A novel TOA estimation method with effective NLOS error reduction

It is well known that non-line-of-sight （NLOS） error has been the major factor impeding the enhancement of accuracy for time of arrival （TOA） estimation and wireless positioning. This article proposes a novel method of TOA estimation effectively reducing the NLOS error by 60%, comparing with the traditional timing and synchronization method. By constructing the orthogonal training sequences, this method converts the traditional TOA estimation to the detection of the first arrival path （FAP） in the NLOS multipath environment, and then estimates the TOA by the round-trip transmission （RTT） technology. Both theoretical analysis and numerical simulations prove that the method proposed in this article achieves better performance than the traditional methods.     

NLOS error mitigation with information fusion algorithm for UWB ranging systems

This article puts forward a scalar weighting information fusion (IF) smoother with modified biased Kalman filter (BKF)and maximum likelihood estimation (MLE) to mitigate the ranging errors in ultra wid...     

Blind Multiuser Detection for Long Code Multipath DS-CDMA Systems with Bayesian MC Techniques

This paper presents an efficient Bayesian blind multiuser receiver for long code multipath CDMA systems. The proposed receiver employs the adaptive sampling method for the Bayesian inference procedure to estimate the data symbols and multipath parameters. Compared to the other reported Bayesian Monte Carlo receivers for long code multipath CDMA systems, the proposed one achieves a faster convergence and a lower computational complexity to attain comparable performance. Simulation results are presented to demonstrate the effectiveness of the proposed Bayesian blind multiuser receiver. Qian Yu received the B. S. and M. S. degree in control theory and applications in 1997 and 2000, respectively, from Northwestern Polytechnical University (NWPU), Xian, China. She is currently working toward the Ph.D. degree in the Division of Information Engineering of EEE, Nanyang Technology University, Singapore. Her general research interests are in the area of signal processing for wireless communication systems. Dr Guoan Bi received a B.Sc degree in Radio communications, Dalian University of Technology, PRC, 1982, M.Sc degree in Telecommunication Systems and Ph.D degree in Electronics Systems, Essex University, UK, 1985 and 1988, respectively. Since 1991, he has been with the school of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. His current research interests include DSP algorithms and hardware structures and digital signal processing for communications. Dr. Liren Zhang is currently an Associate Professor in the School of Electrical and Electronic Engineering, Nanyang Technological University (NTU). He received his B.Eng. degree from Shandong University in 1982, M.Eng degree from the University of South Australia in 1988, and Ph.D from the University of Adelaide, Australia in 1990, all in electrical engineering. From 1990 to 1995 he was a Senior Lecturer in the Department of Electrical and Computer Systems Engineering, Monash University, Australia.Dr Zhang has vast experience as an engineer, academic and researcher in the field of multimedia communications, switching and signaling, teletraffic engineering, network modeling and performance analysis for ATM networks, high speed data networks, mobile networks, satellite networks and optical networks. He has published more than 100 research papers in international journals and conferences. He has been the associate editor for the Journal of Computer Communications since 2000.     

Image recovery from Fourier domain measurements via classification using Bayesian approach and total variation regularization

In this paper we propose a Potts–Markov prior and total variation regularization associated with Bayesian approach to simultaneously reconstruct and segment piecewise homogeneous images in Fourier synthesis inverse problem. When the observed data do not fill uniformly the Fourier domain which is the case in many applications in tomographic imaging, or when the phase of the signal is lacking as in optical interferometry the results obtained by deterministic methods are not satisfactory. Such inverse problem is known to be nonlinear and ill-posed. It then needs to be regularized by introducing prior information. The particular a priori information on which we rely is the fact that the image is composed of a different regions finite known number. Such an appropriate modeling of the image gives the possibility of compensating the lack of information in the data thus giving satisfactory results. We define the appropriate Potts–Markov model to define parameters of label regions for such images and total variation to be used in a Bayesian estimation framework.     

