ISAR (Inverse Synthetic Aperture Radar) imaging method, device and equipment based on improved harmonic wavelet transform

Abstract

The invention relates to an ISAR (Inverse Synthetic Aperture Radar) imaging method, device and equipment based on improved harmonic wavelet transform. The method comprises the following steps: acquiring a windowed signal segment after noise reduction; calculating signal energy of the windowed signal segment, and dynamically defining a weighting function according to the signal energy; constructing an adaptive harmonic wavelet basis matrix based on the window function, the weighting function and the frequency modulation factor; performing regularization processing on the adaptive harmonic wavelet basis matrix to obtain a regularized basis matrix; transforming the windowed signal segment according to the regularization basis matrix to obtain a transformation coefficient; and constructing a time-frequency matrix based on the transformation coefficient, and reconstructing the target image according to the time-frequency matrix to obtain a final ISAR image. According to the method, the ISAR imaging quality can be effectively improved, and a radar image with higher resolution, minimum image entropy and better contrast is generated.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing imaging technology, and in particular, to an ISAR imaging method, device, and equipment based on improved harmonic wavelet transform. Background Art

[0002] Inverse Synthetic Aperture Radar (ISAR) technology is a high-resolution radar imaging technology that realizes the reconstruction of a two-dimensional high-resolution image of a target through the movement of the target relative to the radar. In practical applications, the target may be affected by various factors, resulting in acceleration or deceleration of its motion trajectory. The deviation between these actual motion characteristics and the uniform motion assumption will introduce additional errors during the imaging process, affecting the quality and accuracy of the final image. The quality of the ISAR image depends to a large extent on the effectiveness of the signal processing algorithm adopted.

[0003] Traditional ISAR imaging methods usually rely on Fourier transform for frequency-domain analysis. However, when facing non-cooperative targets with fast rotation or complex shapes, this method is prone to problems such as image blurring and resolution degradation. Because during the ISAR imaging process, the motion state of the target will cause the echo signal to exhibit complex multi-component non-stationary characteristics, while the Fourier transform assumes the stationarity of the signal, which does not hold in many practical application scenarios, thus further limiting its application effect.

[0004] To accurately reflect the motion trajectory and Doppler characteristics of the target scattering points, high-precision time-frequency analysis of the echo signal is required. Currently, common time-frequency analysis methods include Short-Time Fourier Transform (STFT), Wigner-Ville Distribution (WVD), and wavelet transform, etc. However, these methods still have problems such as limited resolution, cross-term interference, or ambiguous time-frequency structure when processing non-stationary signals. Summary of the Invention

[0005] Based on this, it is necessary to provide an ISAR imaging method, device, and equipment based on improved harmonic wavelet transform that can avoid ambiguous time-frequency structure, improve imaging accuracy, and anti-interference ability for the above technical problems.

[0006] An ISAR imaging method based on improved harmonic wavelet transform, the method includes: Obtain a windowed signal segment after noise reduction; Calculate the signal energy of the windowed signal segment, and dynamically define a weight function according to the signal energy magnitude; Construct an adaptive harmonic wavelet basis matrix based on a window function, the weight function, and a frequency modulation factor; Regularize the adaptive harmonic wavelet basis matrix to obtain a regularized basis matrix; transform the windowed signal segment according to the regularized basis matrix to obtain transformation coefficients; Construct a time-frequency matrix based on the transformation coefficients, and reconstruct the target image according to the time-frequency matrix to obtain the final ISAR image.

[0007] An ISAR imaging device based on improved harmonic wavelet transform, the device includes: A signal segment acquisition module, configured to acquire a windowed signal segment after noise reduction; A weight function construction module, configured to calculate the signal energy of the windowed signal segment, and dynamically define a weight function according to the magnitude of the signal energy; A harmonic wavelet basis matrix construction module, configured to construct an adaptive harmonic wavelet basis matrix based on a window function, the weight function, and a frequency modulation factor; A regularization module, configured to regularize the adaptive harmonic wavelet basis matrix to obtain a regularized basis matrix; A transformation module, configured to transform the windowed signal segment according to the regularized basis matrix to obtain transformation coefficients; An ISAR image reconstruction module, configured to construct a time-frequency matrix based on the transformation coefficients, and reconstruct the target image according to the time-frequency matrix to obtain the final ISAR image.

[0008] A computer device, including a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps of the ISAR imaging method based on improved harmonic wavelet transform are implemented.

