基于改进稀疏度自适应匹配算法的免授权非正交多址接入上行传输多用户检测

免授权非正交多址接入技术(NOMA)结合多用户检测技术(MUD),能够满足大规模机器通信(mMTC)场景中的大连接量、低信令开销和低时延传输等需求。在基于压缩感知(CS)的MUD算法中,活跃用户数往往作为已知信息,而实际通信系统中很难准确估计。基于此,该文提出一种改进稀疏度自适应匹配的多用户算法(MSAMP-MUD)。该算法首先利用广义Dice系数匹配准则选择与残差最匹配的原子,更新用户支撑集;当残差能量接近噪声能量时,终止迭代,从而获得最终支持集;否则,采取上述准则更新用户支撑集,提高支撑集中活跃用户数估计精度。在迭代过程中,根据最近两次残差能量之比,选取不同的迭代步长,以降低检测迭代次数。仿真结果表明,所提算法与传统基于CS的MUD算法相比,误码率降低约9%,迭代次数减少约10%。     

利用频率掩蔽法设计均匀和非均匀余弦调制滤波器组

给出了一种均匀非均匀余弦调制滤波器组的设计方法。采用了频率掩蔽法设计原型滤波器,利用迭代算法优化原型滤波器的脉冲响应系数,目标函数表示为脉冲响应系数的四次方,非均匀余弦调制滤波器组通过合并均匀余弦调制滤波器组的相邻的滤波器得到,这种方法简单有效。最后的Matlab实例仿真验证了此算法的有效性。     

基于EM-SBL迭代的稀疏SIMO信道频域盲均衡算法

针对单输入多输出系统下稀疏信道均衡问题,提出了一种新的基于最大似然准则的频域迭代均衡算法.首先将多天线联合均衡问题建模为非完整观测数据集下频域信号序列的最大似然估计问题,利用期望最大化算法进行近似迭代求解,最终得到各个单频信号加权求和形式的均衡输出表达式.在每次迭代过程中,算法依次完成均衡输出的更新和信道参数联合条件后验分布的更新.考虑到信道固有的稀疏特性,在求解信道参数联合条件后验时,引入具有稀疏促进作用的先验分布对信道系数加以约束,使用稀疏贝叶斯学习迭代求解信道参数联合条件后验.仿真结果表明,本文算法具有较好的收敛特性和稳态性能,在中高信噪比条件下可以获得接近信道已知条件下的稳态系统误符号率性能.     

一种新的双基地MIMO雷达收发角和多普勒频率联合估计方法

该文提出了一种新的双基地MIMO雷达收发角和多普勒频率联合估计方法。利用匹配滤波器输出的信号特点,采用最小二乘法从信号中分别提取出多目标的多普勒矢量、接收角矢量和发射角矢量,然后采用迭代算法对这3个矢量进行更新以完成收发角和多普勒频率的联合估计。该方法复杂度低,运算时间少,且能够实现自动配对。仿真结果表明,与基于PARAFAC的方法相比,该文方法更易获得高精度的收发角和多普勒频率估计,具有更高的可靠性。     

一种基于稀疏分解的窄带信号频率估计算法

针对窄带多分量信号频率估计问题,该文提出一种基于稀疏分解的频率估计算法,能够同时对多个窄带信号的频率进行估计。首先利用传统方法进行频率预估计,然后根据频率预估计的结果建立冗余字典,对信号进行稀疏表示,最后通过匹配追踪算法得到精确的频率估计。该算法极大地减小了字典的长度和稀疏分解的运算量,而且在迭代过程中利用了全局信息更新残差向量,估计结果更为精确,在低信噪比情况下性能也较为稳健。仿真结果验证该算法的有效性和正确性。     

基于非正交分解的频率估计算法

高分辨率频率估计是信号处理许多领域研究的热点,并有着广泛应用.通过引入非正交分解思想,提出了一种基于稀疏分解的高分辨率频率估计新算法.由于原子库的冗余性,稀疏分解得到信号更加灵活的表示.算法中稀疏分解利用Matehing Pursuit(MP)算法实现,由分解原子的参数,可得到频率估计.与传统基于DFT频率估计方法相比,新算法可以获得更高的频率分辨率.尤其在低信噪比环境下,算法有明显优势,并且新算法在欠采样情况下仍然适用.理论分析及计算机仿真结果证实了算法的有效性.     

