Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area Power Systems

This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.

Low Complexity Minimum Mean Square Error Channel Estimation for Adaptive Coding and Modulation Systems

Performance of the Adaptive Coding and Modulation(ACM) strongly depends on the retrieved Channel State Information(CSI),which can be obtained using the channel estimation techniques relying on pilot symbol transmission.Earlier analysis of methods of pilot-aided channel estimation for ACM systems were relatively little.In this paper,we investigate the performance of CSI prediction using the Minimum Mean Square Error(MMSE)channel estimator for an ACM system.To solve the two problems of MMSE:high computational operations and oversimplified assumption,we then propose the Low-Complexity schemes(LC-MMSE and Recursion LC-MMSE(R-LC-MMSE)).Computational complexity and Mean Square Error(MSE) are presented to evaluate the efficiency of the proposed algorithm.Both analysis and numerical results show that LC-MMSE performs close to the wellknown MMSE estimator with much lower complexity and R-LC-MMSE improves the application of MMSE estimation to specific circumstances.

Recursive weighted least squares estimation algorithm based on minimum model error principle

Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matrix and filter parameters are difficult to be determined,which may result in filtering divergence.As to the problem that the accuracy of state estimation for nonlinear ballistic model strongly depends on its mathematical model,we improve the weighted least squares method(WLSM)with minimum model error principle.Invariant embedding method is adopted to solve the cost function including the model error.With the knowledge of measurement data and measurement error covariance matrix,we use gradient descent algorithm to determine the weighting matrix of model error.The uncertainty and linearization error of model are recursively estimated by the proposed method,thus achieving an online filtering estimation of the observations.Simulation results indicate that the proposed recursive estimation algorithm is insensitive to initial conditions and of good robustness.

Mobile channel estimation for MU-MIMO systems using KL expansion based extrapolation

In multi-user multiple input multiple output （MU-MIMO） systems, the outdated channel state information at the transmit- ter caused by channel time variation has been shown to greatly reduce the achievable ergodic sum capacity. A simple yet effec- tive solution to this problem is presented by designing a channel extrapolator relying on Karhunen-Loeve （KL） expansion of time- varying channels. In this scheme, channel estimation is done at the base station （BS） rather than at the user terminal （UT）, which thereby dispenses the channel parameters feedback from the UT to the BS. Moreover, the inherent channel correlation and the parsimonious parameterization properties of the KL expan- sion are respectively exploited to reduce the channel mismatch error and the computational complexity. Simulations show that the presented scheme outperforms conventional schemes in terms of both channel estimation mean square error （MSE） and ergodic capacity.

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of promi-nently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing(MIMO–OFDM).One among an effective solution for channel estimation in wireless communication system,spe-cifically in different environments is Deep Learning(DL)method.This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder(CNNAE)classifier for MIMO-OFDM systems.A CNNAE classi-fier is one among Deep Learning(DL)algorithm,in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another.Improved performances are achieved by using CNNAE based channel estimation,in which extension is done for channel selection as well as achieve enhanced performances numerically,when compared with conventional estimators in quite a lot of scenar-ios.Considering reduction in number of parameters involved and re-usability of weights,CNNAE based channel estimation is quite suitable and properlyfits to the video signal.CNNAE classifier weights updation are done with minimized Sig-nal to Noise Ratio(SNR),Bit Error Rate(BER)and Mean Square Error(MSE).

LMMSE-based SAGE channel estimation and data detection joint algorithm for MIMO-OFDM system

A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output （MIMO） - orthogonal frequency division multiplexing （OFDM） system using linear minimum mean square error （LMMSE）- based space-alternating generalized expectation-maximization （SAGE） algorithm. In the proposed algorithm, every sub-frame of the MIMO-OFDM system is divided into some OFDM sub-blocks and the LMMSE-based SAGE algorithm in each sub-block is used. At the head of each sub-flame, we insert training symbols which are used in the initial estimation at the beginning. Channel estimation of the previous sub-block is applied to the initial estimation in the current sub-block by the maximum-likelihood （ML） detection to update channel estimatjon and data detection by iteration until converge. Then all the sub-blocks can be finished in turn. Simulation results show that the proposed algorithm can improve the bit error rate （BER） performance.

Image enhancement via MMSE estimation of Gaussian scale mixture with Maxwell density in AWGN

In optical techniques,noise signal is a classical problem in medical image processing.Recently,there has been considerable interest in using the wavelet transform with Bayesian estimation as a powerful tool for recovering image from noisy data.In wavelet domain,if Bayesian estimator is used for denoising problem,the solution requires a prior knowledge about the distribution of wavelet coeffcients.Indeed,wavelet coeffcients might be better modeled by super Gaussian density.The super Gaussian density can be generated by Gaussian scale mixture(GSM).So,we present new minimum mean square error(MMSE)estimator for spherically-contoured GSM with Maxwell distribution in additive white Gaussian noise(AWGN).We compare our proposed method to current state-of-the-art method applied on standard test image and we quantify achieved performance improvement.