[0009] The ISAR imaging method, device, and equipment based on the improved harmonic wavelet transform provided by the present invention have the following beneficial effects: First, a windowed signal segment after noise reduction is obtained. The windowed signal segment after noise reduction can effectively filter out noise interference, ensure the accuracy of coefficient estimation, avoid the time-frequency structure ambiguity caused by noise interference and uneven signal intensity, and improve the imaging quality. Calculate the signal energy of the windowed signal segment, and dynamically define the weight function according to the signal energy size, which can improve the sensitivity of the wavelet to the changes of high-energy signals and enhance the ability to capture signal details. Construct an adaptive harmonic wavelet basis matrix based on the window function, weight function, and frequency modulation factor. This matrix can be adaptively adjusted according to the characteristics of the windowed signal segment, enhance the adaptability of the basis function in the frequency domain, can perform time-frequency analysis on the signal more accurately, locate the signal components more accurately in the time-frequency domain, effectively avoid the time-frequency structure ambiguity, and make the distribution of the signal in the time-frequency domain clearer and distinguishable. Perform regularization processing on the adaptive harmonic wavelet basis matrix to reduce the influence of noise and irrelevant frequency components, and enhance the stability and reliability of the transformation coefficients. Perform transformation on the windowed signal segment according to the regularized basis matrix, realizing the efficient extraction and analysis of the harmonic components of the signal. Construct a time-frequency matrix based on the transformation coefficients, and reconstruct the target image according to the time-frequency matrix to obtain the final ISAR image. The present invention can effectively improve the quality of ISAR imaging and generate radar images with higher resolution, minimum image entropy, and better contrast. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the structures shown in these drawings.

[0011] Figure 1 It is a schematic flowchart of the ISAR imaging method based on the improved harmonic wavelet transform provided in Embodiment 1; Figure 2 It is a schematic diagram of the imaging result of the RD method provided in Embodiment 1; Figure 3 It is a schematic diagram of the imaging result of the STFT method provided in Embodiment 1; Figure 4 It is a schematic diagram of the imaging result of the SPWVD method provided in Embodiment 1; Figure 5 It is a schematic diagram of the imaging result of the method proposed by the present invention provided in Embodiment 1; Figure 6 It is a structural block diagram of the ISAR imaging device based on the improved harmonic wavelet transform provided in Embodiment 2; Figure 7 It is the internal structure diagram of the computer device provided in Embodiment 3.

[0012] The realization of the object, functional features and advantages of the present invention will be further described in conjunction with the embodiments with reference to the accompanying drawings. Detailed implementation manners

[0013] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0014] It can be understood that the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present invention.

[0015] Next, the embodiments of the present invention will be described in detail in conjunction with the accompanying drawings in the embodiments of the present invention.

[0016] Embodiment 1 It can be understood that the Adaptive Harmonic Wavelet Transform (AHWT), as a time-frequency analysis tool, has been used in the processing of ISAR signals to improve the clarity and resolution of images. The existing adaptive harmonic wavelet transform methods have problems such as asymmetric application of window functions and incomplete construction of basis matrices when applied to ISAR imaging, resulting in limited imaging results, unsatisfactory image resolution and contrast.

[0017] Based on this, this embodiment discloses an ISAR imaging method based on improved harmonic wavelet transform. By utilizing the multi-scale time-frequency analysis characteristics of harmonic wavelets, a high-resolution time-frequency image of the target echo is constructed to improve the imaging accuracy and anti-interference ability. First, obtain the windowed signal segment after noise reduction. The windowed signal segment after noise reduction can effectively filter out noise interference, ensure the accuracy of coefficient estimation, avoid the time-frequency structure ambiguity caused by noise interference and uneven signal intensity, and improve the imaging quality. Calculate the signal energy of the windowed signal segment, and dynamically define the weight function according to the signal energy size, which can improve the sensitivity of the wavelet to the changes of high-energy signals and enhance the ability to capture signal details. Construct an adaptive harmonic wavelet basis matrix based on the window function, weight function, and frequency modulation factor. This matrix can be adaptively adjusted according to the characteristics of the windowed signal segment, enhance the adaptability of the basis function in the frequency domain, can perform time-frequency analysis on the signal more accurately, locate the signal components more accurately in the time-frequency domain, effectively avoid the time-frequency structure ambiguity, and make the distribution of the signal in the time-frequency domain clearer and distinguishable. Regularize the adaptive harmonic wavelet basis matrix to reduce the influence of noise and irrelevant frequency components, and enhance the stability and reliability of the transformation coefficients. Perform transformation on the windowed signal segment according to the regularized basis matrix to achieve the efficient extraction and analysis of the harmonic components of the signal. Construct a time-frequency matrix based on the transformation coefficients, and reconstruct the target image according to the time-frequency matrix to obtain the final ISAR image. The present invention can effectively improve the quality of ISAR imaging, generate radar images with higher resolution, minimum image entropy, and better contrast.

