Small Target Detection Using Two-Dimensional Least Mean Square (TDLMS) Filter Based on Neighborhood Analysis

TDLMS is a general adaptive filter algorithm and when applied to infrared small target detection, traditional structure and implementation of TDLMS may cause some problems in this field. This paper presents a new TDLMS filter structure and implementation incorporating neighborhood analysis and data fusion, which is capable of acquiring and analyzing more information from the vicinity of the target, leading to a more prominent detection result. This enables TDLMS filter to perform better and become more suitable in the field of small target detection. Experiments showed the efficiency of the proposed algorithm.     

Performance evaluation of 2-D adaptive prediction filters fordetection of small objects in image data

This work studies the performance of dimensional least mean square (TDLMS) adaptive filters as prewhitening filters for the detection of small objects in image data. The object of interest is assumed to have a very small spatial spread and is obscured by correlated clutter of much larger spatial extent. The correlated clutter is predicted and subtracted from the input signal, leaving components of the spatially small signal in the residual output. The receiver operating characteristics of a detection system augmented by a TDLMS prewhitening filter are plotted using Monte-Carlo techniques. It is shown that such a detector has better operating characteristics than a conventional matched filter in the presence of correlated clutter. For very low signal-to-background ratios, TDLMS-based detection systems show a considerable reduction in the number of false alarms. The output energy in both the residual and prediction channels of such filters is shown to be dependent on the correlation length of the various components in the input signal. False alarm reduction and detection gains obtained by using this detection scheme on thermal infrared sensor data with known object positions is presented.     

A New Variable Step Size Control Method for the Transform Domain LMS Adaptive Algorithm

This paper develops a new adaptive step size control method for the transform domain least mean-square (TDLMS) adaptive algorithm. The time-varying step size is an efficient approximation of an optimal step size based on a proposed cost function, therefore leading to significant improvements in the performance characteristics of the TDLMS algorithm. Mean-square analysis is provided, and an expression for the steady-state excess mean-squared error (MSE) is derived. Simulation experiments confirm the algorithm's enhanced convergence properties and verify the theoretical steady-state MSE results.     

基于邻域分析TDLMS滤波器的红外小目标检测

使用基本的TDLMS滤波器可以用来进行小目标检测,但是由于其结构和算法的限制,在进行目标检测的过 程中会产生一些困难.提出了一种基于邻域分析的TDLMS滤波器,改造了滤波器输入窗口结构,并加入了新的数 据处理模块,使其能够更多地获取和利用目标邻域内的信息,并获得更为显著的检测效果.实验证明了该算法的有 效性.     

基于领域分析TDLMS滤波器的红外小目标检测

使用基本的TDLMS滤波器可以用来进行小目标检测,但是由于其结构和算法的限制,在进行目标检测的过程中会产生一些困难.提出了一种基于邻域分析的TDLMS滤波器,改造了滤波器输入窗口结构,并加入了新的数据处理模块,使其能够更多地获取和利用目标邻域内的信息,并获得更为显著的检测效果.实验证明了该算法的有效性.     

New Variable Step-Sizes Minimizing Mean-Square Deviation for the LMS-Type Algorithms

The least-mean-square-type (LMS-type) algorithms are known as simple and effective adaptation algorithms. However, the LMS-type algorithms have a trade-off between the convergence rate and steady-state performance. In this paper, we investigate a new variable step-size approach to achieve fast convergence rate and low steady-state misadjustment. By approximating the optimal step-size that minimizes the mean-square deviation, we derive variable step-sizes for both the time-domain normalized LMS (NLMS) algorithm and the transform-domain LMS (TDLMS) algorithm. The proposed variable step-sizes are simple quotient forms of the filtered versions of the quadratic error and very effective for the NLMS and TDLMS algorithms. The computer simulations are demonstrated in the framework of adaptive system modeling. Superior performance is obtained compared to the existing popular variable step-size approaches of the NLMS and TDLMS algorithms.     

Reduced-complexity transform-domain adaptive algorithm with selective coefficient update

This paper proposes a new low-complexity transform-domain (TD) adaptive algorithm for acoustic echo cancellation. The algorithm is based on decomposing the long adaptive filter into smaller subfilters and employing the selective coefficient update (SCU) approach in each subfilter to reduce computational complexity. The resulting algorithm combines the fast converging characteristic of the TD decomposition technique and the benefits of the SCU of low complexity with minimal performance losses. The improvement in convergence speed comes at the expense of a corresponding increase in misadjustment. To overcome this problem, a hybrid of the proposed algorithm and the standard TD LMS algorithm (TDLMS) is presented. The hybrid algorithm retains the fast convergence speed capabilities of the original algorithm while allowing for low final MSE. Simulations show that the hybrid algorithm offers a superior performance when compared to the standard TDLMS algorithm with less computational overhead.     

