Image generation method and device, equipment, medium and product
By transforming and correcting the signal vector in a bi-base synthetic aperture radar system, the problem of poor image quality caused by channel error is solved, and clear and accurate image generation is achieved.
Patent Information
- Application Number
- CN202511984744.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-25
- Publication Date
- 2026-03-20
AI Technical Summary
In dual-base synthetic aperture radar systems, existing technologies struggle to accurately correct channel errors, resulting in poor image quality.
By acquiring the reflected echo signal and transforming it to the range-Doppler domain, the sample covariance matrix is determined and eigenvalue decomposition is performed to generate the steering matrix. The signal vector is then corrected using the error correction matrix, and signal reconstruction and imaging processing are performed.
It significantly improves image quality, eliminates ghosting, and provides clear target outlines, thus resolving the issue of image reconstruction performance degradation caused by channel errors.
Smart Images
Figure CN121703813A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of data processing technology, and in particular relates to a method, apparatus, device, medium and product for generating images. Background Technology
[0002] Synthetic Aperture Radar (SAR) technology has been widely used in Earth observation. Azimuth multichannel SAR technology, by sparsely arranging multiple receiver sub-channels in space, can resolve the contradiction between high resolution and wide mapping bandwidth in traditional single-channel SAR systems. However, when this technology is extended from single-base systems to bistatic synthetic aperture radar (BiSAR) systems, the complexity of signal propagation paths and geometric relationships increases dramatically due to the separation of transmit and receive signals, making accurate estimation and compensation of channel errors a significant technical challenge.
[0003] Currently, the main methods for channel error estimation in azimuth multi-channel SAR systems include internal calibration, orthogonal subspace, and signal subspace comparison. However, when applied to azimuth multi-channel spaceborne BiSAR systems, these methods all suffer from inaccurate error correction, resulting in poor image quality. Summary of the Invention
[0004] This application provides an image generation method, apparatus, device, medium, and product that can improve the quality of images generated based on a dual-base synthetic aperture radar system.
[0005] In a first aspect, embodiments of this application provide a method for generating an image, comprising: In a dual-base synthetic aperture radar system with multiple azimuth channels, the auxiliary satellite acquires reflected echo signals received by multiple receiving sub-channels. The reflected echo signal is transformed from the two-dimensional time domain to the range Doppler domain to obtain a frequency domain signal including multiple Doppler units; In the range-Doppler domain, based on the signal vectors of multiple different range gates of each Doppler unit, the sample covariance matrix is determined, and the sample covariance matrix is subjected to eigenvalue decomposition to obtain the actual signal subspace. The sample covariance matrix is used to characterize the correlation of signal vectors of multiple different sub-channels of each Doppler unit. For each Doppler cell, a steering matrix is generated based on the sample covariance matrix of the Doppler cell and the parameters in the pre-acquired bi-base synthetic aperture radar system. For each Doppler unit, based on the actual signal subspace and the steering matrix, the error correction matrix is determined with the goal of matching the actual signal subspace of the Doppler unit after linear transformation with the space spanned by the steering matrix of the Doppler unit. Based on the error correction matrix corresponding to each Doppler unit, the signal vectors of different sub-channels received by each Doppler unit are corrected to obtain the corrected signal vectors. Based on the corrected signal vectors, signal reconstruction and imaging processing are performed to generate the dual-base synthetic aperture radar image corresponding to the auxiliary satellite.
[0006] Secondly, embodiments of this application provide an image generation apparatus, comprising: The acquisition module is used to acquire the reflected echo signals received by the auxiliary satellite in a dual-base synthetic aperture radar system based on multiple receiving sub-channels in the azimuth multi-channel system. The transformation module is used to transform the reflected echo signal from the two-dimensional time domain to the range Doppler domain to obtain a frequency domain signal including multiple Doppler units; The determination module is used to determine the sample covariance matrix in the range-Doppler domain based on the signal vectors of multiple different range gates of each Doppler unit, and to perform eigenvalue decomposition on the sample covariance matrix to obtain the actual signal subspace. The sample covariance matrix is used to characterize the correlation of signal vectors of multiple different sub-channels of each Doppler unit. The generation module is used to generate a steering matrix for each Doppler cell, based on the sample covariance matrix of the Doppler cell and the parameters in the pre-acquired bi-base synthetic aperture radar system. The determination module is also used to determine the error correction matrix for each Doppler unit based on the actual signal subspace and the steering matrix, with the goal of matching the actual signal subspace of the Doppler unit after linear transformation with the space spanned by the steering matrix of the Doppler unit. The generation module is also used to perform error correction on the signal vectors of different sub-channels received by each Doppler unit based on the error correction matrix corresponding to each Doppler unit, to obtain the corrected signal vector, and to perform signal reconstruction and imaging processing based on the corrected signal vector to generate a dual-base synthetic aperture radar image corresponding to the auxiliary satellite.
