基于约束最小均方的噪声抑制MOE盲多用户检测

针对最小输出能量(minimum output energy,MOE)检测器的权向量由于受到噪声的影响而导致性能下降的问题,设计了一种新的MOE检测器,并且利用约束最小均方(least mean square,LMS)算法得到权向量,提出DS-CDMA系统下一种基于约束LMS的MOE盲噪声抑制多用户检测算法.该算法将权向量和噪声子空间正交,消除了权向量中的噪声分量,降低了噪声输出能量,从而使系统的性能优于常规的LMS盲多用户检测.仿真结果证明了所提算法的有效性和优越性.     

基于子空间约束RLS的半盲多用户检测算法

考虑实际的MC-CDMA上行链路,深入研究了基于RLS算法实现的MOE(RLS-MOE)盲自适应多用户检测,提出了MC-CDMA系统下一种基于子空间约束RLS的半盲多用户检测算法.在MOE盲多用户检测的基础上,利用小区内用户的已知扩频码设计了一种MOE半盲多用户检测器.将子空间方法和RLS算法相结合提出一种基于子空间约束的RLS 算法,使用该算法自适应得到MOE的权向量.本文算法利用所有已知用户的扩频码抑制了小区内用户的干扰,子空间约束的RLS 算法降低了噪声的影响,从而改善了系统的性能.修正的PASTd算法实现了信号的自适应跟踪,大大降低了计算量.仿真实验表明,本文算法的输出信干噪比和误码率性能优于RLS-MOE盲多用户检测,更接近于最优值.     

一种改进的MOE盲多用户检测算法

针对最小输出能量(MOE)准则,分析并研究了直扩码分多址(DS-CDMA)通信系统中的三种盲多用户检测算法,即最小均方(LMS)算法,递推最小二乘(RKS)以及判决反馈变步长MOE检测算法(DF-MOE).仿真实验了同步DS-CDMA系统中平稳信道以及非平稳信道下三种算法抑制多址干扰(MAI)的能力,并且根据各自特点,提出将RLS算法和DF-MOE算法相结合,使其更好地适应于动态系统.     

Alpha稳定分布噪声中的韧性投影 近似子空间跟踪算法

 为了克服投影近似子空间跟踪算法(PAST)在脉冲噪声环境下性能的退化,本文以Alpha 稳定分布为脉冲噪声模型,依据韧性的M估计方法提出了一种新的代价函数,并推导出基于递归最小M估计的韧性投影近似自适应信号子空间跟踪算法(RLM_-PAST).由于采用了适合噪声模型的M估计函数,新算法与采用递归最小二乘估计的子空间跟踪算法相比,在稳定分布脉冲噪声环境下具有更好的性能.把新方法应用于波达方向(DOA)估计,数值仿真结果表明了该算法的有效性.     

基于Sigmoid框架的非负最小均方算法

脉冲噪声会导致非负算法在迭代过程中存在过大的误差值,进而破坏算法的稳定性使其性能严重下降,对此该文提出一种基于Sigmoid框架的非负最小均方算法(SNNLMS).该算法将传统的非负代价函数嵌入Sigmoid框架中得到新的代价函数,新的代价函数具有抑制脉冲噪声影响的特性.此外,为了增强SNNLMS算法在稀疏系统识别问题上的鲁棒性,该文还提出基于反比例函数的反比例Sigmoid非负最小均方算法(IP-SNNLMS).仿真结果表明SNNLMS算法有效地解决了脉冲噪声造成的失调问题;IP-SNNLMS增强了算法鲁棒性,改进了算法在稀疏系统识别问题中收敛速率上的缺陷.     

基于Sigmoid框架的非负最小均方算法

脉冲噪声会导致非负算法在迭代过程中存在过大的误差值,进而破坏算法的稳定性使其性能严重下降,对此该文提出一种基于Sigmoid框架的非负最小均方算法(SNNLMS).该算法将传统的非负代价函数嵌入Sigmoid框架中得到新的代价函数,新的代价函数具有抑制脉冲噪声影响的特性.此外,为了增强SNNLMS算法在稀疏系统识别问题...     

特征空间算法及仿真

本文的算法利用特征分解得到信号子空间,然后将基于线性约束最小方差准则(LCMV)的权向量投影到信号子空间得到特征空间算法的权向量,并对算法进行了仿真.仿真结果表明算法具有较好的性能、很快的收敛速度.     