Fast LMS/Newton Algorithms for Stereophonic Acoustic Echo Cancelation

This paper presents a new class of adaptive filtering algorithms to solve the stereophonic acoustic echo cancelation (AEC) problem in teleconferencing systems. While stereophonic AEC may be seen as a simple generalization of the well-known single-channel AEC, it is a fundamentally far more complex and challenging problem to solve. The main reason being the strong cross correlation that exists between the two input audio channels. In the past, nonlinearities have been introduced to reduce this correlation. However, nonlinearities bring with it additional harmonics that are undesirable. We propose an elegant linear technique to decorrelate the two-channel input signals and thus avoid the undesirable nonlinear distortions. We derive two low complexity adaptive algorithms based on the two-channel gradient lattice algorithm. The models assume the input sequences to the adaptive filters to be autoregressive (AR) processes whose orders are much lower than the lengths of the adaptive filters. This results in an algorithm, whose complexity is only slightly higher than the normalized least-mean-square (NLMS) algorithm; the simplest adaptive filtering method. Simulation results show that the proposed algorithms perform favorably when compared with the state-of-the-art algorithms.     

基于频域块LMS算法的实时虚拟暗室测试方法

针对电磁辐射现场测试被测设备信号和干扰未知的情况,提出了一种基于频域块最小均方算法的实时虚拟暗室测试方法。该方法采用双通道接收机,根据测试通道和背景通道中干扰信号的相关性设计自适应滤波器,在频域对背景通道信号滤波以趋近测试通道中的干扰分量,采用瞬时双通道信号迭代更新滤波器系数,滤波器系数收敛后系统输出中只有被测设备信号。仿真与分析表明,该方法在背景通道有无被测设备信号泄露的情况下都能有效抑制干扰,与基于时域最小均方算法的方法相比,在滤波器长度相同的情况下其计算复杂度更低,适用于实时现场测试。     

基于自适应背景预测的无源毫米波弱小目标检测

在分析毫米波辐射图像特性的基础上,对基于单帧的二维最小均方误差（Two-dimensional LMS）背景预测算法进行改进,充分利用连续帧图像中小目标的运动信息和背景的缓变特性,设计出基于连续帧的TDLMS滤波器。针对TDLMS算法对步长μ选取困难的问题,本文采用变步长TDLMS自适应算法。实验证明,该滤波算法能在两帧图像时达到最优的背景预测效果,有效滤除杂波,提高图像信杂比。     

采用剪切波变换的红外弱小目标背景抑制

提出了一种将剪切波变换与贝叶斯统计机理相结合的背景抑制新方法来解决红外搜索跟踪系统探测复杂空中和地面背景杂波中的弱小目标这一难题.根据红外图像中目标和背景杂波的不同分布特性,首先,采用剪切波变换对原始红外图像进行多尺度和多方向分解,获得原始图像的多尺度和方向细节特征,然后,通过应用高斯尺度混合模型进行处理,从而将红外图...     

An adaptive ARMA four-line lattice filter for spectral estimationwith frequency weighting

A method for designing an adaptive four-line lattice filter which can perform frequency-weighting spectral estimation, which provides more accurate spectral estimation for some frequency bands than for others, is proposed. Using a suitable frequency-weighting function, denoted as an ARMA (autoregressive moving-average) model, an estimated spectrum is obtained by arbitrarily weighing some frequency bands more heavily than others. if the frequency-weighting function has the property of a low-pass filter, the spectrum of the reference model can be estimated accurately with a reduced ARMA order in the low-frequency band. Spectra of time-varying models can be estimated with an exponentially weighted sliding window, and the input signal of the reference model can be estimated by assumption. The order-update and the time-update recursive formulas and the frequency-weighting method for the filter are described. The algorithm is verified by experimental results     

非线性Volterra系统的总体全解耦自适应滤波

研究输入、输出观测数据均受噪声干扰时的非线性Volterra系统的全解耦自适应滤波问题.基于总体最小二乘技术和Volterra滤波器的伪线性组合结构,运用约束优化问题的分析方法研究Volterra滤波过程,从而建立了一种总体全解耦自适应滤波算法.并建立了分析该算法收敛性能的参数反馈调整模型,分析表明,该算法可使各阶Volterra核稳定地收敛到真值.仿真实验的结果表明,当输入、输出观测数据均受噪声干扰时,总体全解耦自适应滤波算法的鲁棒抗噪性能和滤波精度均优于全解耦LMS自适应滤波算法.     

