Terahertz sar motion target parameter-free self-focusing method, device and equipment

Abstract

This application relates to a parameter-free autofocusing method, apparatus, and device for terahertz SAR moving targets. The method includes: initially focusing radar echo data to obtain a coarse-focused image and delineating the target region of interest; constructing a coupled optimization function using the azimuth phase compensation vector as the optimization variable, combining a data fidelity term and a block L1 norm regularization term (characterizing the continuous scattering cluster characteristics of the target); introducing frequency domain auxiliary variables to construct an augmented Lagrangian function, building an iterative framework based on ADMM, synchronously updating the target block sparse scattering coefficients and the azimuth phase compensation vector (applying unit amplitude constraints to achieve pure phase compensation), and solving for the optimal compensation vector; using it to compensate the echo data to complete autofocusing. This method effectively solves problems such as complex echo phase and defocusing in traditional methods, demonstrating significant advantages.

Description

Technical Field

[0001] This application relates to the field of synthetic aperture radar imaging technology, and in particular to a terahertz SAR moving target parameter-free self-focusing method, apparatus and device. Background Technology

[0002] Synthetic Aperture Radar (SAR) constructs a virtual aperture through aircraft motion to achieve high-resolution imaging of target areas, and has important applications in fields such as civilian remote sensing. Terahertz SAR, as a novel SAR technology, operates in the frequency band of 0.1THz to 10THz, combining the penetration of microwave SAR with the high resolution of optical imaging, and can achieve centimeter-level imaging, making it particularly suitable for high-precision detection of concealed targets and identification of small moving targets. However, terahertz SAR imaging of moving targets faces two major challenges: first, the instability of the aircraft platform, where even small track deviations and high-frequency micro-vibrations of the fuselage can introduce nonlinear phase errors; second, the unknown velocity components of non-cooperative moving targets generate additional Doppler information. These two types of phase terms are coupled with each other, causing defocusing phenomena such as trailing and blurring of moving targets in the imaging results, seriously affecting the accuracy of target identification and feature extraction.

[0003] Autofocus technology is a core method for solving defocusing in SAR imaging. Essentially, it restores a clear image of the target by estimating and compensating for phase errors. Based on whether they rely on prior imaging model information, autofocus methods are divided into parametric and non-parametric methods: Parametric methods require a pre-defined phase error model (such as a quadratic or cubic phase model of a moving target, or a multi-component sinusoidal phase model of high-frequency vibrations), but this is suitable for scenarios where the error form is known and is difficult to adapt to the complex coupled phase of terahertz SAR; non-parametric methods do not require a pre-defined model and directly estimate the phase error through data-driven estimation, making them more suitable for scenarios with unknown and complex errors, and are the preferred solution for imaging moving targets in terahertz SAR.

[0004] However, existing terahertz SAR moving target imaging technologies suffer from three core defects. First, the phase gradient autofocus (PGA) algorithm is prone to failure and produces poor focusing results. This is because the moving target lacks a clear strong point or the strong point is obscured by clutter, making it impossible to extract an effective phase gradient. Furthermore, the complex phase errors introduced by the small motion of the carrier aircraft in the terahertz band produce ghosting, which easily captures ghosting rather than the real target within the window during iteration. Second, time-frequency analysis methods such as the fractional Fourier transform (FrFT) have limited applicability and insufficient compensation capabilities. They are only applicable to Doppler parameter estimation under ideal carrier aircraft motion conditions. Additional phase errors introduced by non-ideal carrier aircraft motion will severely interfere with the estimation results. Moreover, they can only compensate for the second-order phase of the moving target, and cannot eliminate higher-order phase errors when the target has acceleration, affecting the focusing effect. Third, the method based on L1 norm sparsity constraints has poor adaptability. This method is only applicable to isolated point sparse targets. However, in high-resolution terahertz SAR scenarios, ground moving targets exhibit continuous block sparse characteristics, which the L1 norm cannot effectively characterize. During the focusing process, the effective scattering information of the target is easily filtered out. Summary of the Invention

[0005] Therefore, it is necessary to provide a terahertz SAR moving target parameter-free self-focusing method, device, and equipment that can accurately extract effective phase information, adapt to complex motion scenarios, and ensure focusing effect in order to address the above-mentioned technical problems.

[0006] A terahertz SAR method for parametric autofocusing of moving targets, the method comprising:

[0007] Acquire radar echo data, wherein the radar echo data is the echo data of ground moving targets in the terahertz SAR imaging scenario;

[0008] The radar echo data is preliminarily focused and imaged to obtain a coarse focused image of the moving target, and the region of interest of the moving target is delineated based on the coarse focused image;

[0009] For the region of interest, the azimuth phase compensation vector is used as the optimization variable. Combined with the image domain data fidelity term and the block L1 norm regularization term, a coupled optimization objective function is constructed. The block L1 norm regularization term is used to realize the structured sparse characterization of the continuous scattering cluster characteristics of moving targets in the terahertz band.