[0018] As Figure 1 shown, the ISAR imaging method based on improved harmonic wavelet transform provided by the present invention includes the following steps: Step 201, obtain the windowed signal segment after noise reduction.

[0019] Step 202, calculate the signal energy of the windowed signal segment, and dynamically define the weight function according to the signal energy size.

[0020] Step 203, construct an adaptive harmonic wavelet basis matrix based on the window function, weight function, and frequency modulation factor.

[0021] Step 204, regularize the adaptive harmonic wavelet basis matrix to obtain a regularized basis matrix; perform transformation on the windowed signal segment according to the regularized basis matrix to obtain transformation coefficients.

[0022] Step 205, construct a time-frequency matrix based on the transformation coefficients, and reconstruct the target image according to the time-frequency matrix to obtain the final ISAR image.

[0023] In the specific implementation process of step 201, multiple adaptive harmonic wavelet transforms are applied to the echo data in each range cell based on adaptive harmonic wavelet transform to simultaneously capture the time and frequency characteristics of the signal. However, in the actual scenario, the measured echo is often contaminated by noise, which greatly reduces the clarity of the time-frequency representation and destroys the accuracy of target feature extraction. Therefore, an effective denoising mechanism is crucial for suppressing noise while retaining the intrinsic details of the scattering centers.

[0024] Based on this, for obtaining the radar echo signal, a window function is used to segment the radar echo signal to obtain several windowed signal segments; then, the windowed signal segments are denoised to obtain the denoised windowed signal segments.

[0025] Specifically, using a window function to segment the radar echo signal to obtain several windowed signal segments includes: Determine the window function, and the expression is: ; Based on the window function length , combined with the sliding window method to segment the radar echo signal, determine the start index and end index of the window to obtain several windowed signal segments; among them, the expression of the start index is: ; The expression of the end index is: ; In the formula, represents the window function; represents the index variable of the window function; represents the window function length; represents the current number of time points; represents the number of sampling points in the azimuth unit.

[0026] It can be understood that to ensure the locality and symmetry of the harmonic wavelet transform in the time domain, a Hamming window is used to segment the target signal. The role of each window is to isolate a part of the signal so that harmonic wavelet analysis can be performed within this segment of the signal. Using the sliding window method to segment the radar echo signal is convenient for local feature analysis.

[0027] Denoise the windowed signal segments to obtain the denoised windowed signal segments, and the expression is: ; In the formula, represents the denoised windowed signal segment; represents the windowed signal segment before denoising; represents the noise threshold, which is defined as: ; It can be understood that based on the noise threshold processing method, denoising the windowed signal segment can effectively suppress low-intensity noise components.

[0028] In the specific implementation process of step 202, in scenarios involving non-uniform target rotation or complex maneuvers, the ISAR echo signal exhibits transient and non-stationary characteristics in the time-frequency domain. Traditional fixed-window methods often fail to capture sudden Doppler discontinuities or short-lived high-energy components, resulting in discontinuities or dilution of scatterers in the time-frequency diagram, ultimately reducing the clarity and accuracy of the final image. Based on this, an adaptive window function strategy is proposed in this embodiment, which can detect changes in signal energy within different time intervals and automatically adjust the corresponding window weights. In this way, high-energy segments are highlighted to enhance prominent scatterers, while weights are moderately reduced in low-energy segments to suppress noise, alleviating the feature loss or "blurring" often introduced by fixed windows in the time-frequency spectrum.

[0029] Specifically, the expression for signal energy is: ; Define the weight function dynamically according to the signal energy size, and the expression for the weight function is: ; In the formula, represents the number of sampling points of the azimuth unit; represents the dynamic weight; represents the regularization parameter; represents the adaptive step parameter; represents the signal energy.

[0030] It is worth noting that the energy-driven strategy used for basis matrix construction in this embodiment does not introduce additional window segmentation or overlap, nor does it change the length of the analysis window. Therefore, its impact on the overall algorithm complexity is small, and in scenarios involving non-stationary ISAR echo Doppler measurements, it can significantly improve the resolution and enhance the robustness.

