采用最小似然bit同步的2PSK系统的性能分析

本文通过对最小似然bit同步定时误差及其与误码率关系的讨论,分析了采用最小似然bit同步的2pck七系统的性能。

基于分类统计的PolInSAR植被高度最大似然估计

极化干涉SAR是一种集极化和干涉SAR优势于一体的新型遥感技术。结合两层植被随机体散射模型和极化分解技术,基于极化干涉SAR数据的概率分布统计特征,提出一种利用参数迭代求解预测模型和测量值最小似然距离的植被高度反演方法。该方法克服了传统最大似然估计方法需已知地表散射特征参数的约束,减少了计算复杂性。最后通过极化干涉SAR仿真数据实验分析,文中算法相对于三阶段反演算法提高了植被高度估计的精度,验证了算法的有效性。

产品可靠性的Bootstrap回归统计分析方法

针对产品可靠性统计分析中,经常面临的小样本问题或误差项分布不明确的问题,将Bootstrap方法引入到产品可靠性的回归统计分析,提出了基于极大似然-最小二乘估计(ML-LSE)二步法的产品可靠性Bootstrap统计分析方法;同时通过对Bootstrap估计值进行纠偏处理,提高了小样本条件下或误差项分布不明确时产品可靠性的统计精度,并求得某型电连接器在正常应力水平下可靠性特征值的区间估计值。统计模拟的结果表明,经纠偏处理后的Bootstrap回归统计分析方法,所得产品可靠性特征值的估计精度能满足置信度的要求。

基于STLS的卫星惯量矩阵在轨估计

提出了一种采用结构总体最小二乘(Structured total least squares,STLS)进行卫星惯量矩阵在轨估计的方法,与当前估计方法相比,该方法在考虑敏感器测量噪声时能获得一致估计。首先由动量守恒定律得到估计方程,针对该方程的特点定义了惯量矩阵的STLS估计,并使用结构总体最小范数(Structured total least norm,STLN)算法进行求解。证明了当噪声为高斯分布时该STLS估计为极大似然估计,给出了该STLS估计具有一致性的充分条件,仿真结果验证了文章所提估计方法的有效性。

Novel K-best detection algorithms for MIMO system

Aiming at the optimum path excluding characteristics and the full constellation searching characteristics of the K-best detection algorithm, an improved-performance K-best detection algorithm and several reduced-complexity K-best detection algorithms are proposed. The improved-performance K-best detection algorithm deploys minimum mean square error （MMSE） filtering of a channel matrix before QR decomposition. This algorithm can decrease the probability of excluding the optimum path and achieve better performance. The reducedcomplexity K-best detection algorithms utilize a sphere decoding method to reduce searching constellation points. Simulation results show that the improved performance K-best detection algorithm obtains a 1 dB performance gain compared to the K- best detection algorithm based on sorted QR decomposition （SQRD）. Performance loss occurs when K = 4 in reduced complexity K-best detection algorithms. When K = 8, the reduced complexity K-best detection algorithms require less computational effort compared with traditional K-best detection algorithms and achieve the same performance.

一种基于局部ML的DFT-S-OFDM检测算法

离散傅里叶变换扩频正交频分复用(DFT-S-OFDM)作为一种单载波调制方案,与传统正交频分复用相比具有较小的峰均功率比(PAPR),并且已被3GPP采用作为其长期演进项目(LTE)的上行调制方案。针对该方案,提出了一种改进的基于局部最大似然(ML)的DFT-S-OFDM检测算法,首先挑选一阶最小均方误差-最大似然(MMSE-ML)检测后最有可能的2个星座符号位置,进而在这2个位置上做最大似然检测。仿真结果表明,提出的算法相比二阶MMSE-ML,在大幅度降低计算复杂度的基础上,性能损失很小;相比一阶MMSE-ML,能获得0.5 dB^0.7 dB的增益,而所需的额外计算量较少。

Characteristics for wind energy and wind turbines by considering vertical wind shear

The probability distributions of wind speeds and the availability of wind turbines were investigated by considering the vertical wind shear. Based on the wind speed data at the standard height observed at a wind farm, the power-law process was used to simulate the wind speeds at a hub height of 60 m. The Weibull and Rayleigh distributions were chosen to express the wind speeds at two different heights. The parameters in the model were estimated via the least square(LS) method and the maximum likelihood estimation(MLE) method, respectively. An adjusted MLE approach was also presented for parameter estimation. The main indices of wind energy characteristics were calculated based on observational wind speed data. A case study based on the data of Hexi area, Gansu Province of China was given. The results show that MLE method generally outperforms LS method for parameter estimation, and Weibull distribution is more appropriate to describe the wind speed at the hub height.

