双原型离散傅里叶变换调制滤波器组的快速设计方法

针对大规模的离散傅里叶变换(DFT)调制滤波器组设计算法复杂度高的问题,该文提出一种基于无约束优化的快速设计算法。该算法将两个原型滤波器的设计问题归结为一个无约束优化问题,将滤波器组的传递失真,混叠失真以及原型滤波器阻带能量的加权和作为目标函数。进而,采用双迭代机制来求解该优化问题。在单步迭代中,运用矩阵求逆的等效条件和Toeplitz矩阵求逆的快速算法,显著地降低了迭代的计算代价。仿真对比表明,与已有的设计算法相比,新算法计算代价低 ,可以得到整体性能更好的滤波器组,并且可以快速设计大规模的滤波器组。     

基于线性迭代的DFT调制滤波器组的设计算法

该文研究了单原型的DFT调制滤波器组的设计方法。在该方法中,滤波器组的设计问题被归结为一个无约束的优化问题,其目标函数为滤波器组的传递失真、混叠失真和原型滤波器的阻带能量的加权和。结合线性化方法,原型滤波器通过迭代求解。在单步迭代中,原型滤波器的系数通过解析式求解得到。仿真表明,与传统的设计算法相比,本文方法设计所得的DFT调制滤波器组重构误差减小了约40dB,阻带衰减提高了约2dB。并且新算法的计算复杂度明显低于传统算法。     

一种设计近似完全重构非均匀余弦调制滤波器组的新算法

该文提出了一种设计近似完全重构非均匀余弦调制滤波器组的新算法。针对现有合并算法中非均匀滤波器组性能无法直接控制优化的缺点,新算法把非均匀滤波器组的设计问题归纳为一个关于原型滤波器的无约束优化问题,其中目标函数是非均匀滤波器组传递失真与原型滤波器阻带能量的加权和,最后利用线性迭代算法求解该优化问题。理论分析和数值实验表明,新算法获得的非均匀余弦调制滤波器组比现有算法设计的滤波器组整体性能更佳。     

用于宽带接收的近似完全重构滤波器组的改进设计

本文提出了一种可用于宽带接收信号近似完全重构的余弦调制滤波器组。首先将滤波器组的设计转化为原型滤波器的设计,利用代价函数保证其通带平稳和阻带衰减,针对已有算法中幅度失真较大的问题,文中利用代价函数保证过渡带具有平方根余弦滚降特性,并且为了避免加强目标函数约束所带来的误差,对通带截止频率进行调整使得原型滤波器的3dB通带截止频率等于理想滤波器的通带截止频率。文中还推导了以滤波器系数为变量,代价函数的闭合解表达式,并针对滤波器设计复杂度高的问题,采用了基于迭代求解原型滤波器的方法。实验仿真表明,该方法得到的调制滤波器组较已有设计方法具有更好的性能。      

基于频域插值的信道化原型滤波器设计

针对信道化原型滤波器设计复杂度较高的问题,提出一种满足线性相位要求的原型滤波器频域插值设计算法。该算法采用无约束迭代优化办法获得了阻带衰减较高的低阶滤波器模型,然后根据所推导的频域插值模型构建满足线性相位特性的原型滤波器频谱,最后通过快速傅里叶逆变换获得了满足信道化滤波器组完全重构特性要求的原型滤波器。所提设计算法将频域插值与迭代优化相结合,有效地解决了设计高阻带衰减的原型滤波器时待优化参数数目较大的问题,并且不会产生镜像频谱,省去了镜像抑制滤波器的设计。仿真结果表明,所提算法相比传统的时域插值设计算法具有显著的性能改善,并且降低了设计复杂度。     

基于LDL算法的大规模矩阵求逆加速器设计及其FPGA实现

矩阵求逆是工程计算中的基本问题，在大规模MIMO系统、阵列信号处理以及图像信号处理等应用中，大规模矩阵求逆的处理速度对系统性能至关重要，但传统矩阵求逆方法运算复杂度高、并行性低且消耗大量存储空间，不利于硬件加速。针对大规模矩阵求逆硬件加速问题，文中研究了基于LDL分解的矩阵求逆算法，并提出了一种基于该算法的大规模矩阵求逆加速架构。利用LDL分解后三角矩阵对角线元素全为1的特点，对矩阵进行分块迭代设计，减少了求逆运算的计算量，提高了计算速度。文中基于Xilinx Virtex7 FPGA设计实现了该加速器，实验结果表明，在128阶矩阵下，吞吐量达105.2 Inv·s^-1,最高时钟频率达200 MHz。与现有矩阵求逆加速方案相比，该设计占用的硬件资源更少，且具有更高的性能。     

一种快速抗间歇采样转发干扰波形和滤波器联合设计算法

该文研究了一种快速的抗间歇采样转发干扰波形和滤波器联合设计方法.基于罚函数和帕累托最优化原理给出了联合设计模型的优化数学模型.推导优化波形和滤波器过程中矩阵迹的解析表达式,有效降低了算法的计算复杂度.提出了一种基于平方迭代加速方法,解决了主分量最小化方法目标函数近似造成的算法收敛速度降低问题,进一步加快了算法运行速度....     

