通讯约束下的线性系统状态降维与估计

本文面向状态估计,考察了通讯功率受限时线性动态系统状态的降维问题.为了满足平行信道传输数据的维数限制和通讯功率约束,采取降低状态维数的方法,通过传输信号的新息,提高传输效率,利用有限的通信资源,使得接收端的状态估计达到最优.本文采用差分脉冲编码调制系统(DPCM),基于最小误差熵估计准则和Kalman估计算法,得出了最优的状态降维矩阵的设计方法,并且对随机系统的可估计性以及对相应确定性系统的能观性进行了分析.分析和仿真结果表明,这种设计方法在传输信号满足通讯功率限制的条件下可以使接收端的状态估计性能达到最优.

相关观测误差条件下Kalman滤波器的一种设计方法

在分析噪声相关性对Kalman滤波器性能影响的基础上,文章推导一种相关观测误差条件下卡尔曼滤波器的设计方法。该方法利用误差的相关矩阵信息,对量测矩阵和观测误差协方差阵做去相关校正,实现准最优的Kalman滤波,并获得正确的状态误差估计。仿真证明了算法的效果。

连续切换非方广义线性系统的观测器设计

针对一类连续切换非方广义线性系统,设计了广义状态观测器,并分析了状态估计误差的稳定性。首先通过矩阵的初等变换将连续切换非方广义线性系统的观测器设计问题转换为连续切换广义线性系统的观测器设计问题,之后设计第i个子系统的子观测器,可根据第i个子观测器构造这个广义切换系统的观测器。当观测器是全维观测器,且每个子系统都可检测时,状态估计误差甚至可以在任意切换下指数收敛到零。最后,通过数值实例和Matlab仿真分析说明了观测器设计方法简单可行,观测误差较其他方法的收敛速度快,证实了该方法的有效性和正确性。

HIGH SNR SUM CAPACITY ANALYSIS OF BD MIMO BC SYSTEMS WITH IMPERFECT CSI

We investigate the sum capacity of Block Diagonalization precoding Multiple Input Mul-tiple Output Broadcast Channels(BD MIMO BC) with imperfect Channel State Information(CSI) at the base station.Since it is difficult to obtain the exact expression,a lower and an upper bounds of the sum capacity under Gaussian channel estimation errors are drived instead.Analyses show that the gap between two bounds is considerably tight at all Signal to Noise Ratio(SNR) region.From the lower bound of the sum capacity,we can see that the multiplexing gain tends to be zero at high SNR region,which indicates that the BD MIMO BC system with channel estimation errors is interference-limited at high SNR.

A novel joint navigation state error discriminator based on iterative maximum likelihood estimation

To break through the limitations of traditional discriminators used in vector tracking loops, this paper presents an iterative maximum likelihood estimation(IMLE) method for extracting navigation state errors from multi-satellite signals. The IMLE method takes into account both computational cost and estimation accuracy. The associated gradient vector and Hessian matrix of the MLE cost function are derived. The characteristics of the proposed joint discriminator are analyzed based on the properties of the MLE cost function,gradient vector, and Hessian matrix. The effectiveness of IMLE is verified by Monte Carlo simulation.

Multi-cell massive MIMO transmission with coordinated pilot reuse

In this paper, we propose a coordinated pilot reuse(CPR) approach to reduce the pilot overhead for multi-cell massive multi-input multi-output transmission. Unlike the conventional multi-cell pilot reuse approach in which pilots can only be reused among different cells, the proposed CPR approach allows pilots to be reused among both inter-cell and intra-cell user equipments, and thus, pilot overhead can be efficiently reduced. For spatially correlated Rayleigh fading channels, we first present a CPR-based channel estimation method and a low complexity pilot allocation algorithm. Because CPR might lead to additional pilot interference, we develop a statistically robust uplink receiver and downlink precoder that takes channel estimation errors into account. The proposed uplink receiver and downlink precoder are robust to channel state information inaccuracy, and thus, can guarantee a certain transmission performance. Monte-Carlo simulations illustrate the significant performance improvement in net spectral efficiency offered by the proposed CPR approach.

Non-gaussian Test Models for Prediction and State Estimation with Model Errors

Turbulent dynamical systems involve dynamics with both a large dimensional phase space and a large number of positive Lyapunov exponents. Such systems are ubiqui- tous in applications in contemporary science and engineering where the statistical ensemble prediction and the real time filtering/state estimation are needed despite the underlying complexity of the system. Statistically exactly solvable test models have a crucial role to provide firm mathematical underpinning or new algorithms for vastly more complex scien- tific phenomena. Here, a class of statistically exactly solvable non-Gaussian test models is introduced, where a generalized Feynman-Ka~ formulation reduces the exact behavior of conditional statistical moments to the solution to inhomogeneous Fokker-Planck equations modified by linear lower order coupling and source terms. This procedure is applied to a test model with hidden instabilities and is combined with information theory to address two important issues in the contemporary statistical prediction of turbulent dynamical systems： the coarse-grained ensemble prediction in a perfect model and the improving long range forecasting in imperfect models. The models discussed here should be use- ful for many other applications and algorithms for the real time prediction and the state estimation.