calculation, resulting in limitations of the algorithm in practical applications. Therefore, it is necessary to apply an optimization algorithm to the AJTF decomposition process to accelerate the processing speed;

[0004] In the prior art, the publication number is CN106772373A, and the name is a SAR imaging method for any ground moving target, which mainly solves the problem of defocusing of moving targets caused by range migration in the prior art, including:

[0005] 1) Construct a side-looking radar echo signal model;

[0006] 2) After performing range pulse compression on the target echo signal, transform it to the range frequency domain;

[0007] 3) Construct a reference function from the range frequency domain expression of the pulse compression signal;

[0008] 4) Take the complex conjugate of the reference function and multiply it by the range-frequency domain expression of the slow target pulse compression signal, and then perform a Fourier transform on the result in the azimuth direction to obtain the two-dimensional range-azimuth frequency domain;

[0009] 5) Perform an inverse Fourier transform in the range direction on the two-dimensional frequency domain to obtain the target signal amplitude spectrum;

[0010] There is no need to estimate the parameters of the target. Only using the fast Fourier transform can achieve good imaging effects without interpolation operations, and it can be used for the tracking and recognition of any unknown moving target.

[0011] In the prior art, the publication number is CN115236626A, and the name is a multi-channel radar sea clutter suppression method and system based on time-frequency analysis. It also discloses a multi-channel radar sea clutter suppression method and system based on time-frequency analysis, including:

[0012] Step S1: Calculate the coherence time of the sea clutter using the sea state series;

[0013] Step S2: Window the sea clutter data using the coherence time of the sea clutter and transform the sea clutter data into the time-frequency domain;

[0014] Step S3: Suppress the sea clutter using the subspace projection method based on the time-domain sliding window in the time-frequency domain;

[0015] Step S4: Restore the suppressed clutter data to the post-Doppler domain and then perform the second-stage suppression;

[0016] It can achieve effective compensation for multi-channel radar sea clutter, fully considering the time decoherence characteristics of the sea clutter, so as to achieve more robust suppression of the sea clutter in the space-time-frequency domain.

[0017] Therefore, the accurate estimation of the mc-PPS signal is the key to realizing the imaging of complex moving targets by spaceborne SAR;

[0018] Existing deficiencies: How to design an optimization algorithm that can further improve the search speed and global optimal search ability in time-frequency decomposition, speed up the processing speed, and on this basis, propose a method for constructing a parametric time-frequency distribution to reduce the influence of cross terms and sidelobes;

[0019] How to adapt to signal environments with different complexities; Whether in a high-noise or low-noise environment, the system can adjust the predetermined threshold of the cumulative energy ratio according to the current conditions, thereby optimizing the signal processing effect;

[0020] Regarding the co-evolutionary particle swarm optimization (PSO) algorithm used for the contradictory constraints between computational complexity and global convergence in the AJTF algorithm, how to design a particle swarm combination partitioning and information interaction sharing method to estimate and extract multiple PPS signal components in parallel, and further improve the search speed and global optimal search ability in time-frequency decomposition is the direction that needs to be improved currently;

[0021] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0022] The object of the present invention is to provide a high-resolution spaceborne SAR ship target imaging processing method under complex sea conditions to solve the problems raised in the above background art.

[0023] To achieve the above object, the present invention provides the following technical solutions:

[0024] A high-resolution spaceborne SAR ship target imaging processing method under complex sea conditions is improved based on the AJTF algorithm to obtain an adaptive joint time-frequency imaging processing method, hereinafter referred to as FDE-AJTF for short. The specific steps include:

[0025] Step S1: Obtain the SAR echo signal data of a moving ship target under complex sea conditions and output it in the form of mc-PPS signal. Define the PPS signal component of a single strong scattering point as ;

[0026] Step S2: Construct the phase compensation basis function , take the conjugate of the PPS signal component, and adjust the length and overlap degree of the time window parameter to match the time-frequency characteristics of the high-sea-condition ship target echo signal;

[0027] Step S3: Use the phase compensation basis function to correct the polynomial phase error, determine the polynomial phase error of the SAR echo signal in SAR imaging, and take the conjugate inner product of the adjusted time window parameter with the SAR echo signal and the phase compensation basis function as the objective function;

[0028] Multiply the SAR echo signal by the phase compensation basis function to obtain the compensated signal , and perform Fourier transform on the result of the compensated signal to the frequency domain to obtain the compensated spectrum in the image ;

[0029] Step S4: Based on the signal corrected in Step S3, define the objective function for adaptive joint time-frequency processing as follows:

[0030] ;

[0031] where, represents the parameter term to be estimated. After imaging processing, the peak frequency point is determined according to the occurrence position of the maximum value in the spectrum, so as to obtain the parameter . Then, the search for all the remaining parameters is completed. represents the frequency point corresponding to the maximum value in the spectrum . , represents the Fourier transform of the product of the PPS signal component and the phase compensation basis function ; represents that the first estimated parameter is the frequency point ;