[0010] By introducing frequency domain auxiliary variables and constructing an augmented Lagrangian function, a joint iterative optimization framework is built based on the alternating direction multiplier method to realize the synchronous alternating update of the target block sparse scattering coefficient and the azimuth phase compensation vector, and the optimal azimuth phase compensation vector is obtained by solving. In the process of updating the azimuth phase compensation vector, a unit amplitude constraint is applied, and only phase information is carried to achieve pure phase compensation.

[0011] The optimal azimuth phase compensation vector is used to perform phase compensation on the radar echo data to complete the autofocus imaging of the moving target.

[0012] In one embodiment, preliminary focusing imaging of the radar echo data includes:

[0013] The radar echo data is sequentially subjected to line frequency modulation processing and residual video phase removal processing;

[0014] Range-directed Fourier transform is performed on the processed echo data to complete range-directed pulse compression;

[0015] The Keystone transform is used to correct range cell migration in the pulse-compressed echo data;

[0016] A compensation function is constructed using the aircraft speed to compensate the corrected echo data, resulting in a coarse-focused image of the moving target.

[0017] In one embodiment, defining the region of interest for the moving target based on the coarse-focused image includes:

[0018] The constant false alarm rate (CFAR) detection algorithm is used to detect moving targets in the coarse-focused image;

[0019] Based on the moving target detection results, the coordinate range of the moving target pixels is extracted, the region of interest of the moving target is defined, and the coarse focusing result of the target within the region of interest and the initial value of the target reflectance coefficient are obtained.

[0020] In one embodiment, the coupling optimization objective function is expressed as:

[0021]

[0022] In the above formula, and These represent the azimuth phase compensation vector and the target reflection coefficient, respectively. The Frobenius norm represents the image domain data fidelity term. Represents the regularization parameter. This represents the block L1 norm.

[0023] In one embodiment, when solving the joint iterative optimization framework:

[0024] The frequency domain auxiliary variables are updated based on a two-dimensional block soft thresholding operator, with fixed target reflection coefficient, azimuth phase compensation vector, and two-dimensional dual variables.

[0025] With fixed frequency domain auxiliary variables, azimuth phase compensation vector and two-dimensional dual variables, the derivative of the optimization objective is taken and set to 0. The analytical solution is then used to update the target reflection coefficient of the frequency domain block sparse.

[0026] By fixing auxiliary variables, target reflection coefficient, and two-dimensional dual variables, the frequency domain optimization target is transformed into the azimuth time domain, and the azimuth phase compensation vector is obtained by solving for each azimuth time domain point.

[0027] Update the two-dimensional dual variables in the frequency domain according to ADMM theory.

[0028] In one embodiment, the iteration termination condition of the joint iterative optimization framework is:

[0029] If the relative residual between two consecutive iterations is less than the preset convergence threshold, the iteration terminates.

[0030] After the iteration terminates, the azimuth phase compensation vector obtained in the current iteration is taken as the optimal azimuth phase compensation vector.

[0031] This application also provides a terahertz SAR moving target parameter-free self-focusing device, the device comprising:

[0032] A radar echo data acquisition module is used to acquire radar echo data, which is the echo data of ground moving targets in a terahertz SAR imaging scenario.

[0033] The preliminary focusing imaging module is used to perform preliminary focusing imaging on the radar echo data to obtain a coarse focused image of the moving target, and to delineate the region of interest of the moving target based on the coarse focused image.

[0034] The coupling optimization objective function construction module is used to construct a coupling optimization objective function for the region of interest, using the azimuth phase compensation vector as the optimization variable and combining the image domain data fidelity term and the block L1 norm regularization term. The block L1 norm regularization term is used to realize the structured sparse characterization of the continuous scattering cluster characteristics of moving targets in the terahertz band.

[0035] The iterative solution module is used to introduce frequency domain auxiliary variables, construct an augmented Lagrangian function, build a joint iterative optimization framework based on the alternating direction multiplier method, realize the synchronous alternating update of the target block sparse scattering coefficient and the azimuth phase compensation vector, and solve for the optimal azimuth phase compensation vector. The azimuth phase compensation vector is subject to a unit amplitude constraint during the update process and carries only phase information to achieve pure phase compensation.

[0036] The phase compensation module is used to perform phase compensation on the radar echo data using the optimal azimuth phase compensation vector to complete the autofocus imaging of the moving target.

[0037] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in the above-described terahertz SAR moving target parametric autofocusing method.