[0007] Thirdly, embodiments of this application provide an electronic device, the device comprising: Processor and memory storing computer program instructions; The image generation method described in the first aspect is used by the processor to execute computer program instructions.
[0008] Fourthly, embodiments of this application provide a computer storage medium storing computer program instructions, which, when executed by a processor, implement the image generation method described in the first aspect.
[0009] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when processed by a processor, implements the image generation method of the first aspect described above.
[0010] The image generation method, apparatus, device, medium, and product provided in this application embodiment acquire reflected echo signals received by an auxiliary satellite in a dual-base synthetic aperture radar system with multiple receiving sub-channels based on multiple receiving channels. The reflected echo signals are transformed to the range-Doppler domain to obtain frequency domain signals. In the range-Doppler domain, for each Doppler unit, a sample covariance matrix can be determined based on signal vectors with multiple different range gates. The sample covariance matrix is then subjected to eigenvalue decomposition to obtain the actual signal subspace. A steering matrix is then generated based on the sample covariance matrix and parameters. The goal is to match the space spanned by the ideal steering matrix after linear transformation of the actual signal subspace. An error matrix is generated based on the actual signal subspace and the steering matrix to make the generated error matrix more accurate. Finally, the signal vectors of different sub-channels are corrected based on the accurate error matrix to obtain corrected signal vectors. A clear and accurate image is generated by reconstructing and imaging the corrected signal vectors. Attached Figure Description
[0011] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0012] Figure 1 This is a schematic diagram of a dual-base synthetic aperture radar system provided for some embodiments of this application.
[0013] Figure 2 This is a flowchart illustrating an image generation method provided in some embodiments of this application.
[0014] Figure 3 This is a schematic flowchart illustrating another image generation method provided in some embodiments of this application.
[0015] Figure 4 This is a flowchart illustrating another image generation method provided in some embodiments of this application.
[0016] Figure 5 This is a flowchart illustrating another image generation method provided in some embodiments of this application.
[0017] Figure 6 This is a schematic diagram of an image generation apparatus provided for some embodiments of this application.
[0018] Figure 7This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0019] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.
[0020] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.
[0021] Before describing the technical solutions provided in the embodiments of this application, in order to facilitate understanding of the embodiments of this application, this application first specifically explains the problems existing in the related technologies: Bistatic Synthetic Aperture Radar (BiSAR) is a new type of radar system, such as... Figure 1 As shown, Figure 1 This is an example of a BiSAR system where the transmitter (primary satellite) and receiver (secondary satellite) are placed in different locations. The transmitter emits a linear frequency modulated signal, and the ground reflections collected by the receiver along the motion trajectory are processed through complex signal and mathematical methods to be equivalent to a virtual antenna much larger than its actual physical size. To fundamentally and effectively resolve the basic contradiction in the requirement for high pulse repetition frequency (PRF) between high resolution and wide mapping bandwidth in traditional SAR systems, existing technologies combine digital beamforming (DBF) technology with azimuth multi-channel joint processing to simultaneously achieve high resolution and wide mapping bandwidth.
[0022] The new azimuth multi-channel SAR system innovatively utilizes multiple receiving sub-channels in the same direction (such as...) with a lower pulse repetition frequency. Figure 1 The auxiliary satellites RX1, RX2 and RX3 in the satellite simultaneously receive a wide beam emitted by a digital synthesizer. This spatial multisampling compensates for the inadequacy of temporal sampling and effectively overcomes the fundamental limitation of minimum antenna area.
[0023] However, due to various non-ideal external conditions, such as complex electromagnetic environment characteristics and inconsistent antenna patterns across channels, systematic errors inevitably arise in each receiving sub-channel of the satellite payload. Channel errors will cause multi-channel SAR signals to deviate from the ideal model and lead to mismatches in reconstruction filters or steering vectors, resulting in a severe deterioration in signal reconstruction performance and ultimately poor image quality. Therefore, accurately correcting the errors between channels is crucial for the reconstruction of non-uniform linear frequency modulated signals.
[0024] In the field of multi-channel error estimation algorithms, common algorithms include: Internal Calibration Method (ICM), Orthogonal Subspace Method (OSM), Signal Subspace Comparison Method (SSCM), Minimum Mean Square Error Distortion-Free Response Method (MVDRM), and Orientation Autocorrelation Method (ASCM). However, all of these methods suffer from inaccurate phase error estimation, which leads to poor image quality.
[0025] Based on this, embodiments of this application provide an image generation method, apparatus, device, medium, and product that can solve the above-mentioned problems.