[0031] In the specific implementation process of step 203, a frequency modulation factor is first defined. It can be understood that when the ISAR target undergoes non-stationary motion, traditional FT-based methods usually lead to serious energy dispersion and a decline in imaging quality. To overcome this limitation, this embodiment introduces a frequency modulation term into the adaptive harmonic wavelet basis matrix. The frequency modulation factor enables polynomial behavior to be directly captured in the adaptive harmonic wavelet basis matrix, effectively "reshaping" the frequency axis distribution and enhancing the time-frequency focusing ability. Therefore, it can better match and reconstruct the dynamic phase fluctuations caused by target motion. The core idea is to convert the system's prior estimate of time-varying Doppler into an appropriate "bias correction" at the basis function level. The use of the frequency modulation factor can significantly improve the performance of time-frequency analysis, improve the phase characteristics, and enhance the time-frequency resolution, making the time-frequency representation more accurate and reliable.

[0032] For each frequency index , the expression of the frequency modulation factor is: ; In the formula, represents the frequency modulation factor; represents the imaginary unit; represents the number of sampling points of the azimuth unit.

[0033] Then, based on the window function , the weight function and the frequency modulation factor , an adaptive harmonic wavelet basis matrix is constructed. The expression of the adaptive harmonic wavelet basis matrix is: ; In the formula, represents the adaptive harmonic wavelet basis matrix; represents the base of the natural logarithm; represents the adaptive step parameter; represents the frequency index; represents the number of sampling points of the azimuth unit.

[0034] In the specific implementation process of step 204, to ensure the stability of the signal and reduce the influence of noise, the adaptive harmonic wavelet basis matrix is regularized to obtain a regularized basis matrix, and the expression is: ; In the formula, represents the regularized basis matrix; represents the adaptive harmonic wavelet basis matrix; represents the Frobenius norm; represents a minimum value to prevent division by zero.

[0035] The windowed signal segment is transformed according to the regularization basis matrix to obtain transformation coefficients, and the calculation expression is: ; The transformation coefficients are subjected to non-linear enhancement processing to obtain the enhanced transformation coefficients, and the expression is: ; In the formula, represents the transformation coefficients; represents the regularization basis matrix; represents the windowed signal segment after noise reduction; represents the enhanced transformation coefficients.

[0036] It can be understood that the harmonic wavelet transform is essentially the projection of the signal and the harmonic wavelet basis matrix, representing the time-frequency characteristics of the signal at different frequencies and scales. By performing non-linear enhancement processing on the transformation coefficients, it helps to highlight the high-amplitude features and suppress the low-amplitude noise at the same time.

[0037] In the specific implementation process of step 205, based on the enhanced transformation coefficients , the samples collected by each range cell form a time series, and finally a time-frequency matrix is obtained, which can be expressed as: ; For each range cell, a time-frequency matrix is constructed to obtain the final time-frequency representation of the ISAR image, which is defined as: ; In the formula, represents the time-frequency matrix; represents the number of sampling points in the azimuth unit; represents the time-frequency representation of the ISAR image; the matrix represents the number of sampling points in the range cell.

[0038] The method provided by the present invention first performs noise threshold processing on the window signal to effectively suppress the low-intensity noise components. By setting an appropriate threshold, the noise interference is filtered out to ensure the accuracy of coefficient estimation and improve the imaging quality.

[0039] For the signal in each time window, its signal energy is calculated, and the weight function is dynamically adjusted based on the signal energy size. Specifically, the higher the signal energy, the more significant the corresponding weight adjustment, so as to improve the sensitivity of the harmonic wavelet to the changes of high-energy signals and enhance the ability to capture signal details.

[0040] During the construction of the adaptive harmonic wavelet basis matrix, a frequency modulation factor is introduced to enhance the adaptability of the adaptive harmonic wavelet basis matrix in the frequency domain. By modulating the frequency characteristics of the adaptive harmonic wavelet basis matrix, it can better fit the instantaneous frequency change of the signal, improving the accuracy and resolution of spectral analysis.

[0041] An adaptive harmonic wavelet basis matrix is constructed for each windowed signal segment. This basis matrix combines the adaptive weight calculation based on signal energy and the frequency modulation term, and through regularization processing, reduces the influence of noise and irrelevant frequency components, enhancing the stability and reliability of the transformation coefficients.

[0042] Using the adaptive harmonic wavelet basis matrix proposed by the present invention for transformation can achieve efficient extraction and analysis of the harmonic components of the signal, making the extraction of harmonic components more efficient and accurate, thereby improving the resolution and contrast of the final ISAR image.

