自适应减少复杂度的盲最大似然序列估计

 基于逐幸存路径处理原理和自适应选择幸存路径的思想,本文提出了一种自适应减少计算复杂度的盲最大似然序列估计新算法.通过分析和推导,给出了一种近似估计网格图最小欧式距离的方法,并利用该估计值对幸存路径进行取舍,在网格搜索中仅保留少数幸存路径来进行信道参数和发送符号序列的联合盲估计.理论分析和计算机仿真结果表明,对严重符号干扰信道,在较高信噪比条件下,本文提出的新算法具有较理想的误符号率性能和较低的计算复杂度.     

Blind multiuser detection: a subspace approach

A new multiuser detection scheme based on signal subspace estimation is proposed. It is shown that under this scheme, both the decorrelating detector and the linear minimum-mean-square-error (MMSE) detector can be obtained blindly, i.e., they can be estimated from the received signal with the prior knowledge of only the signature waveform and timing of the user of interest. The consistency and asymptotic variance of the estimates of the two linear detectors are examined. A blind adaptive implementation based on a signal subspace tracking algorithm is also developed. It is seen that compared with the previous minimum-output-energy blind adaptive multiuser detector, the proposed subspace-based blind adaptive detector offers lower computational complexity, better performance, and robustness against signature waveform mismatch. Two extensions are made within the framework of signal subspace estimation. First, a blind adaptive method is developed for estimating the effective user signature waveform in the multipath channel. Secondly, a multiuser detection scheme using spatial diversity in the form of an antenna array is considered. A blind adaptive technique for estimating the array response for diversity combining is proposed. It is seen that under the proposed subspace approach, blind adaptive channel estimation and blind adaptive array response estimation can be integrated with blind adaptive multiuser detection, with little attendant increase in complexity     

Blind DOA and polarization estimation for polarization-sensitive array using dimension reduction MUSIC

A novel blind direction-of-arrival (DOA) and polarization estimation algorithm for polarization-sensitive uniform linear array using dimension reduction multiple signal classification (MUSIC) is proposed in this paper. The proposed algorithm utilizes the signal subspace to obtain an initial estimation of DOA, then estimates more accurate DOA through a one-dimensional (1-D) local searching according to the initial estimation of DOA, and finally obtains polarization parameter estimation via the estimated polarization steering vectors. The proposed algorithm, which only requires a one-dimension local searching, can avoid the high computational cost within multi-dimensional MUSIC algorithm. The simulation results reveal that the proposed algorithm has better DOA and polarization estimation performance than both estimation of signal parameters via rotational invariance technique algorithm and trilinear decomposition algorithm. Furthermore, the proposed algorithm can be suitable for irregular array geometry, obtain automatically paired multi-dimensional parameter estimation, and avoid multi-dimensional searching. Simulation results verify the effectiveness of the proposed algorithm.     

Reactance domain MUSIC algorithm for electronically steerable parasitic array radiator

Conventional adaptive array antenna processing must observe signals on all of the array antenna elements. However, because the low-cost electronically steerable parasitic array radiator (ESPAR) antenna has only a single-port output, none of the signals on the antenna's parasitic elements can be observed. A direct application of most of the algorithms for the conventional adaptive array antenna is impractical. In this paper, A technique of estimation of direction-of-arrivals (DoAs) is proposed for the ESPAR antenna. This technique is based on the modified MUltiple SIgnal Classification (MUSIC) algorithm. The correlation matrix used in the MUSIC algorithm is estimated from the signal received through the single-port output of the ESPAR antenna as it switches over a set of antenna patterns. Simulation results show that DoAs can be estimated by the reactance domain MUSIC algorithm for ESPAR antennas. Furthermore, the statistical performance on estimation error variance of the reactance domain MUSIC estimator is analyzed and compared with the Crame/spl acute/r-Rao lower bound. Analytic and empirical results show that high-resolution DoAs estimation can be achieved by using the reactance domain MUSIC algorithm for ESPAR antennas.     

基于压缩感知的MIMO-OFDM系统自适应信道估计

针对超密集组网中导频复用将产生导频干扰,严重影响移动用户下行链路信道估计准确性的问题,提出了一种使用短导频的幂函数稀疏度自适应匹配追踪(Power Sparsity Adaptive Matching Pursuit,PSAMP)算法.该算法由稀疏度预估计和追踪重构两部分构成.首先通过幂函数试探得到一个略小于真实稀疏度的预估值,再通过压缩采样匹配追踪重构信号,改善估计结果;若不能成功重构,则逐渐增加信号原子数量.仿真结果表明,相较于传统自适应压缩感知重建算法,所提的P SAMP算法在高信噪比区域具有更好的信道估计性能.     

