ISAR imaging method, device and equipment based on improved harmonic wavelet transform

Abstract

The present invention relates to an ISAR imaging method, device, and equipment based on an improved harmonic wavelet transform. The method comprises: obtaining a windowed signal segment after noise reduction; calculating the signal energy of the windowed signal segment and dynamically defining a weight function based on the signal energy; constructing an adaptive harmonic wavelet basis matrix based on the window function, the weight function, and a frequency modulation factor; regularizing the adaptive harmonic wavelet basis matrix to obtain a regularized basis matrix; transforming the windowed signal segment according to the regularized basis matrix to obtain transformation coefficients; constructing a time-frequency matrix based on the transformation coefficients, and reconstructing a target image according to the time-frequency matrix to obtain a 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.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing imaging, 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) is a high-resolution radar imaging technique that reconstructs a two-dimensional, high-resolution image of a target based on its motion relative to the radar. In practical applications, a target's motion trajectory may be affected by a variety of factors, causing acceleration or deceleration. Deviations between these actual motion characteristics and the uniform velocity assumption introduce additional errors into the imaging process, affecting the quality and accuracy of the final image. The quality of ISAR images depends heavily on the effectiveness of the signal processing algorithms employed.

[0003] Traditional ISAR imaging methods typically rely on Fourier transforms for frequency domain analysis, but this approach is prone to image blur and resolution degradation when dealing with rapidly rotating or complex non-cooperative targets. This is because the target's motion during ISAR imaging causes the echo signal to exhibit complex, multi-component, non-stationary characteristics. The Fourier transform assumes signal stationarity, which does not hold true in many practical applications, further limiting its effectiveness.

[0004] To accurately reflect the motion trajectory and Doppler characteristics of target scatterers, high-precision time-frequency analysis of echo signals is required. Currently, commonly used time-frequency analysis methods include short-time Fourier transform (STFT), Wigner-Ville distribution (WVD), and wavelet transform. However, these methods still suffer from limited resolution, cross-interference, or fuzzy time-frequency structures 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 to avoid time-frequency structure ambiguity, improve imaging accuracy and anti-interference ability, in order to address the above technical problems.

[0006] An ISAR imaging method based on improved harmonic wavelet transform, the method comprising:

[0007] Get the windowed signal segment after noise reduction;

[0008] Calculating the signal energy of the windowed signal segment and dynamically defining a weight function according to the signal energy;

[0009] Constructing an adaptive harmonic wavelet basis matrix based on the window function, the weight function and the frequency modulation factor;

[0010] Regularizing the adaptive harmonic wavelet basis matrix to obtain a regularized basis matrix; transforming the windowed signal segment according to the regularized basis matrix to obtain transformation coefficients;

[0011] A time-frequency matrix is constructed based on the transformation coefficients, and a target image is reconstructed according to the time-frequency matrix to obtain a final ISAR image.

[0012] An ISAR imaging device based on improved harmonic wavelet transform, comprising:

[0013] A signal segment acquisition module, used to obtain the windowed signal segment after noise reduction;

[0014] 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 signal energy;

[0015] A harmonic wavelet basis matrix construction module is used to construct an adaptive harmonic wavelet basis matrix based on a window function, the weight function and a frequency modulation factor;

[0016] A regularization module, configured to perform regularization processing on the adaptive harmonic wavelet basis matrix to obtain a regularized basis matrix;

[0017] a transformation module, configured to transform the windowed signal segment according to a regularized basis matrix to obtain transformation coefficients;

[0018] The ISAR image reconstruction module 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 a final ISAR image.

[0019] A computer device comprises a memory and a processor, wherein the memory stores a computer program and the processor implements the steps of the ISAR imaging method based on improved harmonic wavelet transform when executing the computer program.

