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.     

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.     

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...     

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。     

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.     

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.     

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.     

An improved Markov Chain Monte Carlo method for MIMO iterative detection and decoding

Recently, a new soft-in soft-out detection algorithm based on the Markov Chain Monte Carlo （MCMC） simulation technique for Multiple-Input Multiple-Output （MIMO） systems is proposed, which is shown to perform significantly better than their sphere decoding counterparts with relatively low complexity. However, the MCMC simulator is likely to get trapped in a fixed state when the channel SNR is high, thus lots of repetitive samples are observed and the accuracy of A Posteriori Probability （APP） estimation deteriorates. To solve this problem, an improved version of MCMC simulator, named forced-dispersed MCMC algorithm is proposed. Based on the a posteriori variance of each bit, the Gibbs sampler is monitored. Once the trapped state is detected, the sample is dispersed intentionally according to the a posteriori variance. Extensive simulation shows that, compared with the existing solution, the proposed algorithm enables the markov chain to travel more states, which ensures a near-optimal performance.     

一种有效减小非视距传播影响的TOA定位方法

本文基于移动通信环境中非视距(NLOS)传播时延服从指数分布的特性,提出了一种有效减小NLOS影响的定位方法.该方法首先利用测量的波达时间(TOA)和NLOS传播时延的统计特性估计由NLOS引起的附加时延;然后从测量的波达时间中减去附加时延,得到对LOS传播时间的估计,进而估计移动台的位置;最后,对不同时刻估计的移动台位置进行平滑处理,进一步减小NLOS的影响.采用该方法对移动台的位置进行的估计是一种无偏估计,不需要增加系统成本,计算简单,是一种非常实用的定位方法.仿真结果证明了该方法的有效性.     

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.     

一种适用于移动传感器网络的增强型蒙特卡罗定位跟踪算法

该文提出一种在低锚节点密度的移动传感器网络中实现定位跟踪的方法。利用受控的洪泛方式提高锚节点利用效率,采用遗传交叉操作加快预测阶段的抽样,采用插值方法对节点运动速度及方向进行预测,利用位置估计精度优于自身的1跳邻居节点的信息强化滤波条件。仿真实验结果表明,该文算法与传统算法相比加快了收敛速度,提高了定位精度,改善了在低锚节点密度时的性能。     

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。     

Fast convergent monte carlo receiver for OFDM systems

The paper investigates the problem of the design of an optimal Orthogonal Frequency Division Multiplexing (OFDM) receiver against unknown frequency selective fading. A fast convergent Monte Carlo receiver is proposed. In the proposed method, the Markov Chain Monte Carlo (MCMC) methods are employed for the blind Bayesian detection without channel estimation. Meanwhile, with the exploitation of the characteristics of OFDM systems, two methods are employed to improve the convergence rate and enhance the efficiency of MCMC algorithms.One is the integration of the posterior distribution function with respect to the associated channel parameters, which is involved in the derivation of the objective distribution function; the other is the intra-symbol differential coding for the elimination of the bimodality problem resulting from the presence of unknown fading channels. Moreover, no matrix inversion is needed with the use of the orthogonality property of OFDM modulation and hence the computational load is significantly reduced. Computer simulation results show the effectiveness of the fast convergent Monte Carlo receiver.     

减轻TOA和AOA定位系统非视距影响的方法

结合圆位置线误差、直线位置线误差与参数误差的关系,该文提出了一种利用所有基站测量的波达时间与最近一个基站测量的所有多径的到达角进行混合定位的方法.在该方法中,对波达时间用测量的波达时间总是大于等于视距传播时间进行约束,对到达角用GBSBCM确定的最大角度扩展进行约束,以有效减小非视距传播的影响,提高定位精度.仿真结果证明了该方法的有效性。     

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。     

机载合成孔径雷达高度计高程参数贝叶斯估计

机载合成孔径雷达高度计(SARA)由于具有高航向分辨率,因此受到广泛关注。然而,现有的SARA地面高程重跟踪方法多基于最小二乘算子,高程参数估计精度和算法抑噪性能均存在上限,容易造成高程参数估计结果过拟合,对复杂高程变化适应能力有限。为此,该文提出一种基于参数化贝叶斯统计学习方法的机载SARA重跟踪算法(PR-Bayes)。通过引入目标场景地形先验概率模型,并结合模型驱动机器学习方法,可实现对目标高程信息重跟踪可信估计,从而有效避免估计参数过拟合问题。该算法基于布朗模型(BM)对SARA回波进行复杂模型参数反演,并设计哈密顿蒙特卡洛(HMC)统计采样器,实现对目标场景地形高度的参数估计。基于该文所提算法,分别通过点目标模拟和DEM半实物模拟对该算法进行有效性验证及高程参数估计精度验证,并通过实测数据验证该算法的实用性。     

A cooperative Bayesian and lower bound estimation in dynamic framed slotted ALOHA algorithm for RFID systems

A novel estimation scheme that combines Bayesian and lower bound estimating radio frequency identification tag population size is proposed. The developed methodology is based on the fusion between the Bayesian and lower bound estimating techniques. It turns out that the fusion rule is built up thanks to an existing linear relationship between the cited techniques. Simulation results show that the developed technique significantly improves the accuracy of the estimating tag quantity and presents less estimation error. Also, the resulting advanced dynamic framed slotted ALOHA protocol considerably improves the performance and efficiency of the radio frequency identification anti‐collision compared with the most recent protocols using others estimating methods.