直接序列扩频信号PN序列盲估计方法

在非协作信息侦测情况下,提出了一种实用的直接序列扩频(DS-SS)通信信号PN码序列的快速盲估计方法。该方法根据DS-SS基带信号的特点,首先采用二阶循环统计量估计PN码周期,然后利用分段互相关平均估计PN码同步始点;并在准确预估计这2个参数的基础上,将接收信号以PN码周期分成多个时段,利用多重互相关方法估计PN码序列。理论分析与仿真表明,该方法在截获信号信噪比很低的情况下有很好的估计性能,且无需任何先验知识,对PN码码型也没有任何限制,是一种有效的快速盲估计方法。     

基于DFT的MC-CDMA系统信道估计算法

基于导频辅助的最小平方(LS)算法是MC-CDMA中常用的信道估计算法,它运算量低,实现简单,但信道估计精度差。该文讨论了MC-CDMA的导频插入方式,提出一种基于离散傅里叶变换(DFT)的信道估计算法。该算法将LS信道估计循环前缀长度外的时域响应值置零,并设定阈值忽略循环长度内的噪声和无效径响应。该算法保留了LS算法运算量小和实现简单的优点,大大降低了噪声对信道估计精度的影响,仿真结果验证了算法的有效性。     

一种实用自适应PID控制器

本文介绍了一种实用自适应PID控制器,它是以MCS-51系列单片微机为核心,采用PID控制规律结合参数辨识的控制算法,实现了PID参数完全在线自动整定。该控制器适用于被控制对象未知或慢时变的场合,既可替代常规PID控制器,也可作为分布式系统的低级智能控制器。     

基于调制函数的振动力学模型全参数估计

为提高振动力学模型的精度,课题组对振动力学参数展开研究。基于经典系统力学模型通过在定义域内进行级数展开,重构动力学模型;应用分部积分法及引入调制函数将微分问题转化为关于加速度的积分方程,以便于获取加速度信息作为输入;采用最小二乘法估计刚度、阻尼值,应用雅克比正交级数来表示未知力,从而实现动力学模型全参数的估计。通过估计值和真实值对比验证了课题组所提出的估计方法的准确性。该方法可实现未知参数下的动力学建模,具有一定工程应用价值。     

自动接发球机发球过程损耗时间与误差补偿研究

为提高青少年篮球运动技能等级标准中所用测试器材自动篮球接发球机的发球精度,课题组对发球机构发球过程损耗时间进行研究并建立误差补偿模型。通过RS232通信协议实现驱动电机与上位机的通信,采集自动接发球机在不同发球场景下的发球过程中电机由于负载改变而导致力矩变化过程的时间长度,从而获得在发球过程损耗时间长度与摩擦轮间距、转速之间关系,并以此建立时间误差补偿模型。实验表明：发球机构发球损耗时间随着转速增大而增大,随着摩擦轮间距增大而减小,发球机构损耗时间对发球精度影响大,不可忽略。考虑发球过程时间误差补偿的发球模型,发球误差理论上可最多减少30.80%,可提高发球精度。     

包含测度的椭圆方程解的最大模的估计

对包含测度的椭圆方程 ,证明解的有界性已颇为困难 ,对解的最大模作出估计尤其困难。本文只对其中一种特殊情形作出解最大模的先验估计。     

On Rates of Convergence for Bayesian Density Estimation

Abstract.  We consider the problem of estimating a compactly supported density taking a Bayesian nonparametric approach. We define a Dirichlet mixture prior that, while selecting piecewise constant densities, has full support on the Hellinger metric space of all commonly dominated probability measures on a known bounded interval. We derive pointwise rates of convergence for the posterior expected density by studying the speed at which the posterior mass accumulates on shrinking Hellinger neighbourhoods of the sampling density. If the data are sampled from a strictly positive, α -Hölderian density, with α  ∈ ( 0,1] , then the optimal convergence rate n^{− α / (2 α +1)} is obtained up to a logarithmic factor. Smoothing histograms by polygons, a continuous piecewise linear estimator is obtained that for twice continuously differentiable, strictly positive densities satisfying boundary conditions attains a rate comparable up to a logarithmic factor to the convergence rate n ^−4/5 for integrated mean squared error of kernel type density estimators.     

Tests and Confidence Intervals for an Extended Variance Component Using the Modified Likelihood Ratio Statistic

Abstract.  The large deviation modified likelihood ratio statistic is studied for testing a variance component equal to a specified value. Formulas are presented in the general balanced case, whereas in the unbalanced case only the one-way random effects model is studied. Simulation studies are presented, showing that the normal approximation to the large deviation modified likelihood ratio statistic gives confidence intervals for variance components with coverage probabilities very close to the nominal confidence coefficient.     

Nonparametric density estimation from data with a mixture of Berkson and classical errors

The author considers density estimation from contaminated data where the measurement errors come from two very different sources. A first error, of Berkson type, is incurred before the experiment: the variable X of interest is unobservable and only a surrogate can be measured. A second error, of classical type, is incurred after the experiment: the surrogate can only be observed with measurement error. The author develops two nonparametric estimators of the density of X, valid whenever Berkson, classical or a mixture of both errors are present. Rates of convergence of the estimators are derived and a fully data‐driven procedure is proposed. Finite sample performance is investigated via simulations and on a real data example.     

Estimating the Upper Support Point in Deconvolution

Abstract.  We consider estimation of the upper boundary point F ⁻¹ (1) of a distribution function F with finite upper boundary or 'frontier' in deconvolution problems, primarily focusing on deconvolution models where the noise density is decreasing on the positive halfline. Our estimates are based on the (non-parametric) maximum likelihood estimator (MLE) of F . We show that (1) is asymptotically never too small. If the convolution kernel has bounded support the estimator (1) can generally be expected to be consistent. In this case, we establish a relation between the extreme value index of F and the rate of convergence of (1) to the upper support point for the 'boxcar' deconvolution model. If the convolution density has unbounded support, (1) can be expected to overestimate the upper support point. We define consistent estimators , for appropriately chosen vanishing sequences ( β _n ) and study these in a particular case.