[0020] The ISAR imaging method, device, and apparatus 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 time-frequency structure ambiguity caused by noise interference and uneven signal strength, and improve imaging quality. The signal energy of the windowed signal segment is calculated, and a weight function is dynamically defined according to the signal energy. This can improve the sensitivity of the wavelet to changes in high-energy signals and enhance the ability to capture signal details. An adaptive harmonic wavelet basis matrix is constructed based on the window function, weight function, and frequency modulation factor. The matrix can be adaptively adjusted according to the characteristics of the windowed signal segment, enhancing the adaptability of the basis function in the frequency domain, more accurately performing time-frequency analysis on the signal, more accurately locating signal components in the time-frequency domain, effectively avoiding time-frequency structure ambiguity, and making the signal distribution in the time-frequency domain clearer and more discernible. The adaptive harmonic wavelet basis matrix is regularized to reduce the influence of noise and irrelevant frequency components and enhance the stability and reliability of the transform coefficients. The windowed signal segments are transformed according to the regularized basis matrix, enabling efficient extraction and analysis of the signal's harmonic components. A time-frequency matrix is constructed based on the transformation coefficients, and the target image is reconstructed using the time-frequency matrix to obtain the final ISAR image. This method can effectively improve the quality of ISAR imaging, generating radar images with higher resolution, minimized image entropy, and improved contrast. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0022] Figure 1 Schematic diagram of the process of the ISAR imaging method using the improved harmonic wavelet transform provided in Example 1;

[0023] Figure 2 Schematic diagram of the imaging results of the RD method provided in Example 1;

[0024] Figure 3 Schematic diagram of the imaging results of the STFT method provided in Example 1;

[0025] Figure 4 Schematic diagram of the imaging results of the SPWVD method provided in Example 1;

[0026] Figure 5 Schematic diagram of the imaging results of the method of the present invention provided in Example 1;

[0027] Figure 6 This is a structural block diagram of the ISAR imaging device using the improved harmonic wavelet transform provided in Example 2;

[0028] Figure 7 This is a diagram of the internal structure of the computer device provided in Example 3.

[0029] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0031] 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 fact that ordinary technicians in this field can implement it. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0032] The following describes the implementation of the present invention in detail with reference to the accompanying drawings in the embodiments of the present invention.

[0033] Example 1

[0034] As can be understood, the Adaptive Harmonic Wavelet Transform (AHWT), as a time-frequency analysis tool, has been used in ISAR signal processing to improve image clarity and resolution. However, existing AHWT methods, when applied to ISAR imaging, suffer from issues such as asymmetric windowing and incomplete basis matrix construction, resulting in limited imaging results and suboptimal image resolution and contrast.

[0035] Based on this, this embodiment discloses an ISAR imaging method based on an improved harmonic wavelet transform. This method utilizes the multi-scale time-frequency analysis characteristics of harmonic wavelets to construct high-resolution time-frequency images of target echoes, improving imaging accuracy and anti-interference capabilities. First, a windowed signal segment after noise reduction is obtained. This windowed signal segment can effectively filter out noise interference, ensure the accuracy of coefficient estimation, avoid time-frequency structure ambiguity caused by noise interference and uneven signal strength, and improve imaging quality. The signal energy of the windowed signal segment is calculated, and a weight function is dynamically defined based on the signal energy. This can improve the wavelet's sensitivity to changes in high-energy signals and enhance its ability to capture signal details. An adaptive harmonic wavelet basis matrix is constructed 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, enhancing the adaptability of the basis function in the frequency domain. This allows for more precise time-frequency analysis of the signal, more accurate positioning of signal components in the time-frequency domain, effectively avoiding time-frequency structure ambiguity, and making the signal distribution in the time-frequency domain more clearly discernible. Regularization is performed on the adaptive harmonic wavelet basis matrix to reduce the impact of noise and irrelevant frequency components, enhancing the stability and reliability of the transform coefficients. Windowed signal segments are transformed according to the regularized basis matrix, enabling efficient extraction and analysis of the signal's harmonic components. A time-frequency matrix is constructed based on the transform coefficients, and the target image is reconstructed using the time-frequency matrix to obtain the final ISAR image. This method can effectively improve the quality of ISAR imaging, generating radar images with higher resolution, minimized image entropy, and improved contrast.

[0036] like Figure 1 As shown, the ISAR imaging method based on improved harmonic wavelet transform provided by the present invention includes the following steps:

[0037] Step 201: Obtain a windowed signal segment after noise reduction.

[0038] Step 202: Calculate the signal energy of the windowed signal segment and dynamically define a weight function based on the signal energy.

[0039] Step 203: construct an adaptive harmonic wavelet basis matrix based on the window function, the weight function and the frequency modulation factor.

[0040] Step 204 , 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.

[0041] 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 a final ISAR image.

