光纤陀螺随机漂移分析与建模方法

随机漂移是影响光纤陀螺精度的重要因素。根据光纤陀螺静态漂移测试信号,利用Allan方差法对光纤陀螺随机漂移误差进行了分离和辨识,并分析了系数拟合过程中最小二乘法的病态矩阵问题。最后探讨了利用AIC准则和长自回归法建立光纤陀螺随机漂移ARMA模型的方法,为进一步对光纤陀螺输出信号进行滤波处理奠定了基础。     

基于小波神经网络的MEMS陀螺输出预测方法

为了解决弹载MEMS陀螺实时输出预测问题,提高陀螺输出预测精度并减小计算量,通过分析弹载陀螺信号时间序列特性,结合小波分析和神经网络两种方法的优势,提出了一种基于小波神经网络的MEMS陀螺输出预测方法。选取并优化神经网络结构参数,利用实测陀螺数据对建立的小波神经网络进行训练,并预测出未来一段较短时间的陀螺输出值。与陀螺实测值和ARMA模型预测结果的对比发现,小波神经网络方法有效地提高了MEMS陀螺输出预测的精度、减小了计算量,从而验证了该方法的有效性和精度。     

Exact expected values of variance estimators for simulation

We formulate exact expressions for the expected values of selected estimators of the variance parameter (that is, the sum of covariances at all lags) of a steady‐state simulation output process. Given in terms of the autocovariance function of the process, these expressions are derived for variance estimators based on the simulation analysis methods of nonoverlapping batch means, overlapping batch means, and standardized time series. Comparing estimator performance in a first‐order autoregressive process and the M/M/1 queue‐waiting‐time process, we find that certain standardized time series estimators outperform their competitors as the sample size becomes large. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Predicting the cost performance of inventory control systems by retrospective simulation

When an inventory-control system is designed to utilized statistical demand information in computing an (s,S) policy, it may be necessary to use performance forecasts to calibrate the relationship between control parameters and performance measures. Using a simulation model to test prediction by retrospective simulation we estimated the bias and variance of forecasts for expected costs and other operating characteristics. The results show the sensitivity of the prediction bias to changes in demand, cost, and lead time parameters and to choices made in system design.     

基于自校正卡尔曼滤波器的信息融合算法

设计了一种新的基于ARMA模型自校正卡尔曼滤波器及其信息融合的方法,从而避免了经典卡尔曼滤波器需要精确知道系统的模型参数和噪声统计特性的缺点.仿真结果表明,在未知部分模型参数和噪声统计特性的情况下,自校正卡尔曼滤波器的滤波性能非常接近稳态最优卡尔曼滤波器;基于自校正卡尔曼滤波器的信息融合滤波性能接近基于稳态最优卡尔曼滤波器的信息融合滤波性能.     

一种新的自适应粒子滤波算法*

在对实时性要求高的系统中,粒子数的选择对粒子滤波至关重要,需要在滤波精度与计算复杂度乏间取折衷.提出了一种基于决策规则的自适应粒子滤波算法,在定义综合性能代价函数的基础上,推导出粒子数与滤波误差方差之间的关系,使得在粒子滤波过程中,可以根据实际滤波情况在线调节粒子数,以使滤波的综合性能达到最优.通过Monte Carlo仿真实验表明,新算法在与基本粒子滤波算法(PF)保持同等精度的条件下,大大降低了算法的计算量,约为PF的六分之一.     

自适应采样数粒子滤波算法

基于统计决策规则提出自适应采样数粒子滤波算法,在定义综合性能风险函数的基础上,推导出粒子数与滤波误差方差之间的关系式,使得在跟踪过程中,可以根据目标的机动情况在线调节粒子数,以使跟踪性能达到最优。在Matlab仿真平台下进行了闪烁噪声下的机动目标跟踪实验,结果表明,自适应采样数粒子滤波算法是一种有效的机动目标跟踪方法,跟踪性能较基本粒子滤波算法提高了3.7倍。     

Combining standardized time series area and Cramér–von Mises variance estimators

We propose three related estimators for the variance parameter arising from a steady‐state simulation process. All are based on combinations of standardized‐time‐series area and Cramér–von Mises (CvM) estimators. The first is a straightforward linear combination of the area and CvM estimators; the second resembles a Durbin–Watson statistic; and the third is related to a jackknifed version of the first. The main derivations yield analytical expressions for the bias and variance of the new estimators. These results show that the new estimators often perform better than the pure area, pure CvM, and benchmark nonoverlapping and overlapping batch means estimators, especially in terms of variance and mean squared error. We also give exact and Monte Carlo examples illustrating our findings.© 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Assessing the value of demand sharing in supply chains