Fast RLS-type algorithm for unbiased equation-error adaptive IIR filtering based on approximate inverse-power iteration

In this paper, a fast approximate inverse-power (AIP) iteration is applied to compute recursively the total least squares (TLS) solution for unbiased equation-error adaptive infinite impulse response (IIR) filtering, which is established by approximating the well-known inverse-power iteration with Galerkin method. The AIP algorithm is based on an interesting modification of the inverse-power iteration in which the first entry of the parameter vector is constrained to the negative one. The distinctive feature of the proposed algorithm lies in its high computational efficiency, which is characterized by efficient computation of the fast gain vector (FGV), adaptation of the interesting modification of the inverse-power iteration, and rank-one update of the augmented correlation matrix. The shift structure of the input data vector is exploited to develop a fast algorithm for computing the gain vector. This FGV algorithm can be implemented at a numerical cost lower than the well-known fast Kalman algorithm. Since the first entry of the parameter vector has been fixed as the negative one and the weight vector is updated along the input data vector, a very efficient AIP algorithm is obtained by using the FGV algorithm. The proposed AIP algorithm is of computational complexity O(L) per iteration. Moreover, with no need to use the well-known matrix-inversion lemma, the AIP algorithm has another attractive feature of numerical stability. The proposed algorithm is shown to have global convergence. Simulation examples are included to demonstrate the effectiveness of the proposed AIP algorithm.     

Multivariate ARMA modeling by scalar algorithms

An algorithm for multichannel autoregressive moving average (ARMA) modeling which uses scalar computations only and is well suited for parallel implementation is proposed. The given ARMA process is converted to an equivalent scalar, periodic ARMA process. The scalar autoregressive (AR) parameters are estimated by first deriving a set of modified Yule-Walker-type equations and then solving them by a parallel, order recursive algorithm. The moving average (MA) parameters are estimated by a least squares method from the estimates of the input samples obtained via a high-order, periodic AR approximation of the scalar process     

有限域上超椭圆曲线离散对数问题的错误攻击

In this paper, we present two explicit inva-lid-curve attacks on the genus 2 hyperelliptic curve o-ver a finite field. First, we propose two explicit attack models by injecting a one-bit fault in a given divisor. Then, we discuss the construction of an invalid curve based on the faulted divisor. Our attacks are based on the fact that the Hyperelliptic Curve Scalar Multiplica-tion (HECSM) algorithm does not utilize the curve parameters and We consider three hyperelliptic curves as the attack targets. For curve with security level 186 (in bits), our attack method can get the weakest inva-lid curve with security level 42 (in bits); there are 93 invalid curves with security level less than 50. We al-so estimate the theoretical probability of getting a weak hyperelliptic curve whose cardinality is a smooth integer. Finally, we show that the complexity of the fault attack is subexponential if the attacker can freely inject a fault in the input divisor. Cryptosystems based on the genus 2 hyperelliptic curves cannot work against our attack algorithm in practice.     

基于帧间相关性的最大后验估计语音增强算法

通过讨论纯净语音分量的概率分布特征以及相邻分量间的统计相关特性,在自适应K-L变换(KLT,Karhunen-Loève Transform)域给出了一种新的语音信号统计模型,然后基于该信号模型,利用最大后验(MAP,Maximum a Posterior)估计理论提出了一种新型的单通道语音增强算法.该算法充分考虑到在KLT域相邻时刻语音分量间存在的相关信息,利用信号的高斯模型假设条件,以联合概率密度函数的形式将这种相关信息融合到MAP中,获得纯净语音分量的估计.算法不仅结构简单利于实现,且有效地避免了传统算法对语音分量估计的不足.仿真结果表明本文算法在客观和主观测试中都具有较好的语音增强效果.     

ARMA谱的高阶AR估计方法

正 1.问题的提出 ARMA过程及其z变换可以表为     

利用直接矩阵求逆和临界采样子带自适应滤波器的快速系统辨识

在许多应用中,子带自适应滤波器结构已经显示了其在计算和性能上的优点。基于最近提出的一个采用临界采样滤波器组的子带自适应结构,该文引入了子带直接矩阵求逆(DMI)算法。在保持了该算法快速收敛优点的同时,利用相关矩阵块三对角的特殊结构,降低了该算法的计算复杂度。理论分析及计算机实验显示,子带直接矩阵求逆算法只需经过较少的更新次数自适应子滤波器自由度的两倍,就能够收敛到高于最小均方误差的3dB附近。     

A unified study of burst assembly in optical burst switching networks

This article presents a comprehensive study of burst assembly in optical burst switching (OBS) networks. This investigation aims to provide a unified study of burst assembly algorithms. After analyses of conventional assembly algorithms, we present an adaptive burst assembly algorithm: data-length time-lag product assembly algorithm. Numerical results show that the proposed algorithm takes the real-time traffic state into account to adapt burst length and its injected intervals. It obtains a more acceptable performance. Moreover, we investigated the impact of burst assembly applied at the edge of OBS networks. We analyze the Hurst parameter of the input traffic to the output traffic that results from the burst assembly. Two formulas are presented to describe changes of the Hurst parameter. Theoretical results are validated through extensive simulations in time-based, length-based, and adaptive assembly algorithms, respectively.