自适应滤波技术在扩频通信系统抗窄带干扰中的应用

自适应滤波技术在扩频通信窄带干扰抑制的应用中,需要一种快速收敛的自适应算法以便能跟踪快速变化的干扰信号。文章提出将基于LSL自适应算法的格型滤波器应用于直接序列扩频通信系统中。仿真结果表明,LSL自适应算法在线性预测中具有快速收敛特性,并且在收敛过程中具有数值稳定性,从而显著改善了系统的抗窄带干扰能力。     

DVB-T前向预测自适应信道估计算法的研究

提出了一种基于导频的前向预测自适应信道估计算法。该算法在最小平方误差(LS)算法的基础上采用2维插值方法估计出无线信道的频率响应。在时间维方向上,对当前符号的导频位置根据系统软判决信噪比和多普勒影响的估计进行自适应插值。在频域维方向上直接进行低通滤波。     

一种改进的变步长LMS算法

LMS算法由于简单而获得了广泛的应用,大量的深入研究不断地改善了它的性能。LMS算法存在收敛速度和稳态失调之间的固有冲突,变步长因子可以获得二者之间的有效平衡。对已有的一些变步长LMS自适应滤波算法进行了分析,在此基础上提出一种改进的变步长LMS算法,步长因子同时考虑了指数为预测误差的一次和二次幂的2项。算法在保持较快收敛速度的同时,获得更优的稳态预测误差。对比仿真实验证明了算法的优越性。     

自适应最小二乘格型算法在天线阵列中的应用

在天线阵列中对天线阵列信号分析的算法有LMS和RLS等，主要介绍了自适应最小二乘格型算法LSL对天线信号进行分析，并对算法过程展开介绍。     

不需双端检测的声回波对消算法研究

声回波对消中双端对讲情况下的近端话音对自适应算法有很大影响。为避免双端话音检测,在滤波型LMS算法基础上,用远端信号和误差输出信号的和代替远端信号去激励预测误差滤波器,降低近端话音的影响。另为进一步提高算法抗近端干扰的能力,做了变步长的改进,首先将步长反比于输出信号预测误差的短时功率,其次将步长正比于预测误差的互相关系数。实验表明,文中提出的两算法在近端话音出现时表现出较好的性能,其中第二种有更好的稳态失调。     

基于预测方式选择的可逆信息隐藏

为了提高信息隐藏算法的性能,提出一种基于预测 方式选择和直方图平移的灰度图像可逆信息隐藏算法。通过两种相对准确的预测方法对像素 进行预 测,对突变像素进行标记,标记像素不做任何处理。最后选择其中一种相对准确 的预测值方法作为最终预测方法,求出预测误差并利用预测误差扩展和直方图平 移技术进行数据嵌入。算法自适应地选择最佳预测方法,充分利用图像冗余,有 效提高嵌入容量(EC)。实验结果显示,本文算法充分利用邻近像素间的关联性 ,在保证EC的同时有效提高了峰值信噪比(PSNR)和算法的整体性能。     

基于双向预测的高光谱图像无损压缩

提出了一种基于双向预测的高光谱图像无损压缩算法。该算法首先采用自适应波段选择算法选出信息量较大的波段,然后利用聚类算法对这些波段的谱向矢量进行分类预处理。为了便于组织谱间预测过程,根据相邻波段相关性大小进行自适应波段分组,采用双向预测的方法去除谱间相关性。通过在参考波段和预测波段中定义三维上下文预测结构,在聚类结果的基础上,对各个像素分别训练最优的预测系数,从而实现当前波段的有效预测。对AVIRIS型高光谱图像的实验结果表明,该方法可获得较好的无损压缩性能。     

