一种多输入多输出系统的盲辨识算法

多输入多输出线性系统的盲辨识问题可以利用输出信号的高阶累积量来解决.针对已有的一个线性MIMO系统辨识方法没有充分利用累积量矩阵固有结构的不足,提出一个改进算法,从而提高估计性能.并通过计算机仿真作了验证.     

基于多信道信号增强的卷积混迭语音信号盲分离的后处理方法

由于频域盲源分离方法无法精确解决排列模糊问题,分离出的信号质量受到很大影响.本文提出一种后处理方法以进一步消除不同信源间的空间干扰和噪声且无需增加额外的先验信息.首先在频域盲分离方法中利用分裂语谱技术把一个M×N的多输入多输出(MIMO)混迭系统分裂成N个单输入多输出(SIMO)系统,再对每个SIMO系统分别利用传递函数比和广义旁瓣相消器来重构源信号.仿真实验表明该算法具有良好的性能.     

基于MIMO系统的MDCMA盲均衡算法

基于对线性多输入多输出(MIMO)系统的自适应盲源分离和盲均衡的研究,为了能够有效恢复输入信号,提出了一种在MIMO系统中引入抖动处理技术的修正抖动符号误差恒模算法.仿真结果表明,该算法可以在仅已知输入信号统计量的情况下跟踪信道变化,并且有效克服多个均衡器的输出可能锁定到相同的源信号问题,抑制ISI和MUI,恢复所有发射天线的发送信息.     

多输入多输出衰落信道的最小互信息盲均衡

提出了多输入多输出衰落信道的基于广义高斯分布近似的最小互信息盲均衡器.采用输出信号的广义高斯分布近似,基于互信息最小化目标函数自适应调整均衡器的系数.比较了基于广义高斯分布近似和非线性变换的两种最小互信息盲均衡算法.仿真实验表明基于广义高斯分布近似的方法比非线性变换方法有更大的星座图距离,更快的收敛速率和更好的误码性能.     

基于量子遗传算法的多通道通信信号盲反卷积算法研究

本文在分析多输入多输出盲反卷积的网络结构和算法模型的基础上，提出了一种基于输出信号上下文信息的盲反卷积算法，并提出一种采用量子遗传算法的新的优化求解方法，对仿真的通信信号分离的结果表明算法的有效性。     

信道盲辨识的新方法

只要单输入多输出(SIMO)信道的公因式满足本文提出的充分必要条件,那么本文方法就可以有效地辨识这些带公零点的信道.本文算法由以下步骤组成:先借助整数约束二次优化盲检测属于给定字符集的发送信号,然后根据这些估得的发送信号辨识传输信道.仿真结果表明:本文新算法明显优于现有的文献算法.     

基于时延分集的MIMO多径信道盲估计算法

发送时延分集在空时编码中是一种有效可行的方法。本文提出了一种基于发送时延分集的多径信道估计方法,指出如果发送时延大于信道时延,多输入多输出（MIMO）信道可以转化为特殊的单输入多输出信道,通过对信道输出进行子空间分解可以估计出信道参数。仿真结果显示了本文算法的有效性。     

基于多小波双变换的非线性卫星信道盲均衡算法

针对非线性卫星信道Volterra盲均衡系统收敛缓慢、计算复杂高等不足,提出了基于多小波双变换的非线性卫星信道盲均衡算法.该算法用Wiener均衡器代替Volterra均衡器,减小了均衡器结构的复杂性;用平衡正交多小波对Wiener均衡器的输入信号进行变换,降低了输入信号的自相关性;在Wiener均衡器输出端增加一级判决反馈滤波器,同时对其输入信号作平衡多小波变换,又降低了判决反馈滤波器输出信号的自相关性.仿真结果验证了该算法的有效性.     