[0038] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described terahertz SAR moving target parametric autofocusing method.

[0039] The aforementioned terahertz SAR moving target parametric autofocusing method, apparatus, and device obtain a coarse focused image of the moving target by performing preliminary focusing imaging on radar echo data. Based on the coarse focused image, the region of interest (ROI) of the moving target is delineated. For the ROI, an azimuth phase compensation vector is used as the optimization variable. Combined with image domain data fidelity terms and block L1 norm regularization terms, a coupled optimization objective function is constructed. The block L1 norm regularization term is used to realize the structured sparse characterization of the continuous scattering cluster characteristics of the moving target in the terahertz band. Frequency domain auxiliary variables are introduced to construct an augmented Lagrangian function. A joint iterative optimization framework is constructed based on the alternating direction multiplier method to realize the synchronous alternating update of the target block sparse scattering coefficient and the azimuth phase compensation vector. The optimal azimuth phase compensation vector is obtained by solving the problem. The azimuth phase compensation vector is subject to a unit amplitude constraint during the update process and carries only phase information to achieve pure phase compensation. The optimal azimuth phase compensation vector is used to perform phase compensation on the radar echo data to complete the autofocusing imaging of the moving target. This method effectively solves the core problems in terahertz SAR moving target imaging, such as complex echo phase, continuous scattering cluster characteristics of the target, and the tendency of traditional methods to defocus and poor adaptability, and has significant technical advantages. Attached Figure Description

[0040] Figure 1 This is a flowchart illustrating a parameter-free autofocusing method for a moving target using terahertz SAR in one embodiment.

[0041] Figure 2 This is a schematic diagram of the geometric model between a terahertz airborne SAR system and a moving ground target in one embodiment.

[0042] Figure 3 This method was used to image a moving target in a field experiment. Figure 3 (a) is a schematic diagram before focusing. Figure 3 (b) is a schematic diagram after focusing;

[0043] Figure 4This is a structural block diagram of a parameterless self-focusing device for a moving terahertz SAR target in one embodiment.

[0044] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0046] To address the problems existing in current terahertz SAR imaging technology, such as complex echo phase of moving targets, difficulty in adapting to the continuous scattering cluster characteristics of targets, easy defocusing during self-focusing, and poor scene adaptability, such as Figure 1 As shown, a parameter-free autofocusing method for moving targets using terahertz SAR is provided, which specifically includes the following steps:

[0047] Step S100: Acquire radar echo data, which is the echo data of ground moving targets in the terahertz SAR imaging scenario.

[0048] Step S110: Perform preliminary focusing imaging on the radar echo data to obtain a coarse focused image of the moving target, and delineate the region of interest of the moving target based on the coarse focused image.

[0049] Step S120: For the region of interest, using the azimuth phase compensation vector as the optimization variable, and combining the image domain data fidelity term and the block L1 norm regularization term, a coupled optimization objective function is constructed. The block L1 norm regularization term is used to realize the structured sparse characterization of the continuous scattering cluster characteristics of moving targets in the terahertz band.

[0050] Step S130: Introduce frequency domain auxiliary variables, construct augmented Lagrangian functions, build a joint iterative optimization framework based on the alternating direction multiplier method, realize the synchronous alternating update of the target block sparse scattering coefficient and the azimuth phase compensation vector, and solve for the optimal azimuth phase compensation vector. In the process of updating the azimuth phase compensation vector, a unit amplitude constraint is applied, and only phase information is carried to achieve pure phase compensation.

[0051] Step S140: Phase compensation is performed on the radar echo data using the optimal azimuth phase compensation vector to complete the autofocus imaging of the moving target.

[0052] Considering the complex echo phase of moving targets in terahertz SAR imaging scenarios due to multiple factors such as track offset, aircraft vibration, and non-cooperative moving targets, resulting in severe defocusing of moving targets in SAR images, this paper proposes a parameter-free autofocusing method for terahertz SAR moving targets based on block sparsity constraints. First, a coarse-focused image is generated. Then, considering the physical characteristics of terahertz moving targets exhibiting continuous scattering clusters in SAR images, a target optimization function is constructed based on the target block sparsity characteristics. The coupled optimization problem is decomposed into sub-problems iteratively solved using the Alternating Direction Method of Multipliers (ADMM), including updating auxiliary variables, target reflection coefficients, and phase vectors. Finally, the iteration terminates when the relative residual converges, and the optimized phase vector is used to compensate for the target echo data, completing the focusing process.

[0053] In step S100, the imaging scene is affected by multiple factors such as track deviation, aircraft vibration and non-cooperative moving targets. The radar system uses a linear frequency modulated (LFM) pulse signal as the transmitted waveform. The echo data is coupled with complex phase errors introduced by target motion and non-ideal motion of the aircraft.

