79 results about "Inverse synthetic aperture radar" patented technology

The invention is suitable for the technical field of radar signal processing, and provides a space target SFM three-dimensional reconstruction method based on a multi-frame ISAR image, and the method comprises the steps: carrying out the preprocessing and imaging of large-angle inverse synthetic aperture radar ISAR echo data of a space target, and obtaining a multi-frame ISAR image sequence; extracting scattering points in each frame of ISAR image sequence according to an orthogonal matching pursuit algorithm, and performing feature matching on adjacent frames of ISAR image sequences to obtain a scattering point set; obtaining instantaneous radar sight line information, and constructing a projection matrix from a space target scattering center three-dimensional coordinate to an ISAR two-dimensional pixel plane according to the instantaneous radar sight line information; and obtaining a three-dimensional reconstruction result according to the scattering point set and the projection matrix in combination with a triangulation principle in an incremental motion recovery structure SFM method. According to the method, the robustness and precision of three-dimensional reconstruction can be improved.

The invention discloses a satellite-borne inverse synthetic aperture radar motion compensation imaging calibration integration method, equipment and a medium, and relates to the technical field of radar signal processing and radar imaging. The objective of the invention is to solve the problems of inaccurate motion compensation and difficult large-rotation-angle imaging in ISAR imaging. According to the method, a particle swarm optimization algorithm is adopted to optimize motion parameters, optimal motion parameters are obtained, and the motion parameters comprise relative translation motion parameters and relative rotation motion parameters of a target and a radar; and obtaining focusing and calibration imaging results based on the optimal motion parameters and echo data reflected by the target. The influence of the translation compensation effect on rotation parameter estimation is reduced, more accurate parameter estimation and better imaging focusing performance are realized, and the ISAR imaging problem under the condition of violent relative movement in the rendezvous scene is solved.

A method for recognizing maneuvering space targets based on a complex region graph transformer relates to the field of inverse synthetic aperture radar image processing. The present invention aims to solve the problem that rapid maneuvers of space targets can cause large posture changes in ISAR images, and since ISAR images usually exist in the form of complex values, general real domain networks can lead to the loss of key phase information in ISAR images. First, the present invention uses a contrastive learning module to train ISAR image block pairs to adapt to changes in the target ISAR image. Secondly, the PGT feature extraction module extracts local and global context features of the image blocks through a graph reasoning method and a Transformer framework to obtain a more effective representation of the target. Finally, the graph recognition module updates the features of the nodes and edges between nodes formed by feature embedding, and outputs the classification probability and classification results of the target.

The invention discloses an ISAR (Inverse Synthetic Aperture Radar) multi-pulse accumulation cross-correlation distance and phase correction system based on an FPGA (Field Programmable Gate Array). The ISAR multi-pulse accumulation cross-correlation distance and phase correction system comprises an upper computer and the FPGA, the upper computer is used for controlling the inverse synthetic aperture radar system to transmit a signal and receiving an echo signal; performing de-line frequency modulation on the echo signal to obtain an echo signal after pulse compression, and preprocessing the echo signal to obtain a to-be-processed echo signal; quantizing the echo signal to be processed, acquiring a quantized echo signal, transmitting the quantized echo signal to the FPGA, and receiving the distance returned by the FPGA and the echo signal after phase correction; and the FPGA is used for executing a multi-pulse cumulative cross-correlation envelope alignment algorithm to perform alignment processing on the quantized echo signal, obtaining a corrected echo signal and returning the corrected echo signal. The register transfer stage circuit based on the multi-pulse cumulative cross-correlation envelope alignment algorithm is designed based on the FPGA, high-parallelization acceleration based on the multi-pulse cumulative cross-correlation envelope alignment algorithm module is achieved, and the processing speed and efficiency of the algorithm are remarkably improved.

