1016 results about "Inverse synthetic aperture radar" patented technology

A synthetic aperture radar (SAR) compensates for ionospheric distortions based upon measurement of the group delay, particularly when operating in the VHF / UHF band. The SAR is based upon a multi-input multi-output (MIMO) technique for estimating the effective ionospheric conditions, which is referred to as the group delay approach. The group delay approach is divided into a 1-dimensional (range) approach and a 2-dimensional (range and cross-range) approach. The group delay measures the effective or observed TEC, which is used to reduce the ionospheric distortion.

The dual synthetic aperture array system processes returns from the receiving arrays. The two identical receiving arrays employing displaced phase center antenna techniques subtract the corresponding spectrally processed data to cancel clutter. It is further processed that a moving target is detected and its velocity, angular position and range is measured, in or out of the presence of clutter. There are many techniques presented in the disclosure. These techniques are basically independent but are related based on common set of fundamental set of mathematical equations, understanding of radar principles and the implementations involved. These many techniques may be employed singly and / or in combination depending on the application and accuracy required. They are supported by a system that includes, optimization of the number of apertures, pulse repetition frequencies, DPCA techniques to cancel clutter, adaptive techniques to cancel clutter, motion compensation, weighting function for clutter and target, and controlling the system in most optimum fashion to attain the objective of the disclosure.< / PTEXT>

The invention discloses a 3D terrain imaging system of the interferometric synthetic aperture radar (InSAR) and an elevation mapping method thereof, which mainly solve the problems that the existing InSAR has bad imaging pragmaticality and can not implement 3D elevation mapping on the fast-changing terrain and the transilient terrain. The system comprises three sub-aperture antennas, a radar transmitter, a radar receiver and an imaging data processor; the imaging signal processor comprises a SAR image processing unit and an InSAR image processing unit. The invention receives radar echo through the three sub-apertures, then conducts SAR imaging process on the radar echo respectively received by the three sub-apertures, and then conducts InSAR imaging process on the obtained SAR complex pattern, wherein the InSAR imaging process comprises image registration, phase filtering and phase unfolding based on cluster analysis. The processed InSAR phase unfolded image is processed with an elevation inversion to recover a three dimensional digital elevation map. The invention has the advantages of wide adaptability to mapped terrains, and high imaging effectiveness, therefore, the invention can be used in the mapping of the 3D terrain.

The invention provides an SAR (synthetic aperture radar) image change detection algorithm based on non-supervision depth network learning. The algorithm includes the steps: 101 starting an SAR image change detection method based on a non-supervision depth nerve network; 102 performing FCM (fuzzy c-mean) joint classification for two registered SAR images of different time phases in the same area to obtain rough change detection results; 103 selecting noiseless points with large possibility to serve as training samples of the depth network according to initial change detection results; 104 inputting sample points to be trained to the designed depth nerve network to be trained; 105 inputting two images to be detected to the trained depth nerve network to obtain a final change detection result map; 106 finishing the SAR image change detection method based on the non-supervision depth nerve network. Ohm= {ohm1 and ohm2}. Construction links of a difference map are avoided, sensitivity of noise is improved to a certain extent, and detection efficiency and detection accuracy are remarkably improved.

The invention discloses a CFAR (constant false alarm rate) detection and depth learning-based SAR (synthetic aperture radar) target detection method. The method includes the following steps that: (1) SAR images are obtained; (2) a training sample set is expanded; (3) the network structure of a Faster-RCNN model is constructed; (4) a trained RPN model is obtained; (5) a trained Fast-RCNN model is obtained; (6) a finely-adjusted RPN network is obtained; (7) a trained Faster-RCNN model is obtained; and (8) target detection is carried out. With the CFAR (constant false alarm rate) detection and depth learning-based SAR (synthetic aperture radar) target detection method of the invention adopted, end-to-end image-level detection is realized, and major problems in existing SAR target detection technologies can be solved. The method has good detection performance under complex conditions.

A detection method of double-threshold constant false alarm movement object on double-base synthetic aperture radar includes finalizing distance compression of various pulse-sampling, dividing orientation time to be multiple CPI, carrying out FFT in each CPI to enter orientation frequency domain, comparing data amplitude of frequency channel at external of clutter region with the first threshold and outputting point trace, making coagulation on point trace, making Hough transform in point trace domain and making decision of the second threshold.