[0054] Imaging geometry between a terahertz airborne SAR platform and a moving target in frontal-side mode, such as Figure 2 As shown. In In the coordinate system, The axis indicates the direction of the aircraft's speed along its flight path. The axis indicates the direction of distance from the ground. The axis is perpendicular to the horizontal plane and pointing upwards. For the height of the aircraft, For the speed of the carrier aircraft, This is a bottom-view perspective. During the synthesis aperture time... T s Inside, point target Movement to Q Furthermore, the moving target moves along the plane above the ground. X shaft and Y The speeds of the shafts are respectively v x and v y . This represents the zero Doppler slant range. Therefore, the instantaneous slant range from radar to the target can be expressed as:

[0055] (1)

[0056] In formula (1), Indicates slow time. This represents the initial slant range at which the radar reaches the moving target, and is independent of slow time. This represents the change in slant distance caused by the target's motion. It represents the change in slant range caused by non-ideal motion of the aircraft, including vibration error and track error.

[0057] In step S110, preliminary focusing imaging of radar echo data is performed, including: sequentially performing line frequency modulation processing and residual video phase removal processing on the radar echo data; performing range-direction Fourier transform on the processed echo data to complete range-direction pulse compression; using Keystone transform to perform range cell migration correction on the pulse-compressed echo data; constructing a compensation function using the aircraft speed; compensating the corrected echo data; and obtaining a coarse focused image of the moving target.

[0058] In this embodiment, it is assumed that the SAR system uses an LFM pulse signal as the transmitted waveform, which is as follows:

[0059] (2)

[0060] In formula (2), It is a rectangular window function. and These represent the fast time and pulse width, respectively. and These represent the center frequency and modulation frequency of the transmitted signal, respectively. After demodulation and removal of residual video phase, the echo signal can be represented as:

[0061] (3)

[0062] In formula (3), Indicates the amplitude of the echo signal. It represents the speed of light. It indicates the equivalent fast time. Expressed as the instantaneous slant range difference relative to the reference distance, where, Subsequently, range-direction Fourier transform is performed on the demodulated echo to achieve range-direction pulse compression, which can be expressed as:

[0063] (4)

[0064] In formula (4), The Sinking function is specifically represented as: , This represents the range frequency. From formula (4), it can be seen that the sinc envelope contains... Range cell migration is introduced into the range compression signal, which can be corrected using the Keystone transform. The corrected echo signal can be expressed as:

[0065] (5)

[0066] In formula (5), This represents the reference slope difference, specifically expressed as... Then, a compensation function is constructed using the aircraft speed to eliminate the interference of known information on the Doppler information. The compensation function can be expressed as:

[0067] (6)

[0068] After the above operations were completed to compensate, the initial focusing was achieved.

[0069] In this embodiment, defining the region of interest (ROI) of a moving target based on a coarsely focused image includes: using a constant false alarm rate (CFAR) detection algorithm to detect the moving target in the coarsely focused image; extracting the coordinate range of the moving target pixels based on the moving target detection results; defining the ROI of the moving target; and obtaining the coarse focusing result of the target within the ROI and the initial value of the target reflectance coefficient.

[0070] Specifically, based on the CFAR detection results, the Region of Interest (ROI) of the moving target can be determined. Let the distance dimension of the ROI be... M azimuth dimension is N The coarse focusing result of the target within the ROI is as follows: ,and The target reflectance coefficient is Therefore, it is necessary to construct an azimuth phase compensation factor. The compensated target result can be expressed as .

[0071] To address the physical characteristics of moving targets in the terahertz band exhibiting continuous scattering clusters in SAR images, this paper proposes a sparse regularization constraint method based on a block-level soft thresholding operator. Unlike traditional L1-norm sparse constraints that independently penalize isolated scattering points, this method introduces a structured block-level norm threshold. In the range-Doppler domain, a block-based shrinkage operation is used to achieve a structured sparse representation of the continuous scattering region. This design effectively maintains the spatial continuity of the target's scattered energy while suppressing noise and sidelobes, better aligning with the characteristics of strong reflection, multiple scattering centers, and continuous spatial distribution of terahertz band targets. This provides a structure-fidelity prior information foundation for subsequent high-precision motion compensation.