实现单通道MPSK信号盲分离的MCMC新算法

本文根据单通道接收两路MPSK混合信号在过采样下的基本模型,针对粒子滤波算法在单通道信号盲分离中的性能瓶颈以及高复杂度问题,提出了基于MCMC方法的新算法。该算法对接收信号进行过采样处理,能够利用更多的波形信息,从而有效抑制噪声的影响。新算法利用Gibbs采样估计MPSK调制符号的后验概率,近似实现了贝叶斯最优估计,并利用最小二乘法实现参数的迭代估计。理论分析与仿真实验表明,相对粒子滤波算法,本文提出的新算法在误码率性能以及复杂度方面具有良好的表现。      

改进粒子滤波算法在目标状态估计中的应用

针对粒子滤波算法(PF)建议性函数的选择问题和粒子匮乏现象,提出了改进粒子滤波算法.该算法利用无迹卡尔曼滤波(UKF)产生建议性分布,提高估计精度;采用马尔科夫蒙特卡罗法(MCMC)保持粒子多样性,抑制粒子匮乏现象.仿真结果表明该算法的目标状态估计精度明显优于PF、UPF、PF-MCMC和PF-EKF-MCMC算法.     

Bayesian separation of spectral sources under non-negativity and full additivity constraints

This paper addresses the problem of separating spectral sources which are linearly mixed with unknown proportions. The main difficulty of the problem is to ensure the full additivity (sum-to-one) of the mixing coefficients and non-negativity of sources and mixing coefficients. A Bayesian estimation approach based on Gamma priors was recently proposed to handle the non-negativity constraints in a linear mixture model. However, incorporating the full additivity constraint requires further developments. This paper studies a new hierarchical Bayesian model appropriate to the non-negativity and sum-to-one constraints associated to the sources and the mixing coefficients of linear mixtures. The estimation of the unknown parameters of this model is performed using samples obtained with an appropriate Gibbs algorithm. The performance of the proposed algorithm is evaluated through simulation results conducted on synthetic mixture data. The proposed approach is also applied to the processing of multicomponent chemical mixtures resulting from Raman spectroscopy.     

A Bayesian Lasso via reversible-jump MCMC

Variable selection is a topic of great importance in high-dimensional statistical modeling and has a wide range of real-world applications. Many variable selection techniques have been proposed in the context of linear regression, and the Lasso model is probably one of the most popular penalized regression techniques. In this paper, we propose a new, fully hierarchical, Bayesian version of the Lasso model by employing flexible sparsity promoting priors. To obtain the Bayesian Lasso estimate, a reversible-jump MCMC algorithm is developed for joint posterior inference over both discrete and continuous parameter spaces. Simulations demonstrate that the proposed RJ-MCMC-based Bayesian Lasso yields smaller estimation errors and more accurate sparsity pattern detection when compared with state-of-the-art optimization-based Lasso-type methods, a standard Gibbs sampler-based Bayesian Lasso and the Binomial-Gaussian prior model. To demonstrate the applicability and estimation stability of the proposed Bayesian Lasso, we examine a benchmark diabetes data set and real functional Magnetic Resonance Imaging data. As an extension of the proposed RJ-MCMC framework, we also develop an MCMC-based algorithm for the Binomial-Gaussian prior model and illustrate its improved performance over the non-Bayesian estimate via simulations.     

SEMI-BLIND CHANNEL ESTIMATION OF MULTIPLE-INPUT／MULTIPLE-OUTPUT SYSTEMSBASED ON MARKOV CHAIN MONTE CARLO METHODS

This paper addresses the issues of channel estimation in a Multlple-Input/Multiple-Output (MIMO) system. Markov Chain Monte Carlo (MCMC) method is employed to jointly estimate the Channel State Information (CSI) and the transmitted signals. The deduced algorithms can work well under circumstances of low Signal-to-Noise Ratio (SNR). Simulation results are presented to demonstrate their effectiveness.     