We consider the problem of assessing the value of demand sharing in a multistage supply chain in which the retailer observes stationary autoregressive moving average demand with Gaussian white noise (shocks). Similar to previous research, we assume each supply chain player constructs its best linear forecast of the leadtime demand and uses it to determine the order quantity via a periodic review myopic order‐up‐to policy. We demonstrate how a typical supply chain player can determine the extent of its available information in the presence of demand sharing by studying the properties of the moving average polynomials of adjacent supply chain players. The retailer's demand is driven by the random shocks appearing in the autoregressive moving average representation for its demand. Under the assumptions we will make in this article, to the retailer, knowing the shock information is equivalent to knowing the demand process (assuming that the model parameters are also known). Thus (in the event of sharing) the retailer's demand sequence and shock sequence would contain the same information to the retailer's supplier. We will show that, once we consider the dynamics of demand propagation further up the chain, it may be that a player's demand and shock sequences will contain different levels of information for an upstream player. Hence, we study how a player can determine its available information under demand sharing, and use this information to forecast leadtime demand. We characterize the value of demand sharing for a typical supply chain player. Furthermore, we show conditions under which (i) it is equivalent to no sharing, (ii) it is equivalent to full information shock sharing, and (iii) it is intermediate in value to the two previously described arrangements. Although it follows from existing literature that demand sharing is equivalent to full information shock sharing between a retailer and supplier, we demonstrate and characterize when this result does not generalize to upstream supply chain players. We then show that demand propagates through a supply chain where any player may share nothing, its demand, or its full information shocks (FIS) with an adjacent upstream player as quasi‐ARMA in—quasi‐ARMA out. We also provide a convenient form for the propagation of demand in a supply chain that will lend itself to future research applications. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 515–531, 2014     

Design of follow‐up experiments for improving model discrimination and parameter estimation

One goal of experimentation is to identify which design parameters most significantly influence the mean performance of a system. Another goal is to obtain good parameter estimates for a response model that quantifies how the mean performance depends on influential parameters. Most experimental design techniques focus on one goal at a time. This paper proposes a new entropy‐based design criterion for follow‐up experiments that jointly identifies the important parameters and reduces the variance of parameter estimates. We simplify computations for the normal linear model by identifying an approximation that leads to a closed form solution. The criterion is applied to an example from the experimental design literature, to a known model and to a critical care facility simulation experiment. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

GPS动态定位中的自适应扩展卡尔曼滤波算法

针对全球定位系统 (GPS)提出一种扩展卡尔曼滤波器算法。这种滤波器算法直接从GPS接收机输出的定位结果入手 ,将各种误差因素的影响等效为一个总误差 ,对GPS接收机的机动载体的加速度采用当前统计模型 ,并利用线性卡尔曼滤波器进行动态定位数据的处理。本模型简单 ,实时性好 ,滤波后定位精度得到提高。     

Threshold incentives over multiple periods and the sales hockey stick phenomenon

In this article, we study threshold‐based sales‐force incentives and their impact on a dealer's optimal effort. A phenomenon, observed in practice, is that the dealer exerts a large effort toward the end of the incentive period to boost sales and reach the threshold to make additional profits. In the literature, the resulting last‐period sales spike is sometimes called the hockey stick phenomenon (HSP). In this article, we show that the manufacturer's choice of the incentive parameters and the underlying demand uncertainty affect the dealer's optimal effort choice. This results in the sales HSP over multiple time periods even when there is a cost associated with waiting. We then show that, by linking the threshold to a correlated market signal, the HSP can be regulated. We also characterize the variance of the total sales across all the periods and demonstrate conditions under the sales variance can be reduced. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

ICAO二阶阶跃畸变模型下的SAIM接收机测距精度分析

测距精度是评估卫星自主完好性监测（SAIM）接收机在导航信号畸变环境下工作性能的一个重要指标,论文从数学上推导出了SAIM接收机在国际民航组织（ICAO）二阶阶跃畸变模型下采用早晚相干跟踪处理时测距精度与畸变模型参数的解析表达式,理论分析和仿真结果表明数字畸变将导致接收机伪码鉴相器输出曲线存在过零点偏差,数字畸变量基本不影响接收机测距方差;模拟畸变将扭曲相关峰函数和鉴相器输出曲线,但鉴相器输出基本上不存在过零点偏差,模拟畸变参数、前端滤波器带宽和早晚码间距共同影响接收机的测距方差。     

反馈干扰系统的自校正控制方法

为实现系统跟踪期望输出及系统未知参数的在线闭环辨识,针对带反馈干扰的未知参数系统,结合最小方差控制算法和增广最小二乘法设计了自校正调节器。首先通过对反馈通道干扰的分析,给出了系统的等效模型;其次,按系统输出最小方差的原理设计了控制器,实现了系统跟踪期望输出;第三,运用增广最小二乘法对系统参数的辨识,给出未知参数的无偏一致估计,实现了参数的在线闭环辨识。通过对未知参数的辨识,用所辨识的参数计算调节器的参数,完成对控制律的修改,实现了系统跟踪期望输出和参数的在线闭环辨识。最后,利用该方法对实例进行了仿真研究。仿真结果表明:所提方法简单可行。     

自适应交互多模型火控跟踪算法

以舰载近程防空火控系统的跟踪预测为背景,提出一种自适应的交互式多模型跟踪算法,模型集合取为"当前"统计模型和匀速直线模型.该算法采用后验估计的方法在线辩识及自适应调整上述两个模型的噪声方差,并且采用加权系数限定准则,实现机动目标运动模式与模型集合的自适应.仿真计算证明了该模型算法的有效性.     