[0072] In step S120, the azimuth phase compensation vector is used. To optimize the variables, a coupled optimization objective function is constructed by combining the data fidelity term of the minimum image domain and the block sparsity regularization term, which is expressed as:

[0073] (7)

[0074] In formula (7), and These represent the azimuth phase compensation vector and the target reflection coefficient, respectively. The azimuth phase compensation vector is the core carrier used to correct the echo phase error, while the target reflection coefficient characterizes the inherent scattering characteristics of a moving target in the terahertz band. The Frobenius norm, also known as the image domain data fidelity term, is used to constrain the target scattering coefficients obtained after optimization to maintain a high degree of fit with the grayscale information of the coarsely focused image within the region of interest, ensuring that the optimization process does not deviate from the true scattering characteristics of the target. This represents the regularization parameter, used to balance the weights of data fidelity terms and block sparsity regularization terms, preventing any single term from excessively dominating the optimization result. The block L1 norm, which constitutes the block sparse regularization term, is a key design feature adapted to the characteristics of continuous scattering clusters of moving targets in the terahertz band.

[0075] Specifically, It can be defined as:

[0076] (8)

[0077] In formula (8), express The b The blocks are divided based on the spatial distribution characteristics of continuous scattering clusters from terahertz moving targets. The target reflection coefficients within the region of interest are divided into several continuous pixel blocks of a preset size, ensuring that each pixel within a block corresponds to a continuous scattering region of the target. The core function of block sparsity is that all pixels within a block share a single L2 norm, thereby forcing the scattering characteristics of pixels within the block to remain consistent and guaranteeing the local continuity of the target's scattered energy. Simultaneously, by constraining only a few blocks to have non-zero L2 norms, a global sparse representation of the target's scattering region is achieved, effectively suppressing background clutter and sidelobe interference. Therefore, the essence of this constraint is "local continuity, global sparsity," which not only accurately adapts to the physical characteristics of continuous scattering clusters from terahertz moving targets but also provides a reliable prior constraint for the accurate estimation of subsequent phase errors.

[0078] Furthermore, the objective function represented by formula (7) is and The coupled optimization problem can be decomposed into three subproblems using the alternating direction multiplier method, and iterated until convergence. Core logic: In Domain application of block sparsity constraints, in The domain-optimized azimuth phase compensation vector is transformed between domains using the Fast Fourier Transform (FFT).

[0079] In step S130, introduce Auxiliary variables of the domain Construct the augmented Lagrangian function, expressed as:

[0080] (9)

[0081] In formula (9), For two-dimensional dual variables, For ADMM penalty parameters; This indicates the inner product of Frobenius.

[0082] Furthermore, the core idea of ​​ADMM is to decompose a complex optimization problem into several more manageable subproblems, and then iteratively approach the optimal solution. Therefore, the following steps can be performed iteratively, implemented using FFT. and The domain transformation eventually converges to the optimal azimuth phase compensation vector. .

[0083] In this embodiment, when solving the joint iterative optimization framework: first, the target reflection coefficient, azimuth phase compensation vector, and two-dimensional dual variables are fixed. The frequency domain auxiliary variables are updated based on the two-dimensional block soft threshold operator. Then, the frequency domain auxiliary variables, azimuth phase compensation vector, and two-dimensional dual variables are fixed. The derivative of the optimization objective is calculated and set to 0. The analytical solution is used to update the target reflection coefficient of the frequency domain block sparse. Next, the auxiliary variables, target reflection coefficient, and two-dimensional dual variables are fixed. The frequency domain optimization objective is converted into the azimuth time domain. The azimuth phase compensation vector is obtained for each azimuth time domain point. Finally, the two-dimensional dual variables of the frequency domain are updated according to ADMM theory to complete one iteration process.

[0084] Specifically, fixed azimuth phase compensation vector Target reflectance Frequency domain auxiliary variables Update the two-dimensional dual variable The optimization objective is:

[0085] (10)

[0086] Therefore, the analytical solution is The two-dimensional block soft thresholding operator for the domain can be expressed as:

[0087] (11)

[0088] In formula (11), as auxiliary variables The bEach block, the core logic is to ensure that all pixels within the block are either synchronously preserved or reset to zero, that is, if the L2 norm of the block is greater than a threshold. If the value is less than the threshold, all pixels within the block are retained proportionally to ensure continuity; if the value is less than the threshold, all pixels within the block are reset to zero to suppress noise and clutter.

[0089] Next, the fixed azimuth phase compensation vector Two-dimensional dual variables Frequency domain auxiliary variables ,renew Block sparse target reflectance in the domain The optimization objective is:

[0090] (12)

[0091] right Taking the derivative and setting it to 0, the analytical solution is:

[0092] (13)

[0093] Furthermore, the reflectivity of a fixed target Frequency domain auxiliary variables Two-dimensional dual variables ,renew Azimuth phase compensation vector in the domain The optimization objective is:

[0094] (14)

[0095] Through domain transformation, The optimization objective of the domain is transformed into The optimization objective of the domain, Perform an inverse FFT (IFFT) to obtain the orientation. The target time-domain signal of the domain is represented as:

[0096] (15)

[0097] And because Therefore, the optimization objective is equivalent to:

[0098] (16)

[0099] because Only with slow time Changes, for each location in the time domain The optimization objective is:

[0100] (17)

[0101] Further, expand and ignore For irrelevant terms, the optimization objective is equivalent to:

[0102] (18)

[0103] In formula (18), Indicates to Take the conjugate. Therefore, the analytical solution can be expressed as:

[0104] (19)

[0105] In formula (19), This indicates the phase when taking a complex number. It can be seen that... Only by Time-domain signal of coarse focusing front signal and target reflectance coefficient The calculations are complete, requiring no estimation of any phase parameters; it is entirely data-driven.