CHANNEL ESTIMATION TECHNIQUE IN MULTI-ANTENNA AF RELAY COMMUNICATION SYSTEMS

The channel estimation technique is investigated in OFDM communication systems with multi-antenna Amplify-and-Forward(AF) relay.The Space-Time Block Code(STBC) is applied at the transmitter of the relay to obtain diversity gain.According to the transmission characteristics of OFDM symbols on multiple antennas,a pilot-aided Linear Minimum Mean-Square-Error(LMMSE) channel estimation algorithm with low complexity is designed.Simulation results show that,the proposed LMMSE estimator outperforms least-square estimator and approaches the optimal estimator without error in the performance of Symbol Error Ratio(SER) under several modulation modes,and has a good estimation effect in the realistic relay communication scenario.

A New Class of Biased Linear Estimators in Deficient-rank Linear Models

In this paper, we define a new class of biased linear estimators of the vector of unknown parameters in the deficient_rank linear model based on the spectral decomposition expression of the best linear minimun bias estimator. Some important properties are discussed. By appropriate choices of bias parameters, we construct many interested and useful biased linear estimators, which are the extension of ordinary biased linear estimators in the full_rank linear model to the deficient_rank linear model. At last, we give a numerical example in geodetic adjustment.

基于SDW-MMSE的广义特征值稳健波束形成方法

最大输出信噪比(Signal-to-noise ratio,SNR)准则下,广义特征值(Generalized eigenvalue,GEV)波束形成存在复系数难以控制的问题,在复杂的声学环境中容易导致输出信号严重失真。针对复系数估计问题,本文提出一种基于最小均方误差(Minimum mean square error,MMSE)的复系数估计方法,并通过引入语音失真权重因子(Speech distortion weight,SDW),调节降噪效果和语音失真之间的权重关系,进而提出了基于SDW-MMSE的广义特征值稳健波束形成方法。通过最大似然法估计目标信号和噪音信号的功率谱,进而求解主广义特征向量。进一步基于SDW-MMSE估计复系数,将复系数与主广义特征向量相结合,从而得到基于SDW-MMSE的广义特征值稳健波束形成滤波向量。仿真实验结果表明,本文提出的波束形成方法可有效消除相干噪声和非相干噪声,具有输出信噪比高、语音失真少等稳健性能。

Lower bound on BER performance for maximal ratio combining with weighting errors

The theoretical lower bounds on mean squared channel estimation errors for typical fading channels are presented by the infinite-length and non-causal Wiener filter and the exact closed-form expressions of the lower bounds for different channel Doppler spectra are derived. Based on the obtained lower bounds on mean squared channel estimation errors, the limits on bit error rate （BER） for maximal ratio combining （MRC） with Gaussian distributed weighting errors on independent and identically distributed （i. i. d） fading channels are presented. Numerical results show that the BER performances of ideal MRC are the lower bounds on the BER performances of non-ideal MRC and deteriorate as the maximum Doppler frequency increases or the SNR of channel estimate decreases.

基于统计模型的麦克风阵列语音增强算法

提出一种基于统计模型的麦克风阵列语音增强算法。为了估计第一通道语音信号谱幅度平方,假设第一通道语音信号谱的实部和虚部相互独立,并服从方差相同的高斯分布。先利用贝叶斯公式计算第一通道语音信号谱幅度平方的后验概率,再利用数学期望的计算公式得到第一通道语音信号谱幅度平方的最小均方误差估计,最后用第一通道观测语音信号的谱角度来估计第一通道语音信号的谱角度。实验结果表明,所提出的算法优于三种传统的语音增强算法。

Improved estimator of the continuous-time kernel estimator

There have been many papers presenting kernel density estimators for a strictly stationary continuous time process observed over the time interval [0, T ]. However the estimators do not satisfy the property of mean-square continuity if the process is mean-square continuous. In this paper we present a modified kernel estimator and substantiate that the modified estimator satisfies the property of mean-square continuity. In a simulation study the results show the modified estimator is better than the original estimator in some cases.

基于机载多通道雷达迭代超分辨估计的前视成像

波达角估计算法用于机载多通道雷达前视成像时可以突破瑞利极限,实现同一波束主瓣宽度内的多目标分辨,改善成像的方位向分辨率,然而天线波束覆盖有限且其快速扫描使得可用于协方差矩阵估计的数据样本缺乏,导致对目标位置和幅度估计出现误差。该文提出了一种基于单快拍迭代超分辨处理的多通道雷达前视成像算法,通过对单个空域快拍的迭代谱估计可获得目标的准确位置和幅度信息,再通过多个脉冲的非相干累积得到前视方位高分辨成像。仿真和实测数据处理结果表明,所提算法具有分辨多目标的能力,相较于传统前视成像算法显著提高了前视图像的方位分辨率,同时保证了点目标的精确重构和面目标的轮廓重构。