An incoherent ladar transmitter (12) adapted for use with synthetic aperture processing. The system (12) includes a first mechanism (44, 48, 50) for generating a laser beam (18). A second mechanism (44, 68) records phase information pertaining to the laser beam (18) and subsequently transmits the laser beam (18) from the system in response thereto. A third mechanism (40) receives a reflected version (20) of the laser beam and provides a received signal in response thereto. A fourth mechanism (72) corrects the received signal based on the phase information recorded by the second mechanism (44, 68). In a more specific embodiment, the ladar system (12) includes a synthetic aperture processor (46) for correcting the received signal based on the phase information and providing a corrected laser signal in response thereto. The synthetic aperture processor (46) includes a mechanism (76) for applying, a Discrete Fourier Transform (DFT) to the corrected laser signal to obtain high frequency resolution and cross-range resolution. A fifth mechanism (48) constructs a range-Doppler image based on the corrected laser signal and the movement of the ladar system (12).

The invention discloses a polarimetric SAR (Synthetic Aperture Radar) image classification method based on an SDIT (Secretome-Derived Isotopic Tag) and an SVM (Support Vector Machine). The method comprises the implementation steps of (1) inputting an image, (2) filtering, (3) extracting scattering and polarization textural features, (4) combining and normalizing the features, (5) training a classifier, (6) predicting classification, (7) calculating precision and (8) outputting a result. Compared with an existing method, the polarimetric SAR image classification method based on the SDIT and the SVM enables the empirical risk and the expected risk to be minimal at the same time, and has the advantages of high generalization capability and low classification complexity and also the advantages of describing the image characteristics comprehensively and meticulously and improving the classification precision, and in the meantime, the polarimetric SAR image classification method has a good denoising effect, and further is capable of enabling the outlines and edges of the polarimetric SAR images to be clear, improving the image quality, and enhancing the polarimetric SAR image classification performance.

The invention provides a time sequence InSAR (Interferometric Synthetic Aperture Radar) deformation monitoring method and a device based on a polynomial inversion model. The method comprises the following steps of: combining N SAR (Synthetic Aperture Radar) single look complexes of a certain region to generate M interference pictures and generate M differential phase pictures; calculating an average coherent coefficient picture and extracting high coherent points; establishing a polynomial inversion model by carrying out difference again on a differential phase of the two adjacent high coherent points; solving relative polynomial deformation and a relative elevation error of the adjacent points respectively integrating by taking a certain high coherent point provided with known deformation amount and a DEM (Dynamic Effect Model) error as a reference point to obtain the polynomial deformation and the elevation error of each high coherent point; after a phase of the polynomial inversion model is obtained, subtracting the phase of the polynomial inversion model from the differential phase of the high coherent points to obtain a residual phase; and extracting residual deformation from the differential phase to be overlapped with the polynomial deformation to obtain ground surface deformation information of the high coherent points. The method provides a solution for highly-precisely monitoring the ground surface deformation.

The invention discloses a sparse dynamic ensemble selection-based SAR (synthetic aperture radar) image terrain classification method, which mainly solves the problem that the speed of the conventional dynamic ensemble selection algorithm and the conventional dynamic classifier selection algorithm for terrain classification in SAR images is low. The implementation process of the sparse dynamic ensemble selection-based SAR image terrain classification method is as follows: (1) a wavelet energy feature is extracted from an SAR image to be classified; (2) training data is acquired from the SAR image to be classified; (3) the SAR image to be classified is regionalized to obtain data to be classified; (4) training samples are utilized to learn ensemble systems; (5) a dictionary is learnt for each class of training data, and a synthetic dictionary is obtained; (6) dynamic ensemble selection is carried out on each atom in the synthetic dictionary; (7) samples to be classified are sparsely coded; (8) the samples to be classified are marked according to a sparse coefficient and classifier ensembles corresponding to the atoms; (9) the marks of the samples to be classified are mapped onto pixels in the SAR image, so that a terrain classification result is obtained. The sparse dynamic ensemble selection-based SAR image terrain classification method has the advantages of high speed and good classification effect, and can be used for SAR image target identification.

