New autoregressive (AR) order selection criteria based on the prediction error estimation

The most important problem in data modeling using the AR model is the order selection. Some AR order selection criteria estimate the prediction error and choose the order that minimizes this estimated prediction error. All of these criteria use the same formula for estimating the prediction error from the residual variance for all AR models. However, experimental results show that the relationship between the prediction error and the residual variance depends on the AR model. In this paper, we introduce new formulas for estimating the prediction error using the residual variance. These formulas depend on the AR model, and are obtained through assuming a white Gaussian noise as the input noise to the AR model and assuming that the least-squares-forward (LSF) method is used for estimating the AR coefficients. The performance of the new order selection criteria introduced in this paper is compared with other AR order selection criteria using simulated data. Results show that the new criteria have good performance in estimating the prediction error and in selecting an appropriate order for the AR model.     

AR order selection in the case when the model parameters are estimated by forgetting factor least-squares algorithms

During the last decades, the use of information theoretic criteria (ITC) for selecting the order of autoregressive (AR) models has increased constantly. Because the ITC are derived under the strong assumption that the measured signals are stationary, it is not straightforward to employ them in combination with the forgetting factor least-squares algorithms. In the previous literature, the attempts for solving the problem were focused on the Akaike information criterion (AIC), the Bayesian information criterion (BIC) and the predictive least squares (PLS). In connection with PLS, an ad hoc criterion called SRM was also introduced. In this paper, we modify the predictive densities criterion (PDC) and the sequentially normalized maximum likelihood (SNML) criterion such that to be compatible with the forgetting factor least-squares algorithms. Additionally, we provide rigorous proofs concerning the asymptotic approximations of four modified ITC, namely PLS, SRM, PDC and SNML. Then, the four criteria are compared by simulations with the modified variants of BIC and AIC.     

应用预测最小二乘准则的AR模型判阶方法

本文通过多种AR模型的判阶准则的比较提出了应用最小二乘预测误差的滑窗预测最小二乘(Sliding Window Predictive Least Squares, SWPLS)判阶准则.采用这种准则的主要优点除了准确的判阶性能外,对于时变AR模型具有良好的跟踪特性,同时算法容易实现在线实时处理.文中主要对时变模型参数和时变模型阶数的多种情况进行了判阶模拟,验证了文中提出的滑窗最小二乘预测判阶准则的有效性.     

AR模型功率谱估计在抗干扰中的DSP实现

本文在AR模型(自回归模型)功率谱估计方法的基础上,对其在抗干扰领域中的应用进行了研究,提出了自适应滤除干扰信号的方案,并对该方案进行了DSP实现。实验结果表明,该方案在自适应去除多个干扰信号方面是行之有效的。     

一种改进的AR谱估计方法

分析了现有的基于最小二乘法的AR参数模型的谱估计算法在信噪比较低时估计效果差的原因，提出了一种基于协方差成形最小二乘法的改进的参数模型AR谱估计算法。这种算法建立了以线性模型的真实输出与估计输出的均方误差为模型的代价函数，并选择满足一定约束条件的线性变换估计使得该均方误差最小。仿真结果表明，这种算法虽然是有偏估计，但在信噪比不高的情况下，估计效果优于Yule—Walker等参数模型AR谱估计方法，而在信噪比较高的情况下，二者估计效果相当。     

基于AR功率谱和ZAM变换的FSK-BPSK复合信号参数估计

为了解决频率编码-二相编码(FSK-BPSK)复合调制信号参数估计精度低的问题,提出利用自回归(AR)模型功率谱估计联合ZAM(Zhao,Atlas,and Marks)变换的方法.首先,利用AR模型功率谱估计方法对信号相位不敏感的特点,对FSK-BPSK复合调制信号的跳频分量数以及跳频码元序列进行估计;然后,利用ZAM分布对相位变化极其敏感的特点,提取信号ZAM分布的各跳频频率下的截面,对相位编码的码元宽度和跳频码元宽度进行估计.仿真实验表明,在全频段信噪比不小于0 dB时,AR模型功率谱联合ZAM变换的方法能够精确地估计出该复合信号的各参数.     

Bandlet image estimation with model selection

To estimate geometrically regular images in the white noise model and obtain an adaptive near asymptotic minimaxity result, we consider a model selection based bandlet estimator. This bandlet estimator combines the best basis selection behavior of the model selection and the approximation properties of the bandlet dictionary. We derive its near asymptotic minimaxity for geometrically regular images as an example of model selection with general dictionary of orthogonal bases. This paper is thus also a self-contained tutorial on model selection with orthogonal bases dictionary.     