DS-CDMA系统中基于最大特征值的ESB算法

针对DS-CDMA系统,在基于特征空间(ESB)自适应波束形成算法的基础上提出了一种基于最大特征值的改进算法,该算法利用阵列接收信号协方差矩阵最大特征值所对应的特征向量构成子空间,摈弃了权向量在干扰信号子空间及噪声子空间中的分量.改进算法结构简单、运算量低.计算机仿真和分析证明该算法性能要优于常规ESB算法的性能.     

一种全最小二乘算法及其在非线性滤波器中的应用

针对输入输出观测数据均含有噪声的滤波问题,提出了一种稳定的总体最小二乘自适应算法。该算法以系统的增广权向量的瑞利商(RQ)与对增广权向量的最后元素的约束的和作为总损失函数,利用梯度最陡下降原理导出权向量的自适应迭代算法,并将该算法应用于非线性Volterra滤波器。研究了算法的稳定性能,提出的算法不仅有良好的收敛性能,而且在权向量的自适应迭代时不需要标准化处理,使得算法的实施更为简单。仿真实验表明,无论在线性系统或非线性系统,本文算法的收敛性能,鲁棒抗噪性能和稳态收敛精度明显高于其它同类总体最小二乘算法。     

脉冲噪声下基于Renyi熵的分数低阶双模盲均衡算法

针对脉冲噪声下盲均衡器难以快速收敛并有效抑制噪声的问题,该文提出一种基于Renyi熵的分数低阶双模盲均衡算法。该算法将Renyi熵与分数低阶统计量相结合并用作代价函数来更新盲均衡器权向量,利用Renyi熵提高算法的收敛速度,利用分数低阶统计量增强算法对脉冲噪声的抑制能力。为了提升系统稳健性,该文进一步提出双阈值加权判决法,通过设置双阈值并引入非线性加权函数,使得两种代价函数之间的切换更为平滑。在不同脉冲性噪声、不同信道环境下进行仿真实验,结果表明,该文算法既能有效抑制脉冲噪声,又具有较快的收敛速度。     

Robust M-estimate adaptive filtering

An M-estimate adaptive filter for robust adaptive filtering in impulse noise is proposed. Instead of using the conventional least-square cost function, a new cost function based on an M-estimator is used to suppress the effect of impulse noise on the filter weights. The resulting optimal weight vector is governed by an M-estimate normal equation. A recursive least M-estimate (RLM) adaptive algorithm and a robust threshold estimation method are derived for solving this equation. The mean convergence performance of the proposed algorithm is also analysed using the modified Huber (1981) function (a simple but good approximation to the Hampel's three-parts-redescending M-estimate function) and the contaminated Gaussian noise model. Simulation results show that the proposed RLM algorithm has better performance than other recursive least squares (RLS) like algorithms under either a contaminated Gaussian or alpha-stable noise environment. The initial convergence, steady-state error, robustness to system change and computational complexity are also found to be comparable to the conventional RLS algorithm under Gaussian noise alone     

Fast reduced-rank STAP algorithm based on Gram–Schmidt orthogonalisation for airborne radar

In the reduced-rank space-time adaptive processing (STAP) methods, especially the principal component (PC) analysis STAP method, a set of dominant eigenvectors must be obtained by singular value decomposition of the space-time covariance matrix. Therefore, it is very difficult to be applied in practical system due to the intense computational complexity. In order to reduce the computational burden, a fast reduced-rank STAP algorithm based on Gram–Schmidt (GS) orthogonalisation is proposed in this article. In the proposed GS-PC STAP method, the clutter subspace is reconstructed by the GS orthogonalisation of training samples. Then, the STAP adaptive weight vector is calculated by orthogonally projecting the quiescent weight vector into clutter subspace, which can hold fast convergence measure of effectiveness (MOE) and require less computational complexity by compared with the conventional PC method. Based on the simulated data and multichannel airborne radar measurements data, the corresponding convergence MOE and the clutter suppression performances are verified in the article.     

异步DS-CDMA系统盲空时信道估计及多用户检测

该文提出了适用于频率选择性瑞利衰落信道中的异步DS-CDMA系统盲空时信道估计及多用户检测算法。通过研究多径信号码空间和数据矢量空间,采用噪声子空间技术进行异步DS-CDMA系统盲空时信道参数估计,同时利用了多径传播和接收机同步失调的特性,以利于把盲线性滤波优化技术应用于稳健的干扰抑制。使用一种修改的ULV更新算法进行噪声子空间跟踪,该算法不需要相关矩阵的秩估计,直接估计噪声子空间,不进行信号子空间跟踪。并且研究了线性约束最小方差(LCMV)盲空时多用户检测及其基于Householder变换约束最小均方算法(HCLMS)的自适应实现。仿真结果验证了该文算法的有效性。     