Power system harmonic signal estimation and retrieval for activepower filter applications

This paper presents a new adaptive infinite impulse response (IIR) line enhancer (LE) comb filter configuration for the purpose of power system harmonic signal estimation and retrieval. The approximate maximum likelihood (AML) algorithm is employed for the parameter update. The proposed solution is characterised by modest computational burden, effective tracking capabilities and provides the retrieved harmonic components with little or no distortion. The retrieved power system harmonics may be obtained on an individual basis or as a composite signal. Practical test results are included which show the performance achieved by the proposed technique     

Comparative study of four adaptive frequency trackers

We study and compare four algorithms for adaptive retrieval of slowly time-varying multiple cisoids in noise: the adaptive notch filter, the multiple frequency tracker, the adaptive estimation scheme, and the hyperstable adaptive line enhancer. The local behavior of the algorithms in a neighborhood of their equilibrium state [assuming high signal-to-noise ratio (SNR) and large data sample] for a two-cisoid signal is treated in a similar way to the linear filter approximation technique used for a single-cisoid case. The validity of the results is confirmed by computer simulations     

QR methods of O(N) complexity in adaptive parameter estimation

Recent attention in adaptive least squares parameter estimation has been focused on methods derived from the QR factorization owing to the fact that the QR-based algorithms are much more numerically stable and accurate than the traditional pseudo-inverse-based algorithms, also known as normal equation-based algorithms, even though the former is usually much slower than the latter. This paper presents a fast adaptive least squares algorithm for the parameter estimation of linear and some nonlinear time-varying systems. The algorithm is based on Householder transformations. As verified by simulation results, this algorithm exhibits good numerical stability and accuracy. In addition, the new algorithm requires computation and storage with order of O(N) rather than O(N²) where N is the number of unknown parameters to be estimated. This algorithm can be easily extended to construct other kinds of algorithms, such as the generalized adaptive least squares algorithm, the augmented matrix algorithm, and the maximum likelihood algorithm     

Time Delay Estimation Method Based on Canonical Correlation Analysis

The localization of sources has numerous applications. To find the position of sources, the relative delay between two or more received signals for the direct signal must be determined. The generalized cross-correlation method is the most popular technique; however, an approach based on eigenvalue decomposition (EVD) is another popular one that utilizes the eigenvector of the minimum eigenvalue. The performance of the eigenvalue decomposition (EVD) based method degrades in low SNR and reverberation, because it is difficult to select a single eigenvector for the minimum eigenvalue. In this paper, we propose a new adaptive algorithm based on Canonical Correlation Analysis (CCA) to extend the operation SNR to the lower SNR and reverberation. The proposed algorithm uses an eigenvector that corresponds to the maximum eigenvalue in the generalized eigenvalue equation (GEVD). The estimated eigenvector contains all required information for time delay estimation. We have performed simulations with uncorrelated, correlated noise and reverberation for several SNRs, to show that time delays can be more accurately estimated (especially for low SNR) a CCA based algorithm versus the adaptive EVD algorithm.     

二值传感器网络的分布式稀疏LMS算法

在基于无线传感器网络的参数估计中,每个节点在数据采集、存储、处理和传输等方面的能力是有限的。二值传感器网络中的每个节点只能提供低精度1比特测量值,与能够提供模拟测量值（无限精度）的传感器相比,二值传感器有较低的使用成本。如何利用低成本二值传感器网络获得较好的参数估计性能近些年已引起广泛关注,基于该二值传感器网络,论文提出了一种分布式稀疏参数估计的自适应最小均方（LMS）算法。该算法采用稀疏惩罚最大似然优化,并结合期望最大化和LMS方法,获得稀疏信号的在线估计。仿真实验表明,尽管只采用1比特测量,提出的算法仍具有较好的收敛性,并且稳定状态的估计误差接近于非1比特测量的同类算法。      

基于特征分解MUSIC算法的DOA估计

对于非相干信号源,基于特征分解的多重信号分类算法是一种具有高分辨率的波达方向估计算法,通过计算机仿真比较了它与Bartlett和Capon算法的性能,并分析了信号源数目估计值大于或小于真实值时接收信号入射角的估计结果.研究了一种新的自适应加权空间平滑算法,提高了MUSIC算法对于相干信源DOA估计的性能.仿真结果表明该算法可以有效地去除期望信号与干扰信号之间的相关性,在相干干扰方向上形成深的零陷,在平滑次数相同的情况下新算法比常规空间平滑算法得到更高的输出信干噪比.     

多径衰落信道下基于子空间的RLS盲多用户检测

本文在RLS盲检测算法的基础上,利用子空间的概念,构建了基于子空间的RLS多用户盲检测算法,在仅仅需要知道目标用户的特征序列和定时的条件下,自适应地估计检测向量,通过理论分析表明,改进的检测算法在运算复杂度上低于满秩RLS算法[7].仿真结果表明,改进的检测算法收敛性能优于满秩RLS算法,同时在特征序列畸变条件下表现出健壮件也远优于满秩RLS检测算法.     