基于动态交替投影的失配滤波器快速设计算法

雷达脉冲压缩处理中,通常需要使用匹配滤波器以获得最优的输出信噪比。然而,对于常用雷达波形,其匹配滤波器输出距离旁瓣较高,易产生目标遮蔽效应,而通过优化设计失配滤波器可以在牺牲一定输出信噪比的条件下有效抑制距离维旁瓣。另外,在实际应用中一般需要对系统失真进行周期性标校,进而根据经过系统失真后的雷达波形进行失配滤波器设计,所以需要高效率的失配滤波器设计算法。然而,现有的失配滤波器设计算法通常涉及矩阵求逆等复杂计算过程,其计算复杂度高且不利于硬件实现。本文提出一种基于动态交替投影的失配滤波器快速设计算法,可实现在最小峰值旁瓣值、积分旁瓣值、加权积分旁瓣值等准则下的距离维旁瓣抑制,且信噪比损失、主瓣展宽可控。本算法适用于多种脉冲压缩波形,算法过程仅需用到简单计算及快速傅里叶变换,适用于硬件的高效率设计与实现。      

大规模MIMO系统混合线性迭代信号检测算法

针对大规模多输入多输出(MIMO)系统中线性最小均方误差(MMSE)信号检测算法复杂的高维矩阵求逆难以用于实际工程的问题,文章基于矩阵分块思想并结合Neumann级数展开算法,提出了一种低复杂度的混合迭代算法.利用MMSE算法中加权矩阵逆矩阵的Neumann级数二阶展开作为其分块矩阵求逆的迭代初始值,可以有效提高算法收...     

低复杂度大规模MIMO信号检测算法研究

原有的信号检测算法ZF(迫零)和MMSE(最小均方误差)不可避免地要对矩阵求逆,但是大规模MIMO中天线数量可能是上百根,复杂度随着天线数量呈指数增加,所以在大规模MIMO系统中直接对矩阵求逆任何机器都无法承担的。因此在不损失性能的前提下,提出一种通过迭代计算对矩阵近似求逆的方法来取代传统的矩阵精确求逆,从而起到算法复杂度降低的效果。     

Theory and design of signal-adapted FIR paraunitary filter banks

We study the design of signal-adapted FIR paraunitary filter banks, using energy compaction as the adaptation criterion. We present some important properties that globally optimal solutions to this optimization problem satisfy. In particular, we show that the optimal filters in the first channel of the filter bank are spectral factors of the solution to a linear semi-infinite programming (SIP) problem. The remaining filters are related to the first through a matrix eigenvector decomposition. We discuss uniqueness and sensitivity issues. The SIP problem is solved using a discretization method and a standard simplex algorithm. We also show how regularity constraints may be incorporated into the design problem to obtain globally optimal (in the energy compaction sense) filter banks with specified regularity. We also consider a problem in which the polyphase matrix implementation of the filter bank is constrained to be DCT based. Such constraints may also be incorporated into our optimization algorithm; therefore, we are able to obtain globally optimal filter banks subject to regularity and/or computational complexity constraints. Numerous experiments are presented to illustrate the main features that distinguish adapted and nonadapted filters, as well as the effects of the various constraints. The conjecture that energy compaction and coding gain optimization are equivalent design criteria is shown not to hold for FIR filter banks     

二进制猫群算法在大规模MIMO天线选择中的应用

在多输入多输出(MIMO)系统中,天线选择技术平衡了系统的性能和硬件开销,但大规模MI-MO系统收发端天线选择复杂度问题一直没有得到很好的解决.基于信道容量最大化的准则,采用两个二进制编码字符串分别表示发射端和接收端天线被选择的状态,提出将二进制猫群算法(BCSO)应用于多天线选择中,以MIMO系统信道容量公式作为猫群的适应度函数,将收发端天线选择问题转化为猫群的位置寻优过程.建立了基于BCSO的天线选择模型,给出了算法的实现步骤.仿真结果表明所提算法较之于基于矩阵简化的方法、粒子优化算法具有更好的收敛性和较低的计算复杂度,选择后的系统信道容量接近于最优算法,非常适用于联合收发端天线选择的大规模MIMO系统中.     

