基于时间的通信导航兼备系统定位误差分析

通信导航兼备系统已成为未来通信导航系统发展方向 ,由美国发展起来的联合战术信息分发系统(JTIDS)是典型代表。研究了通信和导航发展的历程及互相结合的途径 ,分析了JTIDS的工作方式和信号特征 ,讨论了JTIDS通信和导航能力 ;运用导航定位误差分析理论着重对JTIDS的相对导航原理及定位误差进行了深入分析 ,首次给出了该系统的定位误差场分布图和系统工作区。研究和分析结果可为建设和发展通信导航兼备系统提供重要参考     

利用GDOP对蜂窝移动通信系统移动台定位的方法

针对基于网络的到达时间 (TOA)定位系统 ,分析了定位误差与移动台 (MS)位置及参数测量误差的关系 ,结合实际中能同时接收到移动台信号的基站 (BS)数有限、定位误差对移动台位置敏感的特点 ,提出了直接用解析法计算所有位置线的交点 ,然后用几何淡化因子 (GDOP)及参数测量误差的均方差对交点进行加权平均来估计移动台位置的方法。该方法同时考虑了参数误差与几何淡化因子对定位精度的影响 ,不需要矩阵求逆 ,运算量小 ,速度快。适用于存在直达波 (LOS)信号的蜂窝通信环境。     

流水施工组织的工期计算

本文通过实例着重探讨在流水施工中各种情况的工期计算方法。     

A super-resolution algorithm for spectral estimation and time series extrapolation

Recent developments in the signal processing field of electrical engineering have resulted in several frequency domain methods of extrapolating a time series. Insight gained in testing one such method, the Papoulis algorithm, has been used to suggest modifications which greatly improve its performance under most operating conditions where real data are concerned. The modified Papoulis method thus developed has been applied to electricity load forecasting over the short and medium term, as well as to world economic and energy data, to assess the cyclic structure present in each series about a trend.     

Linear constraints,robust-weighting and efficient composite modeling

Recent studies have shown that composite forecasting produces superior forecasts when compared to individual forecasts. This paper extends the existing literature by employing linear constraints and robust regression techniques in composite model building. Security analysts forecasts may be improved when combined with time series forecasts for a diversified sample of 261 firms with a 1980-1982 post-sample estimation period. The mean square error of analyst forecasts may be reduced by combining analyst and univariate time series model forecasts in constrained and unconstrained ordinary least squares regression models. These reductions are very interesting when one finds that the univariate time series model forecasts do not substantially deviate from those produced by ARIMA (0,1,1) processes. Moreover, security analysts' forecast errors may be significantly reduced when constrained and unconstrained robust regression analyses are employed.     

Forecast comparisons of four models of U.S. interest rates

In this paper we compare the out of sample forecasts from four alternative interest rate models based on expanding information sets. The random walk model is the most restrictive. The univariate time series model allows for a richer dynamic pattern and more conditioning information on own rates. The multivariate time series model permits a flexible dynamic pattern with own- and cross-series information. Finally, the forecasts from the MPS econometric model depend on the full model structure and information set. In theory, more information is preferred to less. In practice, complicated misspecified models can perform much worse than simple (also probably misspecified) models. For forecasts evaluated over the volatile 1970s the multivariate time series model forecasts are considerably better than those from simpler models which use less conditioning information, as well as forecasts from the MPS model which uses substantially more conditioning information but also imposes ‘structural’ economic restrictions.     

A unified view of statistical forecasting procedures

A large number of statistical forecasting procedures for univariate time series have been proposed in the literature. These range from simple methods, such as the exponentially weighted moving average, to more complex procedures such as Box–Jenkins ARIMA modelling and Harrison–Stevens Bayesian forecasting. This paper sets out to show the relationship between these various procedures by adopting a framework in which a time series model is viewed in terms of trend, seasonal and irregular components. The framework is then extended to cover models with explanatory variables. From the technical point of view the Kalman filter plays an important role in allowing an integrated treatment of these topics.     

The accuracy of extrapolation (time series) methods: Results of a forecasting competition

In the last few decades many methods have become available for forecasting. As always, when alternatives exist, choices need to be made so that an appropriate forecasting method can be selected and used for the specific situation being considered. This paper reports the results of a forecasting competition that provides information to facilitate such choice. Seven experts in each of the 24 methods forecasted up to 1001 series for six up to eighteen time horizons. The results of the competition are presented in this paper whose purpose is to provide empirical evidence about differences found to exist among the various extrapolative (time series) methods used in the competition.     

Fitting of growth curves over time when the data are obtained from a single realization

This paper focuses on the general problem of forecasting the maximum value of a time series which by the nature of the data must approach an asymptotic value. Examples of such series include the growth of organisms, the concentration of a chemical reagent during a reaction occurring over time or the amount of a fossil fuel resource which has been discovered or produced as a function of time. The approach taken below differs from the usual models for this type of data in that it assumes that an unobserved time series is actually driving the process, and that the observed data series is a function of the unobserved process. In the case of fossil fuels the unobserved series might be a measure of the exploratory drilling, the number of production days in a given time period or even the amount of fiscal resources devoted to exploratory activities. A maximum likelihood method is developed for estimating the parameters of the process, especially the maximum S, and the covariance structure of the estimators is developed. The methodology is illustrated on an example of oil production. Finally, methods are developed for forecasting the data into the near future.     

飞机结构强度试验中声源定位技术的研究与验证

针对飞机结构强度试验中飞机结构损伤位置定位困难这一问题,采用基于时差(time difference of arrival,TDOA)的定位方法,比较了最小二乘法和最优化方法的定位精度;并对影响定位精度的因素进行了分析讨论。然后采用十六元八边形声阵列建立了定位系统,在试验现场对机翼盒段的敲击声进行定位。结果表明声阵列能够消除混响及低频噪声的影响,具有良好的定位精度,适用于飞机结构强度试验。