基于支持向量机的非线性信道盲均衡算法研究

针对非线性信道盲均衡问题,考察了一种基于支持向量机的单输入单输出(SISO)盲均衡算法,该算法利用通信信号的常数模特性,将非线性盲均衡问题转化为非线性支持向量回归问题。在此基础上,本文利用分集技术,将该算法拓展至单输入多输出(SIMO)的情况。对两种算法进行的计算机仿真表明,基于支持向量机的SISO盲均衡算法能够有效地抑制信道中的非线性码间干扰;本文提出新算法由于更好地利用了信道的时空特性,具有剩余平均模误差小的优点。     

APES算法在MIMO雷达参数估计中的稳健性研究

 多输入多输出(MIMO,Multiple-Input Multiple-Output)雷达用多个发射天线同时发射多个独立信号照射目标,并使用多个接收天线接收目标回波信号.本文研究了MIMO雷达中参数估计的稳健性问题.本文应用幅度相位估计(APES,Amplitude and Phase EStimation)技术,利用目标的方位角最大似然估计值,得到了衰落向量的APES估计算法.考虑到方位角估计的不准确性,借鉴稳健的Capon波束形成器的设计思想,本文推导了衰落向量的稳健的APES估计算法.仿真实验表明,衰落向量的APES算法与稳健的APES算法性能十分接近.因此,衰落向量的APES估计算法是稳健的.     

盲分离算法在MIMO雷达和通信中的应用研究

将盲分离算法应用于多输入多输出(MIMO)雷达抗干扰和MIMO通信符号检测中。首先,利用信号相互之间以及与干扰之间的独立性,通过盲源分离算法,将各个信号分离出来;然后,雷达中通过匹配处理,完成信号检测;通信中利用少量的训练序列完成信号的匹配以及相位和幅度的校正。仿真结果表明:无论在雷达或通信中,均可获得优良的性能。     

A two-stage algorithm for MIMO blind deconvolution of nonstationarycolored signals

A new two-stage algorithm is proposed for the deconvolution of multi-input multi-output (MIMO) systems with colored input signals. While many blind deconvolution algorithms in the literature utilize high order statistics of the output signal for white input signals, the additional information contained in colored input signals allows the design of second-order statistical algorithms. In fact, practical signal sources such as speech signals do have distinct, nonstationary, colored power spectral densities. We present a two-stage signal separation approach in which the first step utilizes a matrix pencil between output auto-correlation matrices at different delays, whereas the second stage adopts a subspace method to identify and deconvolve MIMO systems     

Transfer function matrix identification in MIMO systems via Walsh functions

A method of identification of the transfer function matrix of a multi-input multi-output (MIMO) linear time invariant system is presented. The approach is based on employing the Walsh spectra of input output signals in an algorithm that yields the unknown initial conditions along with the system parameters to be useful in practical situations wherein the input-output data is available on an arbitrary but active period of time.     

Multichannel system identification and deconvolution: performancebounds

We consider the problem of estimating the parameters of an unknown multi-input multi-output (MIMO) linear system and the related problem of deconvolving and recovering its inputs. Only the system outputs are assumed to be observable. The system inputs are assumed to be non-Gaussian. We derive simple closed-form asymptotic expressions for the Cramer-Rao lower bound (CRLB) for the system parameters, as well as lower bounds on the signal reconstruction performance. These show that the identification/deconvolution performance depend on the accuracy with which the location (mean) and the scale (standard deviation) parameters of the input probability density functions can be identified from observation of the input signals     

Partial input/output decoupling in time-delay control systems

The decoupling problem is considered for a class of multi-input multi-output time-delay systems, the parameters of which do not satisfy the conditions for total decoupling, i.e. the conditions for decouplin all input/output pairs. It is shown that in this case it is possible to decouple number of input/output pairs equal to the rank of the decoupling matrix. The partial decoupling procedure is illustrated by means of an example.     