混响背景下基于高阶统计量的动目标检测方法

针对混响背景中的动目标检测问题，根据基阵接收数据经过波束形成与匹配滤波后的输出结果计算高阶统计量，并将其视作观测空间。基于此空间中混响和目标回波的差异，利用多ping的高阶统计量构造特征向量，计算特征向量之间的马氏距离作为混响和目标差异的量化标准，再依据最大一致条件功效检测准则选择门限检测方法。波形数据仿真与海上实录数据检验均表明该方法的检测性能优于单ping波束形成及匹配滤波方法。通过蒙特卡洛仿真获得不同信混比下的ROC曲线，与单ping检测相比，在保证虚警概率小于0.01，检测概率大于0.5的条件下，最小可检测信混比降低约6dB。     

用于目标跟踪的自适应广义调频波形设计算法

提出了一种用于雷达目标跟踪的自适应广义调频波形设计算法。该算法根据跟踪器的动态需求,以广义调频信号为样板波形自适应设计下一时刻的发射波形,其目的是使预测的目标跟踪均方误差最小化,并假定高信噪比条件,且目标跟踪运动模型和观测模型均为线性。利用与某一波形相对应的克拉美-罗下限(CRLB)以及卡尔曼滤波器,通过最小化预测的跟踪均方误差来实现广义调频波形的自适应设计。仿真结果表明:在信噪比相同的情况下,与使用固定参数、自适应参数的线性调频波形设计算法相比,所提出的算法能够获得更低的目标跟踪均方误差。     

基于正交最小二乘估计的非线性时间序列的预测

在对非线性时间序列的短期预测中经常采用局部线性化的预测算法 ,原有的算法使用普通最小二乘法 (LS)估计近似线性模型的参数。对于存在噪声的数据 ,该算法的数值稳定性较差。本文在对非线性空间进行局部线性化的基础上 ,采用正交最小二乘方法 (OLS)对线性模型同时进行结构选择与参数辨识 ,改善了数值的病态特性 ,增强了算法的稳定性     

数字化扩频接收机的自适应抗干扰滤波方案研究

讨论了自适应抗干扰滤波在最小频移键控的直接序列扩频(DS-MSK)数字化接收机中的应用问题,对非线性滤波加以改进,提出了部分解扩非线性滤波(PDNF)结构,将快速更新子带自适应滤波(FRSAF)算法用于PDNF结构以提高收敛速度。结合FRSAF算法的PDNF自适应抗干扰滤波方案在收敛速度、稳健性和输出信噪比等方面明显优于结合LMS算法的传统非线性抗干扰滤波方案,仿真结果验证了上述结论。     

Allocation of quality improvement targets based on investments in learning

Purchased materials often account for more than 50% of a manufacturer's product nonconformance cost. A common strategy for reducing such costs is to allocate periodic quality improvement targets to suppliers of such materials. Improvement target allocations are often accomplished via ad hoc methods such as prescribing a fixed, across‐the‐board percentage improvement for all suppliers, which, however, may not be the most effective or efficient approach for allocating improvement targets. We propose a formal modeling and optimization approach for assessing quality improvement targets for suppliers, based on process variance reduction. In our models, a manufacturer has multiple product performance measures that are linear functions of a common set of design variables (factors), each of which is an output from an independent supplier's process. We assume that a manufacturer's quality improvement is a result of reductions in supplier process variances, obtained through learning and experience, which require appropriate investments by both the manufacturer and suppliers. Three learning investment (cost) models for achieving a given learning rate are used to determine the allocations that minimize expected costs for both the supplier and manufacturer and to assess the sensitivity of investment in learning on the allocation of quality improvement targets. Solutions for determining optimal learning rates, and concomitant quality improvement targets are derived for each learning investment function. We also account for the risk that a supplier may not achieve a targeted learning rate for quality improvements. An extensive computational study is conducted to investigate the differences between optimal variance allocations and a fixed percentage allocation. These differences are examined with respect to (i) variance improvement targets and (ii) total expected cost. For certain types of learning investment models, the results suggest that orders of magnitude differences in variance allocations and expected total costs occur between optimal allocations and those arrived at via the commonly used rule of fixed percentage allocations. However, for learning investments characterized by a quadratic function, there is surprisingly close agreement with an “across‐the‐board” allocation of 20% quality improvement targets. © John Wiley & Sons, Inc. Naval Research Logistics 48: 684–709, 2001