基于子带分解的自适应滤波

本文对基于子带分解的自适应滤波做了研究，给出子带分解下的包含子带间滤波的最优维纳解和ＬＭＳ算法，并分析了其收敛性能和计算复杂度，与传统的ＬＭＳ算法相比，基于子带分解的自适应滤波具有更好的性能，计算机模拟结果也体现了这一点。     

Nonuniform Subband Adaptive Filtering With Critical Sampling

Adaptive subband structures have been proposed with the objective of increasing the convergence speed and/or reducing the computational complexity of conventional adaptive algorithms, mainly for applications that require a large number of adaptive coefficients. In this paper, we present a nonuniform subband structure with critical sampling, which is capable of modeling an arbitrary finite-impulse response (FIR) system with reduced aliasing. A least-mean-square (LMS)-type adaptation algorithm with normalized step sizes, which works at the lowest downsampling rate and minimizes the average of the subband squared errors, is derived for the proposed structure. A convergence analysis of the adaptation algorithm is presented, from which its convergence rate and steady-state mean-square error can be estimated.     

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.     

A Subband Adaptive Filtering Algorithm Employing Dynamic Selection of Subband Filters

In 2007, Wang and Shyu proposed a scalable secret image sharing scheme in which three sharing styles, namely the multisecret, priority, and progressive modes, are designed to encode an image with diverse revealing effects. Their scheme is a (2, n ) sharing method where each generated shadow image is half size of the original image. This letter proposes a general (t,n), 2 ? t ? n, scalable secret image sharing scheme with the same revealing effects. Notably, the size of each generated shadow image is only (2n-t)/n ² times of the original image. The smaller size of the generated shadow images makes their transmission and storage more efficient.     

A Low-Complexity Delayless Selective Subband Adaptive Filtering Algorithm

Adaptive filters of significant order, requiring high computational complexity, are necessary in many applications such as acoustic echo cancellation and wideband active noise control. Successful approaches to lessen the computational complexity of such filters are subband methods, and partial updating schemes where only a part of the filter is updated at each instant. To avoid the time delay introduced by the subband-splitting, delayless structures which reconstructs a fullband filter, producing delayless output, from the adaptive subband filters have been proposed. This paper proposes a delayless subband adaptive filter partial updating scheme, where the general idea is to only update the most misadjusted subband filter(s). Analysis in terms of mean square deviation is presented and shows that the fullband filter convergence speed is significantly increased, even for flat spectrum signals, as compared to traditional periodic subband filter update with the same computational complexity. Echo cancellation simulations with an artificial system to verify the analysis, using both flat spectrum signals and speech, is also presented, as well as offline calculations using signals from a real system.      

DSSS通信中基于快速更新子带自适应滤波的窄带干扰抑制

本文面向直接序列扩频(DSSS)通信中的窄带干扰抑制,将分块更新子带自适应滤波的高频谱分隔特性和直接变换自适应滤波的逐点更新特性结合起来,提出了一种快速更新子带自适应(FRSAF)算法,给出了算法的迭代因子收敛界和快速实现结构。理论分析表明：该算法收敛迅速、迭代稳健,其性能明显优于经典子带自适应滤波算法和DCT/DFT-LMS算法,应用于DSSS通信可以得到优良的干扰抑制效果。仿真结果验证了上述结论。     

Energy-efficient Hardware Architecture and VLSI Implementation of a Polyphase Channelizer with Applications to Subband Adaptive Filtering

Polyphase channelizer is an important component of subband adaptive filtering systems. This paper presents an energy-efficient hardware architecture and VLSI implementation of polyphase channelizer, integrating algorithmic, architectural and circuit level design techniques. At algorithm level, low complexity polyphase channelizer architecture is derived using multirate signal processing approach. To reduce the computational complexity in polyphase filters, computation sharing differential coefficient (CSDC) method is effectively used as an architectural level technique. The main idea of CSDC is to combine the strength of augmented differential coefficient method and subexpression sharing. Efficient circuit-level techniques: low power commutator implementation, dual-VDD scheme and novel level-converting flip-flop (LCFF), are also used to further reduce the power dissipation. The proposed polyphase channelizer consumes 352 mW power with throughput of 480 million samples per second (MSPS). A test chip has been fabricated in 0.18 μm CMOS technology and its functionality is verified. Chip measurement results show that the dual-VDD implementation achieves a total power saving of 2.7 X.     

自适应滤波用于失真补偿的探讨

提出用自适应滤波原理补偿系统失真,通过在系统前面插入一非线性自适应滤波器对系统进行逆模拟来实现减少系统失真的目的。将RLS算法用于伏特拉自适应滤波器,通过仿真得到了一些结果。     

Dual Sign Algorithm for Adaptive Filtering

A new algorithm, which is a variant of the sign algorithm, is proposed for the adaptive adjustment of an FIR digital filter with an aim of improving the original convergence characteristics, yet retaining the advantage of hardware simplicity. Based on a recently proposed theory for the sign algorithm, a practical design method is derived for the new algorithm, and it is shown by computer simulation that the new algorithm in fact performs significantly better than the original algorithm.     

一种变正则化矩阵的改进多带结构子带自适应滤波算法

定正则化因子的改进多带结构子带自适应滤波(IMSAF)算法在取得收敛速度快和稳态失调误差小之间存在冲突.根据系统噪声抵消原理,设定子带后验误差功率等于子带噪声功率,本文提出了变正则化矩阵的IMSAF算法来解决这一问题.仿真结果证明,所提算法可以同时达到收敛速度快、稳态失调误差小以及追踪速度快等优势.     

