Comparison of a new radar ocean imaging model with SARSEX internal wave image data

Abstract

Good agreement is demonstrated between both L-band and X-band SARSEX internal wave image data and the predictions of a new radar ocean imaging model that incorporates Bragg, specular, and composite scattering effects. It then follows that a two-step hydrodynamic modulation process, as hypothesized by Hughes and Gower, Thompson and Gasparovic, and Watson, does not appear lo be required to explain why the SARSEX L-band and X-band internal wave image modulations are comparable.     

Bistatic scattering analysis for two-dimensional rough sea surfaces using an angular composite model

An angular composite model (ACM) is applied to analyse the bistatic scattering from two-dimensional sea surfaces. When the local scattered angle lies in the specular region, Kirchhoff approximation (KA) is used to evaluate the specular reflection, which predominates the total scattering in this region; the small perturbation method (SPM) is employed to deal with the diffuse reflection dominated by small roughness, as the local scattered angle is situated out of the specular region. This avoids dwelling on the tough question about how to choose the proper cut-off wave number like in the well-known two-scale model (TSM). Numerical illustrations are carried out for bistatic scattering coefficients versus wind speed, incident angle, incident frequency and polarization state, respectively. The comparison of numerical results of the ACM and other experimental and theoretical models in several configurations shows that the ACM is robust to give accurate numerical evaluations for bistatic scattering of rough sea surfaces.     

基于Lambert-Phong模型的图像盲取证方法

由于现有的图像盲取证方法中所使用的光照模型不能有效地表征物体表面的实际光照效果,提出Lambert-Phong光照模型。该模型同时考虑光照的漫反射和镜面反射,利用该光照模型对无限光源模式下的图像进行蓄意修改检测。实验结果表明,Lambert-Phong光照模型能较准确地计算出图像中不同目标的光照方向,有效地判别出图像是否经过蓄意修改。     

Comparison of parametric sparse recovery methods for ISAR image formation

A parametric sparse representation model of the inverse synthetic aperture radar(ISAR)signal has been proposed recently,and the ISAR signal is decomposed as a summation of many basis-signals determined by the target rotation rate.Based on the parametric sparse representation model,several sparsity-driven algorithms are proposed to retrieve both the target rotation rate and the ISAR image.In this paper,four parametric sparse recovery algorithms are compared mainly in three aspects:the accuracy of the rotation rate estimation,the ISAR image quality and the computational load.Numerical examples are presented to show the advantages and disadvantages for each method.     

利用序列ISAR图像获取空间目标3-D信息的方法

如何从空间目标序列性二维(2-D,Two-Dimentional)逆合成孔径雷达(ISAR,Inverse Synthetic Aperture Radar)成像获取目标的三维(3-D)信息,是目标特征自动识别(ATR,Automatic Target Recognition)技术的重要研究课题。利用双向射线跟踪(BART,Bidirectional Analytic Ray Tracing)方法,计算连续多角度观测条件下空间目标的电磁散射数据,并由此获取空间目标的ISAR序列2-D图像。再利用KLT(Kanade-Lucas-Tomasi)特征跟踪算法,跟踪提取2\|D序列ISAR图像中的特征点(强散射点),获得其2-D坐标。然后,基于正交因式分解法(OFM,Orthographic Factorization Method),计算强散射点的3\|D坐标,获取空间目标的3-D信息。通过简单六棱柱模型,验证重构算法的精度;并以ENVISAT卫星模型为例,给出强散射点的3-D重构结果。结果表明,本文对空间目标3\|D信息获取方法能有效地从ISAR序列2-D图像中重构目标的三维信息。     

矿井电磁波传输路径损耗改进模型

针对基于接收信号指示强度的基本路径损耗模型建立在发射节点与接收节点为视距基础上,不符合矿井电磁波传输特点的问题,提出了一种矿井电磁波传输路径损耗改进模型。该改进模型在基本模型只有衰减因子参数的基础上,增加了多径参数系数,并采用最小二乘法计算这2个参数。计算及Matlab仿真结果表明,采用该改进模型得到的结果更接近实测值,误差更小,且接收功率和传输衰减性随着节点间距的增加而减小。     

