Underdetermined direction of arrival estimation with nonuniform linear motion sampling based on a small unmanned aerial vehicle platform

Uniform linear array(ULA)radars are widely used in the collision-avoidance radar systems of small unmanned aerial vehicles(UAVs).In practice,a ULA's multi-target direction of arrival(DOA)estimation performance suffers from significant performance degradation owing to the limited number of physical elements.To improve the underdetermined DOA estimation performance of a ULA radar mounted on a small UAV platform,we propose a nonuniform linear motion sampling underdetermined DOA estimation method.Using the motion of the UAV platform,the echo signal is sampled at different positions.Then,according to the concept of difference co-array,a virtual ULA with multiple array elements and a large aperture is synthesized to increase the degrees of freedom(DOFs).Through position analysis of the original and motion arrays,we propose a nonuniform linear motion sampling method based on ULA for determining the optimal DOFs.Under the condition of no increase in the aperture of the physical array,the proposed method obtains a high DOF with fewer sampling runs and greatly improves the underdetermined DOA estimation performance of ULA.The results of numerical simulations conducted herein verify the superior performance of the proposed method.

THE LINEAR SAMPLING METHOD FOR RECONSTRUCTING A PENETRABLE CAVITY WITH UNKNOWN EXTERNAL OBSTACLES

We consider the interior inverse scattering problem for recovering the shape of a penetrable partially coated cavity with external obstacles from the knowledge of measured scattered waves due to point sources.In the first part,we obtain the well-posedness of the direct scattering problem by the variational method.In the second part,we establish the mathematical basis of the linear sampling method to recover both the shape of the cavity,and the shape of the external obstacle,however the exterior transmission eigenvalue problem also plays a key role in the discussion of this paper.

Analysis of the Linear Sampling Method for Boundary Reconstruction of Chiral Obstacle in Electromagnetic Scattering Problems

In this paper, we consider the inverse scattering by chiral obstacle in electromagnetic fields, and prove that the linear sampling method is also effective to determine the support of a chiral obstacle from the noisy far field data.

Improved response surface method based on sample point selection strategies

In the reliability analysis of complex structures,response surface method(RSM)has been suggested as an efficient technique to estimate the actual but implicit limit state function.A set of sample points are needed to fit to the implicit function.It has been noted that the accuracy of RSM depends highly on the so-called sample points.However,the technique for point selection has had little attention.In the present study,an improved response surface method(IRSM)based on two sample point selection techniques,named the direction cosines projected strategy(DCS)and the limit step length iteration strategy(LSS),is investigated.Since it uses the sampling points selected to be located in the region close to the original failure surface,and since it needs only one response surface,the IRSM should be accurate and simple in practical structural problems.Applications to several typical examples have helped to elucidate the successful working of the IRSM.

无人机高光谱岩性和矿物识别技术研究

无人机高光谱遥感技术以其高空间分辨率和高光谱分辨率的技术优势,在大比例尺区域地质填图领域可以发挥重要作用。利用无人机搭载全谱段高光谱成像仪(HD_SAVI)获取0.2 m高空间分辨率影像数据,采用经验线性法对所获取数据进行光谱重建,重建后无人机高光谱影像光谱曲线与地面实测光谱曲线谱形基本一致,典型特征吸收峰位置偏差均在1个像元以内。采集岩性样本构建岩性知识库,利用深度学习算法基于无人机高光谱影像数据开展岩性识别;岩性识别结果与大比例尺地质图对比,岩性空间分布基本一致,地质体边界准确且较清晰。参考典型矿物光谱库进行矿物识别。与同一地区CASI/SASI航空高光谱数据地质填图结果进行对比分析,结果表明:两者提取的矿物在空间分布上具有较高的一致性,但无人机低空高光谱影像空间分辨率较高,识别出的矿物种类更加丰富。无人机高光谱遥感技术可以满足无人区、交通不便地区地质勘查,以及重点区或矿区外围等地区大比例尺地质填图的需要。

On a Hybrid Method for Inverse Transmission Eigenvalue Problems

In this paper, we are concerned with the inverse transmission eigenvalue problem to recover the shape as well as the constant refractive index of a penetrable medium scatterer. The linear sampling method is employed to determine the transmission eigenvalues within a certain wavenumber interval based on far-field measurements. Based on a prior information given by the linear sampling method, the neural network approach is proposed for the reconstruction of the unknown scatterer. We divide the wavenumber intervals into several subintervals, ensuring that each transmission eigenvalue is located in its corresponding subinterval. In each such subinterval, the wavenumber that yields the maximum value of the indicator functional will be included in the input set during the generation of the training data. This technique for data generation effectively ensures the consistent dimensions of model input. The refractive index and shape are taken as the output of the network. Due to the fact that transmission eigenvalues considered in our method are relatively small,certain super-resolution effects can also be generated. Numerical experiments are presented to verify the effectiveness and promising features of the proposed method in two and three dimensions.