The invention discloses a large squint sliding spotlight SAR(synthetic aperture radar) imaging processing method, which belongs to the signal processing field. The processing method mainly comprises the following five parts, namely directional Deramp processing, two-dimensional phase compensation, interpolation processing, image coarse focusing and directional secondary focusing; the large squintsliding spotlight SAR imaging processing method has high accuracy and can realize accurate focusing in a full scene by using a secondary focus technology for compensating the spare-varying feature ofa Doppler parameter; in addition, the processing method has high efficiency, since distance migration induced by a reference slant distance in an echo signal is removed so as to greatly reduce data volume of the echo signal and improve the processing efficiency; moreover, the processing method has strong practicability and can realize the high-precision imaging process in a large squint angel mode; compared with the traditional imaging processing method, the large squint sliding spotlight SAR imaging processing method has better practicability.

The invention relates to a design method for the formation configuration of distributed satellites with synthetic aperture radars (SAR). The method comprises the following five operation steps: step 1: designing the formation configuration of the distributed satellites and conforming fly-by path equations; step 2: confirming an optimal base line length range of the distributed satellites in start time; step 3: confirming an optimal base line sequence of the distributed satellites in start time; step 4: confirming track parameters of the formed satellites forming a concentric circle configuration; and step 5: calculating effective base lines and vertical path base lines in a track period. The invention provides the concentric circle formation configuration, uses the optimal base line combined constraint conditions of multi-base line interference SARs as design input parameters and designs a control law of radar antenna visual angles to realize that a distributed satellite SAR system can satisfy the design requirements of the optimal base line combined constraint conditions in any time in a track operation period and a basis is provided for the distributed satellite SAR system to obtain high-precision DEM products by a multi-base line interference SAR treatment method.

This invention relates to improved ultra-wideband synthetic aperture radar and inverse synthetic aperture radar, capable of simultaneously and independently imaging a plurality of spectral and polarimetric channels covering multiple radio frequency octaves. Advances in technologies relating to signal processing, graphical user interfaces, color representations of multi-spectral radar images, low aerodynamic drag polarimetric SAR antenna systems, and synthetic aperture radar aircraft platforms are some of the advancements disclosed herein.

The invention discloses an imaging method for a bistatic forward-looking synthetic aperture radar (SAR). Aiming at the defect existing when the existing method is used for the imaging process of the bistatic forward-looking synthetic aperture radar, the imaging method adopts a bistatic forward-looking SAR point target response two-dimensional frequency spectrum based on the least square polynomial fitting; the frequency spectrum is the least square approximation of the two-dimensional frequency spectrum with the accurate theory. According to the characteristics of bistatic forward-looking invariant SAR direction, variant range and nonlinear and variant Doppler centroid range of range cell migration in an RD (radar domain) domain, bistatic forward-looking SAR range migration correction, secondary-range compression and high-order phase compensation are realized by the frequency spectrum so as to accurately focus the bistatic forward-looking SAR. Compared with the traditional SAR imaging method and the bistatic forward-looking SAR imaging method, the method disclosed by the invention has higher imaging precision.

The invention provides a method for acquiring and imaging data of compressive sensing synthetic aperture radar. The method comprises the following steps of: 1, selecting an observation scene; 2, emitting a Chirp signal by a radar antenna; 3, receiving a distance direction signal of the synthetic aperture radar; 4, constructing a compressive sensing restoring matrix; 5, correcting distance compression and distance migration; 6, constructing an orientation direction filter matrix; and 7, compressing towards the orientation direction. By the method for acquiring and imaging the data, aiming at realizing great reduction of radar stored and transmitted data quantity in the special observation scene and synchronously realizing restoring of a distance direction pulse compression signal and correction of the distance migration, the problem that interpolation calculation cannot be performed on a compressive sensing restoring signal is solved.