[0026] The following is a detailed description of an image generation method provided by an embodiment of this application.
[0027] In some embodiments, such as Figure 2 As shown in the figure, this application provides an image generation method, which may include: S210: Acquires reflected echo signals received by the auxiliary satellite in a dual-base synthetic aperture radar system with azimuth multi-channel based on multiple receiving sub-channels.
[0028] Here, in a dual-base synthetic aperture radar system, the primary satellite can transmit a linear frequency modulated signal to ground objects in low Earth orbit, and the antenna of the secondary satellite can simultaneously receive the reflected echoes from the ground objects using multiple receiving sub-channels along the orbital direction. The reflected echo signals received by the secondary satellite based on multiple receiving sub-channels can be directly acquired.
[0029] S220: Transform the reflected echo signal from the two-dimensional time domain to the range Doppler domain to obtain a frequency domain signal including multiple Doppler units.
[0030] The reflected echo signal can be preprocessed first, for example, by performing range compression, compensating for the four fixed phases of the bistatic single-transmitter multiple-receiver mode response signal, and correcting the Doppler center frequency. Then, the reflected echo signal of each sub-channel is subjected to an azimuth fast Fourier transform, transforming the signal from the two-dimensional time domain to the range-Doppler domain to obtain the corresponding frequency domain signal.
[0031] For example, the aliasing spectrum of the m-th receiving sub-channel can be expressed in the range-Doppler domain as: (1) here, The reflected echo signal from the m-th receiving sub-channel of the auxiliary star in the distance-Doppler domain. For distance gate, The Doppler frequency corresponding to the azimuth time. For amplitude error, For phase error, For composite channel error, I The maximum value of the fuzziness index is given by the number of spectral fuzziness indices, which is defined as ( 2I+1 ), The azimuth spectrum period. For time delay difference, This is an additive noise term.
[0032] Here, the time delay difference is: (2) in,
[0033] and These are the azimuth frequencies of the primary and secondary stars, respectively. and These are the oblique angles of the primary star and the secondary star, respectively. This represents the position information of the m-th sub-channel relative to the reference channel. For the speed of the auxiliary star, The speed of the main star, The slant distance of the beam center of the main star, The slant distance of the beam center of the auxiliary star.
[0034] Using the vector representation method, the aliasing spectrum of the m-th receiving sub-channel in the range-Doppler domain can be expressed as: (3) in,
[0035] S230: In the range-Doppler domain, based on the signal vectors of multiple different range gates of each Doppler unit, the sample covariance matrix is determined, and the sample covariance matrix is subjected to eigenvalue decomposition to obtain the actual signal subspace. The sample covariance matrix is used to characterize the correlation of signal vectors of multiple different sub-channels of each Doppler unit.
[0036] For each Doppler cell, the sample covariance matrix corresponding to that Doppler cell can be determined using the signal vectors of multiple adjacent range gates. Specifically, the sample covariance matrix... The calculation formula is: (4) in, It is the number of samples at the distance gate. Represents Doppler units The signal vectors of multiple different adjacent distance gates.
[0037] The covariance matrix of this sample can be decomposed into eigenvalues to obtain multiple eigenvalues. and the corresponding feature vectors, and The space spanned by the eigenvectors corresponding to the relatively large eigenvalues is taken as the actual signal subspace. U s .
[0038] Here, the eigenvalue decomposition of the sample covariance matrix can be expressed as: (5) here, It is composed of relatively small The noise subspace spanned by the eigenvectors corresponding to each eigenvalue.
[0039] S240: For each Doppler element, a steering matrix is generated based on the sample covariance matrix of the Doppler element and the parameters in the pre-acquired bi-base synthetic aperture radar system.
[0040] Based on pre-acquired parameters from the bistatic synthetic aperture radar (BSAR) system, such as the velocity, beam center slant range, and beam angle of the primary satellite, the velocity, beam center slant range, and beam angle of the secondary satellite, and the position information of each receiving sub-channel in multiple receiving sub-channels, the time delay difference can be determined. Then, the two-dimensional Doppler spectral structure of the azimuth multi-channel spaceborne BiSAR system can be observed using Capon spatial spectrum analysis, and the ambiguity index of each Doppler unit can be selected to accurately construct the guidance matrix of the BiSAR system. .
[0041] S250: For each Doppler unit, based on the actual signal subspace and the steering matrix, the error correction matrix is determined with the goal of matching the actual signal subspace of the Doppler unit after linear transformation with the space spanned by the steering matrix of the Doppler unit.
[0042] The result is obtained from the linear transformation of the guidance matrix. Zhang Cheng's space and actual signal subspace Following the principle of consistency, an optimization function can be established that includes the spatial spectral matrix and the error correction matrix. Based on this optimization function, the error correction matrix can be determined. .