拉普拉斯分布参数的近似贝叶斯估计

拉普拉斯分布是刻画尖峰厚尾数据的重要分布之一.本文提出拉普拉斯分布两参数具有显式解的线性近似贝叶斯估计,通过理论证明和数值模拟验证了线性近似贝叶斯估计相比其他估计的优越性,并考察了线性近似贝叶斯估计随着样本量增加的渐近性质.

Assessment of different genetic distances in constructing cotton core subset by genotypic values

One hundred and sixty-eight genotypes of cotton from the same growing region were used as a germplasm group to study the validity of different genetic distances in constructing cotton core subset. Mixed linear model approach was employed to unbiasedly predict genotypic values of 20 traits for eliminating the environmental effect. Six commonly used genetic distances(Euclidean,standardized Euclidean,Mahalanobis,city block,cosine and correlation distances) combining four commonly used hierarchical cluster methods(single distance,complete distance,unweighted pair-group average and Ward's methods) were used in the least distance stepwise sampling(LDSS) method for constructing different core subsets. The analyses of variance(ANOVA) of different evaluating parameters showed that the validities of cosine and correlation distances were inferior to those of Euclidean,standardized Euclidean,Mahalanobis and city block distances. Standardized Euclidean distance was slightly more effective than Euclidean,Mahalanobis and city block distances. The principal analysis validated standardized Euclidean distance in the course of constructing practical core subsets. The covariance matrix of accessions might be ill-conditioned when Mahalanobis distance was used to calculate genetic distance at low sampling percentages,which led to bias in small-sized core subset construction. The standardized Euclidean distance is recommended in core subset construction with LDSS method.

Analysis of a Linear Sampling Method for Identification of Obstacles

Some difficulties are pointed out in the methods for identification of obstacles based on the numerical verification of tile inclusion of a function in the range of an operator. Numerical examples are given to illustrate theoretical conclusions. Alternative methods of identification of obstacles are mentioned： the Support Function Method （SFM） and the Modified Rayleigh Conjecture （MRC） method.

SAMPLED-DATA STATE ESTIMATION FOR NEURAL NETWORKS WITH ADDITIVE TIME–VARYING DELAYS

In this paper, we consider the problem of delay-dependent stability for state estimation of neural networks with two additive time–varying delay components via sampleddata control. By constructing a suitable Lyapunov–Krasovskii functional with triple and four integral terms and by using Jensen's inequality, a new delay-dependent stability criterion is derived in terms of linear matrix inequalities(LMIs) to ensure the asymptotic stability of the equilibrium point of the considered neural networks. Instead of the continuous measurement,the sampled measurement is used to estimate the neuron states, and a sampled-data estimator is constructed. Due to the delay-dependent method, a significant source of conservativeness that could be further reduced lies in the calculation of the time-derivative of the Lyapunov functional. The relationship between the time-varying delay and its upper bound is taken into account when estimating the upper bound of the derivative of Lyapunov functional. As a result, some less conservative stability criteria are established for systems with two successive delay components. Finally, numerical example is given to show the superiority of proposed method.

Overview of multirate sampled-data control system

The basic analysis and synthesis approaches for multirate sampled-data control system are reviewed. After giving the definition and some properties of multirate system are given, its origination, development and design methods are discussed in detail. Finally, some remarks, expectations and conclusions on the present research status and the research directions are given.

基于LRnet模型的混合散射体反演问题研究

探讨了声环境中利用远场信息重建混合散射体的逆散射问题,提出了一种混合散射体形状的重建方法。首先,采用线性采样法获取混合散射体形状的部分先验信息,例如障碍物和裂缝的个数与形状参数,基于门控思想和残差思想构建了形状参数反演模型(LRnet),模型参数通过自学习更新。然后根据远场信息和混合散射体形状的先验信息反演混合散射体边界的形状参数,在此基础上重建混合散射体的边界形状。最后,进行了多次数值实验以验证所提出的方法。结果表明,该方法可以有效地反演混合散射体的形状。

基于拉丁超立方采样技术的半不变量法随机潮流计算

新能源发电的兴起加剧了电力系统运行中的不确定性和关联性,传统潮流计算方法无法适应新环境下电网分析和评估的要求。提出一种可考虑输入变量相关性的基于拉丁超立方采样技术的半不变量法随机潮流计算方法。该算法综合了模拟法精度高、适用性广和半不变量法计算速度快的优点,采用分段线性化潮流模型减小截断误差,并通过Cholesky分解解决了半不变量法只能处理独立变量的难题。对IEEE 30和IEEE 118节点系统的测试验证了所提方法的准确性、快速性和实用性。

