Improvement of Attributed Scattering Center Extraction by Using SAR Super-Resolution Preprocessing

Synthetic aperture radar(SAR)is able to acquire high-resolution method using the active microwave imaging method.SAR images are widely used in target recognition,classification,and surface analysis,with extracted features.Attribute scattering center(ASC)is able to describe the image features for these tasks.However,sidelobe effects reduce the accuracy and reliability of the estimated ASC model parameters.This paper incorporates the SAR super-resolution into the ASC extraction to improve its performance.Both filter bank and subspace methods are demonstrated for preprocessing to supress the sidelobe.Based on the preprocessed data,a reinforcement based ASC method is used to get the parameters.The experimental results show that the super-resolution method can reduce noise and suppress sidelobe effect,which improve accuracy of the estimated ASC model parameters.

Three-dimensional positions of scattering centers reconstruction from multiple SAR images based on radargrammetry

A method and procedure is presented to reconstruct three-dimensional(3D) positions of scattering centers from multiple synthetic aperture radar(SAR) images. Firstly, two-dimensional(2D) attribute scattering centers of targets are extracted from 2D SAR images. Secondly, similarity measure is developed based on 2D attributed scatter centers' location, type, and radargrammetry principle between multiple SAR images. By this similarity, we can associate 2D scatter centers and then obtain candidate 3D scattering centers. Thirdly, these candidate scattering centers are clustered in 3D space to reconstruct final 3D positions. Compared with presented methods, the proposed method has a capability of describing distributed scattering center, reduces false and missing 3D scattering centers, and has fewer restrictionson modeling data. Finally, results of experiments have demonstrated the effectiveness of the proposed method.

Scattering center modeling for low-detectable targets

The scattering centers(SCs)of low-detectable targets(LDTs)have a low scattering intensity.It is difficult to build the SC model of an LDT using the existing methods because these methods mainly concern dominant SCs with strong scattering contributions.This paper presents an SC modeling approach to acquire the weak SCs of LDTs.We employ the induced currents at the LDT to search SCs,and the joint time-frequency transform together with the Hough transform to separate the scattering contributions of different SCs.Particle swarm optimization(PSO)is applied to improve the estimation results of SCs.The accuracy of the SC model built by this approach is verified by a full-wave numerical method.The validation results show that the SC model of the LDT can precisely simulate the signatures of high-resolution images,such as high-resolution range profile and inverse synthetic aperture radar(ISAR)images.

Scattering Center Modeling Using Adaptive Segmental Compressive Sampling

In order to deal with aliasing distortions of Doppler frequencies shown in time-frequency representation( TFR) with aspect undersampling,an approach using adaptive segmental compressive sampling according to the aspect dependencies of the scattering centers is proposed. The random noise problem induced by compressive sampling is solved by employing a series of signal processing techniques of filtering,image transformation and Hough Transform. Three examples are presented to verify the effectiveness of this approach. The comparisons between the built models and the precise scattered fields computed by a well-validated full-wave numerical method are investigated,and the results showgood agreements between each other.

Scattering Center Extraction of UWB Radio Fuze's Target Based on Polar Hough Transform

In order to extract the feature information of ultra wide-band (UWB) radio fuze target and give full play to the warhead's strike ability, a method based on polar Hough transform for scattering centers extraction of the target was proposed in this paper. It firstly utilized the fuze scanning to obtain the distance and azimuth information of the target's main scattering centers at different times, i.e. the track information of scattering centers under the polar coordinates, then used the polar Hough transform to transform the track into the parameter space in order to accumulate the dots and drew 3-D parameter space diagram, in which the peak points corresponded to the target's scattering centers. The simulation results indicate that the method can not only extract scattering centers efficiently and accurately, but also has strong anti-noise performance, and the algorithm is simple and easy to be implemented in engineering.

Size-Controllable Metal Chelates as Both Light Scattering Centers and Electron Collection Layer for High-Performance Polymer Solar Cells

An electron collection layer(ECL)between a photoactive overlay and an electrode plays a crucial role in optimizing the light field and charge extraction in bulk-heterojunction(BHJ)polymer solar cells(PSCs).However,the typical thickness of the photoactive layer is thinner than its optical path lengths,limiting further improvement of light absorption and device performance.