ISAR imaging via sparse frequency-stepped chirp signal

Frequency-stepped chirp signal can simplify the designation of radar system.However,it has a shortcoming of Doppler ambiguity for high-speed moving targets.Therefore,it is of great significance to study how to increase its equivalent pulse repeat frequency.The back scattering field of the ISAR target has strong sparsity;that is to say,most energy is contributed merely by a few scattering centers.Hence,based on the theory of the sparse signal representation,a novel method for ISAR imaging via sparse frequency-stepped chirp signals is proposed by analyzing the signal model of the target.In the proposed method,part of sub-pulses of the frequency-stepped chirp signal is randomly selected to transmit,and then the 2D high-resolution image of the target can be constructed by sparse signal decomposition.At the cost of computational resources,the method can effectively resolve the problem of Doppler ambiguity,decrease the sidelobes and obtain a super-resolution image.Furthermore,the validity of the proposed approach is confirmed by the results of numerical simulations and real data.     

Ad hoc网络中一种带预测的路由算法

在自组网中,由于网络节点的移动性及拓扑结构的易变性,设计稳定的路由成为最受关注的问题。根据可靠性为多路径路由选择更多的可靠路径,以满足自组网中多路径传输在路径的数量和质量方面的需求,是多路径路由技术中的一个重要研究课题。为此,基于GRID模型和预测模型提出了一种带预测的稳定不相交备用路由算法,其利用有效限制路由查询包的泛洪区域,并结合预测策略和节点不相交路径算法来选择一条最稳定的不相交备用路由,从而进一步提高该路由算法的性能。模拟结果显示,与其他3个多路径路由相比较,该算法是一个有效的自组网路由算法。     

无线传感器网络多路径缠绕模型及其容错路由机制

针对无线传感器网络(WSN)不相交多路径路由中的路径过长问题和缠绕多路径路由中的容错削弱问题,提出一种多路径缠绕模型及其容错路由机制。首先,提出将多路径缠绕量化的思想,通过建立多路径缠绕模型将多路径的缠绕性量化,并通过容错概率模型建立多路径缠绕性与路由容错性的理论关联;其次,基于多路径缠绕模型,通过局部调整多路径的相交度建立容错路由机制。实验结果表明,在典型多路径路由算法——顺序分配路由(SAR)和能量有效容错多路径路由(EEFTMR)上应用该容错路由机制,能有效提高算法的数据包传输成功率,并一定程度上优化算法的能量有效性和端到端传输延迟。     

多径传播环境下的无源UHF RFID系统电波传播模型

针对自由空间模型在预测射频识别系统识别距离时存在的偏差,综合考虑射频识别系统应用的多径传播环境,建立一种无源超高频射频识别系统电波传播模型,并重点分析了前向链路路径损耗的主要影响因素及其计算方法。基于该电波传播模型,探索性地提出实际环境下的无源超高频射频识别应用模拟思路。仿真和测量结果表明,该模型在预测无源超高频射频识别系统识别距离时更为准确。     

Extending Backward Polygon Beam Tracing to Glossy Scattering Surfaces

Backward polygon beam tracing methods, that is beam tracing from the light source (L), are well suited to gather path coherency from specular (S) scattering surfaces. These methods are useful for modelling and efficiently simulating caustics on diffuse (D) surfaces; an effect due to LS⁺D transport paths. This paper generalizes backward polygon beam tracing to include a glossy (G) scattering surface. To this end the details of a beam tracing lumped model and implementation of L(S | G)D transport paths are presented. Although we limit the discussion to short transport paths, we show that backward beam tracing is faster than photon mapping by an order of magnitude for rendering caustics from glossy and specular surfaces.     

