Optimal tracking of time-varying channels: a frequency domainapproach for known and new algorithms

In this paper, we developed a systematic frequency domain approach to analyze adaptive tracking algorithms for fast time-varying channels. The analysis is performed with the help of two new concepts, a tracking filter and a tracking error filter, which are used to calculate the mean square identification error (MSIE). First, we analyze existing algorithms, the least mean squares (LMS) algorithm, the exponential windowed recursive least squares (EW-RLS) algorithm and the rectangular windowed recursive least squares (RW-RLS) algorithm. The equivalence of the three algorithms is demonstrated by employing the frequency domain method. A unified expression for the MSIE of all three algorithms is derived. Secondly, we use the frequency domain analysis method to develop an optimal windowed recursive least squares (OW-RLS) algorithm. We derive the expression for the MSIE of an arbitrary windowed RLS algorithm and optimize the window shape to minimize the MSIE. Compared with an exponential window having an optimized forgetting factor, an optimal window results in a significant improvement in the h MSIE. Thirdly, we propose two types of robust windows, the average robust window and the minimax robust window. The RLS algorithms designed with these windows have near-optimal performance, but do not require detailed statistics of the channel     

Frequency-selective fading channel estimation with a polynomialtime-varying channel model

A rectangular-windowed least-squares estimator using a polynomial model of the time-varying channel taps is proposed for estimating the impulse response of a frequency-selective fading channel. This method provides a significant improvement in mean square identification error (MSIE) over the conventional least mean squares (LMS) and the exponentially weighted recursive least squares (EW-RLS) algorithms without a polynomial model. A detailed study of the effects of channel parameters, such as the fading rate and the signal-to-noise ratio, on the proposed method is carried out. The performance of the method depends on the window size of the least squares estimator and the polynomial order being used. Algorithms to obtain the approximately optimal window size and polynomial order are proposed and are shown to perform well     

A realization method of an ARMAX lattice filter

This paper proposes a realization method of an ARMAX lattice filter for frequency-weighting ARMAX model identification. The proposed lattice filter uses an exponentially weighted sliding window for the same application as the extended least squares (ELS) achieves. Based on the proposed structure, the algorithm can perform the frequency-weighting model identification more easily than the ELS. Further, applied to the ARMAX model identification, the proposed algorithm requires fewer multiplications than the ELS does     

Phase Noise Pre-correction Scheme for SC-FDMA Signals in LTE-Uplink

This paper proposes a novel carrier Phase noise (PN) pre-correction scheme with an adaptive PN prediction algorithm for Single-carrier Frequency-division multiple-access (SC-FDMA) systems to alleviate degrada-tion due to the PN. Our proposed PN prediction algorithm is a modified polynomial fitting algorithm which is based on receding horizon principle. The parameters of the pre-diction model are optimized by using the algorithm on PN samples of the local oscillator signal in a training window. By using the optimized prediction model parameters and the latest PN samples, we can predict future PN samples. Then these predicted PN samples are put into our pro-posed PN pre-correction scheme and the SC-FDMA sym-bols at the transmitter are pre-compensated. Due to the absence of the radio frequency delay device, the proposed scheme has a low hardware complexity. Simulation results show that our proposed scheme can greatly reduce the ef-fect of the PN on the transmitted SC-FDMA signal.     

Detection of chemical oxygen demand in water based on UV absorption spectroscopy and PSO-LSSVM algorithm

A method of detecting chemical oxygen demand (COD) of water based on ultraviolet (UV) absorption spectra is proposed. The modeling and analysis of the standard samples and the actual water samples are carried out respectively. For the standard solution samples, the univariate linear models based on single wavelengths and the partial least square (PLS) model based on synergy interval partial least square (SiPLS) and moving window partial least square (MWPLS) are established. For the actual water samples, different pre-processing methods are used. SiPLS and MWPLS are used to select the characteristic bands. The least squares support vector machine algorithm optimized by particle swarm optimization (PSO-LSSVM) algorithm is used to establish the prediction model, and the prediction results of various models are compared. The results show that the optimal model is PSO-LSSVM which uses SiPLS to select the characteristic bands of the first derivative spectra (preprocessing method). The determination coefficient of the prediction set is 0.963 1, and the root mean square error of prediction (RMSEP) is 2.225 4 mg/L. PSO-LSSVM algorithm has good prediction performance for the analysis of COD in actual water samples by UV spectra. This paper provides a new design idea for the research and development of water quality detection optical sensor.     