High-quality audio compression using an adaptive wavelet packetdecomposition and psychoacoustic modeling

This paper presents a technique to incorporate psychoacoustic models into an adaptive wavelet packet scheme to achieve perceptually transparent compression of high-quality (34.1 kHz) audio signals at about 45 kb/s. The filter bank structure adapts according to psychoacoustic criteria and according to the computational complexity that is available at the decoder. This permits software implementations that can perform according to the computational power available in order to achieve real time coding/decoding. The bit allocation scheme is an adapted zero-tree algorithm that also takes input from the psychoacoustic model. The measure of performance is a quantity called subband perceptual rate, which the filter bank structure adapts to approach the perceptual entropy (PE) as closely as possible. In addition, this method is also amenable to progressive transmission, that is, it can achieve the best quality of reconstruction possible considering the size of the bit stream available at the encoder. The result is a variable-rate compression scheme for high-quality audio that takes into account the allowed computational complexity, the available bit-budget, and the psychoacoustic criteria for transparent coding. This paper thus provides a novel scheme to marry the results in wavelet packets and perceptual coding to construct an algorithm that is well suited to high-quality audio transfer for Internet and storage applications     

Adaptive reduced-rank LCMV beamforming algorithms based on joint iterative optimization of filters: Design and analysis

This paper presents reduced-rank linearly constrained minimum variance (LCMV) beamforming algorithms based on joint iterative optimization of filters. The proposed reduced-rank scheme is based on a constrained joint iterative optimization of filters according to the minimum variance criterion. The proposed optimization procedure adjusts the parameters of a projection matrix and an adaptive reduced-rank filter that operates at the output of the bank of filters. We describe LCMV expressions for the design of the projection matrix and the reduced-rank filter. We then describe stochastic gradient and develop recursive least-squares adaptive algorithms for their efficient implementation along with automatic rank selection techniques. An analysis of the stability and the convergence properties of the proposed algorithms is presented and semi-analytical expressions are derived for predicting their mean squared error (MSE) performance. Simulations for a beamforming application show that the proposed scheme and algorithms outperform in convergence and tracking the existing full-rank and reduced-rank algorithms while requiring comparable complexity.     

线性完全重构的二维滤波器组的Groebner基设计

为设计线性完全重构的二维滤波器组,引用了计算代数中的Groebner基方法,根据线性相位条件和完全重构条件,分别设计出二维滤波器组的分析滤波器和综合滤波器的多相元矩阵,给出其参数化形式。根据小波构造理论,利用所设计的分析滤波器组构造出一个对称的纯二维小波。设计结果显示了Groebner基方法的有效性,设计方法更为简单。     

Iterative method for the design of DFT filter Bank

Multirate adaptive filters have numerous advantages such as low computational load, fast convergence, and parallelism in the adaptation. Drawbacks when using multirate processing are mainly related to aliasing and reconstruction effects. These effects can be minimized by introducing appropriate problem formulation and employing sophisticated optimization techniques. In this paper, we propose a formulation for the design of a filter bank which controls the distortion level for each frequency component directly and minimizes the inband aliasing and the residual aliasing between different subbands. The advantage of this problem formulation is that the distortion level can be weighted for each frequency depending on the particular practical application. A new iterative algorithm is proposed to optimize simultaneously over both the analysis and the synthesis filter banks. This algorithm is shown to have a unique solution for each iteration. For a fixed distortion level, the proposed algorithm yields a significant reduction in both the inband aliasing and the residual aliasing levels compared to existing methods applied to the numerical examples.     

Design of Oversampled Interleaved DFT Modulated Filter Bank Using 2Block Gauss-Seidel Method

In this paper, we present several new properties of the recently introduced interleaved DFT modulated filter bank and an efficient algorithm for designing the filter bank. The periodicity and symmetry properties of the overall transfer function and aliasing transfer functions are stated. Then the design of the filter bank is formulated into a constrained optimization problem that jointly minimizes the overall distortion and aliasing distortion subject to fixed bounds on the stopband energy, transition-band energy, and passband flatness of the prototype filters. The constrained optimization problem is solved by the 2block Gauss-Seidel method, which alternatively optimizes the analysis PF pair and the synthesis PF pair. Since the overall distortion and aliasing distortion are jointly minimized, the proposed algorithm can lead to filter banks with small reconstruction error, even when the filter banks behave with a low redundancy ratio and short PFs. The convergence of the proposed algorithm is proved. Numerical examples and comparisons with the existing method are included to demonstrate the performance of the proposed algorithm.