The invention discloses a maneuvering target rotation compensation and ISAR (Inverse Synthetic Aperture Radar) imaging method based on a parameter generation network, and mainly solves the problems that the maneuvering target imaging performance is reduced and ISAR scenes with different signal-to-noise ratios and defect rates are not stable when echoes are deficient and the signal-to-noise ratio is low in the prior art. According to the implementation scheme, the method comprises the following steps: establishing a sparse observation echo model S under a maneuvering target echo defect condition according to an RD imaging principle; generating a plurality of groups of point simulation label images X with scattering point positions randomly distributed and amplitude obeying normal distribution, and combining the point simulation label images X with the S to obtain a training set; constructing a T-layer imaging network comprising a trunk unit and a parameter generation unit according to the sparse observation echo model; using the training set to train the imaging network through back propagation iteration; and inputting echo data of the maneuvering target into the trained imaging network to obtain a final imaging result. According to the method, the maneuvering target rotation parameter estimation and imaging performance under the complex observation condition can be improved, the robustness of the imaging network to different observation conditions is improved, and the method can be used for ship-borne and airborne inverse synthetic aperture radars.

The invention is suitable for the technical field of radar signal processing, and provides a sparse-neighborhood joint constraint-based deep expansion ISAR super-resolution imaging method, which comprises the steps of firstly constructing an ISAR image degradation model, constructing an ISAR super-resolution imaging problem based on the ISAR degradation model and a compressed sensing theory, and solving the ISAR super-resolution imaging problem by using an ADMM (Amplitude Division Multiplexing). The process of solving the ISAR super-resolution imaging problem by the ADMM is expanded into a multi-stage neural network, and finally, the low-resolution ISAR echo is input into the trained neural network to obtain an ISAR super-resolution imaging result. According to the method, the sparsity constraint and the neighborhood amplitude constraint are combined, the corresponding signal model and the ADMM solving algorithm are deduced, the reconstruction capability of the algorithm on complex target details and the representation capability on structural information are effectively improved, and effective super-resolution imaging can be realized based on narrow-band short-aperture echoes.

The invention belongs to the technical field of inverse synthetic aperture radars, and particularly relates to a tidal deformation dynamic modeling and parameter inversion method based on hybrid GRFT. The specific process is as follows: dynamic slope distance and echo modeling: aiming at each scattering point of a target celestial body, combining a radar position and introducing three-dimensional rotation correction caused by rotation to calculate a dynamic slope distance, and simulating a radar echo signal based on the dynamic slope distance; echo processing based on mixed GRFT: performing pulse compression on echo signals, compensating phase errors caused by rotation and deformation in sub-apertures by using the mixed GRFT, and performing non-coherent superposition on GRFT output of each sub-aperture; searching parameters: setting a search range of initial height and dynamic viscosity of each scattering point, setting a fitness function as non-coherent superposition energy synthesis of each scattering point, and searching in the search range until a termination condition is met; and tide deformation quantity calculation: calculating the tide deformation quantity according to the estimated values of the initial height and the dynamic viscosity of the searched scattering points.

The invention discloses an inverse synthetic aperture radar imaging and motion compensation method based on OFDM signals. Interference between distance units is effectively suppressed in a distance compression process by utilizing OFDM signals with enough long cyclic prefixes, and estimation errors are reduced. In order to solve the problem of over-resolution unit migration occurring under high-resolution or long-coherent processing, a vectorization observation model containing low-order and high-order phase error terms of target motion is constructed, and a target image and rotation parameters are jointly estimated through two layers of prior models under a sparse Bayesian framework. In order to enhance the calculation robustness of matrix inversion, joint optimization is carried out by adopting an alternating direction multiplier method, and target rotation parameters are accurately estimated by utilizing a maximum image contrast criterion, so that joint imaging and motion compensation are realized. A simulation result shows that the method has remarkable advantages in the aspects of reducing sidelobe interference, improving resolution and realizing real-time imaging, and is suitable for high-resolution inverse synthetic aperture radar imaging of a non-cooperative target.