稳健的稀疏信号单快拍波达方向估计

通过稀疏重构得到传感器阵列输出数据的稀疏表示模型,研究了单快拍采样情形下的信号到达角(Direction of Arrival,DOA)估计问题。提出了一种基于最小均方误差(Minimum Mean-Square Error,MMSE)准则迭代实现的单快拍到达角估计算法(Iterative Implementation of MMSE,II-MMSE)。该算法将原有的稀疏表示模型中稀疏信号矢量的求解问题,转化为迭代求解稀疏功率对角阵,进而估计多目标信号的DOA。给出了算法的完整实现流程,从理论上分析了II-MMSE算法的迭代收敛性和对阵列模型误差的鲁棒性。仿真结果表明,II-MMSE算法在低信噪比、相干背景、小样本、阵列未校准等条件下都具有良好的测向精度和多目标分辨能力。

A TSE based design for MMSE and QRD of MIMO systems based on ASIP

A Taylor series expansion(TSE) based design for minimum mean-square error(MMSE) and QR decomposition(QRD) of multi-input and multi-output(MIMO) systems is proposed based on application specific instruction set processor(ASIP), which uses TSE algorithm instead of resource-consuming reciprocal and reciprocal square root(RSR) operations.The aim is to give a high performance implementation for MMSE and QRD in one programmable platform simultaneously.Furthermore, instruction set architecture(ISA) and the allocation of data paths in single instruction multiple data-very long instruction word(SIMD-VLIW) architecture are provided, offering more data parallelism and instruction parallelism for different dimension matrices and operation types.Meanwhile, multiple level numerical precision can be achieved with flexible table size and expansion order in TSE ISA.The ASIP has been implemented to a 28 nm CMOS process and frequency reaches 800 MHz.Experimental results show that the proposed design provides perfect numerical precision within the fixed bit-width of the ASIP, higher matrix processing rate better than the requirements of 5G system and more rate-area efficiency comparable with ASIC implementations.

ERROR ESTIMATION OF THE APPROXIMATION ALGORITHM FOR THE WINDY POSTMAN PROBLEM

If we restrict the postman to traversing each edge at most twice in the windypostman problem (WPP), we will get a new problem: 2WPP. An approximation algorithmhas been posed by M. Guan for the WPP. In the present paper, we improve the estimatederror given by M. Guan and show that we can estimate the error for the 2WPP by findinga minimum cost circulation. We also pose a new sufficient condition for the equivalencebetween WPP and 2WPP, which can be checked in polynomial time steps.

一种空时联合抗干扰方法研究

针对星地测控通信链路中的电磁信号干扰,提出空域时域联合的干扰抑制方法。利用地面阵列天线,分离不同方向的各类干扰,等效为接收端多个信道接收的远场信号。通过阵列天线各单元之间的相位差,进行信号波束形成和空间谱估计。采用空时联合干扰抑制方法,将一维的时域和空域干扰抑制拓展至时间与空间联合的二维域内,对干扰信号进行空时二维滤波,并对滤波算法进行了优化设计。通过对单个宽带干扰信号、3个宽带干扰信号的仿真验证,干扰抑制能力达到了80 dB。

无蜂窝大规模MIMO-OTFS系统的上行信道估计方法

无蜂窝MIMO系统中,利用大规模天线与大规模宏分集,构建以用户为中心的架构,用户可以选择多个合适的接入点来获取服务,实现均匀的用户覆盖性能,从而可以更好地满足不断增长的移动通信服务需求。研究了采用OTFS调制的无蜂窝大规模MIMO系统的上行信道估计方法。首先,根据用户的位置关系,对用户划分组群,在有限的资源下服务更多的用户;然后,设计了用户上行导频结构,研究了基于LS和LMMSE的无蜂窝大规模MIMO-OTFS上行信道估计方法。仿真结果表明,提出的上行信道估计方法在不同用户速度和不同信噪比下都能够得到稳健的信道估计结果,同时,在有多个用户的无蜂窝大规模MIMO系统中,并且每个用户在高速移动的情况下,估计的结果也能够保持较高的准确度。

带有色量测噪声的非线性系统Unscented卡尔曼滤波器

传统Unscented卡尔曼滤波器(Unscented Kalman filter,UKF)要求噪声必须为高斯白噪声,无法解决带有色噪声的非线性系统滤波问题.为此,本文提出了一种带有色量测噪声的UKF滤波新算法.首先,基于量测信息增广和最小方差估计,推导出一类带有色量测噪声的非线性离散系统状态的最优滤波框架,接着采用Unscented变换(Unscented transformation,UT)来计算最优框架中的非线性状态后验均值和协方差,进而得到有色量测噪声下UKF滤波递推公式.所设计的UKF新方法能有效地解决传统UKF在量测噪声有色情况下非线性滤波失效的问题,数值仿真实例验证了其可行性和有效性.