改进PVO预测的自适应选块可逆图像水印

为了解决预测误差方法在预测精度和图像不可感 知性上的不足,提出一种基于改进PVO的自适应选块可逆图像水印算 法。首先对原始图像进行分块,并利用子块内相关像素不平均值之间的梯度计算子块 噪声级;然后结合像素值排序预测 算子得到预测误差直方图;最后根据子块噪声级自适应地选择水印的嵌入位置,并采用本 文算法进行水印嵌入。本 算法可以根据原始图像的特性自适应选择水印嵌入的子块,提高了像素预测精度,减少了水 印嵌入产生的失真;提取水印信息 后,可以无失真地恢复原始图像。实验结果表明,本文针对噪声级判断以及PVO算法的改进 策略可以有效提升可逆图像水印 算法的性能,在相同的嵌入容量下,本算法嵌入水印后的图像峰值信噪比较相关文献平均提 高了1.3%左右,具有更好的不可感知性。     

机载SAR图像方位向几何畸变研究

由于大气干扰等原因,SAR平台很难按照理想航迹飞行,造成了SAR图像的散焦和几何畸变。这些几何畸变主要发生在方位向上,因此SAR图像方位向几何畸变校正对于SAR图像应用具有很重要的意义。本文分析了非理想轨迹对SAR图像方位向几何畸变的影响,对常见的视线误差进行了建模。根据距离建模方法,将视线误差建模为加速模型和正弦模型,并对正弦模型进行了重点分析,推导了它的正交多项式展开,得到了正弦变化模型下方位向几何畸变的预测表达式及校正相位。最后通过仿真试验和实测数据处理结果验证了文中算法的有效性。      

基于线性预测系数自适应前后向量化的可变速率MELP语音编码

该文基于LPC的自适应前后向量化技术,提出了一种可变速率的混合激励线性预测MELP语音编码算法。该算法中,采用当前语音帧(前向LPC)或前面某帧已合成语音帧(后向LPC)进行线性预测,当采用后向LPC时,只需传输时间序列编码,故减少了LPC系数的平均编码比特。计算机模拟表明,该算法与标准MELP算法合成的语音质量相当,但显著减少了LPC的传输带宽,从而明显降低了MELP平均编码速率。     

一种具有窄带干扰抑制能力的改进型LSL插值算法

本文提出了一种基于QR分解技术的最小二乘格形插值算法-QRD-LSL插值算法来抑制直扩系统中的窄带干扰.该算法利用直扩信号、噪声信号和窄带干扰信号数据样值间相关特性的差异,通过对直扩系统接收端数据样值序列进行插值运算来估计窄带干扰信号成分,从而达到抑制直扩系统中窄带干扰的目的.由于该算法引入了QR分解过程避免了对数据矩阵求逆运算,并引用动态范围较小的新变量参与阶递推运算,极大程度上提升了算法在稳态特征、收敛速度和计算复杂度等方面的性能.仿真结果表明:QRD-LSL插值算法较LSL插值算法和LSL预测算法至少可获得4dB的处理增益,且计算量与滤波阶数呈线性关系.     

Efficient implementation of error-feedback LSL algorithm

An error feedback LSL algorithm is presented which has a reduced computational complexity and makes use of both a priori and a posteriori prediction errors. A simple numerical convention is proposed to assure numerical stability for poor persistent excitation. Simulation results illustrate that divisions can be implemented with reduced wordlength without leading to a significant decrease in the overall accuracy even in floating-point implementations. This fact allows the use of lookup tables for implementing divisions. Moreover, the proposed algorithm has an inherent parallelism that can be advantageously exploited for fast implementations     

Performance evaluation of distributed localization techniques for mobile underwater acoustic sensor networks

Underwater sensor networks (USN) are used for tough oceanographic missions where human operation is dangerous or impossible. In the common mobile USN architecture, sensor nodes freely float several meters below the surface and move with the force of currents. One of the significant challenges of the mobile USN is localization. In this paper, we compare the performance of three localization techniques; Dive and Rise Localization (DNRL), Proxy Localization (PL) and Large-Scale Localization (LSL). DNRL, PL and LSL are distributed, range-based localization schemes and they are suitable for large-scale, three dimensional, mobile USNs. Our simulations show that, DNRL and LSL can localize more than 90% of the underwater nodes with high accuracy while LSL has higher energy consumption and higher overhead than DNRL. The localization success and accuracy of PL is lower than the other techniques however it can localize underwater nodes faster when small number of beacons are employed.     