自适应邻域尺寸选择的点云法向量估计算法

三维空间中的法向量估计在计算机视觉和表面重建等研究领域中具有重要的意义，基于局部表面拟合的方法是基于点云数据的经典估计方法。为了增强该方法对于不同局部邻域细节尺度的适应性以得到更准确的估计结果，提出了一种基于自适应邻域尺寸选择的点云法向量估计算法。该方法通过分析三维空间点的邻域点在点的梯度上投影来估计点云中各点的邻域分布情况|最后根据不同的分布情况选择不同的邻域大小，根据该邻域范围内的点拟合出的平面求解得到各点的法向矢量。实验结果表明:该方法能够克服邻域半径选择过大或者过小的情况，有效地提高基于局部表面拟合法向矢量求解的正确性。     

An On-Line Calibration Technique for Mismatch Errors in High-Speed DACs

In this paper a novel online calibration technique for high-speed DACs is presented. The approach consists of two elements. The first element is the use of a redundant signed digit (RSD) scheme for the selection of the current sources. This enables a fully digital correction. The second element consists of an adaptive estimation of the correction terms by an LMS algorithm. For this purpose the DAC output signal is low-pass filtered and digitized by an accurate but low-speed calibration ADC. By replicating this analog signal path in the digital domain, the errors can be calculated and used to update the correction weights. The dynamic behavior and the accuracy of the adaptive calibration loop are analyzed both theoretically and through computer simulations, and it is shown that this way a greatly improved accuracy can be obtained.      

An adaptive estimation of periodic signals using a Fourier linearcombiner

Presents an adaptive algorithm for estimating from noisy observations, periodic signals of known period subject to transient disturbances. The estimator is based on the LMS algorithm and works by tracking the Fourier coefficients of the data. The estimator is analyzed for convergence, noise misadjustment and lag misadjustment for signals with both time invariant and time variant parameters. The analysis is greatly facilitated by a change of variable that results in a time invariant difference equation. At sufficiently small values of the LMS step size, the system is shown to exhibit decoupling with each Fourier component converging independently and uniformly. Detection of rapid transients in data with low signal to noise ratio can be improved by using larger step sizes for more prominent components of the estimated signal. An application of the Fourier estimator to estimation of brain evoked responses is included     

LFM信号参数估计的最大似然改进算法

为实现含噪声LFM信号参数的快速检测和精确估计,提出了一种基于延时相关解线调的最大似然估计改进算法,即首先在时域内进行延时相关解线调,然后对解线调后含噪声信号进行经典功率谱估计,得到调频斜率的粗略估计,将此估计值作为初始值,再进行最大似然估计,得到调频斜率的精确估计值,用此精确估计值对原LFM信号进行解线调,再以同样的思路可以得到LFM信号初始频率的最大似然精确估计值。仿真实验证明了该算法的有效性。     

A Robust ICA-Based Adaptive Filter Algorithm for System Identification

This paper proposes a new adaptive filter algorithm for system identification using independent component analysis. The additive noise is considered as an independent component to be separated from the noisy observation and is simultaneously estimated online. The proposed algorithm is derived by minimizing the mutual information between the estimated additive noise and the input signal. The local convergence conditions are also derived. The proposed algorithm can be directly applied to the acoustic echo canceller without any double-talk detector. Some simulations have been carried out to illustrate its effectiveness for synthetic and real speech signals.      

M滤波的自适应背景抑制算法

提出了一种M滤波的自适应背景抑制算法.该算法将目标和观测噪声作为图像背景的混合干扰,依据M估计原理自适应的估计真实背景.算法具有较好的稳健性能,能够自适应的低抗高强度的干扰.仿真和实验表明,与均值滤波(MF)、中值滤波(ModF)以及Wiener滤波(WF)相比,该算法能够更有效地从混合噪声环境下估计背景,增强目标信噪比(SNR).     

Noise‐Robust Speaker Recognition Using Subband Likelihoods and Reliable‐Feature Selection

We consider the feature recombination technique in a multiband approach to speaker identification and verification. To overcome the ineffectiveness of conventional feature recombination in broadband noisy environments, we propose a new subband feature recombination which uses subband likelihoods and a subband reliable‐feature selection technique with an adaptive noise model. In the decision step of speaker recognition, a few very low unreliable feature likelihood scores can cause a speaker recognition system to make an incorrect decision. To overcome this problem, reliable‐feature selection adjusts the likelihood scores of an unreliable feature by comparison with those of an adaptive noise model, which is estimated by the maximum a posteriori adaptation technique using noise features directly obtained from noisy test speech. To evaluate the effectiveness of the proposed methods in noisy environments, we use the TIMIT database and the NTIMIT database, which is the corresponding telephone version of TIMIT database. The proposed subband feature recombination with subband reliable‐feature selection achieves better performance than the conventional feature recombination system with reliable‐feature selection.