几种非线性滤波算法的性能分析

非线性随机动态系统的滤波问题是一类经常遇到的实际应用问题,本文分析了扩展卡尔曼(EKF)、无迹卡尔曼滤波(UKF)和粒子滤波(PF)这三种非线性滤波算法的基本原理和特点以及适应的条件。并通过一个强非线性系统的实验仿真,验证了各自算法的性能。     

MIMO系统中基于卡尔曼滤波的信道预测算法研究

多输入多输出(MIMO)技术是LTE系统中的关键技术之一,能够在不增加带宽的情况下成倍地提高了通信系统的容量和频谱利用率。本文提出了一种多用户MIMO下行链路中基于卡尔曼滤波的信道预测算法。在实际传输时,发射端根据接收端反馈回来的信道信息预测出下一时刻的下行信道信息,仿真结果验证了所提方法的有效性。     

A Kalman filter formulation for noisy regional job data

A problem of great concern to regional economic forecasters is how to know the current local economic status given the delays and noise in provisional data. In this paper the structure of revisions to one of the most important regional data sets, local jobs by industry, is analyzed. A Kalman filter formulation is presented that can be used to improve the monthly estimates of jobs in local industries where large subsequent data revisions are most likely. Two data sets, one for the State of Virginia and one for the Louisville MSA, are used to illustrate the technique. The results, while not conclusive, suggest that the technique may be of practical importance in the monitoring and forecasting of employment activity in mining, construction and other industries for which provisional estimates are subject to the greatest later revision.     

Interpolation and backdating with a large information set

Existing methods for data interpolation or backdating are either univariate or based on a very limited number of series, due to data and computing constraints that were binding until the recent past. Nowadays large datasets are readily available, and models with hundreds of parameters are easily estimated. We model these large datasets with a factor model, and develop an interpolation method that exploits the estimated factors as an efficient summary of all available information. The method is compared with existing standard approaches from a theoretical point of view, by means of Monte Carlo simulations, and also when applied to actual macroeconomic series. The results indicate that our method is rather robust to model misspecification, although traditional multivariate methods also work well while univariate approaches are systematically outperformed. When interpolated series are subsequently used in econometric analyses, biases can emerge, but they are smaller with multivariate approaches, including factor-based ones.     

Time Series Analysis of Repeated Surveys: The State–space Approach

Cross sectional estimates from repeated surveys form a time series { y_t }. These estimates can be viewed as the sum y _t = Y _t + e _t of two processes, { Y _t }, the population process and { e _t }, the survey error process. Serial correlations in the latter series are usually present, mainly due to sample overlap. Other sources of data such as censuses, administrative records and demographic population counts are also available. The state–space modelling approach to the analysis of repeated surveys allows combining information from different sources, incorporating benchmarking constraints in a natural way. Results from these methods seem to compare favourably with those from X-11-ARIMA in filtering out survey errors.     

利用改进的卡尔曼滤波器预测退化设备可靠性

针对退化设备可靠性预测的问题,采用加入次优渐消因子的卡尔曼滤波器预测设备未来时刻退化量均值,进而预测未来时刻的可靠性。本文给出了退化设备的可靠性预测方法和具体算法,这可为确定停机维护时间、维修费用等提供依据,具有重要意义。仿真实验验证了该方法的有效性。     

小型无人机导航系统的探索与研究

针对执行特定海域巡航的小型无人机,提出了GPS/INS组合导航系统的解决方案。首先介绍导航系统的算法和原理,根据任务需求,给出硬件构成及软件核心程序流程,通过仿真,证明在组合系统中采取PID控制以及卡尔曼滤波算法大大提高了导航的精度。     

Invertible and non-invertible information sets in linear rational expectations models

Rational expectations solutions are usually derived by assuming that all state variables relevant to forward-looking behaviour are directly observable, or that they are “…an invertible function of observables” (Mehra and Prescott, 1980). Using a framework that nests linearised DSGE models, we give a number of results useful for the analysis of linear rational expectations models with restricted information sets. We distinguish between instantaneous and asymptotic invertibility, and show that the latter may require significantly less information than the former. We also show that non-invertibility of the information set can have significant implications for the time series properties of economies.     

现代农业信息系统研究

结合信息技术与传统农业，分析了我国现代化农业信息的发展状况，并对农业信息系统的形式提出了建议，构建了现代农业信息系统的框架，引入了信息过滤和数据挖掘相结合的思想，并分析了这些技术的结合，表明了这样的系统对进行农业决策起到了很大的作用，从而说明了现代化农业信息系统对农业发展的重要性。     

供应链中牛鞭效应的模型与分析

对研究牛鞭效应时经常讨论到的三种模型:系统动力学模型、自回归分析的AR(1)模型及Kalman滤波器(KF)模型进行了分析和比较。     

Value investing anomalies in the European stock market: Multiple Value, Consistent Earner, and Recognized Value

Empirical academic studies have consistently found that value stocks outperform glamour stocks and the market as a whole. This article extends prevailing research on existing value anomalies. It evaluates simple value strategies for the European stock market (compared to many other studies that test market data on a country-by-country basis) as well as sophisticated multi-dimensional value strategies that also include capital return variables (Consistent Earner Strategy) and momentum factors (Recognized Value Strategy), the latter reconciling intermediate horizon momentum and long-term reversals of behavioral finance theories. It can be shown that these “enhanced” value strategies can produce superior returns compared to returns of the whole market or “simple” value strategies without capturing higher risks applying traditional risk measures.     

Determining analogies based on the integration of multiple information sources

Forecasting approaches that exploit analogies require the grouping of analogous time series as the first modeling step; however, there has been limited research regarding the suitability of different segmentation approaches. We argue that an appropriate analytical segmentation stage should integrate and trade off different available information sources. In particular, it should consider the actual time series patterns, in addition to the variables that characterize the drivers behind the patterns observed. The simultaneous consideration of both information sources, without prior assumptions regarding the relative importance of each, leads to a multicriteria formulation of the segmentation stage. Here, we demonstrate the impact of such an adjustment to segmentation on the final forecasting accuracy of the cross-sectional multi-state Kalman filter. In particular, we study the relative merits of single and multicriteria segmentation stages for a simulated data set with a range of noise levels. We find that a multicriteria approach consistently achieves a more reliable recovery of the original clusters, and this feeds forward to an improved forecasting accuracy across short forecasting horizons. We then use a US data set on income tax liabilities to verify that this result generalizes to a real-world setting.     

Convergence in Transition Countries – Focus on Investment: Central and Eastern Europe, 1970–1996

Our data on investment in Central and Eastern European economies reveal that, though investment rates were typically high in the 1970s, the marginal efficiency of investment was low. Investment shares begun to decline in the 1980s, before the collapse of the communist system, but there was some recovery in most countries after transition. We use the Kalman filter framework to test for convergence in investment rates. We find some evidence of convergence in Central European countries – former Czechoslovakia, Poland and the countries of the former Yugoslavia. For the remainder of the socialist bloc, however, we were unable to isolate convergence in investment shares. This revised version was published online in July 2006 with corrections to the Cover Date.     

北斗/CDMA组合导航数据融合及其在军事物流中的应用研究

提出了一种新的组合导航定位技术-北斗/CDMA导航定位,进一步研究了如何利用数据融合技术提高定位精度与可靠性。设计了一种简化的联邦卡尔曼滤波器并给出了相应的滤波算法流程。最后提出了北斗/CDMA组合导航定位技术在军事物流监控调度系统中的应用解决方案。