[0032] Take the energy in the main lobe region of the signal spectrum peak in

[0033] as the component intensity of the signal, and the energy in the region outside the main lobe of the sinc function as the residual signal; Through the optimal estimation algorithm processing, the estimated values of the PPS signal in each order component are obtained. The estimated values are defined as

[0034] ;

[0035] where, is the intensity component of the spectrum maximum value, is the optimal estimation result of each order component of the PPS signal, represents the polynomial order, and the change of

[0036] at time t is represented by the nth power of t, and j represents the imaginary unit;

[0037] Step S5: Based on the result of the objective function 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 the PPS signal components of new scattering points and estimate the phase parameters until the residual reaches a predetermined threshold set according to past data analysis or prior knowledge;

[0038] Obtain the average difference change rate corresponding to the signal-to-noise ratio, scatter point density, and signal change rate, and perform analysis and processing to construct a threshold fine-tuning index, which is used to provide a dynamic adjustment strategy for a predetermined threshold;

[0039] Further process the residual signal, process the residual signal in the frequency domain, inverse transform the frequency-domain residual signal to the time domain, and conjugate multiply it with the phase compensation basis function to obtain the time-domain residual signal ;

[0040] Set the threshold values for each order component corresponding to the PPS signal to be When the PPS signal component is lower than the threshold value, subsequent PPS signal components will be lower than the effective components and the iterative search will stop;

[0041] Step S6: Use the co-evolutionary PSO algorithm to optimize the result of Step S5;

[0042] Step S7: Perform simulation and actual data verification on the FDE-AJTF algorithm and the co-evolutionary PSO algorithm, and use the ship target model with complex motion and real high-resolution spaceborne SAR data to evaluate the imaging quality under different complex sea conditions.

[0043] Compared with the prior art, the beneficial effects of the present invention are: through the dynamic adjustment strategy of the fine-tuning index, the system can flexibly adjust the cumulative energy ratio threshold according to 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, it is more in line with the actual situation of the unstable echo of strong scatter points under high sea conditions, improving the robustness of the algorithm;

[0044] Perform steps such as parameter estimation, residual update, and component extraction in the frequency domain, reduce the search dimension, and improve the operation speed; on this basis, a parametric time-frequency distribution construction method is proposed to reduce the influence of cross terms and sidelobes; finally, through simulation and real data verification, after processing with the FDE-AJTF algorithm, the image quality of ships under complex motion conditions can be significantly improved, the ship boundaries and structures can be clearly displayed, and the geometric characteristics, scattering characteristics, and structural characteristics of the ships can be normally obtained, effectively restoring the application ability of spaceborne SAR images in the ocean aspect;

[0045] It can estimate and extract multiple PPS signal components in parallel, further improving the search speed and global optimal search ability in time-frequency decomposition; the processing results of the ship scatter point target model and real high-resolution spaceborne SAR data show that the image quality of ships with complex motion has been significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 Schematic diagram of the overall method flow of the present invention;

[0047] Figure 2 Flowchart of the PSO optimization algorithm of the present invention;

[0048] Figure 3 Process diagram of the co-evolutionary PSO algorithm of the present invention;

[0049] Figure 4 Original result of imaging of complex moving ships by spaceborne SAR of the present invention;

[0050] Figure 5 Result after applying the co-evolutionary PSO algorithm to the real data of the present invention;

[0051] Figure 6 Imaging processing result of simulated moving point targets of the present invention;

[0052] Figure 7 Processing result of the FDE-AJTF compensation process using the co-evolutionary PSO algorithm of the present invention. Detailed implementation manners

[0053] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to specific embodiments.

[0054] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those of ordinary skill in the field to which the present invention belongs. The "first", "second" and similar terms used in the present invention do not denote any order, quantity or importance, but are only used to distinguish different components. The terms such as "comprising" or "including" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left" and "right" are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly. Embodiment

[0055] Please refer to Figures 1 to 7 , the present invention provides a technical solution:

[0056] A method for imaging ship targets by high-resolution spaceborne SAR under complex sea conditions, the specific steps include:

[0057] Improve the classical AJTF method to obtain an adaptive joint time-frequency (FrequencyDomainExtractionforAdaptiveJointTime-Frequency) imaging processing method, hereinafter referred to as FDE-AJTF for short, which specifically includes:

[0058] Step S1: Obtain the SAR echo signal data of a moving ship target under complex sea conditions and output it in the form of mc-PPS signal. Define the PPS signal component of a single strong scattering point as ;

[0059] Step S2: Construct the phase compensation basis function of the echo signal , take the conjugate of the PPS signal component, and adjust the length and overlap degree of the time window parameter to match the time-frequency characteristics of the high-sea condition ship target echo signal;