The invention provides a translation compensation method for an inverse synthetic aperture radar image of a non-cooperative target, and relates to the technical field of radar signal processing and radar imaging, and the method comprises the steps: carrying out the calculation of a target motion and a radar echo signal based on the inverse synthetic aperture radar image of the non-cooperative target, and obtaining a receiving signal; filtering the received signal to obtain a high-resolution range profile sequence; obtaining a motion offset prediction model by using a spatial feature extraction module and a time sequence decoding module; processing the high-resolution range profile sequence by using the trained motion offset prediction model to obtain a translation offset estimation sequence; and constructing a translation compensation item by using the translation offset estimation sequence, performing translation compensation on the compressed echo to obtain a translation compensation result, and completing envelope and phase joint compensation and inverse synthetic aperture radar imaging. According to the method, the problem of translational motion compensation caused by uncertainty and complexity of a non-cooperative target motion mode is solved.

The application relates to a space target joint imaging and attitude inversion method based on a spaceborne platform, and relates to the field of inverse synthetic aperture radar imaging, in particular to a space target joint imaging and attitude inversion method based on a spaceborne platform. The application aims to solve the problems that in the existing space target imaging process of a spaceborne platform, three-dimensional high-order space-variable range migration and phase errors caused by complex relative motion between the target and the platform are difficult to realize high-precision imaging, and the existing attitude estimation algorithm needs to be completely compensated for the rectangular component segmentation and orbit error before the attitude inversion can be realized. The process is as follows: one, a narrowband tracking system of a radar obtains target slant range and a radar line-of-sight unit vector; two, a wedge-shaped transformed signal is obtained; three, spatial Doppler parameter distribution of a special point is obtained; four, an imaging result is obtained; and five, accurate estimation of target attitude parameters is completed through plane coefficient fitting.

The application provides a kind of space target ISAR image component intelligent segmentation and attitude estimation method, through inverse synthetic aperture radar to space target is continuously observed, and utilize distance-Doppler imaging algorithm to the echo is sequentially imaged, obtain the ISAR image sequence of space target;Utilize deep learning network Pix2pixGAN to the ISAR image sequence is segmented, and the segmentation accuracy is higher;After removing the invalid connected region in each component segmentation result, obtain the final segmentation image of each component;Linear structure extraction is carried out to the final each component segmentation image using the minimum circumscribed rectangle method, and the method is lower to the image component segmentation accuracy requirement, and the robustness is stronger;Finally, according to the imaging principle of radar observation and ISAR image, an unconstrained optimization problem for solving three-dimensional attitude is constructed, and a particle swarm optimization algorithm is used for solving, and the solving efficiency is higher, to realize the attitude inversion of space target key component.

The invention discloses an unmanned aerial vehicle ISAR instantaneous imaging method based on differential evolution optimization SPWVD. The method comprises the following steps of performing distance compression, envelope alignment and phase compensation on a received echo signal to obtain processed data; the method comprises the following steps of: processing data, replacing fast Fourier transform (FFT) in an orientation direction in a distance Doppler algorithm with a time-frequency analysis method of smooth pseudo Wiener distribution (SPWVD) by using the processed data to obtain a preliminary distance-instantaneous Doppler image, optimizing two smooth windows of the SPWVD by using a differential evolution (DE) algorithm, and imaging again. According to the method, the inverse synthetic aperture radar algorithm of SPWVD is improved by introducing the differential evolution algorithm optimization process, the imaging quality of the maneuvering target inverse synthetic aperture radar can be effectively improved, the target imaging result is clearer, and the entropy value of the imaging image is smaller.

The invention provides a space target balance mode intelligent discrimination method and apparatus. The method comprises the steps of obtaining an inverse synthetic aperture radar image data set and performing preprocessing; constructing a convolutional neural network and long short-term memory network fusion model, wherein the fusion model comprises a feature extraction module, a time sequence modeling module and a classification module; training the fusion model based on the preprocessed inverse synthetic aperture radar image data set; an inverse synthetic aperture radar image to be recognized is preprocessed and then input into the trained fusion model, and the fusion model outputs a balance mode judgment result corresponding to the inverse synthetic aperture radar image. According to the invention, balance mode discrimination is carried out through the constructed convolutional neural network and long and short-term memory network fusion model, so that the recognition precision and robustness can be improved; and the fusion model is trained by using the preprocessed inverse synthetic aperture radar image data set, so that the fusion model can realize efficient and accurate balance state evaluation in a complex scene.