[0042] In the specific implementation of step 201, ISAR imaging based on adaptive harmonic wavelet transforms applies multiple adaptive harmonic wavelet transforms to the echo data in each range bin to simultaneously capture the time and frequency characteristics of the signal. However, in real-world scenarios, the measured echoes are often contaminated by noise, which significantly reduces the clarity of the time-frequency representation and undermines the accuracy of target feature extraction. Therefore, an effective denoising mechanism is crucial to suppress noise while preserving the intrinsic details of the scattering centers.

[0043] Based on this, in order to obtain the radar echo signal, the radar echo signal is segmented by using the window function to obtain several windowed signal segments; then the windowed signal segments are subjected to noise reduction processing to obtain the noise-reduced windowed signal segments.

[0044] Specifically, the radar echo signal is segmented using a window function to obtain several windowed signal segments, including:

[0045] Determine the window function, the expression is:

[0046] ;

[0047] Based on the window function length , the radar echo signal is segmented by combining the sliding window method, and the window start index and end index are determined to obtain several windowed signal segments; among which, the start index expression is:

[0048] ;

[0049] The ending index expression is:

[0050] ;

[0051] Where, represents the window function; The index variable representing the window function; Indicates the length of the window function; Indicates the current time point; Indicates the number of sampling points in the orientation unit.

[0052] As can be understood, 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. Each window isolates a portion of the signal so that harmonic wavelet analysis can be performed within that segment. The sliding window method is used to segment the radar echo signal, facilitating local feature analysis.

[0053] The windowed signal segment is subjected to noise reduction processing to obtain the noise-reduced windowed signal segment, which is expressed as:

[0054] ;

[0055] Where, represents the windowed signal segment after noise reduction; represents the windowed signal segment before noise reduction; represents the noise threshold, which is defined as:

[0056] ;

[0057] It can be understood that performing noise reduction processing on the windowed signal segment based on the noise threshold processing method can effectively suppress low-intensity noise components.

[0058] During the specific implementation of step 202, in scenarios involving non-uniform target rotation or complex maneuvers, ISAR echo signals exhibit 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 graph, 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 low-energy segments are moderately weighted to suppress noise, reducing the feature loss or "blurring" often introduced by fixed windows in the time-frequency spectrum.

[0059] Specifically, the expression of signal energy is:

[0060] ;

[0061] The weight function is dynamically defined according to the signal energy. The weight function expression is:

[0062] ;

[0063] Where, Indicates the number of sampling points of the orientation unit; Represents dynamic weight; represents the regularization parameter; represents the adaptive step size parameter; Represents the signal energy.

[0064] It is worth noting that the energy-driven strategy used for basis matrix construction in this embodiment does not introduce additional window splits or overlaps, nor does it change the length of the analysis window. Therefore, it has little impact on the overall algorithm complexity and can significantly improve resolution and enhance robustness in scenarios involving non-stationary ISAR echo Doppler measurements.

[0065] During the specific implementation of step 203, the frequency modulation factor is first defined. It can be understood that when the ISAR target undergoes non-stationary motion, the traditional FT-based method usually leads to severe energy diffusion and imaging quality degradation. In order to overcome this limitation, this embodiment introduces a frequency modulation term in the adaptive harmonic wavelet basis matrix. The frequency modulation factor enables the polynomial behavior to be captured directly in the adaptive harmonic wavelet basis matrix, effectively "reshaping" the frequency axis distribution and enhancing the time-frequency focusing capability. Therefore, the dynamic phase fluctuations caused by target motion can be better matched and reconstructed. The core idea is to convert the system's prior estimate of the 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.

[0066] For each frequency index , the frequency modulation factor expression is:

[0067] ;

[0068] Where, represents the frequency modulation factor; represents an imaginary unit; Indicates the number of sampling points in the orientation unit.

[0069] Then based on the window function , weight function and frequency modulation factor Construct the adaptive harmonic wavelet basis matrix. The adaptive harmonic wavelet basis matrix expression is:

[0070] ;

[0071] Where, represents the adaptive harmonic wavelet basis matrix; represents the base of natural logarithms; represents the adaptive step size parameter; represents the frequency index; Indicates the number of sampling points in the orientation unit.

[0072] In the specific implementation process of step 204, in order 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, which is expressed as:

[0073] ;

[0074] Where, 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.

[0075] The windowed signal segment is transformed according to the regularized basis matrix to obtain the transformation coefficient. The calculation expression is:

[0076] ;

[0077] The transformation coefficients are subjected to nonlinear enhancement processing to obtain the enhanced transformation coefficients, which are expressed as follows:

[0078] ;

[0079] Where, represents the transformation coefficient; represents the regularized basis matrix; represents the windowed signal segment after noise reduction; Represents the transform coefficients after enhancement processing.

