New Adaptive All-pass Based Notch Filter for Narrowband/FM Anti-jamming GPS Receivers

The global positioning system (GPS), which provides accurate positioning and timing information, has become a commonly used navigation instrument for many applications. The application of a new adaptive all-pass based notch filter (ANFA) for narrowband/FM interference suppression and frequency estimation in GPS receivers is proposed. An ANFA structure that achieves better unbiased characteristics with its coefficients is employed to accurately estimate the narrowband interfering signals in online fashion. A variable convergence factor that optimizes the maximal mean square error (MSE) reduction in each iteration is applied in a modified adaptive Gaussian–Newton (MAGN) algorithm. The proposed MAGN algorithm can lead to both faster convergence speed and higher estimation accuracy. Simulation results show that the ANFA offers a better performance than conventional linear predictors in terms of the SNR improvement and the mean output power (MOP) under the interference environments of interest.     

Complex Adaptive LMS Algorithm Employing the Conjugate Gradient Principle for Channel Estimation and Equalization

The Complex Block Least Mean Square (LMS) technique is widely used in adaptive filtering applications because of its simplicity and efficiency from a theoretical and implementation standpoint. However, the limitations of the Complex Block LMS technique are slow convergence and dependence on the proper choice of the stepsize or convergence factor. Moreover, its performance degrades significantly in time-varying environments. In this paper, a novel adaptive LMS technique named the Complex Block Conjugate LMS algorithm, CBC-LMS, is presented. Based on the Conjugate Gradient Principle, the proposed technique searches orthogonal directions to update the filter coefficients instead of the negative gradient directions used in the Complex Block LMS algorithm. In addition, the CBC-LMS algorithm derives optimal stepsizes to adjust the adaptive system coefficients at each iteration. As a result, the developed method overcomes the inherent limitations of the existing Complex Block LMS algorithm. The performance of the CBC-LMS technique is tested in wireless channel estimation and equalization applications, using both computer simulations and laboratory experiments. Furthermore, the developed technique is compared to the Complex Block LMS method and a recently proposed method, which is called Complex Optimal Block Adaptive LMS (OBA-LMS). The experimental and simulation results confirm that the proposed CBC-LMS technique achieves faster convergence with comparable accuracy and reduced computational complexity, relative to the existing techniques.     

自适应天线阵列在宽带CDMA上行链路接收机中的应用

本文提出了一种将自适应天线阵列与横向均衡器相结合。采用局部基于码片速率的信道估计和可变步长自适应算法的宽带CDMA上行链路空-时接收机结构。通过理论分析并结合CDMA2000标准上行链路的计算机仿真,证明这种空-时接收机在瑞利衰落信道下,具有较好的收敛性能和接收性能。     

一种脉冲噪声下的自适应均衡算法

由单层神经自适应滤波器和Chebyshev正交多项式滤波器级联构成一个均衡器,基于此均衡器结构,提出一种基于分数低阶统计量的自适应均衡算法(称为NC-NLMP算法).仿真结果表明,对线性信道和非线性信道,NC-NLMP算法的均衡性能均优于传统的FIR横向滤波器的自适应均衡算法和神经网络均衡算法;对于非线性信道,NC-NLMP算法的均衡性能远优于后两者;NC-NLMP算法对高斯噪声和稳定分布脉冲噪声都具有很好的均衡性能,对背景噪声韧性强.     

无线OFDM系统传输信道模型化参数检测

利用导频子载波时频相位相关性统计检测与QAM信号解映射判决误差统计，提出了一种适用于频域导频无线OFDM系统的传输信道模型化参数检测方案，包括信道最大多径时延、最大多普勒频率和信噪比检测；能够有效解决传统的OFDM信道估计算法通常按照最恶劣信道情况上限进行设计，以及基于MMSE准则最优信道估计器中信道先验信息统计运算复杂度高的缺陷；仿真结果表明，在低复杂度条件下可以有效获知当前传输信道模型参数的近似统计信息。     

MIMO-OFDM系统中一种基于自适应滤波的信道估计方法

该文提出了一种适用于MIMO-OFDM系统的基于自适应滤波器的信道估计方法,此方法在不需要任何信道统计信息的前提下,通过自适应滤波的方法对时变信道状态参数进行即时跟踪与估计。仿真结果表明该文提出的基于自适应滤波的信道估计方法,相比于不考虑噪声的基于LS算法的信道估计方法,MSE和BER性能均有很大的提高。其中基于LMS滤波器的信道估计方法具有计算复杂度小的特点;而基于RLS的信道估计方法具有收敛速度快,MSE和BER性能均优于基于LMS方法的特点。     

A Unified Conjugate Gradient Based Approach for Optimal Complex Block FIR Filtering