[0060] Step S3: Use the phase compensation basis function to correct the polynomial phase error, determine the polynomial phase error of the SAR echo signal in SAR imaging, and use the adjusted time window parameter and the conjugate inner product of the SAR echo signal and the phase compensation basis function as the objective function;

[0061] Multiply the SAR echo signal by the phase compensation basis function to obtain the compensated signal , and perform Fourier transform on the result of the compensated signal to the frequency domain to obtain the compensated spectrum in the image ;

[0062] Step S4: Based on the signal corrected in Step S3, define the objective function of the adaptive joint time-frequency processing as:

[0063] ;

[0064] wherein, represents the parameter term to be estimated. After imaging processing, determine the peak frequency point according to the position where the spectrum maximum value appears, so as to obtain the parameter , and then complete the search for all the remaining parameters. N is the total number of parameters; represents the frequency point corresponding to the maximum value in the spectrum , represents the Fourier transform of the product of the PPS signal component and the phase compensation basis function ; represents that the first estimated parameter is the frequency point ​;

[0065] p represents the index marker of the PPT signal, and c represents the index marker after compensation; FT represents the Fourier transform;

[0066] Take The energy in the main lobe region of the signal spectrum peak in as the component intensity of the signal, and the energy in the region outside the main lobe of the sinc function as the residual signal;

[0067] Through the optimal estimation algorithm processing, the estimated values of the PPS signal in each order component are obtained, and the estimated value is defined as , and the calculation formula is as follows:

[0068] ;

[0069] Among them, is The intensity component of the spectrum maximum value, is the optimal estimation result of each order component of the PPS signal, represents the polynomial order, and The change of at time t is represented by the nth power of t, and j represents the imaginary unit;

[0070] Step S5: Based on the result of the objective function 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 the PPS signal components of new scattering points and the estimated phase parameters until the residual reaches a predetermined threshold set according to past data analysis or prior knowledge;

[0071] In each iteration process, extract the statistical features of the signal-to-noise ratio, scattering point density, and signal change rate, and after constructing the corresponding data set, calculate the average difference change rate of these features;

[0072] Perform further processing on the residual signal, process the residual signal in the frequency domain, inverse transform the frequency domain residual signal to the time domain, and conjugate multiply it with the phase compensation basis function to obtain the time domain residual signal ;

[0073] Set the threshold value of the PPS signal component as , when the PPS signal component is lower than the threshold value, the subsequent PPS signal components will be lower than the effective components and the iterative search will stop;

[0074] Step S6: Use the co-evolutionary PSO algorithm to optimize the output result of step S5, specifically including:

[0075] a) Take the PPS signal component as the input initial particle swarm, and define the set of the number of particles as and represents the -th particle among particles;

[0076] b) Set the initial fitness function and calculate the fitness of the

[0077] -th particle in the subgroup P. Define the particle position as the position of the particle's parameter set in the multi-dimensional solution space, and the parameters include the start time, end time of the PPS signal component, and polynomial coefficients of the second order and above;

[0078] c) Divide the input signal into three groups, is the optimal group, is the sub-optimal group, is the random group, and set the initial number of iterations as ;

[0079] The FDE-AJTF time-frequency decomposition is compensated by searching for phase parameters of the second order and above. The maximum value of the spectrum of the phase-compensated signal is used as the fitness evaluation function for extraction. The position of this peak is essentially the azimuth scattering center point position corresponding to the component signal; therefore, the scattering center point position is used as the neighborhood of the optimal group particles and used as the basis for distinguishing the optimal group and the sub-optimal group;

[0080] d) Update the position of the particle swarm. Through random division, divide the random group into two subgroups; that is, , and merge them with the optimal group and the sub-optimal group respectively into two new mixed groups; among them, represents the random group in the -th iteration, and

[0081] are the corresponding two subgroups; this can improve the overall randomness of the population and enable information interaction between the three subgroups;

[0082] e) Update the fitness and the local best particle positions of each group;

[0083] h) Judge whether the algorithm meets the stop conditions, including the maximum number of iterations and the convergence of the global fitness result. The convergence is represented by the fitness ratio of the residual signals of the sub-optimal solutions before and after extraction. If the maximum number of iterations and the fitness ratio conditions are met, execute step i), otherwise, execute step e);

[0084] i) Output the global best parameters of the optimal group and the sub-optimal group to complete the algorithm estimation;

[0085] Step S7: Simulate and verify the FDE-AJTF algorithm and the co-evolutionary PSO algorithm, and evaluate the imaging quality under different complex sea conditions by using the ship target model with complex motion and real high-resolution spaceborne SAR data.

