NEW LOWER BOUNDS FOR LEE DISCREPANCY ON TWO AND THREE MIXED LEVELS FACTORIALS

The objective of this paper is to study the issue of uniformity on asymmetrical designs with two and three mixed levels in terms of Lee discrepancy. Based on the known formulation, we present a new lower bound of Lee discrepancy of fractional factorial designs with two and three mixed levels. Our new lower bound is sharper and more valid than other existing lower bounds in literature, which is a useful complement to the lower bound theory of discrepancies.

CRAMER-RAO BOUNDS OF THE FREQUENCY ESTIMATION IN THE TIME SELECTIVE RADIO CHANNELS WITH DOPPLER SPREAD

The mobile channel is slow fading and time selective, thus the multiplicative and additive noise of the channel will smear the spectral line, or arouse Doppler spread. This spread will make the parameters estimation accuracy degrade. The goal of this paper is to analytically assess this degradation when Carrier Frequency Offset (CFO) and Doppler shift exist jointly. Then the finite-sample Cramer-Rao Lower Bound (CRLB) is derived and close-form asymptotical expression is given for large-sample CRLB. These expressions give insights into the performance room for frequency estimation. Also the variance of Doppler shift estimator is simulated to illustrate the theoretical results.

ON MULTIPATH SIGNAL RESOLUTION AND ITS PERFORMANCE BOUNDS

The muitipath signal resolution is reviewed in this paper.The problemsexisted and to be studied are pointed out.Theoretical analysis of the performance ofthe resolution for deterministic signal in the cases where the signal known or unknownis made.Their corresponding Cramer-Rao lower bounds(CRLB)are obtained.

支持均匀缩放的不等长时间子序列查询方法

作为时序数据分析中的基础技术之一,时间序列的子序列查询旨在寻找与目标序列相似的子序列。现有的子序列查询方法大多仅支持查询与目标序列长度相同的子序列,因而均匀缩放技术常被用于解决子序列查询中的不等长问题。但现有支持均匀缩放的子序列查询技术大多未考虑子序列的Z-标准化,且对查询效率仍有改善的空间。针对该问题,提出一种基于索引技术且支持均匀缩放的子序列查询方法。结合现有索引方法 ULISSE提供的树状数据结构,设计可保证非漏报的下界距离,为索引结构的剪枝提供理论保证,并利用索引中存储的元数据,提出精确K-近邻查询算法。所提方法适用于非归一化和归一化两种场景。实验结果表明,较UCR-US和ULISSE基线方法,该基于索引的不等长子序列查询方法在CAP、GAP两个真实数据集以及随机游走人工合成数据集上均实现了查询效率的显著提升,针对在非归一化和归一化两种场景下的不等长子序列查询,该方法的平均效率提升分别为2.33和2.51倍。

SAR图像目标增强模型最优性能分析

研究用于SAR图像目标增强的lk范数正则化方法的最优性能。从有偏参数估计的统计性能极限角度,将达到渐进最小总方差的参数估计看作目标增强的最优效果。根据Bayes框架以及罚最大似然估计原理,研究了达到最优性能时正则项需满足的条件,该条件与图像大小、信号统计分布以及观测数据噪声方差有关,可为模型设计与迭代算法设计提供依据。并证明了仅当噪声方差较小时lk范数约束才能使得处理结果达到该最优估计性能。仿真实验验证了该结论的正确性。

乘积局部FC-一致空间内的聚合不动点定理和应用(英文)

应用在局部FC-一致空间内对紧闭集值映象得到的一个H imm elberg型不动点定理,对定义在局部FC-一致空间的乘积空间上的紧闭集值映象族建立了新的聚合不动点定理.作为应用,在局部FC-一致空间内对具有下和上界的拟平衡问题组得到了解的存在性定理和得到了新的极小极大定理.这些结果推广了文献中的某些已知结果.

一种高精度的频率估计算法研究

文章提出了一种基于DFT插值的频率估计算法,充分利用峰值及其左右谱线的幅度信息与相位信息进行估计。理论分析证明其是无偏估计,仿真结果表明该算法的性能接近修正克劳美罗限(MCRB),而且工作的信噪比门限低。该算法复杂度低,运算量小,便于实现和应用。

下侧或双侧二重Dirichlet级数收敛性

定义了双侧与下侧二重的Dirichlet级数 ;讨论了它们的几对相关收敛横坐标 ;建立了下侧二重Dirichlet级数的相关一致有界收敛定理 ;建立了该两类级数的Valiron推广公式及Knopp Kojima推广公式 .