[0106] Finally, update Dual variables of the domain , represented as:

[0107] (20)

[0108] In this embodiment, the iteration termination condition of the joint iterative optimization framework is: the iteration terminates when the relative residual between two adjacent iterations is less than a preset convergence threshold. After the iteration terminates, the azimuth phase compensation vector obtained in the current iteration is taken as the optimal azimuth phase compensation vector.

[0109] Specifically, the iteration terminates when the relative residual is less than the convergence threshold, which can be expressed as:

[0110] (twenty one)

[0111] Specifically, after iterative convergence, the optimal azimuth phase compensation vector is obtained. This process, through compensation, yields a clear, focused image. The process can be summarized as follows:

[0112] (twenty two)

[0113] In this method, to address the defocusing problem of target scattering in the frequency domain caused by phase errors, an iterative estimation and amplitude constraint technique for the frequency domain compensation factor is proposed. The core innovation of this method lies in estimating the motion error phase term coupled to the echo frequency domain data through an inverse transform operation. To strictly ensure that the compensation operation corrects only phase distortion without introducing spurious amplitude modulation, the algorithm imposes a strict unit amplitude constraint on the estimated compensation factor in each iteration, forcing it to carry only phase information. This design, mechanistically, avoids image amplitude distortion caused by amplitude-phase coupling in traditional methods, achieving accurate and pure phase compensation for terahertz SAR phase errors.

[0114] Furthermore, this paper constructs a joint iterative optimization framework based on ADMM. This framework innovatively achieves synchronous and alternating updates of the target block sparse scattering coefficients and the spatially varied phase compensation factor. Each iteration includes three core steps: first, phase compensation is performed; second, the target scattering coefficients satisfying the sparse characteristics of the continuous block are updated using the block soft threshold operator; and finally, the dual variables are updated according to ADMM theory to enhance the constraints. The entire process unifies scattering coefficient estimation and phase error compensation under a rigorous convex optimization theoretical framework, ensuring the convergence of the algorithm and the optimality of the solution.

[0115] In this paper, the effectiveness of the proposed method is also verified through experimental testing. In the experimental setup, the terahertz radar center frequency was 220 GHz, the bandwidth was 900 MHz, the aircraft speed was 60 m / s, the aircraft altitude was 1 km, and the synthetic aperture time was 0.2 s. The coarse focusing results of the moving target are as follows: Figure 3 As shown in (a), the moving target exhibits severe defocusing. After imaging using the method of this invention, the result is as follows: Figure 3 As shown in (b), the moving target is refocused, and the gun barrel and fuselage are clearly visible.

[0116] The aforementioned parameterless autofocusing method for terahertz SAR moving targets has significant advantages over existing technologies: Compared to the limitations of traditional L1 norm constraints, which are only suitable for isolated point targets and easily filter out continuous scattering clusters, the block sparsity constraints used in this method can accurately characterize the distribution characteristics of continuous scattering clusters of terahertz moving targets. While effectively suppressing clutter, it significantly improves the retention rate of effective scattering information of the target, making the details of the focused image more complete. Unlike existing methods that rely on strong point targets or preset phase models, this method estimates the frequency domain compensation factor through a data-driven approach, eliminating the dependence on prior imaging models. It can effectively correct the complex phase term coupled with the aircraft error and target motion, significantly improving the adaptability to non-cooperative moving targets in terahertz SAR. Addressing the significant noise interference in the terahertz band and the susceptibility of phase estimation in existing technologies, this method relies on block threshold constraints to complete the noise resistance design, enhancing the noise suppression capability in the phase estimation process. Even in low signal-to-noise ratio scenarios, it can still achieve stable focusing of moving targets.

[0117] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0118] In one embodiment, such as Figure 4 As shown, a terahertz SAR moving target parameter-free self-focusing device is provided, including: a radar echo data acquisition module 200, a preliminary focusing imaging module 210, a coupled optimization objective function construction module 220, an iterative solution module 230, and a phase compensation module 240, wherein:

[0119] The radar echo data acquisition module 200 is used to acquire radar echo data, which is the echo data of ground moving targets in the terahertz SAR imaging scenario.