一种基于高阶谱的广义相位数据时延估计算法

为改善高阶谱相位数据时延估计算法的性能，研究了一种基于高阶谱的广义相位数据时延估计算法。该算法采用基于双谱的相干函数在高阶谱域对相位数据进行加权和加窗处理，以提高时延估计精度。最后用实测的直升机噪声信号进行了仿真，对各种相位数据时延估计算法性能进行了比较，验证了新算法的有效性。     

基于多臂赌博机模型的信道选择

在择机频谱接入系统中,为解决未知信道环境先验知识下的信道选择问题,提出了一种基于多臂赌博机(MAB)模型的改进UCB(Upper Confidence Bound) 索引选择策略。该策略是通过在UCB索引的置信因子中引入收益方差值来调整对未知信道环境的探索过程,以降低探索成本。结合理论证明了本策略有较快的收敛速度,还证明了本策略下的学习后悔值曲线与时隙呈近似对数关系而较缓慢增长。仿真结果表明,与原UCB策略以及贪心算法相比,所提策略更能自适应地选择可用性较好的信道,有效降低学习后悔值并加快其收敛速度,从而提高了系统吞吐量。     

A microprocessor system for surveillance of sensors via autoregressive identification

This paper presents an on-line sensor tester based on autoregressive models. The noisy signal delivered by the sensor is analysed as a response of a linear system to a white noise excitation. This model is identified and then used to obtain an estimation of the frequency and step responses of the sensor.After this identification stage, the model and its characteristics are used to determine whether or not the sensor is in working order. This decision concerning the sensor state is taken following statistical criteria.The main part of the paper discusses the theoretical background of the realized device: choice of the model and order determination, model validation, responses calculation and decision on the sensor state. Results of the preliminary study in simulation are shown. Then the hardware and software of the realized device are described as well as the obtained performances.     

基于AR模型的最大熵磁共振成像重建算法

本文提出了一种基于ＡＲ模型的磁共振成像算法，通过所获得的自回归系数及线性预测误差，替代了传统的快速傅里叶（ＦＦＴ）重建方法，获得了满意的重建图像，由于在实际系统中，只能得到有限的频谱数据，利用传统的ＦＦＴ方法重建磁共振图像，将导致截断伪影和低的分辨率。本算法利用ＡＲ模型外推未知频谱数据，替代ＦＦＴ方法的填零法重建，并利用ＢＵＲＧ算法中的ＡＲ模型参数计算的有效性，不仅消除了截断伪影，抑制噪声提高了分     

闪电AR谱的多重分形特性分析及放电类型的识别

对闪电时域波形的分形研究由于忽略了其频率特性,致使复杂多变的闪电过程的全部特性无法得到充分表征。针对此问题,本文将多重分形理论引入到现代谱估计中,提出了一种基于AR(auto-regressive)谱的闪电电场信号的多重分形特性分析及放电类型的识别方法。首先基于AR模型谱估计法获得闪电电场信号的功率谱,然后,通过多重分形去趋势波动分析(multifractal detrended fluctuation analysis,MF-DFA) 法验证了闪电AR谱序列具有多重分形特性,并进一步对AR谱序列的Hurst指数以及多重分形谱进行了讨论,最后将相关参数作为闪电信号的有效特征值输入支持向量机进行了云闪(intracloud lightning) 和地闪(cloud-to-ground lightning,CG) 不同放电类型的识别。实验结果表明,本文方法对云、地闪信号的有效识别率达到了94%以上,该研究成果对闪电的特性研究与自动化识别技术均具有一定的参考价值。     

基于 Burg 算法功率谱估计的参数选择

介绍了现代功率谱估计中常用的基于自回归（AR）模型的Burg算法,分析了Burg算法中参数选择对功率谱估计的影响,并给出综合最优的Burg算法参数选择方法,最后将本文参数选择方法应用于列车测速系统进行实验验证,准确地估计出了列车的多普勒频率。     

面向新闻文本的分类方法的比较研究

文本分类是根据文档内容将文档分类为预定义类别的过程.文本分类是文本检索系统的必要要求,文本检索系统响应用户的查询检索文本,而文本理解系统以某种方式转换文本,如生成摘要,回答问题或提取数据[1].本文中将运用朴素贝叶斯、支持向量机、K最近邻、fastText这4种方法来进行新闻文本分类,并比较了各种算法的分类性能、复杂度等方面的优缺点,最后评述了精确度和时间2种分类器常用的性能评价指标[2].     