The invention discloses a SAR (Synthetic Aperture Radar) image segmentation technique based on dictionary learning and sparse representation, and mainly solves the problems that the existing feature extraction needs a lot of time and some defects exist in the distance measurement. The method comprises the following steps: (1) inputting an image to be segmented, and determining a segmentation class number k; (2) extracting a p*p window for each pixel point of the image to be segmented so as to obtain a test sample set, and randomly selecting a small amount of samples from the test sample set to obtain a training sample set; (3) extracting wavelet features of the training sample set; (4) dividing the training sample set by using a spectral clustering algorithm; (5) training a dictionary by using a K-SVD (Kernel Singular Value Decomposition) algorithm for each class of training samples; (6) solving sparse representation vectors of the test sample on the dictionary; (7) calculating a reconstructed error function of the test sample; and (8) calculating a test sample label according to the reconstructed error function to obtain the image segmentation result. The invention has the advantages of high segmentation speed and favorable effect; and the technique can be further used for automatic target identification of SAR images.

The invention discloses a ground moving target imaging method for a single-channel synthetic aperture radar (SAR). The method comprises the following steps of: (1) correcting distance curvature; (2) correcting distance migration; (3) estimating Doppler parameters; (4) estimating motion parameters; (5) compressing a direction; and (6) repeating the steps until all moving targets are processed. In the invention, the distance curvature of moving target echo data is corrected by second-order Keystone transform, so the method has the advantage of high-resolution SAR data imaging; by uniformly correcting the distance curvature of all stationary targets and all moving targets, the defect of large computation amount in the prior art is overcome; and by building a second-order moving target imaging model, parameter estimation and imaging of maneuvering moving targets can be performed, and the moving states of the moving targets can be more accurately described from aspects of speed, acceleration and the like.

The invention discloses a fast high-resolution SAR (synthetic aperture radar) image ship detection method based on feature fusion and clustering. The fast high-resolution SAR image ship detection method comprises the following steps: on the basis of the back scattering characteristics of each ground object and the prior information of a ship target in an SAR image, positioning a target potential position index map by an Otsu algorithm and range constraint; on the index map, pre-screening to obtain a detection binary segmentation map by a CFAR (constant false alarm rate) algorithm based on a local contrast; carrying out morphological processing to a detection result, and extracting a potential target slice from the SAR image and a detected binary segmentation map according to a processing result; and carrying out K-means clustering to the extracted slice by a designed identification feature to obtain a final identification result. According to the fast high-resolution SAR image ship detection method based on feature fusion and clustering, the data volume of a detection stage is effectively reduced by pre-processing, and point-to-point detection is not needed / the time of point-to-point detection is saved. Meanwhile, a target identification problem under the condition of insufficient training samples at present can be solved by the designed characteristic and a non-supervision clustering method, the target can be effectively positioned, and the size of the target can be estimated.

The invention discloses a polarized SAR (synthetic aperture radar) image classification method based on a depth PCA (principal component analysis) network and an SVM (support vector machine) classifier. The polarized SAR image classification method includes filtering a polarized SAR image, extracting a shape feature parameter, a scattering feature parameter, a polarization feature parameter and independent elements of a covariance matrix C, and combing and normalizing into new high-dimensional features serving as data to be processed in a next step; according to actual ground feature flags, randomly selecting 10% of data with flags from each type to serve as training samples; whitening the training samples to serve as input to train a first layer of the network, taking a result as input of a second layer to train the second layer of the network, and performing binaryzation and histogram statistics on an output result; taking output of the depth PCA network as a finally learned feature training SVM classifier; whitening test samples, and inputting the test samples into a trained network framework to predict and calculate accuracy; coloring and displaying a classified image and outputting a final result.

The invention provides a compressed sensing synthetic aperture radar (SAR) imaging method based on fractional order fourier transformation, and belongs to the technical field of microwave imaging. For a sparse target scene, a system emits a linear frequency modulation pulse signal; by adopting a method for projecting an echo signal to a low-dimension measurement matrix, measurement data which are much smaller than those required by a Nyquist sampling principle are acquired; due to the echo signal, after a reconstruction matrix is constructed by a simplified fractional order fourier transformation domain sparse mode and a data acquisition mode, a calculated distance pulse pressure result is optimized by a compressed sensing signal reconstruction method and is subjected to distance migration correction and direction pulse pressure to obtain a target scene imaging result. By the imaging method, the measurement data amount of a target scene with a sparse characteristic can be greatly reduced; and pressure on data storage and data transmission can be effectively relieved.