Specular Highlight Removal for Real‐world Images

Removing specular highlight in an image is a fundamental research problem in computer vision and computer graphics. While various methods have been proposed, they typically do not work well for real‐world images due to the presence of rich textures, complex materials, hard shadows, occlusions and color illumination, etc. In this paper, we present a novel specular highlight removal method for real‐world images. Our approach is based on two observations of the real‐world images: (i) the specular highlight is often small in size and sparse in distribution; (ii) the remaining diffuse image can be represented by linear combination of a small number of basis colors with the sparse encoding coefficients. Based on the two observations, we design an optimization framework for simultaneously estimating the diffuse and specular highlight images from a single image. Specifically, we recover the diffuse components of those regions with specular highlight by encouraging the encoding coefficients sparseness using L₀ norm. Moreover, the encoding coefficients and specular highlight are also subject to the non‐negativity according to the additive color mixing theory and the illumination definition, respectively. Extensive experiments have been performed on a variety of images to validate the effectiveness of the proposed method and its superiority over the previous methods.     

双边滤波的改进高光去除

目的 由于非均匀光照条件下,物体表面通常出现块状的强反射区域,传统的去高光方法在还原图像时容易造成颜色失真或者边缘的丢失。针对这些缺点,提出一种改进的基于双边滤波的去高光方法。方法 首先通过双色反射模型变换得到镜面反射分量与最大漫反射色度之间的转换关系,然后利用阈值将图像的像素点分为两类,将仅含漫反射分量的像素点与含有镜面反射分量的像素点分离开来,对两类像素点的最大漫反射色度分别做估计,接着以估计的最大漫反射色度的相似度作为双边滤波器的值域,同时以图像的最大色度图作为双边滤波的引导图保边去噪,进而达到去除镜面反射分量的目的。结果 以经典的高光图像作为处理对象,对含有镜面反射和仅含漫反射的像素点分别做最大漫反射色度估计,再以该估计图作为双边滤波的引导图,不仅能去除镜面反射分量还能有效的保留图像的边缘信息,最大程度的还原图像细节颜色,并且解决了原始算法处理结果中R、G、B三通道相似的像素点所出现的颜色退化问题。用改进的双边滤波去高光算法对50幅含高光的图像做处理,并将该算法与Yang方法和Shen方法分别作对比,结果图的峰值信噪比（PSNR）也分别平均提高4.17%和8.40%,所提算法的处理效果更符合人眼视觉,图像质量更好。结论 实验结果表明针对含镜面反射的图像,本文方法能够更有效去除图像的多区域局部高光,完成对图像的复原,可为室内外光照不匀情况下所采集图像的复原提供有效理论基础。     

A composite model for accurate colorimetric characterization of liquid crystal displays

This paper presents a composite color‐characterization model for Liquid Crystal Displays (LCDs), considering channel dependence and poor chromaticity constancy of primaries. The proposed model consists of two parts, that is, the Piecewise Linear assuming Variation in Chromaticity (PLVC) model and a partition compensation error model. PLVC model can accurately predict CIEXYZ for primaries and calculate the CIEXYZ of any digital input values ( d _r , d _g , d _b ) based on the channel independence assumption. In addition, the prediction error of PLVC model is compensated in RGB subspace using the partition compensation error model. The color difference Δ was used to evaluate the accuracy of the model. The result of this experiment shows that PLVC model performs well for LCDs with channel independence and poor chromaticity constancy of primaries, compared with other characterization models. For channel dependence and poor chromaticity constancy LCDs, our result shows that proposed composite color‐characterization model also has better prediction accuracy than all other tested models. In this study, measurements were conducted in ambient light environment, and the experiment results consist with practical applications.     

High-resolution ISAR imaging of fast rotating targets based on pattern-coupled Bayesian strategy for multiple measurement vectors

Very high resolution inverse synthetic aperture radar (ISAR) imaging of fast rotating targets is a complicated task. There may be insufficient pulses or may introduce migration through range cells (MTRC) during the coherent processing interval (CPI) when we use the conventional range Doppler (RD) ISAR technique. With compressed sensing (CS) technique, we can achieve the high-resolution ISAR imaging of a target with limited number of pulses. Sparse representation based method can achieve the super resolution ISAR imaging of a target with a short CPI, during which the target rotates only a small angle and the range migration of the scatterers is small. However, traditional CS-based ISAR imaging method generally faced with the problem of basis mismatch, which may degrade the ISAR image. To achieve the high resolution ISAR imaging of fast rotating targets, this paper proposed a pattern-coupled sparse Bayesian learning method for multiple measurement vectors, i.e. the PC-MSBL algorithm. A multi-channel pattern-coupled hierarchical Gaussian prior is proposed to model the pattern dependencies among neighboring range cells and correct the MTRC problem. The expectation-maximization (EM) algorithm is used to infer the maximum a posterior (MAP) estimate of the hyperparameters. Simulation results validate the effectiveness and superiority of the proposed algorithm.     