Estimating signal parameters using the nonlinear instantaneousleast squares approach

A novel method for signal parameter estimation is presented, termed the nonlinear instantaneous least squares (NILS) estimator. The basic idea is to use the observations in a sliding window to compute an instantaneous (short-term) estimate of the amplitude used in the separated nonlinear least squares (NLLS) criterion. The effect is a significant improvement of the numerical properties in the criterion function, which becomes well-suited for a signal parameter search. For small-sized sliding windows, the global minimum in the NLIS criterion function is wide and becomes easy to find. For maximum size windows, the NILS is equivalent to the NLLS estimator, which implies statistical efficiency for Gaussian noise. A “blind” signal parameter search algorithm that does not use any a priori information is proposed. The NILS estimator can be interpreted as a signal-subspace projection-based algorithm. Moreover, the NILS estimator can be interpreted as an estimator based on the prediction error of a (structured) linear predictor. Hereby, a link is established between NLLS, signal-subspace fitting, and linear prediction-based estimation approaches. The NILS approach is primarily applicable to deterministic signal models. Specifically, polynomial-phase signals are studied, and the NILS approach is evaluated and compared with other approaches. Simulations show that the signal-to-noise ratio (SNR) threshold is significantly lower than that of the other methods, and it is confirmed that the estimates are statistically efficient. Just as the NLLS approach, the NILS estimator can be applied to nonuniformly sampled data     

Nonlinear adaptive prediction of speech with a pipelined recurrentneural network

New learning algorithms for an adaptive nonlinear forward predictor that is based on a pipelined recurrent neural network (PRNN) are presented. A computationally efficient gradient descent (GD) learning algorithm, together with a novel extended recursive least squares (ERLS) learning algorithm, are proposed. Simulation studies based on three speech signals that have been made public and are available on the World Wide Web (WWW) are used to test the nonlinear predictor. The gradient descent algorithm is shown to yield poor performance in terms of prediction error gain, whereas consistently improved results are achieved with the ERLS algorithm. The merit of the nonlinear predictor structure is confirmed by yielding approximately 2 dB higher prediction gain than a linear structure predictor that employs the conventional recursive least squares (RLS) algorithm     

基于主奇异矢量的L型阵列相干信号二维DOA估计方法

针对现有L型阵列相干信号DOA估计算法精度不高、孔径损失较大的问题,该文提出一种基于主奇异矢量的解相干(L-PUMA)方法以及改进的主奇异矢量法(L-MPUMA)。L-PUMA算法首先对互协方差矩阵进行降噪,再通过奇异值分解得到2维主奇异矢量,然后利用加权最小二乘法得到线性预测方程的多项式系数,该线性预测方程的根即为信号的DOA估计,最后提出一种新的配对算法实现仰角和方位角的配对。L-MPUMA算法利用反向共轭变换构造增广主奇异矢量,进一步提高了数据利用率,克服了信号完全相干时L-PUMA算法性能下降严重的问题,仿真实验验证了所提算法的高效性。     

OFDM系统自适应数字预失真研究

研究了正交频分复用（OFDM）传输系统中高功率放大器（HPA）的自适应预失真方法。针对OFDM信号的高峰平比特性及HPA带来的非线性失真,提出一种基于训练序列的最小均方误差（LMS）算法和递归最小二乘（RLS）算法的组合算法,将其应用到基于记忆多项式模型的数字预失真系统中。用MATLAB构建一个基于该自适应算法的预失真系统。仿真结果表明：该算法能有效的改善放大器的非线性特性。     