三维目标曲线SAR成像的降维搜索算法

曲线合成孔径雷达(CurviLinear Synthetic Aperture Radar,CLSAR)利用雷达平台的单条曲线轨迹就可形成三维成像所需的曲线合成孔径。由于CLSAR采集的数据在三维频率空间是稀疏的,简单地采用非参数化方法所获得的图像几乎无法使用,所以有价值的目标三维像必须采用参数化方法来获得。该文提出一种新的适用于CLSAR的目标三维成像算法。该算法巧妙地利用了接收数据中距离方向与垂直距离方向参数间的弱耦合性,将高维优化问题解耦为低维优化问题,并顺序地估计出相应参数,最后采用一个迭代过程进行参数求精。仿真实验表明,新算法是一种适用于CLSAR的有效的目标三维成像算法。     

NOMA系统基于改进的近似消息传递算法的联合信道估计与多用户检测

针对免调度非正交多址接入(Non-Orthogonal Multiple Access,NOMA)系统,多用户传输场景的上行信道估计(Channel Estimation,CE)与活动用户检测(Active User Detection,AUD)问题可被建模为压缩感知重建问题。本文提出了一种改进的近似消息传递(Approximate Message Passing,AMP)算法——阈值自适应-加约束重加权-近似消息传递(Threshold Adaptive Constrained Reweighted Approximate Message Passing,TA-CR-AMP)算法来联合解决CE和AUD问题。该算法在合适的迭代终止准则下,对AMP算法加入更新稀疏信号稀疏结构的操作,在此基础上对算法引入加约束的重加权,并令阈值自适应变化。仿真结果表明,与AMP算法相比,本文提出的算法以较低的复杂度获得了更加优越的信道估计和活跃用户检测性能。另外,本算法获得了与更加复杂的期望最大-贝叶斯AMP(Expectation Maximization Bayesian Approximate Message Passing, EM-B-AMP)算法相近的性能。      

MTI雷达中基于子空间分解的杂波抑制算法

最优MTI滤波器的权值与目标的多普勒频率有关，传统的MTI雷达需要利用一组能覆盖目标多普勒频率范围的并行滤波器组来实现最佳滤波。本文提出了一种算法，通过子空间分解法直接求取滤波器的权系数，由于无需多普勒滤波器组，系统的复杂度得到了降低。为了适应杂波的变化，本文还推导了该算法基于幂迭代的自适应过程。仿真结果表明：在不同的杂波环境下，该算法均有良好的MTI改善因子。     

结构性稀疏阵列多信道联合估计

多信道估计时,如果利用信道的稀疏性和多信道的相关性,可以提高信道估计性能。本文利用阵列信道的结构性稀疏特性,提出了一种多路分组稀疏LMS算法(Group Sparse LMS,GS-LMS)。该算法将多路信道作为一个整体同时进行自适应信道估计,通过引入Ι_2,1范数,将结构性稀疏先验引入到稀疏LMS算法的代价函数中,导出新的滤波器权系数更新公式。仿真结果表明了在不同信道条件下,本文算法的稳态误差性能明显优于若干现有的稀疏LMS算法。      

基于sigmoid函数的Volterra自适应有源噪声对消器

该文介绍了一种新颖的非线性自适应有源噪声对消器基于sigmoid函数的Volterra自适应有源噪声对消器,并采用输入信号和瞬时误差归一化的LMS自适应算法调整其系数。这种基于sigmoid函数的Volterra自适应有源噪声对消器具有参数少和便于实现的模快化结构等优点。仿真结果表明:这种基于sigmoid函数的Volterra自适应有源噪声对消系统具有良好的抗噪声性能。     

Adaptive subsample delay estimation using windowed correlation

An on-line delay estimation algorithm based upon the windowed average magnitude difference function (AMDF) that is suitable for determining the subsample delay between two noisy signals is introduced. The new estimator uses the AMDF to directly update the delay estimate, which is in turn used to adapt the coefficients of a fractional delay filter (FDF). The new estimator consists predominately of addition and subtraction operations and outperforms conventional FDF based estimators in low signal-to-noise ratio situations.     

Adaptive algorithms for joint time delay estimation and IIRfiltering

A new class of gradient-based algorithms for joint adaptive time delay estimation and IIR filtering for the recursive identification of systems with long pure delays is introduced. The algorithms update an adaptive delay estimate and the coefficients of an IIR filter using familiar gradient-descent methods. In addition, interpolation of the input sequence is used to obtain delays that are not constrained to integer numbers of samples. Three algorithms using a direct form filter parametrization and one using a normalized lattice filter are derived, and computer simulations are used to demonstrate their convergence and tracking properties     

一种Farrow结构数字延时滤波器的设计

普通数字延时滤波器虽然结构简单,但系数计算过程复杂,在延时参数快速变化时,系数更新速度无法满足实时性要求,在工程应用上受限制。采用Farrow结构数字延时滤波器能够更加灵活高效地进行分数延时滤波,延时参数改变时,无需重新计算滤波器系数,更容易在现场可编程门阵列(FPGA)上实现。介绍了一种Farrow结构数字延时滤波器,提出采用基于对称结构的滤波器系数求解方法,并经过加权优化,获得最终Farrow滤波器的系数。系数计算过程中,通过对设计所得Farrow滤波器延时精度和误差的分析,调整加权因子的取值和滤波器阶数,进而提高延时精度。计算机仿真结果证明了加权对称系数求解Farrow滤波器系数方法的有效性和实用性。     