高速铁路的GSM-R无线传播模型校正

为了提高在铁路沿线复杂地形环境下的Hata模型准确度,在研究GSM-R系统中的电波传播预测Hata模型的基础上,提出了一种对Hata传播模型的校正方法。结合郑西实测线路数据对市郊、平原和山区三种地形环境下进行校正,得到了不同地形下的Hata校正模型。通过对校正前后模型的误差对比,校正后的Hata传播模型准确度有显著提高。     

Quantum‐inspired genetic programming model with application to predict toxicity degree for chemical compounds

Cheminformatics plays a vital role in maintaining large amounts of chemical data. The reliable prediction of toxic effects of chemicals in living systems is highly desirable in domains such as cosmetics, drug design, food safety, and the manufacturing of chemical compounds. Toxicity prediction requires several new approaches for knowledge discovery from data to paradigm composite associations between the modules of the chemical compound; the computational demands of such techniques increase greatly with the number of chemical compounds involved. State‐of‐the‐art prediction methods such as neural networks and multilayer regression require either tuning parameters or complex transformations of predictor or outcome variables and do not achieve highly accurate results. This paper proposes a quantum‐inspired genetic programming model to improve prediction accuracy. Genetic programming is utilized to give a linear equation for calculating the degree of toxicity more accurately. Quantum computing is employed to improve the selection of the best‐of‐run individuals and handles parsimony pressure to reduce the complexity of solutions. The results of the internal validation analysis indicated that the quantum‐inspired genetic programming model has better goodness‐of‐fit statistics then and significantly outperforms the neural network model.     

基于Yamaguchi分解模型的全极化SAR图像分类

针对利用Yamaguchi分解模型的四个散射分量直接进行类别归属判断精度不高并且所分类别有限的问题,结合模糊C均值的理论,提出了一种基于Yamaguchi分解模型的全极化SAR分类算法,把四个散射分量组成一组归一化的特征矢量,进行FCM聚类分析。并且用日本机载L波段PiSAR数据验证了该算法具有较高的分类精度和较好的视觉效果。     

A new complex envelope behavioral model for load mismatched power amplifiers

A new dual‐input wideband dynamic Quadratic Poly‐Harmonic Distortion (D‐QPHD) model for unmatched power amplifiers (PAs) is proposed and validated in this article. Compared with the dynamic PHD (D‐PHD) model, the new approach significantly improves the prediction accuracy. As the new model has reduced complexity, it is also more robust against possible rank deficiency problems, but still preserves accuracy, when compared with the recently proposed double‐input double‐output (DIDO) model. The parameters of the new model also have smaller magnitude distribution range compared with the DIDO model, which also improves the model robustness. Hence, it provides a good modeling choice for complex envelope DIDO modeling.     

Neural computation approach for developing a 3D shapereconstruction model

The shape from shading problem refers to the well-known fact that most real images usually contain specular components and are affected by unknown reflectivity. In this paper, these limitations are addressed and a new neural-based 3D shape reconstruction model is proposed. The idea behind this approach is to optimize a proper reflectance model by learning the parameters of the proposed neural reflectance model. In order to do this, new neural-based reflectance models are presented. The feedforward neural network (FNN) model is able to generalize the diffuse term, while the RBF model is able to generalize the specular term. A hybrid structure of FNN-based and RBF-based models is also presented because most real surfaces are usually neither Lambertian models nor ideally specular models. Experimental results, including synthetic and real images, are presented to demonstrate the performance of our approach given different specular effects, unknown illuminate conditions, and different noise environments.