A robust, parallelizable, O(m), a posteriori recursive leastsquares algorithm for efficient adaptive filtering

This article presents a new recursive least squares (RLS) adaptive algorithm. The proposed computational scheme uses a finite window by means of a lemma for the system matrix inversion that is, for the first time, stated and proven here. The new algorithm has excellent tracking capabilities. Moreover, its particular structure allows for stabilization by means of a quite simple method. Its stabilized version performs very well not only for a white noise input but also for nonstationary inputs as well. It is shown to follow music, speech, environmental noise, etc., with particularly good tracking properties. The new algorithm can be parallelized via a simple technique. Its parallel form is very fast when implemented with four processors     

二维滑动矩形窗傅氏变换的快速递推算法

该文利用相邻滑动窗数据之间的关系以及傅氏变换的平移性质,提出一种二维滑动矩形窗傅氏变换的快速递推算法。文中分析了该快速递推算法的复杂度和传统直接计算法的复杂度,证明了新的快速递推法可以大大降低计算复杂性,尤其是在图像尺寸和窗口尺寸较大的场合中。该算法可以改善滑窗傅氏变换或Gabor变换的计算效率。     

A fast learning algorithm for Gabor transformation

An adaptive learning approach for the computation of the coefficients of the generalized nonorthogonal 2-D Gabor (1946) transform representation is introduced. The algorithm uses a recursive least squares (RLS) type algorithm. The aim is to achieve minimum mean squared error for the reconstructed image from the set of the Gabor coefficients. The proposed RLS learning offers better accuracy and faster convergence behavior when compared with the least mean squares (LMS)-based algorithms. Applications of this scheme in image data reduction are also demonstrated.     

A QRD-RLS-Based Predistortion Scheme for High-Power Amplifier Linearization

A digital baseband predistortion (PD) scheme for high-power amplifier (HPA) linearization is proposed and analyzed in this brief. The proposed approach utilizes the QR-decomposition-based recursive least squares (QRD-RLS) algorithm to estimate the memoryless complex polynomial coefficients that characterize the HPA. The inverse polynomial model coefficients corresponding to the PD are similarly extracted using QRD-RLS. The performance of the proposed PD scheme is analyzed via simulations and compared with previously published techniques. Results show that the QRD-RLS-based solution offers improved performance over its comparatives     

二次多项式拟合在分布式光纤传感中的应用

基于布里渊散射的分布式光纤传感中温度和应变与布里渊频移成线性关系,为了提高温度和应变测量的准确性,提出了一种改进的二次多项式拟合算法用于提取布里渊频移。该算法分为两步:首先使用一种改进的中值滤波算法对含噪布里渊谱信号进行预处理,以提高增益峰值定位的准确性；然后截取围绕峰值左右对称的一个线宽的原始布里渊谱进行二次多项式拟合以实现布里渊频移的高精度提取。以布里渊频移误差及峰值定位准确性作为衡量指标,比较研究后确定同一频率下所有空间点对应的布里渊增益作为滤波器的输入。研究了不同扫频间隔和信噪比及不同滤波窗长下改进算法的效果,同时研究了最优窗长的选择问题。结果表明,不同信噪比和扫频间隔下改进算法均能有效提高布里渊频移提取的准确性。随窗口长度增加布里渊频移误差先减少后增加,在扫频间隔为1~10MHz、信噪比为0～40dB情况下,通用的最优窗长为53~163。     

基于LMS算法的多点滑动DFT方法

 根据自适应LMS法,LMS谱分析器可以通过递归运算完成滑动窗口中数据的DFT运算.本文推导了LMS算法及多点滑动DFT运算之间的关系式,并由此提出了一种基于LMS算法的多点滑动DFT运算方法.文章在理论推导的同时,进行了计算机仿真验证.该方法使用方便,可灵活适用于不同的滑动窗口大小及滑动步长参数.     