Partial update and sparse adaptive filters

There has been increasing research interest in developing adaptive filters with partial update (PU) and adaptive filters for sparse impulse responses. On the basis of maximum a posteriori (MAP) estimation, new adaptive filters are developed by determining the update when a new set of training data is received. The MAP estimation formulation permits the study of a number of different prior distributions which naturally incorporate the sparse property of the filter coefficients. First, the Gaussian prior is studied, and a new adaptive filter with PU is proposed. A theoretical basis for an existing PU adaptive filter is also studied. Then new adaptive filters that directly exploit the sparsity of the filter are developed by using the scale mixture Gaussian distribution as the prior. Two new algorithms based on the Student's-t and power-exponential distributions are presented. The minorisation-maximisation algorithm is employed as an optimisation tool. Simulation results show that the learning performance of the proposed algorithms is better than or similar to that of some recently published algorithms     

Parallel genetic algorithm based unsupervised scheme for extractionof power frequency signals in the steel industry

A steel industry has different types of loads, and so the incoming supply voltage of some units becomes distorted thus affecting those systems that depend on a distortionless supply. A novel unsupervised scheme named the recursive hybrid parallel genetic algorithm based line enhancer (RHPGABLE) scheme is proposed, to track the desired power frequency signal from the corrupted one. The RHPGABLE scheme is based on a proposed new crossover operator known as the generalised crossover (GC) operator. The delay and the filter coefficients are estimated recursively to yield optimal solutions. In the recursion of the proposed RHPGABLE algorithm, a parallel genetic algorithm (PGA) based on a coarse-grained approach is employed to estimate the delay, while the filter coefficients are estimated by PGA and a least mean squares (LMS) algorithm. RHPGABLE is an unsupervised scheme in the sense that no a priori knowledge of delay or filter coefficients and the associated training signal component is assumed to be available. The proposed scheme has been tested successfully on both synthetic data and data obtained from the Steel Melting Shop of Rourkela Steel Plant, Orissa, India     

A Block-Floating-Point-Based Realization of the Block LMS Algorithm

An efficient scheme is proposed for implementing the block LMS (BLMS) algorithm in a block-floating-point framework that permits processing of data over a wide dynamic range at a processor complexity and cost as low as that of a fixed-point processor. The proposed scheme adopts appropriate formats for representing the filter coefficients and the data. Using these and a new upper bound on the step size, update relations for the filter weight mantissas and exponent are developed, taking care so that neither overflow occurs, nor are quantities which are already very small multiplied directly. It is further shown how the mantissas of the filter coefficients and also the filter output can be evaluated faster by suitably modifying the approach of the fast BLMS algorithm.     

Information Theoretic Approach to Complexity Reduction of FIR Filter Design

This paper presents a new paradigm of design methodology to reduce the complexity of application-specific finite-impulse response (FIR) digital filters. A new adder graph data structure called the multiroot binary partition graph (MBPG) is proposed for the formulation of the multiple constant multiplication problem of FIR filter design. The set of coefficients in any fixed point representation is partitioned into symbols so that common subexpression identification and elimination become congruent to information parsing for data compression. A minimum number of different pairs or groups of symbols and residues can be used to code a set of coefficients based on their probability and conditional probability of occurrence. This ingenious concept enables the notion of entropy to be applied as a quantitative measure to evaluate the coding density of different compositions of symbols towards a set of coefficients. The minimal vertex set MBPG synthesized by our proposed information theoretic approach results in direct correspondences between the vertices and adders, and edges and physical interconnections. Unlike the common subexpression elimination algorithms based on other graph data structures, the symbol-level information carried in each vertex and the graph isomorphism of MBPG promise further fine-grain optimization in a reduced search space. One such optimization that has been exploited in this paper is the shift-inclusive computation reordering to minimize the width of every two's complement adder to further reduce the implementation cost and the critical path delay of the filter. Experiment results show that the proposed algorithm can contribute up to 19.30% reductions in logic complexity and up to 61.03% reduction in critical path delay over other minimization methods.      

双正交小波提升系数的递推算法与实现

根据双正交小波提升格式的特点,为了得到快速提升小波变换的系数,提出求解提升系数的递推算法。该方法基于前向小波变换的预测和更新过程的递推式,与给定双正交小波滤波器比较系数,求得小波提升系数和尺度系数。实例证明,无论是先预测后更新的提升格式,还是先更新后预测的提升格式,均可用此法求解提升系数。在Matlab7.0平台上,用递推算法编程实现db5.3小波转换成提升格式,完成图像的三级分解。     

Partial-Update L∞ -Norm Based Algorithms

The computational complexity of an adaptive filtering algorithm increases with increasing the filter tap length and therefore, the use of such a filter can become prohibitive for certain applications, especially for real-time implementation. In this paper, we develop low-complexity adaptive filtering algorithms by incorporating the concept of partial updating of the filter coefficients into the technique of finding the gradient vector in the hyperplane based on the L_infin-norm criterion. Two specific partial update algorithms based on the sequential and M-Max coefficient updating are proposed. The statistical analyses of the two algorithms are carried out, and evolution equations for the mean and mean-square of the filter coefficient misalignment as well as the stability bounds on the step size are obtained. It is shown that the proposed partial update algorithm employing the M-Max coefficient updating can achieve a convergence rate that is closest to that of the full update algorithm. Finally, simulations are carried out to validate the theoretical results and study the convergence rate of the proposed algorithms