The Subspace-Based Linear Conjugate CMA in BPSK-Modulated MC-CDMA Systems

A subspace-based linear conjugate constant modulus algorithm (CMA) for blind adaptive multiuser detection in BPSK-Modulated MC-CDMA systems is proposed. First, a linear conjugate CMA algorithm is presented which jointly elaborates the received signal and its complex conjugate to utilize the information contained in the pseudo-autocorrelation of the observable, thus is capable of achieving much better performance. Second, to further improve the performance, the subspace method is applied to the linear conjugate CMA algorithm to obtain a subspace-based linear conjugate CMA algorithm, which mitigates the influence of the noise subspace. Finally, an improved projection approximation subspace tracking with deflation algorithm is proposed for adaptive signal subspace estimation to reduce the computational complexity. The proposed algorithm provides an improved robustness against the signature waveform mismatch, good tracking capability in dynamic environment and better output signal-to-interference-plus-noise-ratio and bit error rate performance. The superiority of the proposed algorithm is demonstrated through computer simulations.     

Performance Analysis of Blind Adaptive MOE Multiuser Receivers Using Inverse QRD-RLS Algorithm

The inverse QR (IQRD) recursive least-squares (RLS) algorithm (IQRD-RLS) is very popular because it has good numerical stability and can be mapped onto COordinate Rotation DIgital Computer (CORDIC) processor-based systolic arrays, which are suitable for very large-scale integrated circuits (VLSI) architecture and real-time applications. In this paper, the blind optimal minimum output energy (MOE) detector which is developed for multiuser detection (MUD) in direct-sequence code-division multiple-access (DS-CDMA) systems is implemented using the linearly constrained IQRD-RLS algorithm. Specifically, the max/min approach is combined with subspace tracking for producing the optimal MOE multiuser detector. A new fast subspace tracking algorithm based on Lagrange multiplier methodology and the IQRD-RLS algorithm is developed. A comparative analysis among the recently emerged channel-estimation techniques is conducted using the IQRD-RLS algorithm. The corresponding robust MOE receivers at low SNR are implemented using the IQRD method, and their performances are assessed in terms of SINR, BER, and computational complexity. A robust multiuser receiver is developed by adding a quadratic inequality constraint to the optimal max/min MOE detector. The feasibility of systolic array implementation of the IQRD-based optimal MOE detector is explored. Several simulation experiments are conducted in a severe near–far environment to analyze the IQRD-based receivers and the subspace tracking algorithms.      

空时分组码MC-CDMA系统盲干扰抑制

该文考虑空时分组码多载波码分多址(MC-CDMA)系统的盲干扰抑制。通过研究多径信号频域码空间和数据矢量空间,采用噪声子空间技术进行盲信道估计。使用一种修改的ULV更新算法进行噪声子空间跟踪,该算法不需要相关矩阵的秩估计,直接估计噪声子空间。为了抑制多址干扰(MAI),提出一种基于投影的辅助矢量(PAV)算法,用前一级滤波矢量的输出重构最大比合并(MRC)滤波矢量,将重构滤波矢量到由基本滤波矢量和前几级辅助矢量张成子空间上的正交投影作为辅助矢量,将前一级滤波矢量和新产生辅助矢量线性合并得到新的滤波矢量。仿真结果验证了该文算法的有效性。     

A robust past algorithm for subspace tracking in impulsive noise

The PAST algorithm is an effective and low complexity method for adaptive subspace tracking. However, due to the use of the recursive least squares (RLS) algorithm in estimating the conventional correlation matrix, like other RLS algorithms, it is very sensitive to impulsive noise and the performance can be degraded substantially. To overcome this problem, a new robust correlation matrix estimate, based on robust statistics concept, is proposed in this paper. It is derived from the maximum-likelihood (ML) estimate of a multivariate Gaussian process in contaminated Gaussian noise (CG) similar to the M-estimates in robust statistics. This new estimator is incorporated into the PAST algorithm for robust subspace tracking in impulsive noise. Furthermore, a new restoring mechanism is proposed to combat the hostile effect of long burst of impulses, which sporadically occur in communications systems. The convergence of this new algorithm is analyzed by extending a previous ordinary differential equation (ODE)-based method for PAST. Both theoretical and simulation results show that the proposed algorithm offers improved robustness against impulsive noise over the PAST algorithm. The performance of the new algorithm in nominal Gaussian noise is very close to that of the PAST algorithm.