[0120] The preliminary focusing imaging module 210 is used to perform preliminary focusing imaging on the radar echo data to obtain a coarse focused image of the moving target, and to delineate the region of interest of the moving target based on the coarse focused image.

[0121] The coupling optimization objective function construction module 220 is used to construct a coupling optimization objective function for the region of interest, using the azimuth phase compensation vector as the optimization variable and combining the image domain data fidelity term and the block L1 norm regularization term. The block L1 norm regularization term is used to realize the structured sparse characterization of the continuous scattering cluster characteristics of the moving target in the terahertz band.

[0122] The iterative solution module 230 is used to introduce frequency domain auxiliary variables, construct an augmented Lagrangian function, build a joint iterative optimization framework based on the alternating direction multiplier method, realize the synchronous alternating update of the target block sparse scattering coefficient and the azimuth phase compensation vector, and solve for the optimal azimuth phase compensation vector. The azimuth phase compensation vector is subject to a unit amplitude constraint during the update process and carries only phase information to achieve pure phase compensation.

[0123] The phase compensation module 240 is used to perform phase compensation on the radar echo data using the optimal azimuth phase compensation vector to complete the autofocus imaging of the moving target.

[0124] Specific limitations regarding the terahertz SAR moving target parameterless autofocusing device can be found in the limitations of the terahertz SAR moving target parameterless autofocusing method above, and will not be repeated here. Each module in the aforementioned terahertz SAR moving target parameterless autofocusing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0125] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a terahertz SAR moving target parameter-free autofocusing method. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0126] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0127] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0128] Acquire radar echo data, wherein the radar echo data is the echo data of ground moving targets in the terahertz SAR imaging scenario;

[0129] The radar echo data is preliminarily focused and imaged to obtain a coarse focused image of the moving target, and the region of interest of the moving target is delineated based on the coarse focused image;

[0130] For the region of interest, the azimuth phase compensation vector is used as the optimization variable. Combined with the image domain data fidelity term and the block L1 norm regularization term, a coupled optimization objective function is constructed. The block L1 norm regularization term is used to realize the structured sparse characterization of the continuous scattering cluster characteristics of moving targets in the terahertz band.

[0131] By introducing frequency domain auxiliary variables and constructing an augmented Lagrangian function, a joint iterative optimization framework is built based on the alternating direction multiplier method to realize the synchronous alternating update of the target block sparse scattering coefficient and the azimuth phase compensation vector, and the optimal azimuth phase compensation vector is obtained by solving. In the process of updating the azimuth phase compensation vector, a unit amplitude constraint is applied, and only phase information is carried to achieve pure phase compensation.

[0132] The optimal azimuth phase compensation vector is used to perform phase compensation on the radar echo data to complete the autofocus imaging of the moving target.

[0133] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0134] Acquire radar echo data, wherein the radar echo data is the echo data of ground moving targets in the terahertz SAR imaging scenario;

[0135] The radar echo data is preliminarily focused and imaged to obtain a coarse focused image of the moving target, and the region of interest of the moving target is delineated based on the coarse focused image;

[0136] For the region of interest, the azimuth phase compensation vector is used as the optimization variable. Combined with the image domain data fidelity term and the block L1 norm regularization term, a coupled optimization objective function is constructed. The block L1 norm regularization term is used to realize the structured sparse characterization of the continuous scattering cluster characteristics of moving targets in the terahertz band.

[0137] By introducing frequency domain auxiliary variables and constructing an augmented Lagrangian function, a joint iterative optimization framework is built based on the alternating direction multiplier method to realize the synchronous alternating update of the target block sparse scattering coefficient and the azimuth phase compensation vector, and the optimal azimuth phase compensation vector is obtained by solving. In the process of updating the azimuth phase compensation vector, a unit amplitude constraint is applied, and only phase information is carried to achieve pure phase compensation.

[0138] The optimal azimuth phase compensation vector is used to perform phase compensation on the radar echo data to complete the autofocus imaging of the moving target.