MIMO雷达正交相位编码信号设计及分析

针对MIMO雷达发射正交波形的要求,提出一种采用相位编码设计MIMO雷达信号的方法。该方法采用遗传优化算法,设计产生一组正交相位编码信号,并分析该组信号的自相关和互相关特性。文章最后分析了采用正交相位编码信号设计MIMO雷达信号存在的一些问题。     

Multichannel Algorithms for Simultaneous Equalization and Interference Suppression

This paper proposes techniques for simultaneous cancellation of intersymbol and interchannel or multi-access interference (ISI and ICI) that shows up in several multi-input, multi-output (MIMO) communication channels. Correlation and kurtosis based optimization criteria are derived for multi-channel decision feedback equalizers (MC-DFE) and compared with the popular Godard algorithm (CMA) and the minimum mean-square error in a decision directed mode (MMSE-DD). The proposed adaptive algorithms are easily extended to a scenario with more than two users with the computational complexity increasing linearly with the number of inputs. Simulation results show that the algorithms converge to the global minimum in a blind environment with channels that introduce moderate distortion.     

Blind identification of FIR MIMO channels by decorrelating subchannels

We study blind identification and equalization of finite impulse response (FIR) and multi-input and multi-output (MIMO) channels driven by colored signals. We first show a sufficient condition for an FIR MIMO channel to be identifiable up to a scaling and permutation using the second-order statistics of the channel output. This condition is that the channel matrix is irreducible (but not necessarily column-reduced), and the input signals are mutually uncorrelated and of distinct power spectra. We also show that this condition is necessary in the sense that no single part of the condition can be further weakened without another part being strengthened. While the above condition is a strong result that sets a fundamental limit of blind identification, there does not yet exist a working algorithm under that condition. In the second part of this paper, we show that a method called blind identification via decorrelating subchannels (BIDS) can uniquely identify an FIR MIMO channel if a) the channel matrix is nonsingular (almost everywhere) and column-wise coprime and b) the input signals are mutually uncorrelated and of sufficiently diverse power spectra. The BIDS method requires a weaker condition on the channel matrix than that required by most existing methods for the same problem.     

Second-order adaptive Volterra system identification based ondiscrete nonlinear Wiener model

The authors present the nonlinear LMS adaptive filtering algorithm based on the discrete nonlinear Wiener (1942) model for second-order Volterra system identification application. The main approach is to perform a complete orthogonalisation procedure on the truncated Volterra series. This allows the use of the LMS adaptive linear filtering algorithm for calculating all the coefficients with efficiency. This orthogonalisation method is based on the nonlinear discrete Wiener model. It contains three sections: a single-input multi-output linear with memory section, a multi-input, multi-output nonlinear no-memory section and a multi-input, single-output amplification and summary section. For a white Gaussian noise input signal, the autocorrelation matrix of the adaptive filter input vector can be diagonalised unlike when using the Volterra model. This dramatically reduces the eigenvalue spread and results in more rapid convergence. Also, the discrete nonlinear Wiener model adaptive system allows us to represent a complicated Volterra system with only few coefficient terms. In general, it can also identify the nonlinear system without over-parameterisation. A theoretical performance analysis of steady-state behaviour is presented. Computer simulations are also included to verify the theory     

A Robust Control Approach to Perfect Reconstruction of Digital Signals

In this paper we present a deterministic worst-case approach for reconstructing discrete-valued signals that have been filtered via dispersive and noisy systems (ldquochannelsrdquo). This approach, which is explored based on robust control ideas and makes no assumption on the noise (distribution or structure) other than a requirement that its magnitude be bounded, can serve as a complement to existing approaches that attempt to reconstruct discrete-valued signals by optimizing probabilistic criteria. The particular problems touched upon are: (i) necessary and sufficient conditions for causal (possibly delayed) perfect reconstruction under deterministic magnitude bounded noise for single-input single-output (SISO) and multi-input multi-output (MIMO) channels; (ii) perfect reconstruction based on decision feedback (DF) structures; and (iii) necessary and sufficient conditions for perfect reconstruction with DF structures in the presence of uncertainties in the channel. The l¹ control theory emerges as the natural key player for analysis and synthesis of perfect reconstructing strategies in this framework.