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In this article, an effective clutter suppression and moving target imaging approach is proposed for the geosynchronous-low earth orbit (GEO-LEO) bistatic multichannel synthetic aperture radar (SAR) system, which is robust for the fast moving target with Doppler centroid ambiguity. For the GEO-LEO bistatic multichannel SAR system, the characteristic of baseline with azimuth invariant in Doppler Fourier transform domain is not tenable. It is difficult to implement clutter suppression by the unified baseline compensation in azimuth Doppler Fourier transform domain. Fortunately, we discover that the baseline can be approximately regarded as a constant in chirp Fourier transform domain for the GEO-LEO bistatic multichannel SAR system. Hence, the corresponding baseline compensation can be achieved in the azimuth chirp Fourier transform domain for the clutter. With an orthogonality vector of clutter, the clutter can be well suppressed and the moving target is extracted. After that, the moving target imaging approach is developed with the baseband Doppler centroid estimation and Doppler ambiguity number estimation. Finally, the theoretical investigations and the proposed approach in this article are validated by some experiments, where the experiments for clutter suppression and experiments for fast moving targets are included. Especially, an experiment with real SAR scattering scene is involved. In addition, some discussions for blind velocity targets are presented.

Due to the constraint of the minimum antenna restriction, there is an irreconcilable contradiction between high resolution and wide swath (HRWS) in a single-channel spaceborne synthetic aperture radar (SAR) system. To address this contradiction, the use of azimuth multichannel system can effectively realize the HRWS SAR imaging by taking the beam forming techniques. In this paper, a novel unambiguous spectrum reconstruction algorithm for space-borne multichannel HRWS SAR imaging is proposed. The proposed method firstly compensates the linear phase structure related to the along-track baseline in the Doppler domain. Then, the covariance matrix is reconstructed along azimuth direction based on adaptive iterative algorithm (IAA). Hence, based on the estimated covariance matrix, an unambiguous signal spectrum can be accurately restored according to the linearly constrained minimum variance (LCMV) criterion. Finally, a well-focused HRWS SAR image can be obtained by applying the typical SAR image algorithm. The major advantage of the proposed method is that it can realize the azimuth ambiguity elimination in a non-homogeneous scene. Both simulated and real data processing results are used to validate the effectiveness of the proposed method.

The P-band frequency is less common in spaceborne synthetic aperture radar (SAR) systems, while has superior abilities on penetration through the vegetation canopy and soil compared with X-, C- and L-bands. As observations from P-band SAR satellite will offer an unique opportunity to estimate forest biomass, map the topography of forest floors and detect buried targets, the demand for such a satellite is getting stronger in China and over the world. This paper aims to report the results we achieved during the feasibility study of P-band SAR satellite, including evaluation of P-band penetration ability with airborne experiments and an overview of applications of P-band SAR data.

Space-borne early warning radar has great military and civil significance in ground, air, and space moving target detection in virtue of its wide-area coverage ability, where air moving target indication (AMTI) is an essential task for space-borne early warning radar. As for the AMTI mode, it is inevitably to handle the ground strong clutters due to the large surveillance area, and space-time adaptive processing (STAP) technique can be successfully applied to suppress the troubled clutter effectively. However, the clutter rejection performance of STAP will be restricted by many non-ideal factors, such as range ambiguity, channel number, and yaw angle induced by earth's rotation; thus it is necessary to analyze the influence of these practical non-ideal factors. In this paper, the multichannel signal model of clutter and air moving target in a space-borne radar (SBR) system are deduced. Then, we analysis the adverse influences of these non-ideal factors on STAP in detail. Finally, some simulation processing results are provided to validate the STAP performance with the consideration of these engineer factors, which can provide some useful guides of SBR system design with AMTI mode.

Using the array technique, the multi-channel synthetic aperture radar (SAR) has the capability of high-resolution imaging, detection, and location of the ground moving targets, which is a powerful tool during the remote sensing of smart city. Recently, the theory of compressive sensing has been applied to radar imaging with data of compressive sampling, which can effectively reduce the burden of current radar system. In this paper, we focus on SAR moving targets imaging from sparse aperture (SA) data with accurate target motion compensation. The procedure of motion compensation is decomposed into two steps: the SPECAN processing in the range frequency and azimuth time domain is first applied and then followed by the residue component correction embedded into sparse imaging. To overcome the SA and target motion, a novel parametric sparse imaging approach is proposed by addressing the problems of Doppler ambiguity and phase errors. In the scheme, a parametric and dynamic dictionary is used to include these two important issues. Then, a modified orthogonal matching pursuit method is presented for high-quality imaging with Doppler ambiguity number estimation and phase error correction, which can deal with the case of multiple moving targets. Finally, experimental analysis is performed to confirm the effectiveness of the proposed algorithm.