This paper proposes a unified block based approach to generate two complex filtering algorithms. The proposed unified approach calculates the complex conjugate gradients as the orthogonal update directions for the adaptive filter coefficients at each iteration. Along each update direction, the time-varying convergence factors tailored for the adaptive filter coefficients are updated based on the complex Taylor series expansion. The general formulation leads to two classes of adaptive algorithms: the Complex Block Conjugate Least Mean Square algorithm with Individual adaptation of parameters, CBCI-LMS, and the Complex Block Conjugate Least Mean Square algorithm, CBC-LMS. The formulation shows that the CBCI-LMS algorithm achieves faster adaptation than the CBC-LMS technique at the expense of an increase in the number of computations per iteration. The performances of these two proposed algorithms are evaluated and compared to existing techniques. In addition, the implementation aspects are examined under a wide range of adaptive conditions. These two generated algorithms are then applied to channel equalization and adaptive array beamforming. Based on the obtained results, the proposed algorithms demonstrate excellent convergence characteristics, in terms of the adaptation speed and accuracy.     

Alias-Free Subband Adaptive Filtering With Critical Sampling

To overcome the limitations of a conventional fullband adaptive filtering, various subband adaptive filtering (SAF) structures have been proposed. Properly designed, an SAF will converge faster at a lower computational cost than a fullband structure. However, its design should consider the following two facts: the interband aliasing introduced by the downsampling process degrades its performance, and the filter bank in the SAF introduces additional computational overhead and system delay. In this paper, to fully exploit the benefits of using an SAF, an almost alias-free SAF structure with critical sampling is proposed. The interband alising is removed from the subband signal by isolating the aliasing using a bandwidth-increased analysis filter. Computer simulations show that the proposed structure converges faster than both an equivalent fullband structure at lower computational complexity and recently proposed SAF structures for a colored input.     

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。     

NGB-W广播信道估计实现算法设计

针对下一代广播电视网无线系统(NGB-W)广播接收信道估计,提出了一种基于二次一维维纳滤波的信道估计实用算法.该算法实时估计信道多普勒频偏、时延扩展和噪声方差等参数,并根据参数估计值分别从离线获得的预选集中选择时域和频域的一维维纳滤波系数.通过Cocentric System Studio(CCSS)平台仿真,给出了参数估计对算法性能的影响,以及所提信道估计算法的均方误差和误块率性能.仿真结果表明该算法在不同移动速度下,与采用理想插值系数信道估计相比性能损失不超过0.4 dB,与理想信道估计相比性能损失在1 dB以内.     

一种不受信道阶数估计影响的SIMO直接盲均衡算法

针对单输入多输出（Single-Input-Multiple-Output, SIMO）模型提出一种完全不需要信道阶数估计的直接盲均衡算法。文章利用接收数据的截短协方差矩阵和信号子空间的关系设计一种零延迟均衡器,并通过信道矩阵和均衡器系数的合响应特性克服了算法相位偏转的问题,最后得到一种对信道阶数估计鲁棒并且没有相位偏转的盲均衡算法。该算法不同于一般子空间类算法,不需要直接对接收信号的协方差矩阵进行信号子空间和噪声子空间的分解,因此对信道阶数估计具有很强的鲁棒性。文章给出了算法的Batch实现过程,同时为更好适应一般时变信道环境和实现实时处理的要求,通过递归迭代得到算法的自适应实现过程。仿真实验表明该算法几乎不受信道阶数过估计或欠估计的影响,同时该算法具有良好的均方误差（Mean Square Error, MSE）和误符号率SER（Symbol Error Rate, SER）性能,并且具有很快的收敛速度。      

在脉冲噪声环境中用于快衰信道估计的改进型算法

该文分析了在存在噪声干扰的情况下,进行估计快衰信道的方法。在无线通信系统中,快衰信道可以采用AR(Auto-Regressive)模型进行预测,而LS (Least Square)算法和自适应Kalman滤波器可以分别对AR模型的参数和信道的冲激响应进行估计,但是这两种算法对噪声干扰非常敏感。该文提出改进型的RLM算法和Kalman 滤波器,并在存在噪声的情况下,使用它们并行对AR参数和信道的冲激响应进行联合估计。仿真结果显示:相比于传统的算法,改进后的算法在联合估计信道时,提高了抵抗大脉冲干扰的能力,加快了待估的参数的收敛速度。     