[0086] Further explanation: Obtain the SAR echo signal data of the moving ship target under complex sea conditions. By adjusting the time window length and overlap degree, make it match the time-frequency characteristics of the high sea state echo signal, and use the conjugate inner product of the adjusted time window parameters, SAR echo signal and basis function as the objective function, specifically including:

[0087] Data acquisition:

[0088] Use a spaceborne synthetic aperture radar (SAR) system to detect a moving ship target, especially under complex sea conditions. In this embodiment, the sea conditions are high waves and strong winds, and obtain its echo signal data; since complex sea conditions will cause the signal to contain various noises and non-stable scattering point characteristics, it is necessary to preprocess the data;

[0089] Initial time window setting:

[0090] According to the sea conditions and target characteristics, first set an initial time window length and overlap degree; the time window length is used to define the time period for signal analysis, while the overlap degree determines the overlapping area ratio between adjacent time windows; the initial time window length is usually estimated according to the frequency range of the signal, and the overlap degree is initially set to 50%, that is, 50% of the area of adjacent windows overlaps;

[0091] Adaptive time window adjustment:

[0092] Apply the short-time Fourier transform (STFT) to the acquired SAR echo signal to obtain its time-frequency distribution map; by analyzing the signal characteristics on the time-frequency map, especially the changes in the instantaneous bandwidth and center frequency, if it is found that the signal in a certain time window changes violently, this means that under high sea conditions, the echo of the scattering points in the signal has great instability; at this time, dynamically adjust the time window length and overlap degree to adapt to these characteristics of the signal; for example, if the bandwidth of the signal fluctuates greatly in a certain time window, the time window length will automatically increase to capture more stable signal characteristics, and at the same time the overlap degree will decrease to avoid unnecessary computational redundancy; through multiple adjustments, the time window parameters are gradually optimized and finally reach the state that best matches the signal characteristics;

[0093] Objective function construction and optimization:

[0094] After each time window adjustment, calculate the matching degree between the adjusted time window parameters and the SAR echo signal; evaluate this matching degree by constructing an objective function, that is, the degree of interaction between the time window parameters and the conjugate of 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 degree are selected to achieve the best signal processing effect;

[0095] Data output and preparation:

[0096] Finally, apply the optimized time window parameters to SAR echo signal processing and perform basis function matching; 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 basis;

[0097] Furthermore, after receiving the SAR echo signal, 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 the new scattering point PPS signal components and estimate the phase parameters until the residual reaches a predetermined threshold set according to past data analysis or prior knowledge, specifically including:

[0098] Select a deterministic method based on the cumulative energy ratio to set the predetermined threshold of the residual, and calculate the energy of the current PPS signal component in each iteration and the cumulative energy , define the cumulative energy ratio The calculation formula is:

[0099] ;

[0100] where represents the energy of the PPS signal component extracted in the k-th iteration step, represents the energy of the PPS signal component extracted in the i-th iteration; represents the total energy of the target signal, defined as the sum of the energies of all extracted PPS signal components;

[0101] Set the predetermined threshold , that is, when , the iteration stops; at this time, the energy ratio 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:

[0102] After receiving the SAR echo signal s(t), first obtain the initial PPS signal components of each scattering point through preliminary processing ;

[0103] Calculate energy of , while calculating the initial residual signal energy ;

[0104] In the k-th iteration, extract the k-th PPS signal component , calculate its energy and update the cumulative energy ratio ;

[0105] Update the residual signal , and calculate its energy ;

[0106] When or is lower than 1% of the total signal energy, stop the iteration; at this time, only weak noise and invalid components are retained in the residual signal.

[0107] Furthermore, in each iteration process, extract the statistical features of the signal-to-noise ratio, scatter point density, and signal change rate, and after constructing the corresponding data set, calculate the average difference change rate of these features, specifically including:

[0108] Label the signal-to-noise ratio, scatter point density, and signal change rate as SNR, SD, and SVR in sequence;

[0109] Set the iteration number set as {1, 2,..., i,..., k}, where k is the total number of iterations, and i represents the index of the i-th iteration;

[0110] In the i-th iteration number, calculate the signal-to-noise ratio as follows:

[0111] ;

[0112] Among them, is the signal energy at the i-th iteration; is the noise energy at the i-th iteration;

[0113] Calculate the SNR difference change amount between adjacent iteration numbers as:

[0114] ;

[0115] Calculate the average of all difference changes:

[0116] ;

[0117] Among them, is the average value of the SNR difference change, is the SNR difference change amount between adjacent iteration numbers;

[0118] In the i-th iteration, the scattering point density is calculated as follows:

[0119] ;

[0120] where, is the number of scattering points at the i-th iteration, and As is the fixed area covered by the signal;

[0121] The change amount of the SD difference between adjacent iterations is calculated as:

[0122] ;

[0123] Calculate the average of all difference changes:

[0124] ;

[0125] where, is the average value of the change in the scattering point density difference, is the change amount of the scattering point density difference between adjacent iterations;

[0126] In the i-th iteration, the signal change rate is calculated as follows:

[0127] ;

[0128] where, is the change in the signal characteristics at the i-th iteration; is the fixed time interval;

[0129] The change amount of the SVR difference between adjacent iteration steps is calculated as:

[0130] ;

[0131] Then, calculate the average of all difference changes:

[0132] ;

[0133] where, is the average value of the change in the signal change rate difference, is the change amount of the signal change rate difference between adjacent iterations.