[0043] S260: Based on the error correction matrix corresponding to each Doppler unit, the signal vectors of different sub-channels received by each Doppler unit are corrected to obtain the corrected signal vector. Based on the corrected signal vector, signal reconstruction and imaging processing are performed to generate the dual-base synthetic aperture radar image corresponding to the auxiliary satellite.
[0044] Using the estimated error correction matrix Amplitude and phase errors are compensated for in the signal vectors received from different channels by each Doppler unit. Then, the non-uniform sampled signal can be reconstructed using the multiple Doppler direction constraint method. Finally, a fuzz-free bi-base synthetic aperture radar image is obtained using the traditional single-base equivalent imaging algorithm.
[0045] This application embodiment acquires reflected echo signals received by an auxiliary satellite in a dual-base synthetic aperture radar system with multiple azimuth channels based on multiple receiving sub-channels. The reflected echo signals are transformed to the range-Doppler domain to obtain frequency domain signals. In the range-Doppler domain, for each Doppler cell, a sample covariance matrix can be determined based on signal vectors with multiple different range gates. The sample covariance matrix is then subjected to eigenvalue decomposition to obtain the actual signal subspace. A steering matrix is then generated based on the sample covariance matrix and parameters. The target is identified by matching the space spanned by the ideal steering matrix after linear transformation of the actual signal subspace. An error matrix is generated based on the actual signal subspace and the steering matrix, making the generated error matrix more accurate. Subsequently, the signal vectors of different sub-channels are corrected based on the accurate error matrix to obtain corrected signal vectors. Signal reconstruction and imaging processing of the corrected signal vectors effectively solve the problem of mismatch in reconstruction filters or steering vectors, thereby significantly suppressing paired false targets (ghosting) in the azimuth direction. The imaging results after correction using this method show significantly improved image quality, with clear target outlines and no ghosting.
[0046] In some embodiments, such as Figure 3As shown, for each Doppler cell, a steering matrix is generated based on the sample covariance matrix of the Doppler cell and the pre-acquired parameters in the bistatic synthetic aperture radar system. This matrix may include: S310: For each Doppler unit, based on the sample covariance matrix, the bandwidth of the frequency domain signal is scanned to obtain the two-dimensional Doppler spectrum structure.
[0047] For each Doppler unit Based on the idea of Capon beamformer, bandwidth scanning can be performed on the frequency domain signal to construct the following steering vector. : (6) Let be the azimuth frequency, and satisfy . , For Doppler bandwidth, For phase difference, This refers to the relative phase shift.
[0048] Based on this scanning vector, a two-dimensional Doppler spectral structure (Capon spectrum) is constructed. Represented as: (7) Let q be the scanning frequency of the q-th scanning point. Let p be the Doppler center frequency of the p-th Doppler element. The guide vector for the q-th scan point. The sample covariance matrix of the p-th Doppler unit.
[0049] S320: Determine the effective ambiguity index set based on the frequencies corresponding to multiple spectral peaks greater than or equal to preset values in the two-dimensional Doppler spectral structure and the center frequency of the Doppler unit.
[0050] In a two-dimensional Doppler spectrum, because the system pulse repetition frequency (PRF) is lower than the Doppler bandwidth, spectral aliasing occurs, resulting in multiple separate spectral peaks on the Capon spectrum. Each peak corresponds to an ambiguity index component. A threshold (preset value) can be set to distinguish between valid signal components and noise. The preset value is typically determined based on the noise floor or the average signal power.
[0051] Then, identify the positions of all spectral peaks in the Capon spectrum whose amplitudes are greater than or equal to the preset value. Record the precise frequency value corresponding to each spectral peak. For center frequency is The theoretical frequency of the i-th ambiguity component of the Doppler unit should be . , This is the pulse repetition frequency. The frequency of each detected spectral peak is compared with this theoretical value, and the corresponding ambiguity index i is calculated as follows: (8) By collecting the ambiguity indices i corresponding to all the identified spectral peaks, we can form the effective ambiguity index set I for that Doppler unit.
[0052] S330: Generate a steering matrix based on parameters and a set of effective fuzzy indices.
[0053] Here, the ideal error-free steering vector components can be calculated based on the parameters, and the steering vectors corresponding to all fuzzy components belonging to the effective fuzzy index set can be combined column-wise to obtain the steering matrix.
[0054] This application embodiment obtains the accurate two-dimensional Doppler spectral structure through bandwidth scanning and determines the effective ambiguity index set based on the two-dimensional Doppler spectral structure. This effectively avoids the introduction of redundant noise subspace interference due to the inclusion of non-existent ambiguity components. Based on the parameters and the effective ambiguity index set, a steering matrix is generated, which enables the steering matrix to accurately match the real signal structure of the current Doppler unit. This makes the error correction matrix determined based on the steering matrix more accurate, significantly improves the quality of signal reconstruction, and thus improves the quality of the generated image.