The invention discloses an ISAR weak component enhancement method and device based on noise perception Transform, and relates to the technical field of image processing, and the method comprises the steps: obtaining a to-be-processed inverse synthetic aperture radar image; processing an inverse synthetic aperture radar image to be processed by adopting the trained phase perception multi-scale Transform network to obtain an enhanced inverse synthetic aperture radar image; wherein the trained phase perception multi-scale Transform network takes preset category data as a training data set to realize collaborative optimization of weak component enhancement and noise suppression, and the phase perception multi-scale Transform network is obtained by training an initial phase perception multi-scale Transform network. Noise can be eliminated while weak components are enhanced.

The present invention relates to a radar imaging method and system, and specifically to an inverse synthetic aperture radar imaging method and system based on co-prime dual-channel downsampling. It overcomes the problems existing in the inverse synthetic aperture radar imaging method under existing downsampling, such as large sampling difficulty, large time error, high hardware implementation difficulty, inability to guarantee imaging performance under low signal-to-noise ratio, and low reliability of sparse reconstruction. First, a co-prime dual-channel ADC sampling combination is used to downsample the inverse synthetic aperture radar echo; then, based on the reconstruction method, a high-resolution range image of sparse reconstruction is obtained, and further a two-dimensional ISAR image is obtained; compared with random sampling, the co-prime dual-channel ADC sampling proposed by the present invention is easy to implement in hardware and has small system error, and it is easier to ensure the accuracy of sampling; at the same time, the influence of noise on inverse synthetic aperture radar imaging is considered, and the influence of noise is considered in the sparse solution process, realizing the reconstruction of a high-performance and high-resolution radar range image under low signal-to-noise ratio.

The invention discloses an inverse synthetic aperture radar imaging method and system with phase distortion and two-dimensional compression sampling, terminal equipment and a medium, and relates to the technical field of radar imaging. The method comprises the following steps: constructing a sparse recovery model containing phase error correction based on a low-rank and sparsity principle; wherein the sparse recovery model is adapted to an inverse synthetic aperture radar echo signal which is subjected to two-dimensional compression sampling and has phase distortion; and based on a unified alternating direction multiplier method framework, solving the sparse recovery model to complete sparse recovery and phase error correction of the two-dimensional compressed sampling signal, and outputting an inverse synthetic aperture radar image. According to the method, the problems that phase errors are ignored in existing inverse synthetic aperture radar imaging and phase correction fails during down-sampling are solved, a clear image can be generated, and the calculation complexity is reduced.

A maneuvering target inverse synthetic aperture radar imaging method relates to the technical field of microwave remote sensing. The objective of the invention is to solve the problem of poor quality of an existing ISAR imaging method. According to the maneuvering target inverse synthetic aperture radar imaging method, the position of a particle swarm is generated randomly, the position of each particle is updated based on a particle swarm algorithm, and the particles are radar scattering points; dividing the particle population after position updating based on a hierarchical clustering method to obtain sub-populations; performing particle swarm optimization based on generalized Radon-Fourier transform on particles in each sub-population to obtain a global optimal position in each sub-population; and reconstructing echo signals of radar scattering points by using all global optimal positions of the population, and performing two-dimensional FFT processing on the reconstructed echo signals to obtain an inverse synthetic aperture radar imaging result of the maneuvering target.