[0043] Enhancing the transformation coefficients can further enhance the effective signal components, improve the imaging contrast, and make the target features more obvious and clear in the image.

[0044] The present invention introduces adaptive parameters and frequency modulation terms to improve the harmonic wavelet transform, constructs an adaptive harmonic wavelet basis matrix, and through comprehensive processing methods such as noise suppression, regularization, and nonlinear enhancement, can effectively improve the quality of ISAR imaging, generating radar images with higher resolution, minimum image entropy, and better contrast.

[0045] In one of the embodiments, the method provided by the present invention is verified.

[0046] As Figure 2 shown, the imaging results of the traditional RD method are presented. It can be seen that although this algorithm can generally display the target contour, there are significant noise interferences and blurred distributions of target scattering points in the image. Especially in the azimuth direction, the energy dispersion is serious, mainly because this method cannot effectively handle the Doppler frequency modulation caused by target motion.

[0047] As Figure 3 shown, the imaging results of the STFT method are presented. Although STFT introduces time-frequency analysis for imaging, due to the limitation of the fixed window length, there is still a problem of insufficient focusing in the area of rapid target maneuvering, and the background noise suppression effect is not ideal.

[0048] As Figure 4 shown, the imaging results of the SPWVD method are presented. This method shows certain advantages in suppressing cross-term interferences, and the target contour is clearer than the previous two methods, but the target shape is distorted. There is still room for improvement in detail retention and noise suppression.

[0049] As Figure 5 shown, the imaging results of the method proposed by the present invention are presented. It can be clearly observed that the method proposed by the present invention not only effectively suppresses background noise, but also achieves significant improvements in target scatter point focusing, edge detail retention, and overall image quality. Especially in the azimuth direction, the energy of the scatter points is more concentrated and the target structure is clearer.

[0050] Although each step in this embodiment Figure 1 is shown in sequence according to the indication of the arrow, these steps are not necessarily executed in the order indicated by the arrow. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, Figure 1 at least a part of the steps in can include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed 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 alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0051] Embodiment 2 Based on the ISAR imaging method based on the improved harmonic wavelet transform in Embodiment 1, this embodiment discloses an ISAR imaging device based on the improved harmonic wavelet transform. As Figure 6 shown, the ISAR imaging device based on the improved harmonic wavelet transform includes: a signal segment acquisition module 401, a weight function construction module 402, a harmonic wavelet basis matrix construction module 403, a regularization module 404, a transformation module 405, and an ISAR image reconstruction module 406, where: The signal segment acquisition module 401 is used to acquire the windowed signal segment after noise reduction.

[0052] The weight function construction module 402 is used to calculate the signal energy of the windowed signal segment and dynamically define the weight function according to the signal energy magnitude.

[0053] The harmonic wavelet basis matrix construction module 403 is used to construct an adaptive harmonic wavelet basis matrix based on the window function, the weight function, and the frequency modulation factor.

[0054] The regularization module 404 is used to perform regularization processing on the adaptive harmonic wavelet basis matrix to obtain a regularized basis matrix.

[0055] The transformation module 405 is used to transform the windowed signal segment according to the regularized basis matrix to obtain transformation coefficients.

[0056] The ISAR image reconstruction module 406 is used to construct a time-frequency matrix based on the transformation coefficients, reconstruct the target image according to the time-frequency matrix, and obtain the final ISAR image.

[0057] In this embodiment, the specific working processes and working principles of the signal segment acquisition module 401, the weight function construction module 402, the harmonic wavelet basis matrix construction module 403, the regularization module 404, the transformation module 405, and the ISAR image reconstruction module 406 are the same as those of the method in Embodiment 1. Therefore, they will not be elaborated in this embodiment. Each of these unit modules can be implemented in whole or in part by software, hardware, and their combination. Each unit module can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of these unit modules.

[0058] Embodiment 3 As Figure 7 shown, a terminal device disclosed in this embodiment includes a transmitter, a receiver, a memory, and a processor. Among them, the transmitter is used to send instructions and data, the receiver is used to receive instructions and data, the memory is used to store computer execution instructions, and the processor is used to execute the computer execution instructions stored in the memory to implement the method in Embodiment 1 above.

[0059] It should be noted that the above memory can be either independent or integrated with the processor. When the memory is set independently, the terminal device further includes a bus for connecting the memory and the processor.

[0060] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0061] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered as the scope recorded in this specification.