最优多步长自适应滤波

本文研究具有最优下降步长序列的自适应滤波。最优一词此处系指：①对于给定的均方误差的初值，使均方误差经给走次数学习后达到最小；②对于均方误差的给定初值和终值，经过最少次数的学习，均方误差由初值下降至终值。对于①②两种不同意义的最优，我们采用动态规划和对数比例分割方法来求各自的最优步长序列。同时提供一组仿真结果。     

一种自适应电子调速器的设计

文章给出了一种自适应电子调速器的基本结构;论述了调速器的工作原理、电路的设计和软件编程方法。进口电子调速器所用的控制算法只限于传统PID,控制效果不理想。文章给出的电子调速器采用先进的自适应控制算法,优化了电路的设计,提高了控制精度,实际应用情况良好。     

A Modular Analog NLMS Structure for Adaptive Filtering

This paper proposes a modular Analog Adaptive filter (AAF) algorithm, in which the coefficient adaptation is carried out by using a time varying step size analog normalized LMS (NLMS) algorithm, which is implemented as an external analog structure. The proposed time varying step size is estimated by using the first element of the crosscorrelation vector between the output error and reference signal, and the first element of the crosscorrelation vector between the output error and the adaptive filter output signal, respectively. Proposed algorithm reduces distortion when additive noise power increases or DC offsets are present, without significatively decreasing the convergence rate nor increasing the complexity of the conventional NLMS algorithms. Simulation results show that proposed algorithm improves the performance of AAF when DC offsets are present. The proposed VLSI structure for the time varying step size normalized NLMS algorithm has, potentially, a very small size and faster convergence rates than its digital counterparts. It is suitable for general purpose applications or oriented filtering solution such as echo cancellation and equalization in cellular telephony in which high performance, low power consumption, fast convergence rates and small size adaptive digital filters (ADF) are required. The convergence performance of analog adaptive filters using integrators like first order low pass filter is analyzed.     

用于扩频信号提取的盲自适应频移滤波器

基于扩频信号的周期平稳特性,提出利用盲自适应频移滤波器(BA-FRESH)提取混杂在同类扩频干扰和白噪声中所需信号的方法。该滤波器不需要预先知道训练信号的统计特性。仿真实验证明,在有用信号和干扰的不同频谱重叠系数下,BA-FRESH滤波器均能够提高输出信噪比,在一定程度上提高扩频系统干扰容限,有利于进一步的信号处理。     

Multiple Adaptive Frequency Filtering for OFDM Channel Estimation

In orthogonal frequency division multiplexing (OFDM) systems using pilots, channel estimation is performed on pilots and then interpolated over the time and the frequency axis. As time-interpolated estimates have a different mean square error than pilot estimates we propose the use of multiple adaptive filters for exploiting the frequency correlation of the channel. Each filter operates on a different configuration of pilot and time-interpolated estimates and is adapted independently from the other filters. As a simpler suboptimal solution we consider also the use of a single filter, whose adaptation however takes into account the reliability of the various estimates. Performance results are reported with reference to the DVB-T and DVB-T2 standards showing that the proposed technique performs similar or better than existing approaches at a much lower complexity.      

降秩盲自适应FRESH滤波器的研究

提出了基于最小均方误差准则(MMSE)的盲自适应FRESH滤波器;给出了基于互谱准则(CSP)、主分量分析(PCA)和多级维纳滤波器(MSWF)等方法的降秩处理,并对不同的方法进行了性能比较。计算机仿真结果表明,这种降秩盲FRESH滤波器能够有效地分离或提取谱重叠信号。     

逐级最优自适应滤波算法

本文提出了应用整体最小二乘技术的递推自适应滤波算法。论证说明了算法的收敛特性。从最小二乘到整体最小二乘的逐级优化使滤波结构具有灵活的配置。通过高级EDA软件SPW的模拟，验证了算法在自适应杂波抑制中的有效性．整个算法具有相对高性能和易实现的特点。     

位移估计自适应滤波算法

基于“当前”统计模型的基础上 ,利用位移预测估计与实时位移估计间的偏差进行自适应方差调整 ,提出了一种新的自适应滤波算法———位移估计自适应跟踪算法 (AdaptiveFilteringAlgorithmofDistanceEstimation简记为ADE)。大量仿真结果表明 ,采用ADE算法既保持了对机动目标的跟踪性能 ,又显著提高了对弱机动目标及非机动目标的跟踪精度     

进化规划用于自适应IIR滤波器的优化设计

针对自适应ＩＲ滤波器（ＡＩＲＦ）潜在的不稳定性和性能指标函数容易陷入局部极小点而导致性能下降等问题，本文将进化规划用于直接、并联、级联和格型结构的ＡＩＩＲＦ的优化设计。基于进化规划的自适应滤波算法不依赖于梯度信息，能够有效地实现ＡＩＩＲＦ参数的全局寻优。大量的仿真实验结果表明不同结构的ＡＩＲＦ用进化规划进行参数寻优，不仅解决了自适应滤波器性能指标函数容易陷入局部极小点的问题，亦解决了ＡＩＲＦ的稳定性问题。     

模糊自适应滤波方法在相对导航系统中的应用

编队飞行中需要确定主机和从机之间的相对位置关系,就是要解决导航定位问题。针对无人机在编队飞行过程中由于机动性较大,惯性元器件测量容易出现偏差,进而影响系统的运动状态方程的情况,或者是在系统噪声与观测噪声的统计特性不能够准确得到的情况,提出了一种新的模糊自适应滤波方法。根据实时得到的量测新息的实际方差与理论方差的差值和量测新息的均值,按照判定条件选择适合的滤波方法,然后由设计的模糊推理系统在线实时调整系统噪声和量测噪声矩阵,或是调整状态误差协方差阵即强跟踪滤波,使无人机编队飞行即使在恶劣的环境下依然保持确定的队形不变。仿真结果表明,该算法具有较好的自适应效果。