The application provides a space target balance mode intelligent discrimination method and device, including obtaining inverse synthetic aperture radar image data sets and preprocessing; a convolutional neural network and a long short-term memory network fusion model are constructed, the fusion model includes a feature extraction module, a time sequence modeling module and a classification module; the fusion model is trained based on the preprocessed inverse synthetic aperture radar image data sets; the inverse synthetic aperture radar image to be identified is preprocessed and input into the trained fusion model, and the fusion model outputs the balance mode discrimination result corresponding to the inverse synthetic aperture radar image. The balance mode discrimination is performed through the constructed convolutional neural network and long short-term memory network fusion model, the recognition accuracy and robustness can be improved; and the fusion model is trained based on the preprocessed inverse synthetic aperture radar image data sets, so that the fusion model can realize efficient and accurate balance state evaluation in a complex scene.

The invention relates to a meshless azimuth super-resolution sparse aperture ISAR (inverse synthetic aperture radar) imaging method and a meshless azimuth super-resolution sparse aperture ISAR imaging system, which are used for performing one-dimensional imaging processing on all effective distance units of received echo to obtain a super-resolution one-dimensional azimuth image of a target and further obtain a super-resolution ISAR image of the target. Sparse representation of missing azimuth echoes in a single distance unit is established based on a meshless compressed sensing framework, super-resolution one-dimensional azimuth imaging is modeled into an underdetermined inverse problem based on OANM and solved through a fast algorithm, and after a positive semidefinite Toeplitz matrix and an echo signal of the azimuth echoes of the distance unit are reconstructed, the sparse representation of the missing azimuth echoes in the distance unit is obtained. Vandermonde decomposition is carried out on the reconstructed Toeplitz matrix to obtain a super-resolution one-dimensional azimuth image, and a super-resolution ISAR image with a complete target is drawn, so that the technical effects of avoiding an off-grid error, being not constrained by a minimum atomic distance and improving the ISAR image quality under sparse aperture and short aperture radar observation conditions are achieved.

The invention relates to the technical field of data transmission mechanisms, in particular to an ISAR (inverse synthetic aperture radar) imaging multi-channel data high-bandwidth dual-mode transmission system based on an FPGA (field programmable gate array). The ISAR imaging multi-channel data high-bandwidth dual-mode transmission system is oriented to a plurality of independently configured data transmission channels, and each data transmission channel works cooperatively with a DDR (double data rate) data processing module A and a DDR data processing module B through a data channel selection module. A multi-channel data transmission function is realized on an FPGA platform, and the requirements of high-real-time signal processing applications such as ISAR imaging and the like on continuous high-bandwidth data transmission are met; two groups of independent DDR data processing modules A and B are adopted, the DDR data processing modules A and B correspond to independent data paths and control logics, one of the DDR data processing modules A and B executes data writing or original sequence reading operation, and the other one of the DDR data processing modules A and B executes transposition reading or other read-write operation in a clock period, so that the data processing efficiency is improved. Therefore, double-channel concurrent access is achieved, and read-write conflicts and pipeline blocking in a traditional single-channel architecture are eliminated.

The invention belongs to the technical field of inverse synthetic aperture radars, and particularly relates to an asteroid point cloud intelligent reconstruction method based on a single-view radar image. The method specifically comprises the steps that an asteroid point cloud intelligent reconstruction network is constructed, the asteroid point cloud intelligent reconstruction network comprises an encoder module and a generator module, a discard layer and a batch normalization layer are introduced behind each convolution layer and hidden layer of the encoder module, the generator module is composed of full connection layers, and the discard layer and the batch normalization layer are introduced behind each full connection layer; carrying out ISAR imaging simulation by using the known asteroid point cloud model to generate an asteroid two-dimensional image; setting a network loss function Loss, inputting the training data set into the asteroid point cloud intelligent reconstruction network, and updating the weight of the network by using a gradient descent method until the Loss value falls within a set range to obtain a trained asteroid point cloud intelligent reconstruction network; and inputting to-be-reconstructed asteroid two-dimensional image data into the network to obtain an asteroid three-dimensional point cloud prediction result.