二水平设计离散偏差和对称化L_2偏差紧的下界

基于现有的均匀性测度公式,利用Langrange乘数法和Taylor公式得到二水平设计离散偏差和对称化L2偏差紧的下界,最后通过2个例子来验证其结论.

均匀性模式及其在试验设计中的应用

均匀性是一个几何概念,通常采用不同的偏差来度量设计的均匀性.但偏差的度量侧重于总体,即考虑所有投影子设计的均匀性总和,而根据效应排序原则,低维投影均匀性应该比高维投影均匀性更重要,基于这一思想许多学者提出了类似于字长型的均匀性模式的新概念,并用它来度量设计的各阶投影均匀性.该文对均匀性模式的文献进行了综述,并给出均匀性模式及相关的最小低阶投影均匀性准则的统一定义.该文还对最小低阶投影均匀性准则与其他各种设计筛选准则之间的联系及其在补设计中的应用等进行了综述,这些联系表明最小低阶投影均匀性准则与因子设计中的广义最小低阶混杂准则、正交性准则、V准则、最近平衡性准则、设计效率准则、E(s2)准则之间是相互等价的,这些准则彼此之间可以互相支持,因此揭示了均匀设计和因子设计之间的内在关系.此外,该文还对现有文献中均匀性模式的下界进行了综述,并将达到下界的MPU设计案例罗列出来以方便实际工作者使用.

组合设计的离散偏差的下界

效应别名是部分因析设计不可避免的问题.由于别名的因子效应会造成数据分析的困扰,因此如何有效的解除别名效应的模糊性是部分因析设计中的重要问题.折叠反转是解除因子别名效应的经典方法.在两种特殊的情形下分别得到了两水平部分因子设计的组合设计的离散偏差的下界,数值例子说明这些下界是紧的.因此,这些下界可以作为寻找饱和正交设计的最优折叠反转方案的标准,并给出了用基于离散偏差的均匀性准则来寻找最优折叠反转方案的理论依据.

Double设计的几个结果

讨论了Double设计在对称化L2-偏差下的均匀性,给出了Double设计为均匀设计的充要条件,同时得到了Double设计的对称化L2-偏差的下界,来评价Double设计的均匀性,最后通过一个例子来验证理论结果。

无线传感器网络中一种改进的分布式加权多维尺度定位算法

本文在无线传感器网络单跳定位误差分析的基础上,分析了多跳节点定位误差的特性,并据此提出针对分布式加权多维尺度定位(Distributed Weighted Multidimensional Scaling,dwMDS)的权值优化算法。在无法获知参考点确切误差的情况下,利用分析出来的克拉美劳下限代替参考点误差并与距离测量误差合并,更准确的反映了多跳定位中的点与点之间的误差,从而有助于设计更优化的权值。仿真结果表明,使用优化权值改进的算法得到的节点定位误差明显减小。

Optimal observation configuration of UAVs based on angle and range measurements and cooperative target tracking in three-dimensional space

This article investigates the optimal observation configuration of unmanned aerial vehicles(UAVs) based on angle and range measurements, and generalizes predecessors' researches in two dimensions into three dimensions. The relative geometry of the UAVs-target will significantly affect the state estimation performance of the target, the cost function based on the Fisher information matrix(FIM) is used to derive the FIM determinant of UAVs' observation in three-dimensional space, and the optimal observation geometric configuration that maximizes the determinant of the FIM is obtained. It is shown that the optimal observation configuration of the UAVs-target is usually not unique, and the optimal observation configuration is proved for two UAVs and three UAVs in three-dimension. The long-range over-the-horizon target tracking is simulated and analyzed based on the analysis of optimal observation configuration for two UAVs. The simulation results show that the theoretical analysis and control algorithm can effectively improve the positioning accuracy of the target. It can provide a helpful reference for the design of over-the-horizon target localization based on UAVs.

Optimal maneuvering strategy of spacecraft evasion based on angles-only measurement and observability analysis

Spacecraft orbit evasion is an effective method to ensure space safety. In the spacecraft’s orbital plane, the space non-cooperate target with autonomous approaching to the spacecraft may have a dangerous rendezvous. To deal with this problem, an optimal maneuvering strategy based on the relative navigation observability degree is proposed with angles-only measurements. A maneuver evasion relative navigation model in the spacecraft’s orbital plane is constructed and the observability measurement criteria with process noise and measurement noise are defined based on the posterior Cramer-Rao lower bound. Further, the optimal maneuver evasion strategy in spacecraft’s orbital plane based on the observability is proposed. The strategy provides a new idea for spacecraft to evade safety threats autonomously. Compared with the spacecraft evasion problem based on the absolute navigation, more accurate evasion results can be obtained. The simulation indicates that this optimal strategy can weaken the system’s observability and reduce the state estimation accuracy of the non-cooperative target, making it impossible for the non-cooperative target to accurately approach the spacecraft.