基于AR模型的功率谱估计

为了更好地模拟计算功率谱,采用了现代功率谱估计中常用的基于AR模型的自相关法和Burg法,通过仿真给出了估计出的功率谱曲线,结果表明,Burg算法在谱分辨率方面有着良好的性能。选择合适的采样点数来确定模型阶数,给出阶数选择的原则。     

A comparative study on dimming control methods for electronicballasts

This paper presents a comparative study on the performance characteristics between the variable switching frequency and variable DC link voltage control schemes for the dimming operation of electronic ballasts using a half-bridge series-resonant parallel-loaded inverter. Attributes under investigations include (1) average input current of the inverter; (2) dimming characteristics; (3) variations of the filament power and starting voltage throughout the dimming range; (4) dead time boundaries for ensuring zero-voltage-switching in the inverter. An approximated fundamental-frequency model that includes the characteristics of the nonlinear lamp resistance and filament resistance is used. Theoretical predictions are favorably verified with the experimental measurements in two 36 W Ballast-lamp systems     

Diversity order of decode-and-forward MIMO relaying with transmit antenna selection

This article analyzes the diversity order of several proposed schemes, where the transmit antenna selection (TAS) strategies are combined with low-complexity decode-and-forward (DF) protocols in the multiple-input multiple-output (MIMO) relaying scenario. Although antenna selection is a suboptimal form of beamforming, it enjoys the advantages of tractable optimization and low feedback overhead. Specifically, this article proposes schemes that combine TAS strategies with fixed decode-and-forward (FDF) and selection decode-and-forward (SDF) protocols. Following that, the asymptotic expressions of outage probabilities are derived and the diversity order of the proposed schemes analyzed. These kinds of combination of transmit antenna selection strategies and low-complexity decode-and-forward protocols can achieve partial diversity order in the MIMO relaying scenario. The numerical simulations verify the analysis.     

一种基于SAR稀疏采样数据的动目标运动参数估计方法

该文针对地面动目标运动参数估计问题进行研究,提出一种利用单天线合成孔径雷达(SAR)稀疏采样数据的动目标2维速度估计方法。首先以目标2维速度为参数构建一个等效参数化模型将动目标回波数据转化为小斜视回波数据,然后利用改进的迭代阈值算法实现不同参数条件下的动目标2维成像,最后以成像结果的图像熵值为优化准则对初始模型参数进行搜索,从而获得准确的动目标运动参数。该方法以稀疏采样数据为输入,可以减少所需数据量,并且能够有效避免多普勒模糊问题,在较低信杂比条件下仍然能够准确估计出目标运动参数。仿真实验结果验证了所提动目标参数估计方法的有效性。     

A comparative study of two postreconstruction beam hardening correction methods

The general nature of postreconstruction beam hardening correction methods is discussed. A methodology for choosing the energy of reconstruction is presented based on a technique of evaluating the "nearness" of two projection data sets. Two previously published postreconstruction beam hardening correction methods are described within a common framework. These methods differ at a number of independent places and so one can produce hybrid methods by interchanging some but not all of the choices. A basic difference between the methods is that one needs only the initial reconstruction during the postreconstruction correcting phase, while the other needs the original projection data as well. Both methods have been implemented and are compared (using a mathematical head phantom) from the points of view of the nearness of the corrected polychromatic projection data to the desired monochromatic projection data and the visual quality of the reconstructions. Variants and hybrids of the two methods are also investigated and recommendations based on the results are presented.     

无线OFDM系统中基于导频的信道估值器的比较分析

本文对无线OFDM系统中基于导频的信道估值器进行了比较分析,对比了四种估值器在均方误差（MSE）上的性能。本文针对时频两维的信道估值,分别分析了时频分离和时频不分离两种处理方法。文中还讨论了信道估值的低阶近似算法,并进行了给定复杂度下的不同估值器的性能对比。结果表明,在相同的复杂度下,使用时频分离的估值器比时频不分离的估值器性能要好,分离估值器经过低阶近似,性能得到了进一步的提高。