[0080] It can be understood that the harmonic wavelet transform is essentially a 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 nonlinear enhancement on the transform coefficients, it helps to highlight high-amplitude features while suppressing low-amplitude noise.

[0081] In the specific implementation process of step 205, based on the transformed coefficients after the enhancement process , the samples collected from each distance unit constitute a time series, and the final time-frequency matrix can be expressed as:

[0082] ;

[0083] The time-frequency matrix is constructed for each distance unit to obtain the final ISAR image time-frequency representation, which is defined as:

[0084] ;

[0085] Where, represents the time-frequency matrix; Indicates the number of sampling points of the orientation unit; Represents the time-frequency representation of ISAR image; matrix Indicates the number of distance unit sampling points.

[0086] The method provided by the present invention first performs noise threshold processing on the window signal to effectively suppress low-intensity noise components. By setting an appropriate threshold, noise interference is filtered out, ensuring the accuracy of coefficient estimation and improving imaging quality.

[0087] The signal energy of each signal within each time window is calculated, and the weight function is dynamically adjusted based on the signal energy. Specifically, the higher the signal energy, the more significant the corresponding weight adjustment, thereby increasing the harmonic wavelet's sensitivity to changes in high-energy signals and enhancing its ability to capture signal details.

[0088] 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 is made to better fit the instantaneous frequency changes of the signal, thereby improving the accuracy and resolution of spectrum analysis.

[0089] An adaptive harmonic wavelet basis matrix is constructed for each windowed signal segment. This basis matrix combines adaptive weight calculation based on signal energy and frequency modulation terms, and uses regularization to reduce the influence of noise and irrelevant frequency components, thereby enhancing the stability and reliability of the transform coefficients.

[0090] By using the adaptive harmonic wavelet basis matrix proposed in the present invention for transformation, efficient extraction and analysis of the signal harmonic components can be achieved, making the extraction of the harmonic components more efficient and accurate, thereby improving the resolution and contrast of the final ISAR image.

[0091] Enhancement processing of the transform coefficients can further enhance the effective signal components, improve imaging contrast, and make the target features more obvious and clear in the image.

[0092] The present invention introduces adaptive parameters and frequency modulation terms to improve the harmonic wavelet transform and constructs an adaptive harmonic wavelet basis matrix. It also performs comprehensive processing through noise suppression, regularization and nonlinear enhancement methods, which can effectively improve the quality of ISAR imaging and generate radar images with higher resolution, minimum image entropy and better contrast.

[0093] In one embodiment, the method provided by the present invention is verified.

[0094] like Figure 2 Figure 2 shows the imaging results of a traditional RD method. While this algorithm can roughly depict the target outline, the image suffers from significant noise interference and a blurred distribution of target scattering points. Energy dispersion is particularly severe in azimuth, primarily due to the method's inability to effectively handle Doppler frequency modulation caused by target motion.

[0095] like Figure 3 Figure 2 shows the imaging results of the STFT method. Although STFT introduces time-frequency analysis for imaging, due to the limitation of fixed window length, there is still a problem of insufficient focus in the target's fast maneuvering area, and the background noise suppression effect is not ideal.

[0096] like Figure 4 Figure 2 shows the imaging results of the SPWVD method. This method demonstrates some advantages in suppressing cross-term interference, resulting in a clearer target outline than the previous two methods, but the target shape is distorted. There is still room for improvement in detail preservation and noise suppression.

[0097] like Figure 5 Figure 2 shows the imaging results of the proposed method. It is clearly observed that the proposed method not only effectively suppresses background noise but also significantly improves target scattering point focus, edge detail preservation, and overall image quality. In particular, in azimuth, the energy of the scattering points is more concentrated, and the target structure is clearer.

[0098] Although this embodiment Figure 1 The steps in the diagram are shown in the order indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may 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 in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0099] Example 2

[0100] Based on the ISAR imaging method based on improved harmonic wavelet transform in Example 1, this embodiment discloses an ISAR imaging device based on improved harmonic wavelet transform, such as Figure 6 As 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, wherein:

[0101] The signal segment acquisition module 401 is used to acquire the windowed signal segment after noise reduction.

[0102] 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.

[0103] The harmonic wavelet basis matrix construction module 403 is used to construct an adaptive harmonic wavelet basis matrix based on a window function, a weight function and a frequency modulation factor.