Rate-Distortion Optimized Motion-Compensated Prediction for Packet Loss Resilient Video Coding

A rate-distortion optimized motion-compensated prediction method for robust video coding is proposed. Contrasting methods from the conventional literature, the proposed approach uses the expected reconstructed distortion after transmission, instead of the displaced frame difference in motion estimation. Initially, the end-to-end reconstructed distortion is estimated through a recursive per-pixel estimation algorithm. Then the total bit rate for motion-compensated encoding is predicted using a suitable rate distortion model. The results are fed into the Lagrangian optimization at the encoder to perform motion estimation. Here, the encoder automatically finds an optimized motion compensated prediction by estimating the best tradeoff between coding efficiency and end-to-end distortion. Finally, rate-distortion optimization is applied again to estimate the macroblock mode. This process uses previously selected optimized motion vectors and their corresponding reference frames. It also considers intraprediction. Extensive computer simulations in lossy channel environments were conducted to assess the performance of the proposed method. Selected results for both single and multiple reference frames settings are described. A comparative evaluation using other conventional techniques from the literature was also conducted. Furthermore, the effects of mismatches between the actual channel packet loss rate and the one assumed at the encoder side have been evaluated and reported in this paper     

Environment-adaptation mobile radio propagation prediction usingradial basis function neural networks

This paper investigates the application of a radial basis function (RBF) neural network to the prediction of field strength based on topographical and morphographical data. The RBF neural network is a two-layer localized receptive field network whose output nodes from a combination of radial activation functions computed by the hidden layer nodes. Appropriate centers and connection weights in the RBF network lead to a network that is capable of forming the best approximation to any continuous nonlinear mapping up to an arbitrary resolution. Such an approximation introduces best nonlinear approximation capability into the prediction model in order to accurately predict propagation loss over an arbitrary environment based on adaptive learning from measurement data. The adaptive learning employs hybrid competitive and recursive least squares algorithms. The unsupervised competitive algorithm adjusts the centers while the recursive least squares (RLS) algorithm estimates the connection weights. Because these two learning rules are both linear, rapid convergence is guaranteed. This hybrid algorithm significantly enhances the real-time or adaptive capability of the RBF-based prediction model. The applications to Okumura's (1968) data are included to demonstrate the effectiveness of the RBF neural network approach     

基于相空间重构与最小二乘支持向量机的时延预测

针对网络控制系统的时延预测问题,提出一种基于相空间重构与最小二乘支持向量机的时延预测方法.首先利用0-1测试法确定时延序列具有混沌特性,引入相空间重构技术提高预测精度.对实际采集的时延序列进行Hurst指数分析,选择最小二乘支持向量机作为预测模型.然后利用C-C方法确定时延序列相空间重构参数,通过递归图确定时延序列的局部可预测性,利用遗传算法对最小二乘支持向量机的参数进行离线优化.最后通过优化后的最小二乘支持向量机并结合相空间重构对时延序列进行在线预测.与其它预测方法进行了仿真对比,结果表明本文方法具有更高的预测精度与更小的预测误差,同时并未降低预测算法的实时性.     

Finite word-length effects in recursive least squares algorithms with application to adaptive equalization

In this paper we provide a summary of recent and new results on finite word length effects in recursive least squares adaptive algorithms. We define the numerical accuracy and numerical stability of adaptive recursive least squares algorithms and show that these two properties are related to each other, but are not equivalent. The numerical stability of adaptive recursive least squares algorithms is analyzed theoretically and the numerical accuracy with finite word length is investigated by computer simulation. It is shown that the conventional recursive least squares algorithm gives poor numerical accuracy when a short word length is used. A new form of a recursive least squares lattice algorithm is presented which is more robust to round-off errors compared to the conventional form. Optimum scaling of recursive least squares algorithms for fixedpoint implementation is also considered.     

Adaptive derivative estimation for delay-constrained accelerationmeasurement

The well-known noise problem in digital differentiation is addressed by means of using adaptive digital filtering for signal pre-processing. Rapidly responding differentiators with low-noise output can be constructed by using the adaptive filter in a predictor configuration. As the prefilter is adaptive, the approximation is more flexible than polynomial fitting. The recursive least-squares adaptive algorithm is used for prediction