[0139] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0140] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0141] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A parameter-free self-focusing method for terahertz SAR moving target, characterized in that, The method includes: Acquire radar echo data, wherein the radar echo data is the echo data of ground moving targets in the terahertz SAR imaging scenario; The radar echo data is preliminarily focused and imaged to obtain a coarse focused image of the moving target, and the region of interest of the moving target is delineated based on the coarse focused image; For the region of interest, using the azimuth phase compensation vector as the optimization variable, and combining the image domain data fidelity term and the block L1 norm regularization term, a coupled optimization objective function is constructed. The block L1 norm regularization term is used to achieve a structured sparse representation of the continuous scattering cluster characteristics of moving targets in the terahertz band. The coupled optimization objective function is expressed as follows: In the above formula, and respectively represent the azimuth phase compensation vector and the target reflection coefficient, represents the Frobenius norm, that is, the image domain data fidelity term, represents the regularization parameter, represents the block L1 norm, represents the compensated target result; By introducing frequency domain auxiliary variables and constructing an augmented Lagrangian function, a joint iterative optimization framework is built based on the alternating direction multiplier method to realize the synchronous alternating update of the target block sparse scattering coefficient and the azimuth phase compensation vector, and the optimal azimuth phase compensation vector is obtained by solving. In the process of updating the azimuth phase compensation vector, a unit amplitude constraint is applied, and only phase information is carried to achieve pure phase compensation. The optimal azimuth phase compensation vector is used to perform phase compensation on the radar echo data to complete the autofocus imaging of the moving target.

2. The terahertz SAR moving target parameter-free autofocusing method according to claim 1, characterized in that, Preliminary focusing and imaging of the radar echo data includes: The radar echo data is sequentially subjected to line frequency modulation processing and residual video phase removal processing; Range-directed Fourier transform is performed on the processed echo data to complete range-directed pulse compression; The Keystone transform is used to correct range cell migration in the pulse-compressed echo data; A compensation function is constructed using the aircraft speed to compensate the corrected echo data, resulting in a coarse-focused image of the moving target.

3. The terahertz SAR moving target parameter-free autofocusing method according to claim 1, characterized in that, The region of interest for a moving target is defined based on the coarse-focused image, including: The constant false alarm rate (CFAR) detection algorithm is used to detect moving targets in the coarse-focused image; Based on the moving target detection results, the coordinate range of the moving target pixels is extracted, the region of interest of the moving target is defined, and the coarse focusing result of the target within the region of interest and the initial value of the target reflectance coefficient are obtained.

4. The terahertz SAR moving target parameter-free autofocusing method according to claim 3, characterized in that, When solving the joint iterative optimization framework: The frequency domain auxiliary variables are updated based on a two-dimensional block soft thresholding operator, with fixed target reflection coefficient, azimuth phase compensation vector, and two-dimensional dual variables. With fixed frequency domain auxiliary variables, azimuth phase compensation vector and two-dimensional dual variables, the derivative of the optimization objective is taken and set to 0. The analytical solution is then used to update the target reflection coefficient of the frequency domain block sparse. By fixing auxiliary variables, target reflection coefficient, and two-dimensional dual variables, the frequency domain optimization target is transformed into the azimuth time domain, and the azimuth phase compensation vector is obtained by solving for each azimuth time domain point. Update the two-dimensional dual variables in the frequency domain according to ADMM theory.

5. The terahertz SAR moving target parameter-free self-focusing method according to claim 4, characterized in that, The iteration termination condition of the joint iterative optimization framework is: If the relative residual between two consecutive iterations is less than the preset convergence threshold, the iteration terminates. After the iteration terminates, the azimuth phase compensation vector obtained in the current iteration is taken as the optimal azimuth phase compensation vector.

6. A terahertz SAR moving target parameter-free self-focusing device, characterized in that, The device includes: A radar echo data acquisition module is used to acquire radar echo data, which is the echo data of ground moving targets in a terahertz SAR imaging scenario. The preliminary focusing imaging module is used to perform preliminary focusing imaging on the radar echo data to obtain a coarse focused image of the moving target, and to delineate the region of interest of the moving target based on the coarse focused image. The coupling optimization objective function construction module is used to construct a coupling optimization objective function for the region of interest, using the azimuth phase compensation vector as the optimization variable, and combining an image domain data fidelity term and a block L1 norm regularization term. The block L1 norm regularization term is used to achieve a structured sparse characterization of the continuous scattering cluster characteristics of moving targets in the terahertz band. The coupling optimization objective function is expressed as follows: In the above formula, and These represent the azimuth phase compensation vector and the target reflection coefficient, respectively. The Frobenius norm represents the image domain data fidelity term. Represents the regularization parameter. Represents the block L1 norm. This represents the target result after compensation; The iterative solution module is used to introduce frequency domain auxiliary variables, construct an augmented Lagrangian function, build a joint iterative optimization framework based on the alternating direction multiplier method, realize the synchronous alternating update of the target block sparse scattering coefficient and the azimuth phase compensation vector, and solve for the optimal azimuth phase compensation vector. The azimuth phase compensation vector is subject to a unit amplitude constraint during the update process and carries only phase information to achieve pure phase compensation. The phase compensation module is used to perform phase compensation on the radar echo data using the optimal azimuth phase compensation vector to complete the autofocus imaging of the moving target.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

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