The Limit-Switched Loop: A Phase-Locked Loop for Burst Mode Operation

In a time-division or burst mode communications systems, synchronization must be achieved within a short preamble time at the beginning of each burst. When used in such a system, a conventional phase-locked loop (PLL) occasionally exhibits a prolonged phase acquisition transient. This effect, known as hangup, occurs at a large value of phase error and may cause loss of a data burst. The present paper investigates a modification of a PLL known as a limit-switched loop (LSL). The LSL offers faster phase acquisition and better hangup immunity than a PLL. The LSL estimates its position in the phase plane at the beginning of acquisition and, if hangup is about to occur, makes a phase correction so as to forestall its occurrence. The structure and properties of the LSL are investigated in this paper.     

基于链路选择学习算法的无设备目标定位

针对压缩感知框架下无设备目标定位(device-free localization,DFL)的字典失配问题,提出一种基于链路选择学习(link selection learning,LSL)算法的DFL方式.由于传统基于阴影模型的字典无法准确表达接收信号强度(received signal strength,RSS)变化与目标位置间的对应关系,本文算法首先在训练阶段通过字典学习的方式更新初始字典; 同时该算法在更新字典的过程中,仅选取置信区域中的链路参与计算,这样既加速了字典学习过程,提高了算法实时性,又滤除了野值链路的影响.室内外实验结果表明,该方法可以有效地消除现有基于阴影模型字典所带来的模型误差,提高定位精度,同时具有运算速度快的优点.     

A state-space approach to adaptive RLS filtering

Adaptive filtering algorithms fall into four main groups: recursive least squares (RLS) algorithms and the corresponding fast versions; QR- and inverse QR-least squares algorithms; least squares lattice (LSL) and QR decomposition-based least squares lattice (QRD-LSL) algorithms; and gradient-based algorithms such as the least-mean square (LMS) algorithm. Our purpose in this article is to present yet another approach, for the sake of achieving two important goals. The first one is to show how several different variants of the recursive least-squares algorithm can be directly related to the widely studied Kalman filtering problem of estimation and control. Our second important goal is to present all the different versions of the RLS algorithm in computationally convenient square-root forms: a prearray of numbers has to be triangularized by a rotation, or a sequence of elementary rotations, in order to yield a postarray of numbers. The quantities needed to form the next prearray can then be read off from the entries of the postarray, and the procedure can be repeated; the explicit forms of the rotation matrices are not needed in most cases     

基于时域特性的并行系统的采样误差估计算法

为了降低并行时间交替采样系统中通道失配误差的 硬性,利用两个标准斜波 信号的时域特性,对斜波信号多次采样,采样点减去偏置误差得到的无偏置采样值,从而求 解采样点的时间误差和增益误差联立方程,计算时间和增益误差。本文算法的采样点数和推导计算量较少,是一种快速而 精确的工程实用算法;并使用Matlab对本文算法进行模拟仿真证明其可行性,并通过Farrow 结构的滤波器对估计所得的通道失配误差进行校正验证,校正后的无杂散动态范围(SFDR,spurious free dynamic range)至少达到50dB。     

Improved macroblock level rate control for the spatially scalable extension of H.264/AVC

An improved rate control algorithm, designed for scalable video coders incorporating interlayer prediction, is proposed. Firstly, a Rate Distortion (RD) model for interlayer prediction involving the spatial enhancement layers is devised. An optimised Mean Absolute Difference (MAD) prediction model for the spatial enhancement layers that considers both the MAD from the spatial base layer in the same frame and the MAD from the corresponding macroblock in previous frames is also proposed. Simulation results show that the resulting algorithm produces accurate rate control with an average bit rate error of less than 0.26%. Compared with the JVT-W043 default rate control algorithm of the JSVM, the proposed algorithm improves the average PSNR by up to 0.53 dB or reduces the bit rate by an average of 10.95%. Furthermore, the proposed algorithm can be combined with the existing rate control scheme for H.264/AVC, resulting in further improvements.