[0055] In some embodiments, such as Figure 4 As shown, the parameters include the velocity of the primary satellite, the slant range of the primary satellite's beam center, the beam angle of the primary satellite, the velocity of the secondary satellite, the slant range of the secondary satellite's beam center, the beam angle of the secondary satellite, and the position information of each receiving sub-channel in the multiple receiving sub-channels of the dual-base synthetic aperture radar system; based on the parameters and the effective ambiguity index set, a steering matrix is generated, which may include: S410: Based on the velocity of the primary satellite, the slant range of the primary satellite's beam center, the beam angle of the primary satellite, the velocity of the secondary satellite, the slant range of the secondary satellite's beam center, the beam angle of the secondary satellite, and the position information of each receiving sub-channel relative to the reference channel, multiple time delay differences are determined. The time delay difference represents the azimuth time offset of each receiving sub-channel relative to the reference channel.
[0056] For the m-th receiving sub-channel, the delay difference can be determined based on the aforementioned expression (2), which will not be elaborated here.
[0057] S420: Input the set of multiple time delay differences and effective ambiguity indices of the Doppler cells into the preset steering vector formula to obtain the steering matrix.
[0058] For the k-th modulus index i in the effective fuzzy index set k Its corresponding guide vector The m-th element is: (9) The steering vectors corresponding to all k effective ambiguity indices are used as column vectors and combined to form the steering matrix of the Doppler cell. for: (10) That is, the final steering matrix is a complex matrix of m×k, whose column space spans the ideal signal space composed of all effective signal components in the current Doppler cell.
[0059] The embodiments of this application determine the time delay difference by parameters, which ensures a high-precision match between the steering matrix and the real signal propagation model. Based on the time delay difference and the effective ambiguity index set, a preset steering vector formula is input to obtain an accurate steering matrix, which is beneficial for generating high-quality images.
[0060] In some embodiments, such as Figure 5 As shown, for each Doppler unit, based on the actual signal subspace and the steering matrix, with the goal of matching the space spanned by the steering matrix of the Doppler unit after linear transformation of the actual signal subspace, the error correction matrix is determined, which may include: S510: For each Doppler unit, determine the spatial spectrum matrix based on the actual signal subspace and the steering matrix, whereby the spatial spectrum matrix is used to characterize the linear transformation relationship between the actual signal subspace and the space spanned by the steering matrix.
[0061] According to signal subspace theory, in the ideal case with no channel error, the actual signal subspace... With ideal guidance matrix The spanned spaces should be completely consistent. However, due to channel errors, a linear transformation relationship exists between them. To characterize this relationship, we can first construct a spatial spectral matrix. as follows: (11) express The orthogonal matrix of the projection matrix, It represents the Hadamah accumulation. Let be the projection matrix of the signal subspace.
[0062] S520: To achieve the goal of matching the actual signal subspace after linear transformation with the space spanned by the steering matrix of the Doppler unit, an optimization function including the spatial spectrum matrix and the error correction matrix is established.
[0063] The goal is to find a space that best matches the space spanned by the error correction matrix and the ideal steering matrix. This can be transformed into a minimization problem.
[0064] Establish the following optimization function: (12) S530: Find the solution that makes the optimization function converge, and obtain the error correction matrix.
[0065] The error correction matrix obtained by solving as follows: (13) This application's embodiments transform the sub-channel error estimation problem into a mathematically constrained optimization problem by establishing a linear transformation relationship between the actual signal subspace and the steering matrix. By solving this problem, the amplitude and phase errors of each sub-channel can be estimated simultaneously and with high precision, resulting in an accurate error correction matrix.
[0066] In some embodiments, an optimization function including a spatial spectrum matrix and an error correction matrix is established, including: The spatial spectrum matrix is diagonally loaded to obtain a diagonal matrix; an optimization function is established based on the diagonal matrix and the error correction matrix.
[0067] Here, the condition for formula (13) above to hold is: It is a positive definite matrix; however, in practice, the matrix... In some Doppler units, the values are close to singularities, which can lead to inaccurate results. Therefore, a diagonal loading method is used to improve the algorithm's robustness against singularities and disturbances. Diagonal loading is performed as follows: (14) Let X be a diagonal matrix, Δ be a diagonal loading factor, and X be the identity matrix.
[0068] The optimization function can be constructed based on the diagonal matrix mentioned above using the above formula (12), which will not be elaborated here.
[0069] The embodiments of this application can effectively improve the condition number of the matrix by diagonally loading the spatial spectrum matrix, making it far from singularity, thereby ensuring the numerical stability of the subsequent matrix inversion and optimization solution process, and enabling the method to operate reliably under various signal conditions (including low signal-to-noise ratio scenarios).