Aiming at the problem of lacking systematic research on the change rules of efficiency characteristics over coil parameters toward magnetically coupled resonant wireless power transmission technology, and combining with engineering practice, it is difficult to guide design of transmission characteristics in practical engineering. For this reason, based on the mutual inductance analysis model of magnetically coupled resonant system, the hardware of the magnetic coupling resonant drive circuit and the full-bridge inverter are designed. And a variety of resonant coils are wound in certain constraining condition. The experimental platform of magnetic coupling resonant wireless energy transmission is built, and the transmission characteristics of each resonant coil model are tested. It research the variation of system power and transmission efficiency with coil radius, number of turns and wire diameter and optimal design parameters of coil. By comparing with the simulation results of mutual inductance theory, it is found that the change law of output power and efficiency of the simulation and experiment are the same, which verifies the correctness of the experimental results. It shows by experiment that the resonant coil satisfies the system transmission requirement under the condition of the coil radius is 157mm, the number of coil turns is 16 turns, and the wire diameter is around 3.65mm. At this time, the output power is 309.8W and the transmission efficiency is up to 80%. Therefore, the resonant system is more reasonable and scientific than other design parameters under the coil structure design.

Limited urban land resources turn the floor area into an issue with more and more attention during the construction process of substations. Compactness and miniaturization have become the developing direction of urban indoor substations in the future. It is found out from the limiting factor of substation area that the outlet method is directly correlated with it. Based on the common outlet schemes, a new outlet method, positive and negative triangle outlet, is proposed. In order to highlight the advantages of positive and negative triangle outlet compared to normal outlet schemes, the analytic hierarchy process (AHP) is adopted to quantitatively evaluate the weights of each outlet scheme under four criteria, including electric power environmental protection, installation and operation difficulty, costs for electrical distribution transformation and area, and their sub-indexes and determine the optimal outlet scheme that meets the requirements. It is feasible to establish the outlet model of indoor substations through AHP based on the actual project situations. The algorithm can realize the quantification of qualitative indexes and determine the optimal outlet scheme so as to provide certain reference for choice of outlet schemes for urban indoor substations.

With the limited urban land resources and people's high demand for living environment, the construction of urban substation is being increasingly denser and miniaturized indoor substation is being widely used. The outlet of substation is closely related to land area, so this paper proposes a new outlet scheme named positive and negative triangle outlet, based on common ways of outlet, to reduce the land area of substation. In order to compare the advantages of this outlet scheme with that of other outlet schemes under different indicators, AGA-AHP (Accelerating Genetic Algorithm Analytic Hierarchy Process) are used to perform quantitative evaluation for weight of various outlet schemes in electricity environmental protection, difficulty of installation, operation and maintenance, distribution cost, and land area, as well as its sub-indicators, and determine the optimal outlet scheme that meets the requirements. According to the actual situation of the project, outlet model of indoor substation is built by AGA-AHP algorithms, and judgment matrix is established by related experts. The algorithms can realize the quantification of qualitative indicators and the output of the optimal outlet scheme, so as to provide some reference to the construction of substation.

Polarimetric synthetic aperture radar (SAR) Interferometry (PolInSAR) becomes a hot topic in SAR application areas, such as terrain classification. However, the system parameter design is mostly based on traditional Polarimetric SAR (PolSAR) or interferometric SAR (InSAR) methods. The accuracy of polarimetric calibration is the major factor for the PolInSAR system design, so it is necessary to analyze the affect of polarimatric calibration errors on terrain classification accuracy. In this paper, error transfer link of typical classification algorithm is investigated, then, the minimum requirements on polarimetric distortions including channel imbalance and crosstalk are investigated for terrain classification application for the first time. The result is verified by E-SAR datasets, which can provide the important theoretical basis and reference for quantitative calibration requirements of classification accuracy.

Phase unwrapping, a process of recovering the real phase from ambiguous one, is one of key techniques for interferometric synthetic aperture radar (InSAR) data processing. The larger interferograms make the phase unwrapping to be a more challenging task. A novel fast phase unwrapping method is proposed in this paper which is very effective in dealing with the large scale datasets. Several sub-networks consisting of residues are constructed in the method. Therefore, smaller networks make the execution more efficient using minimum cost flows(MCF) algorithm. In addition, the method takes an effective strategy to balance all the sub-networks in order to ensure a feasible solution to each sub-network. Experiment with real data demonstrates the effectiveness of the proposed method.