The invention discloses an SAR (synthetic aperture radar) image change detection method combining multi-threshold segmentation with fuzzy clustering. The method mainly aims at overcoming the defect of existing fuzzy clustering algorithms and is used for SAR image change detection by combining multi-threshold segmentation with fuzzy clustering. The implementation steps of the method include: (1), subjecting two SAR images to median filtering; (2), calculating to obtain a logarithmic ratio differential image after normalization; (3), adopting the Otsu method based on standard particle swarm optimization to perform multi-threshold segmentation to the logarithmic ratio differential image after normalization; (4), initializing membership matrixes U0 and U1; (5), adopting the FLICM (fuzzy local information C-means) algorithm to perform fuzzy clustering to pixels which cannot be determined whether changes occur or not after multi-threshold segmentation; (6), deblurring; and (7), outputting change detection results. The multi-threshold segmentation and fuzzy clustering are combined for SAR image change detection, so that change detection time is reduced, and change detection accuracy is improved.

The invention discloses a three-dimensional position reconstructing method based on an ISAR (inverse synthetic aperture radar) image sequence for a scattering point, which comprises the following four links: a target ISAR image gives the distribution information of a target strong scattering point in a radial direction and a transverse direction based on a distance-Doppler high resolution basic principle; data correlation gives a corresponding relationship between two-dimensional projection points in the ISAR image sequence and is realized by utilizing a flight path initialization method through extracting the one-dimensional radial distance information of all the scattering points in an image sequence; an observing matrix is obtained through the following steps: obtaining a target ISAR image sequence through a period of time of sampling, and after the data correlation, and combining the two-dimensional position coordinates of all the corresponding projection points in a sequence to form the observing matrix, so as to form three-dimensional reconstructed known information; and a position matrix is solved through the following step: solving the optimal estimation of a three-dimensional position matrix of a target scattering point from a subspace through carrying out singular value decomposition on the observing matrix and by utilizing a rank theory and using the orthogonality of a projection space as a constraint condition, so as to obtain a target three-dimensional reconstructed image.

The invention discloses a radar foresight super-resolution imaging method which specifically includes: distance toward pulse compression, distance walking corrosion, distance toward inverse fast fourier transform (IFFT), determination of iterations and azimuth deconvolution. The radar foresight super-resolution imaging method performs azimuth modeling on echo scanned by a radar antenna in foresight mode to enable the echo to be in convolution, and estimates front ground feature distribution information in a deconvolution method; and additionally, based on noise statistical property, optimum iterations can be determined according to iteration deconvolution, so that radar can obtain better foresight super-resolution imaging performance. Compared with the background technology, the radar foresight super-resolution imaging method overcomes the restrains on scene of a single-pulse imaging technology and limitation on platform dimension of an array imaging method, further solves the problems of complex synchronization, motion compensation and the like of double-foundation synthetic aperture radar (SAR) and can effectively obtain ground feature distribution information of the area dead ahead the platform.

A method for detecting radar mobile object of single channel synthetic hole diameter based on multivision sub image pair includes forming multiple symmetric sub image pairs by multivision image of synthetic hole diameter radar data, forming each sub image pair by two subvision images, utilizing subvision image cancellation to accumulate cancellation result by two - dimensional united means along position and distance directions for effectively suppressing background clutter interference as well as for realizing energy accumulation of mobile object then applying constant false alarm technique to finalize detection on mobile object.

The invention discloses a saliency-based synthetic aperture radar (SAR) image airfield runway edge detection method, which comprises the following implementation steps: (1) reading in image data; (2) carrying out median filtering on the image data which is read in; (3) carrying out enhanced processing on a filtered and de-noised image; (4) obtaining a salient map of the processed image; (5) rejecting a noisy background area on the basis of the saliency information of the salient map; (6) obtaining a seed growth point to carry out region growing; (7) carrying out aftertreatment; and (8) carrying out edge positioning. According to the saliency-based SAR image airfield runway edge detection method, the airfield runway edge can be quickly detected, the airfield runway edge information can be favorably kept, meanwhile, the interference of the noisy background area is avoided, and the saliency-based SAR image airfield runway edge detection method has better instantaneity and higher detection precision and can be applied to the SAR image airfield runway edge detection.