[0070] In some embodiments, transforming the reflected echo signal from the two-dimensional time domain to the range Doppler domain to obtain a frequency domain signal comprising multiple Doppler units may include: The reflected echo signal is preprocessed to obtain a preprocessed reflected echo signal; the preprocessed reflected echo signal is then transformed from the two-dimensional time domain to the range Doppler domain to obtain a frequency domain signal including multiple Doppler units.
[0071] The reflected echo signal can be preprocessed, including range compression, phase compensation and Doppler center correction, to obtain a preprocessed reflected echo signal. The preprocessed reflected echo signal is then transformed from the two-dimensional time domain to the range-Doppler domain to obtain a frequency domain signal including multiple Doppler units.
[0072] The embodiments of this application preprocess the original echo signal to avoid interference from the fixed phase on the estimation of the relative phase difference between sub-channels, which is the basis for generating high-quality images.
[0073] Based on the same inventive concept, embodiments of this application also provide an image generation apparatus.
[0074] In some embodiments, such as Figure 6 As shown, this application embodiment provides an image generation apparatus, including: The acquisition module 601 is used to acquire the reflected echo signals received by the auxiliary satellite based on multiple receiving sub-channels in a dual-base synthetic aperture radar system with azimuth multi-channel. The transformation module 602 is used to transform the reflected echo signal from the two-dimensional time domain to the range Doppler domain to obtain a frequency domain signal including multiple Doppler units; The determination module 603 is used to determine the sample covariance matrix in the range Doppler domain based on the signal vectors of multiple different range gates of each Doppler unit, and to perform eigenvalue decomposition on the sample covariance matrix to obtain the actual signal subspace, wherein the sample covariance matrix is used to characterize the correlation of signal vectors of multiple different sub-channels of each Doppler unit. The generation module 604 is used to generate a steering matrix for each Doppler element based on the sample covariance matrix of the Doppler element and the parameters in the pre-acquired bi-base synthetic aperture radar system. The determination module 603 is also used to determine the error correction matrix for each Doppler unit based on the actual signal subspace and the steering matrix, with the goal of matching the actual signal subspace of the Doppler unit after linear transformation with the space spanned by the steering matrix of the Doppler unit. The generation module 604 is also used to perform error correction on the signal vectors of different sub-channels received by each Doppler unit based on the error correction matrix corresponding to each Doppler unit, to obtain the corrected signal vector, and to perform signal reconstruction and imaging processing based on the corrected signal vector to generate a dual-base synthetic aperture radar image corresponding to the auxiliary satellite.
[0075] This application embodiment acquires reflected echo signals received by the auxiliary satellite in a dual-base synthetic aperture radar system with multiple receiving sub-channels based on multiple receiving channels through an acquisition module. A transformation module transforms the reflected echo signals to the range-Doppler domain to obtain frequency domain signals. A determination module, in the range-Doppler domain, determines the sample covariance matrix for each Doppler unit based on signal vectors from multiple different range gates, and performs eigenvalue decomposition on the sample covariance matrix to obtain the actual signal subspace. Then, a generation module generates a steering matrix based on the sample covariance matrix and parameters. The determination module uses the space spanned by the ideal steering matrix after linear transformation of the actual signal subspace as the target. An error matrix is generated based on the actual signal subspace and the steering matrix. Finally, the generation module corrects the signal vectors of different sub-channels based on the accurate error matrix to obtain corrected signal vectors. Signal reconstruction and imaging processing are then performed on the corrected signal vectors to generate clear and accurate images.
[0076] In some embodiments, the generation module is configured to: For each Doppler unit, bandwidth scanning is performed on the frequency domain signal based on the sample covariance matrix to obtain the two-dimensional Doppler spectrum structure; Based on the frequencies corresponding to multiple spectral peaks greater than or equal to preset values in the two-dimensional Doppler spectral structure and the center frequency of the Doppler unit, the effective fuzzy index set is determined. Based on the parameters and the effective fuzzy index set, a guidance matrix is generated.
[0077] In some embodiments, the parameters include the velocity of the primary satellite in the dual-base synthetic aperture radar system, the beam center slant range of the primary satellite, the beam angle of the primary satellite, the velocity of the secondary satellite, the beam center slant range of the secondary satellite, the beam angle of the secondary satellite, and the position information of each receiving sub-channel in the multiple receiving sub-channels; the generation module is used for: Based on the velocity of the primary satellite, the slant range of the primary satellite's beam center, the beam angle of the primary satellite, the velocity of the secondary satellite, the slant range of the secondary satellite's beam center, the beam angle of the secondary satellite, and the position information of each receiving sub-channel relative to the reference channel, multiple time delay differences are determined. The time delay difference characterizes the azimuth time offset of each receiving sub-channel relative to the reference channel. By inputting the multiple time delay differences and effective ambiguity index set of the Doppler cells into the preset steering vector formula, the steering matrix is obtained.