The invention discloses a space target instantaneous attitude estimation method and device based on bistatic inverse synthetic aperture radar imaging, and the method comprises the steps: obtaining an ISAR image in a current CPI of a space target obtained by a single-base / bistatic ISAR observation system, and obtaining the position information of the target and a radar; determining an expression of a mapping relation between a projection matrix and attitude parameters of the single-base / double-base ISAR observation system based on the position information; selecting feature points from the two ISAR images, and solving an expression of a mapping relation between a projection matrix and attitude parameters of the single-base / double-base ISAR observation system to obtain two mapping relation matrixes; and estimating the attitude parameters of the space target at the imaging moment based on the two mapping relation matrixes and the relation between the mapping relation and the attitude parameters. According to the method, high-reliability and low-cost instantaneous attitude estimation of the space target can be realized without selecting the same features in two images.

The invention discloses a space target combined imaging and attitude inversion method based on a spaceborne platform, and relates to the field of inverse synthetic aperture radar imaging, in particular to the space target combined imaging and attitude inversion method based on the spaceborne platform. The objective of the invention is to solve the problem that high-precision imaging is difficult to realize due to three-dimensional high-order space-variant range migration and phase error caused by complex relative motion between a target and a platform in the process of space target imaging by the existing satellite-borne platform; and an existing attitude estimation algorithm needs to realize attitude inversion on the premise of rectangular part segmentation and complete orbit error compensation. The method comprises the following steps: 1, a narrow-band tracking system of a radar obtains a target slant distance and a radar line-of-sight unit vector; 2, obtaining a signal after wedge transformation; 3, acquiring spatial Doppler parameter distribution of the dominant display point; 4, obtaining an imaging result; and 5, completing accurate estimation of target attitude parameters through plane coefficient fitting.

The present invention discloses a satellite target identification method and system based on adaptive training for scattering feature extraction and fusion. Specifically, the method comprises the following steps: based on the satellite target echoes received by an inverse synthetic aperture radar, a two-dimensional inverse Fourier transform is performed to obtain an inverse synthetic aperture radar image dataset of the original attitude; an attribute scattering center model dataset of the original attitude is obtained through an inverse parameterized modeling algorithm; the scalability of the attribute scattering center model is utilized to obtain the attribute scattering center model dataset and the inverse synthetic aperture radar image dataset after attitude transformation; a satellite target identification network model is established and trained using an adaptive training method; and an open set recognition algorithm is used to output a probability distribution vector based on the trained model to distinguish between the types of satellite targets of interest and those of non-interest. The present invention improves satellite target identification accuracy and can distinguish non-interested satellite targets, enabling target identification in more complex environments.

The invention discloses a sparse inverse synthetic aperture radar imaging and self-focusing method and device. The method comprises the following steps: respectively initializing a target image matrix and a phase error diagonal matrix by using a zero matrix and a unit matrix; calculating a kth iteration auxiliary matrix according to the target image matrix, the phase error diagonal matrix and the sparse aperture range profile sequence; determining the sparseness of the (k + 1) th iteration based on the kth iteration auxiliary matrix; calculating an iterative value through an iterative formula, and reconstructing a (k + 1) th iterative solution of the function; and when an iteration termination condition is satisfied, iteration is terminated to obtain an estimation signal, otherwise iteration is continued, through the method, a space target image can be efficiently reconstructed from an echo signal obtained from a sparse aperture without any prior knowledge, the influence of a sub-sampling pulse signal on phase angle estimation is solved, and the estimation precision is improved. The quality of the reconstructed image is improved, the algorithm complexity is reduced, and manual parameter adjustment is avoided.

The invention provides an inverse synthetic aperture radar image phase coding modulation method based on motion compensation guidance, which comprises the following steps that: step 1, image modulation equipment reconnaissance receives an ISAR signal, and measures and acquires a signal parameter; 2, designing a phase coding modulation pulse; 3, designing and modulating a target scattering template; 4, designing and generating a motion compensation guide signal; 5, modulating and forwarding signal inter-pulse and intra-pulse phase codes; and step 6, the ISAR carries out calibration and imaging on the received modulation signal to generate a modulated image. According to the method, the correctness of imaging radar self-focusing motion compensation is ensured, and method support is provided for experiment and application of a phase coding modulation image transformation method in an external field. According to the method, the relationship between the coding sequence design and the target image amplitude is not changed, and the amplitude change rule of 0-1 amplitude modulation can be reduced. The improved structure is simple and easy to realize.