[0134] Further explanation: Obtain the average difference change rates corresponding to the signal-to-noise ratio, scattering point density, and signal change rate, and perform analysis and processing to construct a threshold fine-tuning index. The fine-tuning index is used to provide a dynamic adjustment strategy for a predetermined threshold, specifically including:

[0135] Define the fine-tuning index as , and the calculation formula is as follows:

[0136] ;

[0137] Among them, is a dynamically adjusted judgment value, e is the base of the natural logarithm, is a constant to prevent division by zero. In this embodiment, , The specific value is determined according to the experimental data by the expert group, is the time series normalization coefficient, and f(t) is the amplitude of the PPS signal component at time t, is the total observation time, is the variance of the signal amplitude;

[0138] According to the analysis of past data or prior knowledge, the reference values of and are determined to be and ;

[0139] If and The difference between any two values in and the corresponding reference value is more than 10%, then is calibrated as follows; 10% of the difference amplitude can be adjusted based on the experimental demonstration by the expert group system and the analysis by the expert group, and is not limited to this 10% value;

[0140] ;

[0141] Among them, ; is the fine-tuning index after calibration, Determined according to the experimental data by the expert group;

[0142] The effective value range of the fine-tuning index is limited to [0.5, 1.5]. Within this range, [0.5, 1) indicates that the signal processing allows a larger noise component and is applicable to complex signal processing environments; [1, 1.5] indicates that the signal processing requires stricter noise suppression and is applicable to high-quality signal processing situations;

[0143] 1.1) Signal processing in complex environments:

[0144] In complex sea conditions, the signal processing system needs to cope with the violent fluctuations on the ocean surface, multipath effects, wind and wave interference, and the influence of other electromagnetic noise sources, such as the communication signals of other ships; in such an 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;

[0145] Quantification standard: The signal-to-noise ratio (SNR) is in the range of 10 - 20 dB, allowing a relatively low SNR, and the processing method is more tolerant of noise;

[0146] Resolution requirement: The resolution requirement is relatively low, at the level of 5 - 10 meters. The focus is on detecting the target rather than fine imaging;

[0147] Image quality: There is a probability of noise and blurring in the image, but it is sufficient to identify large targets and obtain their approximate positions;

[0148] 1.2) High-quality signal processing scenario:

[0149] 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 a relatively high signal quality to clearly distinguish the details of ship targets and is suitable for applications that require precise target recognition and classification;

[0150] Quantification standard: The signal-to-noise ratio (SNR) is above 20 - 30 dB, requiring a relatively high SNR to ensure precise target imaging;

[0151] Resolution requirement: The resolution requirement is high, below 1 meter at the sub-meter level, in order to clearly image and identify the details of the target;

[0152] Image quality: The image is clear, without obvious noise, and can precisely distinguish and identify different types of targets;

[0153] When takes values in the range of [0.5, 1), the signal processing process allows more noise components; this means that the cumulative energy ratio of the predetermined threshold can be reduced to between 95% and 99% for use in complex signal processing environments;

[0154] The adjusted cumulative energy ratio threshold is and is calculated by the following formula:

[0155] ;

[0156] Example: When , ; in this case, the cumulative energy ratio requirement is 79.2%, allowing a larger proportion of noise residue, suitable for complex signal processing environments;

[0157] If during the signal processing process, , then the cumulative energy ratio ; at this time, the signal processing will tolerate more noise and is used for scenarios with relatively low signal quality or higher noise components;

[0158] When When the value is in the range of [1, 1.5], the signal processing process requires stricter noise suppression; at this time, the predetermined threshold The cumulative energy ratio can be increased to between 100% and 149% to ensure higher signal quality and is applicable to high-quality signal processing scenarios;

[0159] The adjusted cumulative energy ratio threshold is calculated by the following formula:

[0160] ;

[0161] Example: When then ; in this case, noise is strictly suppressed;

[0162] If during the signal processing process , then the cumulative energy ratio ; at this time, the signal processing will strictly suppress noise to ensure a very low signal residual and is applicable to high-quality signal processing scenarios;

[0163] By fine-tuning the dynamic adjustment strategy of the exponent , the system can flexibly adjust the cumulative energy ratio threshold according to the real-time signal conditions; this can ensure the robustness and processing accuracy of the system under different signal conditions.