A general class of nonlinear normalized adaptive filteringalgorithms

The normalized least mean square (NLMS) algorithm is an important variant of the classical LMS algorithm for adaptive linear filtering. It possesses many advantages over the LMS algorithm, including having a faster convergence and providing for an automatic time-varying choice of the LMS stepsize parameter that affects the stability, steady-state mean square error (MSE), and convergence speed of the algorithm. An auxiliary fixed step-size that is often introduced in the NLMS algorithm has the advantage that its stability region (step-size range for algorithm stability) is independent of the signal statistics. In this paper, we generalize the NLMS algorithm by deriving a class of nonlinear normalized LMS-type (NLMS-type) algorithms that are applicable to a wide variety of nonlinear filter structures. We obtain a general nonlinear NLMS-type algorithm by choosing an optimal time-varying step-size that minimizes the next-step MSE at each iteration of the general nonlinear LMS-type algorithm. As in the linear case, we introduce a dimensionless auxiliary step-size whose stability range is independent of the signal statistics. The stability region could therefore be determined empirically for any given nonlinear filter type. We present computer simulations of these algorithms for two specific nonlinear filter structures: Volterra filters and the previously proposed class of Myriad filters. These simulations indicate that the NLMS-type algorithms, in general, converge faster than their LMS-type counterparts     

Nonlinear adaptive bilinear filters for active noise control systems

The reference and error channels of active noise control (ANC) systems may be saturated in real-world applications if the noise level exceeds the dynamic range of the electronic devices. This nonlinear saturation degrades the performance of ANC systems that use linear adaptive filters with the filtered-X least-mean-square (FXLMS) algorithm. This paper derives a bilinear FXLMS algorithm for nonlinear adaptive filters to solve the problems of signal saturation and other nonlinear distortions that occur in ANC systems used for practical applications. The performance of this bilinear adaptive filter is evaluated in terms of convergence speed, residual noise in steady state, and the computational complexity for different filter lengths. Computer simulations verify that the nonlinear adaptive filter with the associated bilinear FXLMS algorithm is more effective in reducing saturation effects in ANC systems than a linear filter and a nonlinear Volterra filter with the FXLMS algorithm.     

一种子带分频段信道估计算法

针对当前高速率通信中信道阶数很长导致信道估计和均衡困难的问题,利用子带 滤波器组近似完全重构的特点,提出一种在子带内进行分频段信道估计、在全频带综合信道 参数的估计方法。该方法较全频带信道估计收敛速度快,收敛误差小,能很好适应恶劣的信 道情况。虽然总的计算量大于全频带信道估计,但由于采用并行计算,所以能大大减少运算 时间。仿真试验表明,在重构误差足够小的情况下,子带数目越多,收敛越快,收敛残差比 全频带信道估计小5 dB左右。     

Online blind equalization algorithm with echo state network based on prediction principle

In view of the nonlinear channel,the online blind equalization algorithm with echo state network was proposed based on prediction principle.In the proposed algorithm,the traditional linear prediction error filter was replaced by the ESN with good nonlinear mapping ability,and recursive least square (RLS) algorithm was used to calculate the output weight of the network to minimize the network prediction error.Then,the amplitude and phase were adjusted.Simulation results show that the proposed algorithm can effectively reduce the distortion caused by nonlinear channel to the transmitted signal for 16QAM signal,which has lower mean square error and faster convergence speed in comparison with other blind equalization algorithms based on prediction principle.     

基于SP-LCCMA的STBC MC-CDMA系统盲信道估计

研究了空时编码多载波码分多址系统（STBC MC—CDMA）盲信道估计技术。根据信道位于信号子空间的特点,提出基于信号子空间投影线性约束恒模算法（SP—LCCMA）的盲信道估计,避免了噪声子空间信道估计的缺点,将估计信道应用于STBC MC—CDMA系统多用户检测。仿真结果表明,提出算法的收敛速度和信干噪比（SINR）性能优于一般恒模算法。     

一种基于RLS算法的自适应时延估计算法

针对基于LMS算法的自适应时延估计算法收敛速度慢的缺点,推导出一种基于RLS算法的自适应时延估计算法,并分析了基于RLS算法的自适应滤波器的均值性能。经仿真证明该算法的收敛速度有了一定提高,并且其时延估计的跟踪性能得到了改善。     

OFDM系统中基于压缩传感理论的信道估计算法

信道估计是OFDM系统中的一项关键技术,信道估计质量的好坏对整个系统的性能有重要的影响。传统的最小均方算法对稀疏信道进行估计时存在精确性差的缺陷。本文利用信道冲激响应的稀疏性,提出了一种基于近似l₀范数的信道估计算法。该算法用三种函数逼近l₀范数,应用梯度下降法和梯度投影算法获得代价函数的最优解,从而得到信道的最稀疏解。仿真实验结果表明:在相同条件下,与基于l₁范数的信道估计算法比较,本文算法的收敛速度快,估计值信噪比高,且均方误差小。