State estimation with quantized innovations in wireless sensor networks: Gaussian mixture estimator and posterior Cramér–Rao lower bound

Since the features of low energy consumption and limited power supply are very impor- tant for wireless sensor networks （WSNs）, the problems of distributed state estimation with quan- tized innovations are investigated in this paper. In the first place, the assumptions of prior and posterior probability density function （PDF） with quantized innovations in the previous papers are analyzed. After that, an innovative Gaussian mixture estimator is proposed. On this basis, this paper presents a Gaussian mixture state estimation algorithm based on quantized innovations for WSNs. In order to evaluate and compare the performance of this kind of state estimation algo- rithms for WSNs, the posterior Cram6r-Rao lower bound （CRLB） with quantized innovations is put forward. Performance analysis and simulations show that the proposed Gaussian mixture state estimation algorithm is efficient than the others under the same number of quantization levels and the performance of these algorithms can be benchmarked by the theoretical lower bound.

二四混水平因子设计在Lee偏差下的均匀性

讨论了二四混水平因子设计在Lee偏差下的均匀性,并给出了Lee偏差的一个下界,该下界可以作为搜索Lee偏差下二四混水平均匀设计的一个基准.

Optimized energy thresholds in a spectral computed tomography scan for contrast agent imaging

Spectral computed tomography(CT) based on photon counting detectors(PCDs) is a well-researched topic in the field of X-ray imaging. When PCD is applied in a spectral CT system, the PCD energy thresholds must be carefully selected, especially for K-edge imaging, which is an important spectral CT application. This paper presents a threshold selection method that yields better-quality images in K-edge imaging. The main idea is to optimize the energy thresholds ray-by-ray according to the targeted component coefficients, followed by obtaining an overall optimal energy threshold by frequency voting. A low-dose pre-scan is used in practical implementations to estimate the line integrals of the component coefficients for the basis functions. The variance of the decomposed component coefficients is then minimized using the Cramer–Rao lower bound method with respect to the energy thresholds. The optimal energy thresholds are then used to take a full scan and gain better image reconstruction with less noise than would be given by a full scan using the non-optimal energy thresholds. Simulations and practical experiments on imaging iodine and gadolinium solutions, which are commonly used as contrast agents in medical applications, were used to validate the method. The noise was significantly reduced with the same dose relative to the non-optimal energy thresholds in both simulations and in practical experiments.

Using self-location to calibrate the errors of observer positions for source localization

The uncertainty of observers＇ positions can lead to significantly degrading in source localization accuracy. This pa-per proposes a method of using self-location for calibrating the positions of observer stations in source localization to reduce the errors of the observer positions and improve the accuracy of the source localization. The relative distance measurements of the two coordinative observers are used for the linear minimum mean square error （LMMSE） estimator. The results of computer si-mulations prove the feasibility and effectiveness of the proposed method. With the general estimation errors of observers＇ positions, the MSE of the source localization with self-location calibration, which is significantly lower than that without self-location calibra-tion, is approximating to the Cramer-Rao lower bound （CRLB）.

Bayesian Multidimensional Scaling for Location Awareness in Hybrid-Internet of Underwater Things

Localization of sensor nodes in the internet of underwater things(IoUT)is of considerable significance due to its various applications,such as navigation,data tagging,and detection of underwater objects.Therefore,in this paper,we propose a hybrid Bayesian multidimensional scaling(BMDS)based localization technique that can work on a fully hybrid IoUT network where the nodes can communicate using either optical,magnetic induction,and acoustic technologies.These communication technologies are already used for communication in the underwater environment;however,lacking localization solutions.Optical and magnetic induction communication achieves higher data rates for short communication.On the contrary,acoustic waves provide a low data rate for long-range underwater communication.The proposed method collectively uses optical,magnetic induction,and acoustic communication-based ranging to estimate the underwater sensor nodes’final locations.Moreover,we also analyze the proposed scheme by deriving the hybrid Cramer-Rao lower bound(H-CRLB).Simulation results provide a complete comparative analysis of the proposed method with the literature.