The invention discloses an ISAR coherent translation compensation and imaging method based on a motion parameter estimation network, and relates to the technical field of imaging, and the method comprises the steps: receiving an echo of a space target inverse synthetic aperture radar; preprocessing the echo to obtain a compensated echo sequence; processing the compensated echo sequence by adopting a trained motion parameter estimation network model to obtain a distance grid offset rough estimation value of the central position of the target in each echo; multiplying the distance grid offset coarse estimation value by the distance resolution unit to obtain a distance coarse estimation value, fitting to obtain a translation parameter coarse estimation value, taking the translation parameter coarse estimation value as a preset imaging parameter to be optimized of an imaging model, and performing iterative optimization to obtain an optimized imaging parameter; obtaining a translation error term of each echo according to the optimized imaging parameters; and compensating the translation error term of each echo, and performing coherent accumulation on each compensated echo to obtain an imaging result. According to the invention, the imaging result is clearer and more accurate.

The invention relates to an SBR-based electrically large target inverse synthetic aperture radar rapid imaging method. The method comprises the steps of setting an imaging range and resolution, setting ISAR imaging parameters, and calculating SBR simulation parameters; obtaining ray path tracking information and ray field intensity tracking information; obtaining a first far-field result and a second far-field result, and combining the first far-field result and the second far-field result to obtain final far-field intensity; and an ISAR image is quickly generated by adopting an imaging algorithm. According to the method, ISAR imaging parameters and an electrically large-size target geometric file are determined, then octree structure division and incident wave setting are carried out, ray tracing far-field calculation is carried out, and finally an ISAR image of a target is obtained through an ISAR imaging algorithm. Through the mode of combining the model scaling and the single-double station equivalent theorem, only one-time ray tracing needs to be carried out at the initial frequency and the incident direction, and the efficiency of SBR-based ISAR imaging is greatly improved.

The invention discloses a ship target cross-view-angle image fusion method based on an ISAR (inverse synthetic aperture radar) image and an optical image, and aims to solve the problems that ship target images of different view angles are not distinguished in an existing ship target recognition method, and only the unique advantages of a radar image and the optical image are combined into a new image for recognition; and whether the visual angles in the two image domains have deviation is not considered, so that the converted image has a distortion phenomenon due to lack of certain visual angle information, and the ship target recognition rate is low. The method comprises the following steps: acquiring an ISAR image and an optical image corresponding to a ship target according to a three-dimensional model of the ship target; and inputting the preprocessed ISAR image and the preprocessed optical image into a Selection GAN network, and outputting a fused image of the ship target. The preprocessed optical image is an optical image under an expected view angle. The visual angle information difference existing in the radar image and the optical image is used for guiding image conversion of the radar image and the optical image, feature fusion of the two image domains is achieved, and therefore the recognition rate of the ship target is increased. Belongs to the technical field of image fusion.

The application discloses a kind of based on differential evolution optimization SPWVD's unmanned plane ISAR instantaneous imaging method, it includes the following steps: the received echo signal is carried out distance compression, envelope alignment and phase compensation and obtains the data after processing;The data after processing is replaced by the fast Fourier transform (FFT) in the azimuth direction in distance Doppler algorithm by the time-frequency analysis method such as smooth pseudo-winer distribution (SPWVD), and the initial distance-instantaneous Doppler imaging is obtained, and then the two smooth windows of SPWVD are optimized by differential evolution (DE) algorithm, and imaging again.The inverse synthetic aperture radar algorithm of the present application is improved by introducing the optimization process of differential evolution algorithm SPWVD, and the imaging quality of the mobile target inverse synthetic aperture radar can be effectively improved, so that the target imaging result is clearer, and the entropy value of the imaging diagram is smaller.