Combined with digital beam forming (DBF), multichannel synthetic aperture radar (MC-SAR) in azimuth can achieve high-resolution and wide-swath (HRWS) SAR imaging. However, inevitable channel errors lead to a great degradation of DBF performance in practice. To address this problem, a novel channel phase error calibration algorithm is proposed in this letter. Theoretical analysis reveals that the signal of the reference channel can be reconstructed from those of other channels. Although signal cancellation can be roughly realized by using this reference signal and the original signal, there are still residues due to the existence of channel phase errors. Therefore by minimizing the difference between these two signals, the channel phase errors can be precisely derived. The effectiveness of the proposed algorithm is validated by experimental results.

This paper proposes a novel approach based on scale invariant feature transform (SIFT) and kernel sparse representation for traffic sign recognition in complex traffic scenes. This module consists of several steps. In the first stage, SIFT is introduced for feature extraction from samples and test targets, respectively. The features are mapping to the kernel space. In the second stage, we construct an over-complete dictionary based on kernel sparse representation. Finally, traffic objects are recognized by computing sparseness and reconstruction residuals in the dictionary. Experiment results show that the proposed approach enhances the class discriminant ability using traffic features with higher recognition preciseness and robustness in complex traffic scenes compared with SVM, SRC.

We propose in this paper a method for the procedural modeling of buildings combing CGA shape grammar with facade image segmentation. The method effectively analyzes the architectural structure and elements and achieves facade image segmentation. Specifically, we generate shape tree of facade image according to image segmentation. We adopt a top-down subdivision scheme to encode rules and a down-top merging scheme to generate 3D models with clear level of detail. Our method can subdivide low-resolution images with certain noise and distinguish prominent architectural elements. We can change the accuracy of models with three-dimensional components so that our models can meet the needs of large-scale urban three-dimensional reconstruction.

This paper presents a scheme of approximating cylindrical models using convolution surfaces and subdivision surfaces. Firstly, an inverse Catmul-Clark subdivision is utilized to approximate the cylindrical models. Then the subdivided mesh is evolved onto the target convolution surface-based shape to produce a natural blending at branches. The initial inverse subdivision reduce the evolution iterations efficiently. Results show that our method approximate the skeletal structures well.

Ship classification is an important step in maritime surveillance utilizing synthetic aperture radar images. In this paper, we focus on the classifier architecture. The paper investigates three individual classifiers, i.e., the K nearest neighbor classifier, the Bayes classifier, and the back-propagation neural network classifier from the viewpoint of discrimination measurements firstly. Then, we propose a SVM combination strategy to fuse the results of individual classifiers. Extensive experiments conducted on the TerraSAR-X SAR images validate the effectiveness of the proposed method.

According to human visual sensitivity, in this paper, we present a new simplification of point cloud based on point saliency. In order to find the nearest K points, we need to divide the space of Point cloud firstly. Secondly, we compute the normal vector and saliency of every point. At last, we combine octree structure and saliency to simplify point cloud data. Experiments show that our method can well preserve the point cloud in visual sensitivity area.

The progressive mesh compression algorithm on the blocks of model based on edge-collapse mesh simplification is proposed to solve some practical problems. At first, we adopt K-means to cluster the mesh triangles. Secondly, we compress each model block. The detail information of different groups obtained by compressing each of blocks are independent of each other. If some details are lost in transmission, other details can also decode independently. We only need to request server to resend the lost part of the groups data. At the same time, it can be decoded regional details depend on our viewpoint when we need to load the complex models. Because our vision can only focus on part region of model, and the area in which we isn't interested can be instead by coarse model.

Advanced Encryption Standard (AES) and Elliptic Curve Cryptosystems (ECC) is the best two algorithms of symmetric encryption technology and asymmetric encryption technology. The paper analyzes the AES algorithm and S-box structure, then the replace plan based on S-box structure is proposed to improve AES encryption algorithm, secondly the ECC algorithm is been discussed. Based on this we put forward an mix encryption scheme of improved AES and ECC. This plan has high operation speed, high security performance and strong usability.

This paper proposes an improved color Set Partitioning in Hierarchical Trees (CSPIHT) algorithm based on multiwavelet. In the proposed image coding algorithm, color RGB image is converted into YCbCr format. Clearly, coding bit stream luminance Y plane is higher than Cb and Cr chrominance planes. The new spatial orientation tree structure is developed between channel planes based on SPIHT coding idea. Combination with mutltiwavelet coefficients, we embedded multiwavelet SPHIT coding for color images. The result shows that this coding method is Valid and performance is superior to SPHIT on 9/7 wavelet.