[0062] The above-described embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.

Claims

1. An ISAR imaging method based on improved harmonic wavelet transform, characterized in that, The method includes: Obtaining a windowed signal segment after noise reduction; Calculating the signal energy of the windowed signal segment and dynamically defining a weight function according to the magnitude of the signal energy; Constructing an adaptive harmonic wavelet basis matrix based on a window function, the weight function, and a frequency modulation factor; Performing regularization processing on the adaptive harmonic wavelet basis matrix to obtain a regularized basis matrix; performing a transform on the windowed signal segment according to the regularized basis matrix to obtain transform coefficients; Constructing a time-frequency matrix based on the transform coefficients and reconstructing a target image according to the time-frequency matrix to obtain a final ISAR image.

2. The ISAR imaging method based on improved harmonic wavelet transform according to claim 1, wherein, Obtaining a windowed signal segment after noise reduction includes: Obtaining a radar echo signal, performing segmentation processing on the radar echo signal using a window function to obtain a plurality of windowed signal segments; Performing noise reduction processing on the windowed signal segments to obtain windowed signal segments after noise reduction.

3. The ISAR imaging method based on improved harmonic wavelet transform according to claim 2, wherein, Performing segmentation processing on the radar echo signal using a window function to obtain a plurality of windowed signal segments includes: Determining a window function, the expression of which is: ； In the formula, represents a window function; represents an index variable of the window function; represents the window function length; Based on the window function length , segment the radar echo signal by combining with the sliding window method, determine the start index and end index of the window, and obtain a number of windowed signal segments.

4. The ISAR imaging method based on improved harmonic wavelet transform according to any one of claims 1 to 3, characterized in that, Dynamically defining a weight function according to the magnitude of the signal energy, the expression of the weight function being: ； wherein, represents the dynamic weight; represents the regularization parameter; represents the adaptive step size parameter; represents the signal energy.

5. The ISAR imaging method based on improved harmonic wavelet transform according to any one of claims 1 to 3, characterized in that, The expression of the frequency modulation factor is: ； Wherein, represents the frequency modulation factor; represents the imaginary unit; represents the frequency index; represents the number of sampling points of the azimuth unit.

6. The ISAR imaging method based on improved harmonic wavelet transform according to any one of claims 1 to 3, characterized in that The expression of the adaptive harmonic wavelet basis matrix is: ； In the formula, represents the adaptive harmonic wavelet basis matrix; represents the dynamic weight; represents the window function; represents the frequency modulation factor; represents the base of the natural logarithm; represents the adaptive step size parameter; represents the frequency index; represents the number of sampling points of the azimuth unit.

7. The ISAR imaging method based on improved harmonic wavelet transform according to claim 6, wherein Performing regularization processing on the adaptive harmonic wavelet basis matrix to obtain a regularized basis matrix, the expression of which is: ； In the formula, represents the regularization basis matrix; represents the adaptive harmonic wavelet basis matrix; represents the Frobenius norm; represents preventing division by zero due to the minimum value.

8. The ISAR imaging method based on improved harmonic wavelet transform according to any one of claims 1 to 3, characterized in that, Performing a transform on the windowed signal segment according to the regularized basis matrix to obtain transform coefficients, the calculation expression being: ； Performing non-linear enhancement processing on the transform coefficients to obtain enhanced transform coefficients, the expression of which is: ； In the formula, represents the transformation coefficient; represents the regularization basis matrix; represents the windowed signal segment after noise reduction; represents the transformation coefficient after enhancement processing.

9. An ISAR imaging device based on improved harmonic wavelet transform, characterized in that, The apparatus includes: A signal segment acquisition module for obtaining a windowed signal segment after noise reduction; A weight function construction module for calculating the signal energy of the windowed signal segment and dynamically defining a weight function according to the magnitude of the signal energy; A harmonic wavelet basis matrix construction module for constructing an adaptive harmonic wavelet basis matrix based on a window function, the weight function, and a frequency modulation factor; A regularization module for performing regularization processing on the adaptive harmonic wavelet basis matrix to obtain a regularized basis matrix; A transform module for performing a transform on the windowed signal segment according to the regularized basis matrix to obtain transform coefficients; An ISAR image reconstruction module for constructing a time-frequency matrix based on the transform coefficients and reconstructing a target image according to the time-frequency matrix to obtain a final ISAR image.

10. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the ISAR imaging method based on improved harmonic wavelet transform according to any one of claims 1 to 8.

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