[0078] In some embodiments, the determining module is configured to: For each Doppler unit, a spatial spectrum matrix is determined based on the actual signal subspace and the steering matrix, wherein the spatial spectrum matrix is used to characterize the linear transformation relationship between the actual signal subspace and the space spanned by the steering matrix; With the goal of matching the actual signal subspace after linear transformation with the space spanned by the steering matrix of the Doppler unit, an optimization function including the spatial spectrum matrix and the error correction matrix is established. Find the solution that makes the optimization function converge, and obtain the error correction matrix.
[0079] In some embodiments, the determining module is configured to: Diagonal loading is applied to the spatial spectral matrix to obtain a diagonal matrix; An optimization function is established based on the diagonal matrix and the error correction matrix.
[0080] In some embodiments, the transformation module is configured to: The reflected echo signal is preprocessed to obtain the preprocessed reflected echo signal; The preprocessed reflected echo signal is transformed from the two-dimensional time domain to the range Doppler domain to obtain a frequency domain signal containing multiple Doppler units.
[0081] The apparatus of the above embodiments is used to implement the corresponding image generation method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.
[0082] Figure 7 A schematic diagram of the hardware structure of an electronic device is provided in the application embodiment.
[0083] The electronic device 700 may include a processor 701 and a memory 702 storing computer program instructions.
[0084] Specifically, the processor 701 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.
[0085] Memory 702 may include mass storage for data or instructions. For example, and not limitingly, memory 702 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 702 may include removable or non-removable (or fixed) media. Where appropriate, memory 702 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 702 is non-volatile solid-state memory.
[0086] In a particular embodiment, memory 702 includes read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.
[0087] Memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to the first aspect of this application.
[0088] The processor 701 reads and executes computer program instructions stored in the memory 702 to implement any of the image generation methods in the above embodiments.
[0089] In one example, the electronic device may also include a communication interface 703 and a bus 704. Wherein, as... Figure 7 The processor 701, memory 702, and communication interface 703 are connected through bus 704 and complete communication with each other.
[0090] The communication interface 703 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.
[0091] Bus 704 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 704 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.
[0092] The electronic devices described above are used to implement the corresponding image generation methods in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.
[0093] Furthermore, in conjunction with the image generation methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the image generation methods in the above embodiments.
[0094] Furthermore, in conjunction with the image generation methods in the above embodiments, this application embodiment can provide a computer program product for implementation. When the instructions of this computer program product are executed by the processor of an electronic device, they implement any of the image generation methods in the above embodiments.
[0095] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.
[0096] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.
[0097] It should also be noted that the exemplary embodiments mentioned in this application describe methods or apparatuses based on a series of steps or devices. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.
[0098] The aspects of this application have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of this application. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.
[0099] The above description is merely a specific embodiment of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.
Claims
1. A method for generating an image, characterized in that, include; In a dual-base synthetic aperture radar system with multiple azimuth channels, the auxiliary satellite acquires reflected echo signals received by multiple receiving sub-channels. The reflected echo signal is transformed from the two-dimensional time domain to the range Doppler domain to obtain a frequency domain signal including multiple Doppler units; In the range-Doppler domain, based on the signal vectors of multiple different range gates of each Doppler unit, a sample covariance matrix is determined, and the sample covariance matrix is subjected to eigenvalue decomposition to obtain the actual signal subspace. The sample covariance matrix is used to characterize the correlation of signal vectors of multiple different sub-channels of each Doppler unit. For each Doppler cell, a steering matrix is generated based on the sample covariance matrix of the Doppler cell and the parameters in the pre-acquired bistatic synthetic aperture radar system. For each Doppler unit, based on the actual signal subspace and the steering matrix, an error correction matrix is determined with the goal of matching the actual signal subspace of the Doppler unit after linear transformation with the space spanned by the steering matrix of the Doppler unit. Based on the error correction matrix corresponding to each Doppler unit, the signal vectors of different sub-channels received by each Doppler unit are corrected to obtain the corrected signal vectors. Based on the corrected signal vectors, signal reconstruction and imaging processing are performed to generate the dual-base synthetic aperture radar image corresponding to the auxiliary satellite.