光伏发电系统可靠性分析及其置信容量计算

大规模光伏发电的接入增加了系统的不确定因素。该文针对光伏发电出力的时序性和随机性,通过天气类型抽样和设备停运概率的分层抽样改进了光伏发电系统的出力模型。将模型应用于并网光伏发电系统的可靠性评估和置信容量的计算中,研究了天气变化、设备故障等随机因素对计算结果的影响,提高了计算的精度。通过对2015年青海省格尔木市并网光伏系统的计算分析,发现电网规模的扩大和光伏电站的分散分布有助于光伏发电置信容量的提升。

直接汞分析仪测定煤中汞含量不确定度评定

对直接汞分析仪测定煤炭中汞含量过程中可能引入的不确定度进行分析和评定。采用棋盘取样法确定样品均匀性不确定度分量,用较严密的密度修正法评估校准溶液质量浓度变化的不确定度分量,用一般的线性拟合法和较严密的非线性拟合法评估校准曲线拟合浓度的不确定度分量。

一种工业CT定量检测炸药装药密度的方法

为了定量检测炸药的装药密度,提出了一种工业CT测量炸药装药密度的线性换算法。用蒸馏水制备已知密度的盐溶液作密度标准件,对该方法进行了验证。用该方法测量了某战斗部产品实际装药密度和某种炸药药柱的密度。结果表明,该法与排水法测量装药密度或药柱密度之间的误差为1%。认为线性换算法简单、实用,能用于检测结构简单样品的装药密度。

基于加速时序蒙特卡洛法的风电场置信容量评估

随着风电资源接入电网规模的增大,如何快速准确地评估待建风电场的置信容量是电力系统规划时需要考虑的问题。文中基于有效载荷容量这一概念,根据新能源接入前后系统可靠性不变的原则,从负荷侧评价了风电资源的置信容量。在计算可靠性指标时,针对当前解析法无法考虑负荷曲线时序性而时序仿真法计算时间较长的问题,提出了一种可靠性指标的加速时序蒙特卡洛计算方法。在此基础上,在计算可靠性较高的系统风电置信容量时,将该方法和线性插值法相结合,避免了采用仿真法计算可靠性指标时的数值波动对弦截法迭代过程造成的不利影响。最后,利用甘青地区电网实际机组、负荷和风电出力数据设计了算例,并验证了算法的正确性。

水文频率分析中的多项式正态变换方法研究

采用多项式正态变换(PNT)方法,把偏态分布转换为正态分布,推求指定频率下正态分布的设计值(分位数),然后通过一一变换转换成原偏态分布对应频率的设计值.以P Ⅲ型分布为例,用Monte Carlo法进行模拟分析,并对PNT法的统计性能与线性矩(L M)法的统计性能进行了比较.结果表明,PNT法的精度略差于L M法的精度.由于PNT法本质上是非参数估计方法,不需要假设水文变量的分布线型,因此该法理论上是一种较为可行的水文极值分析方法.

基于Bayesian方法的参数估计和异常值检测

异常值检测是当前数据分析研究中的一个重要研究领域。模型中的异常值会直接影响建模、参数的估计、预测等问题。基于模型的异常值检测,传统的做法是先对模型参数进行估计,再进行异常值检测。而异常值的存在会影响参数估计,从而导致下一步异常值检测的不可靠;反之异常值检测也会影响参数估计。针对这些不足之处,提出了基于Bayesian方法的参数估计和异常值检测,此方法可以将参数估计和异常值检测同时实现,具体做法是在线性回归模型中引入识别变量,基于Gibbs抽样算法,给出识别变量后验概率的计算方法,通过比较这些识别变量的后验概率进行异常值定位,同时给出参数的估算方法。通过大量的模拟实验,结果表明,与传统方法相比,提出的方法对异常值更灵敏。

基于SVM的直升机飞行状态识别

针对直升机飞行状态识别训练样本数据少而导致识别率不高的问题,提出一种基于支持向量机(support vector machine,SVM)的直升机飞行状态识别方法.首先利用限幅、去野点和均值滤波对飞行数据进行去噪,用最小二乘法对飞行数据进行直线拟合获取变化率,并根据线性相关性提取状态特征参数,以减少数据冗余;然后根据特征参数将飞行状态分为10小类,对每一小类进行SVM分类器设计以提高识别效率;最后利用训练样本训练每个SVM分类器,用训练好的SVM分类器识别直升机全起落飞行状态.通过某型直升机实飞数据进行飞行状态识别实验,并将所提出的方法与RBF神经网络法进行对比,所得结果表明该方法在小样本情况下的识别率有明显提高,可为直升机故障诊断和寿命预测提供依据.