Reliability evaluation for Weibull distribution under multiply type-? censoring

The multiply type-I censoring represented that all units in life test were terminated at different times. For estimations of Weibull parameters, it was easy to compute the maximum likelihood estimation （MLE） and least-squares estimation （LSE） while it was hard to build confidence intervals （CI）. The concept of generalized confidence interval （GCI） was introduced to build CIs of parameters under multiply type-I censoring. Further, GCI based on LSE and GCI based on MLE were proposed. It is mathematically proved that the former is exact and the latter is approximate. Besides, a Monte Carlo simulation study and an illustrative example also Ran out that the GCI method based on LSE yields rather satisfactory results by comparison with the ones based on MLE. It should be clear that the GCI method is a sensible choice to evaluate reliability under multiply type-I censoring.

New Soft Output Viterbi Algorithm for Mobile Communication System

Soft output Viterbi algorithm （SOVA） is a turbo decoding algorithm that is suitable for hardware implementation. But its performance is not so good as maximum a posterior probability（MAP） algorithm. So it is very important to improve its performance. The non-correlation between minimum and maximum likelihood paths in SOVA is analyzed. The metric difference of both likelihood paths is used as iterative soft information, which is not the same as the traditional SOVA. The performance of the proposed SOVA is demonstrated by the simulations. For 1 024-bit frame size and 9 iterations with signal to noise ratio from 1 dB to 4 dB, the experimental results show that the new SOVA algorithm obtains about more 0. 4 dB and 0. 2 dB coding gains more than the traditional SOVA and Bi-SOVA algorithms at bit error rate（BER） of 1 × 10^-4 , while the latency is only half of the Bi-direction SOVA decoding.

ARMA模型构建及MATLAB实现

时间序列是指将某种现象某一个统计指标在不同时间上的各个数值，按时间先后顺序排列而形成的序列。时间序列分析是一种动态数据处理的统计方法，该方法基于随机过程理论和数理统计学方法，研究随机数据序列所遵从的统计规律，该方法是广泛应用和研究实际问题的工具之一，它能恰当的描述历史数据随时间的变化规律。首先介绍了常见的线性时间序列模型，进而给出了模型的构建方法，包括从平稳性、零均值来对数据进行预处理，以及对模型参数的估计和对模型的定阶方法。最后，运用MATLAB软件实现了对真实数据的参数估计，并采用最小二乘估计方法对多维数据进行参数估计。

Despreader for Direct Sequence Spread Spectrum System and Its Performance Analysis

A new type of despreader for direct sequence spread spectrum signal is proposed. Compared with traditional despreaders, the new despreader does not contain hard decision ware or handle binary sequence any more, and the locally stored spread spectrum signals are pre-modulated baseband signals (such as Gaussian minimum shift keying (GMSK) signals), which are much more similar to the received spread spectrum signals. Moreover, the missed detection probability of the despreader is about one order of magnitude lower than that of traditional ones. Based on the maximum likelihood criterion and phase probability density function of demodulated signal, a new method of ana- lyzing the despreaders’ performance is put forward, which is proved to be more accurate than traditional methods according to the numerical results. Finally, an adaptive despreader under different signal-to-noise ratios is given.

A fusion of least squares and empirical likelihood for regression models with a missing binary covariate

Multiply robust inference has attracted much attention recently in the context of missing response data. An estimation procedure is multiply robust, if it can incorporate information from multiple candidate models, and meanwhile the resulting estimator is consistent as long as one of the candidate models is correctly specified. This property is appealing, since it provides the user a flexible modeling strategy with better protection against model misspecification. We explore this attractive property for the regression models with a binary covariate that is missing at random. We start from a reformulation of the celebrated augmented inverse probability weighted estimating equation, and based on this reformulation, we propose a novel combination of the least squares and empirical likelihood to separately handle each of the two types of multiple candidate models,one for the missing variable regression and the other for the missingness mechanism. Due to the separation, all the working models are fused concisely and effectively. The asymptotic normality of our estimator is established through the theory of estimating function with plugged-in nuisance parameter estimates. The finite-sample performance of our procedure is illustrated both through the simulation studies and the analysis of a dementia data collected by the national Alzheimer's coordinating center.