2. The image generation method according to claim 1, characterized in that, For each Doppler cell, a steering matrix is generated based on the sample covariance matrix of the Doppler cell and the pre-acquired parameters in the bistatic synthetic aperture radar system, including: For each Doppler unit, based on the sample covariance matrix, the bandwidth of the frequency domain signal is scanned to obtain a two-dimensional Doppler spectrum structure; Based on the frequencies corresponding to multiple spectral peaks greater than or equal to preset values in the two-dimensional Doppler spectral structure and the center frequency of the Doppler unit, an effective fuzzy index set is determined. The guidance matrix is generated based on the parameters and the set of effective fuzzy indices.
3. The image generation method according to claim 2, characterized in that, The parameters include the velocity of the primary satellite in the dual-base synthetic aperture radar system, the beam center slant range of the primary satellite, the beam angle of the primary satellite, the velocity of the secondary satellite, the beam center slant range of the secondary satellite, the beam angle of the secondary satellite, and the position information of each of the multiple receiving sub-channels. The process of generating the steering matrix based on the parameters and the effective fuzzy index set includes: Based on the velocity of the primary star, the slant range of the primary star's beam center, the beam angle of the primary star, the velocity of the secondary star, the slant range of the secondary star's beam center, the beam angle of the secondary star, and the position information of each receiving sub-channel relative to the reference channel, multiple time delay differences are determined, and the time delay differences characterize the azimuth time offset of each receiving sub-channel relative to the reference channel. The time delay difference of the Doppler unit and the set of effective ambiguity indices are input into a preset steering vector formula to obtain the steering matrix.
4. The image generation method according to claim 1, characterized in that, For each Doppler unit, the error correction matrix is determined based on the actual signal subspace and the steering matrix, with the goal of matching the space spanned by the steering matrix of the Doppler unit after linear transformation with the space spanned by the actual signal subspace of the Doppler unit. This includes: For each Doppler unit, a spatial spectrum matrix is determined based on the actual signal subspace and the steering matrix, wherein the spatial spectrum matrix is used to characterize the linear transformation relationship between the actual signal subspace and the space spanned by the steering matrix; With the goal of matching the actual signal subspace after linear transformation with the space spanned by the steering matrix of the Doppler unit, an optimization function including the spatial spectrum matrix and the error correction matrix is established. Find the solution that makes the optimization function converge, and obtain the error correction matrix.
5. The image generation method according to claim 4, characterized in that, The establishment of the optimization function, which includes the spatial spectral matrix and the error correction matrix, includes: The spatial spectral matrix is diagonally loaded to obtain a diagonal matrix; The optimization function is established based on the diagonal matrix and the error correction matrix.
6. The image generation method according to claim 1, characterized in that, The reflected echo signal is transformed from the two-dimensional time domain to the range Doppler domain to obtain a frequency domain signal comprising multiple Doppler units, including: The reflected echo signal is preprocessed to obtain the preprocessed reflected echo signal; The preprocessed reflected echo signal is transformed from the two-dimensional time domain to the range Doppler domain to obtain a frequency domain signal including multiple Doppler units.
7. An image generation apparatus, characterized in that, include; The acquisition module is used to acquire the reflected echo signals received by the auxiliary satellite in a dual-base synthetic aperture radar system based on multiple receiving sub-channels in the azimuth multi-channel system. The transformation module is used to transform the reflected echo signal from the two-dimensional time domain to the range Doppler domain to obtain a frequency domain signal including multiple Doppler units; The determination module is used to determine the sample covariance matrix in the range Doppler domain based on the signal vectors of multiple different range gates of each Doppler unit, and to perform eigenvalue decomposition on the sample covariance matrix to obtain the actual signal subspace, wherein the sample covariance matrix is used to characterize the correlation of signal vectors of multiple different sub-channels of each Doppler unit. The generation module is used to generate a steering matrix for each Doppler cell based on the sample covariance matrix of the Doppler cell and the parameters in the pre-acquired bi-base synthetic aperture radar system. The determining module is further configured to, for each of the Doppler units, determine an error correction matrix based on the actual signal subspace and the steering matrix, with the goal of matching the space spanned by the steering matrix of the Doppler unit after linear transformation of the actual signal subspace of the Doppler unit. The generation module is further configured to perform error correction on the signal vectors of different sub-channels received by each Doppler unit based on the error correction matrix corresponding to each Doppler unit, to obtain the corrected signal vector, and perform signal reconstruction and imaging processing based on the corrected signal vector to generate the dual-base synthetic aperture radar image corresponding to the auxiliary satellite.
8. An electronic device, characterized in that, The device includes: a processor, and a memory storing computer program instructions; The processor reads and executes the computer program instructions to implement the image generation method as described in any one of claims 1 to 6.
9. A readable storage medium, characterized in that, The readable storage medium stores computer program instructions that, when executed by a processor, implement the image generation method as described in any one of claims 1 to 6.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is processed by a processor, it implements the image generation method as described in any one of claims 1 to 6.