The invention discloses a geometric correction method for a spotlight-mode satellite SAR image. The method comprises the following steps: (1) reading related parameters and echo data, and constructing a positioning equation; (2) calculating the coordinates of the position vector and velocity vector of a satellite in a non-rotating geocentric coordinate system at a moment of radar illumination center according to the spatial geometric relationship between the satellite and the earth; (3) completing slope distance and Doppler frequency correction; (4) acquiring the coordinates as well as longitude and latitude of a certain pixel point (i, j) of the satellite SAR image in the non-rotating coordinate system; (5) determining whether all of the pixel points of the satellite SAR image are processed; and (6) re-dividing the latitude-longitude grid, and outputting the geometric correction result. The method provided by the invention can be used for carrying out row-by-row geometric correction on the satellite SAR image, and can process the satellite SAR image in blocks as required due to the completely independent treatment process between the rows of the satellite SAR image, thereby further improving the treatment efficiency.

The invention provides an SAR (Synthetic Aperture Radar) image based automatic water area detection method. The SAR image based automatic water area detection method comprises the following steps of a first step, performing de-noising processing on an SAR image; a second step, computing the gradient of an input image through an edge detection operator and obtaining a gradient image of the SAR image; a third step, improving the contrast ratio of the SAR image through a contrast ratio stretching conversion algorithm; a fourth step, achieving secondary self-adaption threshold segmentation through a double-peak iterative method and obtaining two binary image; a fifth step, performing post processing on the binary image obtained in the fourth step through morphology and obtaining a crude extracting target candidate area; a sixth step, removing false alarm, marking the target candidate area extracted from the fifth step, performing statistics of the area of every connecting dark spot, setting an area threshold value according to the resolution ratio of the SAR image, removing the dark spots with the area less than the preset area and marking extracted water areas in an original drawing.

The invention discloses an external scaler and calibrating method of new broad-band synthetic aperture radar, which is characterized by the following: modulating the signal of the received synthetic aperture radar through sine amplitude; making two sides of the Doppler signal of synthetic aperture radar of scaler generate symmetrical mirror-image spectrum; utilizing positional filter to extract the mirror-image spectrum; separating the synthetic aperture radar signal from ground clutter. The scaler contains receiving and transmitting antenna, RF mode and modulating mode. The invention realizes external scaling signal separated from ground clutter completely to form loop testing circuit with radar transceiver system, antenna, transmitting path and external scaler without influencing by ground clutter, which improves the precision of external scaler greatly under complex and high-ground clutter condition.

The present invention discloses an SAR (Synthetic Aperture Radar) image target detection method based on a visual attention model and a constant false alarm rate, which mainly solves the problems of a low detection speed and a high clutter false alarm rate in the existing SAR image marine ship target detection technology. The implementation steps of the method are as follows: extracting a saliency map corresponding to an SAR image according to Fourier spectrum residual error information; calculating a saliency threshold, so as to select a potential target area on the saliency map; detecting the potential target area by adopting an adaptive sliding window constant false alarm rate method, and obtaining an initial detection result; and obtaining a final detection result after removing a false alarm from the initial detection result, and extracting a suspected ship target slice, so as to complete a target detection process. The SAR image target detection method based on the visual attention model and the constant false alarm rate provided by the present invention has the advantages of a high calculation speed, a high target detection rate and a low false alarm rate, and meanwhile the method has the advantages of simpleness and easy implementation and can be used for marine ship target detection.

The invention discloses an inverse synthetic aperture radar imaging method for maneuvering targets on the basis of the sparse aperture. The inverse synthetic aperture radar imaging method for the maneuvering targets on the basis of the sparse aperture comprises the implementation steps that (1) original echo data, with the sparse aperture, of an inverse synthetic aperture radar are received; (2) distance compression and envelope alignment processing are carried out; (3) phase correction is accurately carried out; (4) sparse aperture echo signals are reconstructed; (5) fast Fourier transform is carried out to achieve distance-Doppler imaging. According to the inverse synthetic aperture radar imaging method for the maneuvering targets on the basis of the sparse aperture, accurate phase compensation and accurate data reconstruction can be carried out on ISAR imaging processing under the conditions of the maneuvering targets and the sparse aperture, and a more focused high-quality ISAR imaging result is obtained.