[0164] The experimental data analysis of the above content is as follows:

[0165] This embodiment aims to verify the advantages of a dynamic threshold adjustment method based on fine-tuning the exponent in complex signal processing; the experiment measures and calculates the signal-to-noise ratio (SNR), scatter point density (SD), and signal change rate (SVR) in different signal environments, and dynamically adjusts the predetermined threshold to optimize the signal processing effect;

[0166] Signal acquisition: Select three signal sources with different complexities, 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 is between 1 - 10 kHz, and the sampling rate is 100 kHz;

[0167] Initial parameter settings: Set the reference signal-to-noise ratio , scatter point density , signal change rate ;

[0168] The application of the fine-tuning exponent formula is according to the following formula:

[0169] ;

[0170] Calculate the fine-tuning index ; where , , the variance of the signal amplitude is calculated from the actual signal data;

[0171] Signal processing: Through a preset signal processing algorithm, extract the signal energy for each iteration, and calculate the energy of the PPS signal component for each iteration and the total energy ; Subsequently, calculate the cumulative energy ratio , when , stop the iteration and record the energy of the residual signal;

[0172] Dynamic adjustment: According to the average difference change rate of and measured during each iteration, apply the fine-tuning index to dynamically adjust the predetermined threshold , and the specific operation is as follows:

[0173] When and the difference amplitudes between any two values and the reference value are both more than 10%, calculate the calibrated fine-tuning index , and use the calibrated fine-tuning index to adjust the cumulative energy ratio threshold ;

[0174] For the processing of different signal sources, calculate the corresponding value respectively, and record the cumulative energy ratio threshold after each processing;

[0175] Data recording and analysis: Each experiment records the average difference change rate of the signal-to-noise ratio (SNR), scatter point density (SD), signal change rate (SVR) and the final value; Compare and analyze the experimental results of different signal sources to verify the improvement of the fine-tuning index on the signal processing effect;

[0176] The experimental data record table is shown in Table 1 below:

[0177] Table 1 Verification of the improvement of the fine-tuning index on the signal processing effect:

[0178]

[0179] Data analysis:

[0180] For signal sources A and D, due to the low noise, the signal-to-noise ratio has a difference from the reference value of less than 10%, resulting in a low value of the fine-tuning index, indicating that the cumulative energy ratio threshold can be adjusted to a range below 100% to adapt to more complex environmental requirements;

[0181] For signal sources C and F, the noise is high, and the signal-to-noise ratio has a difference from the reference value exceeding 10%, leading to a fine-tuning index increased to above 1; at this time, the cumulative energy ratio threshold is adjusted to above 100% to achieve strict noise suppression to ensure high-quality signal processing effects;

[0182] In the fine-tuning index calculation formula, is a multiple non-linear function based on the signal-to-noise ratio , the scattering point density and the signal change rate , comprehensively considering multiple characteristic parameters of the signal, ensuring the rationality and accuracy of the dynamic adjustment strategy;

[0183] In the table, for signal sources C and F, is high (3.5 and 3.4 dB respectively), and are also correspondingly high, resulting in reaching 1.15 and 1.12, and further obtaining as 1.18 and 1.14 through calibration; this directly affects the increase of the cumulative energy ratio threshold , reaching 116.82% and 113.86% respectively; this indicates that through the dynamic adjustment of the fine-tuning index, the present invention can automatically increase the strictness of signal processing in the face of a high-noise environment to ensure high-quality signal output;

[0184] In contrast, the of signal sources A and D is low (2.8 and 2.9 dB respectively), and the corresponding and are also low, ultimately resulting in being 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 and allow more noise components to exist, thus adapting to complex signal environments;

[0185] In the dynamic adjustment strategy of the present invention, there is a close numerical relationship between the various parameters and in the formula; for example:

[0186] The increase of will directly improve the fine-tuning index because the value multiplied by plays a magnifying role in the exponential part of the formula; when increases by 10%, assuming other conditions remain unchanged, the change of

[0187] will cause to increase by an amplitude between 5% and 15%, depending on the overall noise level of the signal; the increase of also has a significant impact on the fine-tuning index ; due to the existence of the

[0188] function, every one percentage point increase of will bring a doubling change in the exponential part, further amplifying and

[0189] As a part of the denominator, its decrease will increase ; therefore, the decrease of the signal change rate indicates enhanced signal stability, which will increase the tolerance to noise, thereby reducing and enabling the system to have stronger noise suppression ability in high-stability signal processing; For example, in signal source C, when is

[0190] Calculated

[0191] From the analysis of the table and formula, it can be seen that the present invention demonstrates obvious innovation and advantages in the following aspects: Dynamic adaptability: Through the real-time monitoring and calculation of and the dynamic adjustment of the fine-tuning index enables the system to adapt to signal environments of different complexities; whether in a high-noise or low-noise environment, the system can adjust the cumulative energy ratio threshold

[0192] 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;

[0193] 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;

[0194] Further explanation: The calculation formula is as follows:

[0195] ;

[0196] 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;

[0197] Phase compensation basis functions The calculation formula is:

[0198] ;

[0199] 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;

[0200] 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;

[0201] The calculation formula is as follows:

[0202] ;

[0203] in, is a complex constant representing the amplitude and initial phase of the signal; describes the phase part that varies with time;

[0204] The calculation formula is as follows:

[0205] ;