A novel scheme for global scattering center modeling of radar targets

A novel scheme for extracting the global scattering center model of radar targets is proposed in this paper.The 2D/3D scattering center models can be reconstructed based on the wideband measurements at different viewing angles.The sphere spreading of the 1D scattering center projections is exploited.The 1D–2D/3D scatterer map(OTSM)is designed to manifest the high dimensional scattering characteristic of radar targets.The Hough transform and the least squares method are used to extract the stable scattering centers and their scattering coefficients.This modeling method does not need a high density of the spatial grid,which greatly cuts down the necessary original data.The model built in this way describes the stable point scattering mechanisms in a large spatial extent and can be extrapolated to other frequencies in the optical region.Examples verify the validity of both the model and the method.

A modified OMP method for multi-orbit three dimensional ISAR imaging of the space target

The conventional two dimensional(2D)inverse synthetic aperture radar(ISAR)imaging fails to provide the targets'three dimensional(3D)information.In this paper,a 3D ISAR imaging method for the space target is proposed based on mutliorbit observation data and an improved orthogonal matching pursuit(OMP)algorithm.Firstly,the 3D scattered field data is converted into a set of 2D matrix by stacking slices of the 3D data along the elevation direction dimension.Then,an improved OMP algorithm is applied to recover the space target's amplitude information via the 2D matrix data.Finally,scattering centers can be reconstructed with specific three dimensional locations.Numerical simulations are provided to demonstrate the effectiveness and superiority of the proposed 3D imaging method.

Parameter estimation of GTD model and RCS extrapolation based on a modified 3D-ESPRIT algorithm

The noise robustness and parameter estimation performance of the classical three-dimensional estimating signal parameter via rotational invariance techniques(3D-ESPRIT)algorithm are poor when the parameters of the geometric theory of the diffraction(GTD)model are estimated at low signal-to-noise ratio(SNR).To solve this problem,a modified 3D-ESPRIT algorithm is proposed.The modified algorithm improves the parameter estimation accuracy by proposing a novel spatial smoothing technique.Firstly,we make cross-correlation of the auto-correlation matrices;then by averaging the cross-correlation matrices of the forward and backward spatial smoothing,we can obtain a novel equivalent spatial smoothing matrix.The formula of the modified algorithm is derived and the performance of this improved method is also analyzed.Then we compare root-meansquare-errors(RMSEs)of different parameters and the locating accuracy obtained by different algorithms.Furthermore,radar cross section(RCS)of radar targets is extrapolated.Simulation results verify the effectiveness and superiority of the modified 3DESPRIT algorithm.

NEW SAR IMAGE INTERPRETATION METHOD OF AIRCRAFT BASED ON JOINT TIME-FREQUENCY ANALYSIS

With the continuous improvement of Synthetic Aperture Radar(SAR) resolution, interpreting the small targets like aircraft in SAR images becomes possible and turn out to be a hot spot in SAR application research. However, due to the complexity of SAR imaging mechanism, interpreting targets in SAR images is a tough problem. This paper presents a new aircraft interpretation method based on the joint time-frequency analysis and multi-dimensional contrasting of basic structures. Moreover, SAR data acquisition experiment is designed for interpreting the aircraft. Analyzing the experiment data with our method, the result shows that the proposed method largely makes use of the SAR data information. The reasonable results can provide some auxiliary support for the SAR images manual interpretation.

A new feature extraction method using the amplitude fluctuation property of target HRRP for radar automatic target recognition

Due to the aspect sensitivity of high-resolution range profile(HRRP),traditional radar HRRP target recognition methods usually use average profile within some target-aspect region as the target-aspect template.Actually,the amplitude fluctuation property of target HRRP also represents some feature information of the target.Based on the scattering center model,a new feature extraction method using the amplitude fluctuation property of target HRRP is proposed in this paper.The weighted HRRP feature extracted by the new method can represent the scatterer distribution in every range cell,thereby it can describe the scattering property of the target better.The experimental results based on measured data show that the new feature extraction method can greatly improve recognition performances.