一种基于混合MCMC的时域波达方向估计算法

基于马尔科夫链蒙特卡洛（Markov Chain Monte Carlo,MCMC）方法的时域波达方向估计算法通过构造马尔科夫链的方式来对波达方向进行估计,但是现有的算法在马尔科夫链的收敛速度和结果上并没有表现出很好的鲁棒性。为了优化算法的性能,采用多(短)链并行的方式代替原来的长链生成方式,提高了算法收敛的稳定性;并对特定模型下的构造过程进行分析,优化了状态空间,提高了算法的搜索效率;同时结合多混合的MCMC方法,进一步提高了算法估计的精确度和收敛速度。仿真结果表明,改进后的算法对波达方向估计的准确性和实时性都有很大提升。     

一种新的贝叶斯调制分类算法

提出了一种基于马尔可夫链蒙特卡罗(MCMC)的数字调制分类方法。针对存在未知残留载波相位和频率时贝叶斯分类难以实现的问题,采用Metropolis-Hastings(M-H)算法估计边缘似然概率密度,从而在分类性能上保持了贝叶斯分类的理论最优性和稳健性。利用对比实验验证了方法的性能。     

贝叶斯网络结构学习研究

针对贝叶斯网络结构学习方法难以兼顾高准确率和高效率的问题,提出了一种基于Markov Chain Monte Carlo(MCMC)方法的贝叶斯网络结构学习方法的改进.改进包括:使用依赖关系分析,利用统计学的方法对采样空间进行大幅缩减,能够在精确控制准确度的情况下大幅提高时间效率;结合先验知识,从理论角度将先验知识融入评分中得到完全服从后验分布的结果;搜索最优子结构,对于特定的一些结构搜索最优子结构而不是采用贪心的方法,提高了贝叶斯网络结构学习的准确率.通过理论分析可以证明时间复杂度得到了大幅的降低.并且可以在牺牲可预知的准确率的情况下,将指数时间复杂度降为线性时间.大量的数据实验表明,经改进后的方法在时间和准确性上都具有良好的表现.     

Monte Carlo法在平板裂缝天线辐射缝导纳误差分析中的应用

采用MonteCarlo法分析了平板裂缝天线辐射缝的导纳误差。建立辐射缝导纳误差的概率模型,将波导和裂缝的结构参数误差看作在某误差范围内服从正态分布的随机变量。对这些变量进行抽样计算,得到各个结构参数误差对谐振频率和谐振电导的影响,其中缝隙长度和偏置的误差对谐振电导和谐振频率的影响较大,波导窄边和壁厚误差产生的影响较小。根据计算结果调整各结构参数的误差范围,使它们对谐振频率和谐振电导的影响程度接近,在此基础上分析了同时存在这些误差项时对谐振频率和谐振电导的影响,由此提出满足导纳提取精度要求的加工误差指标。最后用Ansoft HFSS软件仿真验证了该方法的正确性。     

基于马尔科夫键蒙特卡洛抽样的最大似然时差-频差联合估计算法

该文针对无源定位中参考信号真实值未知的时差-频差联合估计问题,构建了一种新的时差-频差最大似然估计模型,并采用马尔科夫链蒙特卡洛(MCMC)方法求解似然函数的全局极大值,得到时差-频差联合估计。算法通过生成时差-频差样本,并统计样本均值得到估计值,克服了传统互模糊函数(CAF)算法只能得到时域和频域采样间隔整数倍估计值的问题,且不存在期望最大化(EM)等迭代算法的初值依赖和收敛问题。推导了时差-频差联合估计的克拉美罗界,并通过仿真实验表明,算法在不同信噪比条件下的估计精度优于CAF算法和EM算法,且计算复杂度较低。     

模拟扫描电镜像衬度的体构件Monte Carlo方法

利用Mollte Carlo计算方法可以模拟电子束与样品的相互作用过程,从而了解扫描电子显微学中信号的产生机制,本工作中,我们采用体构件法来产生复杂试样的几何构型,利用光线追踪算法求得散射事件间的步长抽样修正。电子散射的物理模型则采用Mott散射截面描述电子与原子间的弹性相互作用,以及用介电函数理论描述电子与固体的非弹性相互作用,同时还考虑到了二次电子的级联产生过程．以此,我们模拟计算出了若干复杂几何体的二次电子像和背散射电子像。