[0206] where, represents the Fourier transform; represents the result of SAR secondary imaging and is the frequency domain of the signal after compensation; The maximum value in appears at the peak position and is represented by the sinc function, that is at, and this peak position represents the spatial position of the reflection point;

[0207] Taking the energy in the main lobe region of the signal spectrum peak in as the component intensity of the signal, and the energy in the region outside the main lobe of the sinc function as the residual signal, specifically including:

[0208] Set its frequency domain characteristics as follows:

[0209] ;

[0210] where, represents the peak frequency point the left and right neighborhood ranges of, and the minimum value is ; By expanding this neighborhood range, the robustness of the algorithm is improved;

[0211] The main lobe region refers to a symmetric interval around the spectral peak frequency point where , where represents the width of this interval;

[0212] The value of is set to 0 within the main lobe region, indicating that the PPS signal component in this frequency range is "shielded" or "removed";

[0213] Outside the main lobe region, that is, "others", remains consistent with the original spectrum ;

[0214] The calculation formula is as follows:

[0215] ;

[0216] where, represents the inverse Fourier transform; is based on the estimation result The conjugate of the phase compensation basis function; other components can be gradually extracted from the residual function, and finally all PPS signal components are obtained. The linear summation 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 validity of the PPS signal components is detected by setting a threshold value. According to the analysis of the FDE-AJTF time-frequency decomposition objective function, the PPS signal components are extracted one by one according to the signal strength;

[0217] The calculation formula is as follows;

[0218] ;

[0219] where M represents the number of PPS signal components in the echo, represents the m-th PPS signal component, represents the optimal estimation of each PPS signal component, and is the residual after extracting M PPS signal components.

[0220] Furthermore, aiming at 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. Each component of the mc-PPS signal is 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 excellent individuals found during the search migrate as shared information between different subgroups to guide the progress of evolution, thus 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. The population is divided into an optimal group, a suboptimal group, and a random group. Each subgroup represents a subspace in the solution space and also represents a solution to the problem; 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 restricts the search area in the suboptimal solution, but retains the probability of updating the particle position for subgroup improvement; if after a certain iteration, the particles in the suboptimal group have the best fitness of all individuals in the global population, the suboptimal group can be upgraded to the optimal group, which means solving the problem of premature stagnation of particles; the random group has no best particle and is updated together with other groups, improving the randomness of the entire population and the information exchange between groups; when the optimal group obtains a local optimal solution, the suboptimal group retains the ability to search for another optimal solution outside the region. Therefore, multiple components can be extracted simultaneously using this method, effectively improving the global convergence speed;

[0221] 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;

[0222] The fitness calculation formula is as follows:

[0223] ;

[0224] 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;

[0225] Definition The parameter set calculation formula for each particle is as follows;

[0226] ;

[0227] 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;

[0228] 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:

[0229] ;

[0230] in, Indicates the local optimal fitness value of the m′th particle in the gth generation, Represents all position parameter records of the particle from the first iteration to the g iteration, denotes the global optimal fitness value of the g-th generation in the global scope;

[0231] Define the local and global optimal solution positions of the m'-th particle as follows:

[0232] ;

[0233] where, is the local optimal solution position of the current particle in the g-th generation iteration; is the global optimal solution position in the g-th generation iteration;

[0234] Survival of the fittest is carried out according to the local fitness of the randomly recombined mixed group. If the maximum local individual fitness value of the particles in the random subgroup is greater than the minimum local fitness value of the particles in the optimal group, then the survival of the fittest is processed by swapping two particle individuals inside the randomly recombined mixed group; the same processing is also applied to the other mixed group of the random group;

[0235] The expression for the two subgroups to exchange roles is as follows:

[0236] ;

[0237] where, is the maximum value of the local optimal fitness of the individuals in the sub-optimal subgroup, is the maximum value of the local optimal fitness of the individuals in the optimal population, and the symbol represents the individual exchange grouping; and are the particle numbers with the maximum local optimal fitness in the two subgroups of the optimal group and the sub-optimal group respectively, that is:

[0238] ;

[0239] The iteration starts from and takes the three subgroups after updating the individuals and groupings in the previous steps as the three subgroups corresponding to the

[0240] Judge whether the algorithm meets the stop conditions, including the maximum number of iterations and the convergence of the global fitness result, specifically including:

[0241] 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 simultaneously, but if there is only one effective component remaining in the signal, the parameters of the second component are invalid and need to be judged for validity; according to the parameter extraction process, if the sub-optimal solution is the true 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 definition of the fitness ratio used as the convergence judgment basis is as follows:

[0242] ;

[0243] Among them, represents the fitness of the sub-optimal solution during the iteration process, is the fitness of the sub-optimal solution and the residual signal after the optimal component is extracted, is the ratio of the fitness of the sub-optimal solution before and after the optimal component is extracted, is the threshold for judgment, represents valid, represents invalid, and the value in this embodiment is 0.99; according to the characteristics of the mc-PPS signal, when the sub-optimal solution is not the true value solution, its fitness is affected by the extraction of the optimal component, so this ratio is relatively low, while when the sub-optimal solution is the true value solution, the change in its fitness is small; therefore, by discriminating the parameters of the sub-optimal solution, invalid components can be excluded more accurately.