Volume visualization is one of the advanced technologies in the field of medical image 3D visualization, the efficiency of the volume rendering algorithm directly influences the results of volume visualization. In allusion to the ray-casting volume rendering algorithm, a fast look-up empty voxels algorithm is proposed based on AABB, which shortens the length of ray integral and accelerates the volume rendering. The experiment results verify that the new algorithm is effective in raising rendering speed.

Modeling and simulation of special effects in flight simulation visual system is an important field in computer simulation technology and virtual reality technology in recent years. In this paper, we study the simulation of Jet fire based on the particle system. Texture mapping and LOD techniques are used to enhance the operation speed. The result shows that visual effect of Jet fire is true to life, and it can even meet the need of real time animation on generic PC. At the same time, the simulations of rain and snow are given.

A radar imaging algorithm named reciprocal spectrum algorithm (RSA) is proposed in this paper to get higher resolution in azimuth direction with frequency sampling waveform. Theoretical analysis and simulation results show that the algorithm can give better performance than the tradition range Doppler algorithm (RDA) at the cost of peak value reduction at the object points in radar image.

A progressive SAR imaging technique is proposed as a novel SAR raw data processing scheme in this paper. Different from the classic SAR imaging algorithms which focus the SAR raw data as a whole regardless of the backscattering signal, the novel scheme discriminate the backscattering signal and choose the proper ones to be focused progressively according to some application context while the others will be discarded. The new scheme makes the SAR imaging algorithm not only focus the energy back to the scattering points but also form an image more suitable for some special applications. In this paper, a realization of the scheme present via Atomic Decomposition (AD)[1]–[3]. AD helps estimate the parameters of backscattered signal of a scattering point and detach it from the raw data. With the parameters, the backscattering-signal can be reconstructed and focused. Targets or scatter-points in the scene will be imaged progressively in a sequence of their energy due to the greedy natural of matching pursuit employed during AD. Therefore, the contents in the final SAR image can be controlled by an energy threshold. Besides the interested targets can be extracted easily from the background clutter and this may be helpful for the SAR image understanding

The spotlight mode of spaceborne SAR platforms often have to point its payload to immobile target on the ground which means the satellite attitude have to follow a specific trajectory in a fixed time span. In this paper, the attitude angle, angular velocities and angular accelerate are derived. A PD control law with feedforward is developed to achieve the purpose. The approach is verified using numerical simulation with external disturbance and uncertain model.

To improve the accuracy and better fault-tolerant performance for ultra-close spacecraft formation flying, a Global Position System (GPS)/Vision based Navigation (VISNAV) integrated Relative Navigation and Attitude Determination (RNAD) approach was presented in this paper. Onboard GPS was used for RNAD for formation spacecraft by multi-antennas configuration. The same RNAD information was also achieved by VISNAV system separately by using multiple line-of-sight vectors observations from spacecraft to another. Optimal fusion of RNAD information from both GPS/VISNAV subsystems was achieved by a Federal Kalman Filter (FKF) architecture. Simulation results provided later indicate that the integrated system can provide a total improvement of relative navigation and attitude estimation performance in accuracy and fault-tolerant.

Modeling and rendering clouds is an important field in the virtual reality technology in recent years, and it takes an important role in virtual display system, computer games and 3D animation field. In this paper, we study the simulation of clouds from the modeling method, lighting model and the rendering efficiency. As far as the modeling method, we model cloud based on the particle system and fractal method. This reduces the complexity of the simulation. And then the exiting multiple forward scattering illumination models were used. At last, the Billboard and Impostor techniques are used to enhance the operation speed. The result shows that visual effect of 3D cloud scene is true to life, and it can even meet the need of real time animation on generic PC.

This paper presents a multi-label Chinese text categorization system based on Chinese character features and boosting algorithm. This system has been successfully evaluated on the TCM-MED dataset provided by China Academy of traditional Chinese medicine (TCM) and the Reuters-21578 benchmark. We suggest that the TCM-MED dataset can be used as a standard corpus for the Chinese text categorization tasks. We have also carried out experiments to compare the performance of the boosting algorithm with two other traditional algorithms on the same datasets. The results indicate that for the design of a multi-label Chinese text categorization system, the boosting algorithm has a high performance and outperforms the other two algorithms.

Based on the Chinese character representation and the boosting algorithm, a multi-label Chinese text categorization system is demonstrated. This system has been successfully tested on two multi-labeled datasets, namely traditional Chinese medicine (TCM) dataset- TCM-MED and Reuters21578. Experiments have also been carried out to compare the performance of the boosting algorithm with two other traditional algorithms on the two datasets mentioned above. The results indicate that the boosting algorithm outperforms the other two algorithms in Chinese text categorization.