[0104] The regularization module 404 is used to perform regularization processing on the adaptive harmonic wavelet basis matrix to obtain a regularized basis matrix.

[0105] The transformation module 405 is used to transform the windowed signal segment according to the regularized basis matrix to obtain transformation coefficients.

[0106] The ISAR image reconstruction module 406 is used 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.

[0107] 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 in the method of Example 1, and therefore are not further described in this embodiment. Each unit module can be implemented in whole or in part through software, hardware, or a combination thereof. Each unit module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above unit modules.

[0108] Example 3

[0109] like Figure 7 The terminal device disclosed in this embodiment includes a transmitter, a receiver, a memory, and a processor. 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-executable instructions, and the processor is used to execute the computer-executable instructions stored in the memory to implement the method in the above-mentioned embodiment 1.

[0110] It should be noted that the above memory can be independent or integrated with the processor. When the memory is independently provided, the terminal device further includes a bus for connecting the memory and the processor.

[0111] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the 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-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may 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 many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double 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.

[0112] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, 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.

[0113] The above-described embodiments merely illustrate several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that a person skilled in the art would be able to make numerous modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. An ISAR imaging method based on improved harmonic wavelet transform, characterized in that: The method comprises: Get the windowed signal segment after noise reduction; Calculating the signal energy of the windowed signal segment and dynamically defining a weight function according to the signal energy; Constructing an adaptive harmonic wavelet basis matrix based on the window function, the weight function and the frequency modulation factor; Regularizing the adaptive harmonic wavelet basis matrix to obtain a regularized basis matrix; transforming the windowed signal segment according to the regularized basis matrix to obtain transformation coefficients; A time-frequency matrix is constructed based on the transformation coefficients, and a target image is reconstructed 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, characterized in that: Get the windowed signal segment after noise reduction, including: Acquire a radar echo signal, and segmentally process the radar echo signal using a window function to obtain a plurality of windowed signal segments; Noise reduction processing is performed on the windowed signal segment to obtain a noise-reduced windowed signal segment.

3. The ISAR imaging method based on improved harmonic wavelet transform according to claim 2, characterized in that: The radar echo signal is segmented using a window function to obtain a number of windowed signal segments, including: Determine the window function, the expression is: ； Where, represents the window function; The index variable representing the window function; Indicates the length of the window function; Based on the window function length , combining the sliding window method to segment the radar echo signal, determine the window start index and end index, 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: A weight function is dynamically defined according to the signal energy, and the weight function expression is: ； Where, Represents 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 frequency modulation factor expression is: ； Where, represents the frequency modulation factor; represents an imaginary unit; represents the frequency index; Indicates the number of sampling points in the orientation 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 adaptive harmonic wavelet basis matrix expression is: ； Where, represents the adaptive harmonic wavelet basis matrix; Represents dynamic weight; represents the window function; represents the frequency modulation factor; represents the base of natural logarithms; represents the adaptive step size parameter; represents the frequency index; Indicates the number of sampling points in the orientation unit.

7. The ISAR imaging method based on improved harmonic wavelet transform according to claim 6, characterized in that: Regularization processing is performed on the adaptive harmonic wavelet basis matrix to obtain a regularized basis matrix, which is expressed as: ； Where, 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.

8. The ISAR imaging method based on improved harmonic wavelet transform according to any one of claims 1 to 3, characterized in that: The windowed signal segment is transformed according to the regularized basis matrix to obtain the transformation coefficient, which is calculated as follows: ； The transformation coefficients are subjected to nonlinear enhancement processing to obtain the enhanced transformation coefficients, which are expressed as follows: ； Where, represents the transformation coefficient; represents the regularized basis matrix; represents the windowed signal segment after noise reduction; Represents the transform coefficients after enhancement processing.

9. An ISAR imaging device based on improved harmonic wavelet transform, characterized in that: The device comprises: A signal segment acquisition module, used to obtain the 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 signal energy; A harmonic wavelet basis matrix construction module is used 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 perform regularization processing on the adaptive harmonic wavelet basis matrix to obtain a regularized basis matrix; a transformation module, configured to transform the windowed signal segment according to a regularized basis matrix to obtain transformation coefficients; The ISAR image reconstruction module 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 a final ISAR image.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the ISAR imaging method based on improved harmonic wavelet transform according to any one of claims 1 to 8 are implemented.

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