[0244] Furthermore, in summary, for the image problems that occur under high sea state conditions of sub-meter spaceborne SAR, taking the phase compensation function as the basis function and increasing the time window parameter is more in line with the actual situation of the unstable echo of strong scattering points under high sea state, improving the robustness of the algorithm; performing steps such as parameter estimation, residual update, and component extraction in the frequency domain, reducing the search dimension, and improving the operation speed; on this basis, a parametric time-frequency distribution construction method is proposed to reduce the influence of cross terms and sidelobes; finally, through simulation and real data verification, after processing with the FDE-AJTF algorithm, the image quality of ships under complex motion conditions can be significantly improved, the ship boundaries and structures can be clearly displayed, and the geometric characteristics, scattering characteristics, and structural characteristics of the ships can be normally obtained, effectively restoring the application ability of spaceborne SAR images in the ocean aspect;

[0245] A co-evolutionary particle swarm optimization (PSO) algorithm is proposed to address the conflicting constraints between computational complexity and global convergence in the FDE-AJTF algorithm and is applied to the compensation processing of high-resolution spaceborne SAR. In view of the contradiction of non-convex optimal calculation in the multi-dimensional solution space of the FDE-AJTF algorithm, based on a comparison of genetic algorithms and PSO algorithms, this application proposes a co-evolutionary PSO algorithm and designs a method for particle swarm combination partitioning and information interaction sharing, which can estimate and extract multi-PPS signal components in parallel, further improving the search speed and global optimal search ability in time-frequency decomposition. The processing results of the ship scatterer target model and real high-resolution spaceborne SAR data show that the image quality of complex moving ships has been significantly improved, and the overall processing speed has increased by more than 40%. The imaging results verify the efficiency and robustness of the co-evolutionary PSO algorithm.

[0246] To verify the processing ability of the co-evolutionary particle swarm algorithm for high-resolution spaceborne SAR ship target data, a spaceborne SAR space geometric imaging simulation model is established. Signals are simulated at nine points on the hull edge, and the positions of each point are [00, 1000, –1000, 5050, 500, 50 –50, –5050, –500, –50 –50], in meters. The complex motion sea state of the simulated ocean background is level 5. The co-evolutionary PSO algorithm is evaluated from three aspects: the visualization effect of point target simulation, the quality of point targets, and the processing speed. The specific imaging parameters and position information are shown in the following table.

[0247] Table 2 Main simulation parameters

[0248]

[0249] To evaluate the algorithm's performance in processing actual data, real spaceborne SAR data is used for experimental processing. This data is imaged in the sliding spotlight mode with a spatial resolution of 0.5 meters, and the data product format is SLC data. Figure 4 For the spaceborne SAR ship image with complex motion, it can be found that in the original imaging result, due to the complex motion of the sea surface and the three-dimensional motion brought by the waves, the hull appears severely defocused. The ship target image diverges severely in the azimuth direction, and it is no longer possible to distinguish the structure and shape characteristics of the ship. Such an imaging result will not be able to obtain the typical parameters of the ship even if artificial target interpretation or deep learning-based algorithms are used, and it no longer has the ability for marine surveillance applications such as classification and recognition.

[0250] After processing with the FDE-AJTF method optimized by co-evolutionary PSO, the scattering center information can be accurately extracted by obtaining each phase component of the ship; the basic outline of the ship, as well as geometric information such as length and width, are retained in the image. At the same time, the dense scattering center area also reflects the scattering structure characteristics of the ship. It can be clearly found from the image that there is a facility structure in the middle of the hull. Moreover, under the condition of the same HP820 workstation hardware configuration, 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.

[0251] The above formulas are all dimensionless and take their numerical calculations. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0252] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0253] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0254] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this 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 taken as the signal component strength, while the energy in the area outside the main lobe of the sin c function is taken 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 basis 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; 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 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 set of iteration numbers to {1, 2, …, i, …, k}, 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, 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, f(t) 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 of is [0.5,1.5]. Within this range, [0.5,1) means that the signal processing allows a larger noise component, which is suitable for complex signal processing environments; [1,1.5] means that the signal processing requires more stringent noise suppression, which is suitable for high-quality signal processing scenarios; when When the value is in the range of [0.5,1), the signal processing process allows a standard amount of noise components for use in complex signal processing environments. The adjusted cumulative energy ratio threshold is , and is calculated by the following formula: ； when When the value is in the range of [1,1.5], the signal processing process requires stricter noise suppression and is used in high-quality signal processing scenarios. 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 used as the signal component strength, and the energy in the area outside the main lobe of the sin c function is used as the residual signal, which specifically 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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