磁性目标跟踪的后验克拉美罗下限分析与计算

为了求解磁性目标跟踪问题的后验克拉美罗下限(PCRB),提出了PCRB-GMSPPF算法。该算法利用高斯混合采样粒子滤波算法对目标状态的真实后验概率密度分布进行抽样,再通过蒙特卡洛积分法迭代求解每个观测时刻的Fisher信息矩阵,进而得出目标状态估计的PCRB;克服了基于PF算法求解PCRB过程中由于粒子退化和贫化问题造成不能从后验概率分布中正确抽样的缺点;在建立磁性目标跟踪的状态模型和观测模型的基础上进行仿真分析,将求解出的PCRB与采用GMSPPF及PF算法进行跟踪的均方根误差做对比,验证所提的PCRB-GMSPPF算法的有效性,结果表明:针对磁性目标跟踪问题,PCRB-GMSPPF算法较PCRB-PF算法具有更好的准确性,并可用于一般的非线性模型跟踪误差下限分析。     

二维TOA定位中的最小GDOP问题

基于二维TOA定位的Cramer?Rao下界,对传感器测量精度不尽相同的一般情形,推导获得了最优GDOP值及其充要条件,并利用GDOP等值线图进行了验证。结果表明,最佳的GDOP 值是传感器测量精度的简单表达式,或一个带约束条件的组合优化问题的最优值。     

基于遗传算法的陆基导航布站方法研究

陆基导航是一种通过高精度的测距进行定位、定速的系统,其定位、定速精度与布站有密切关系.为优化陆基导航的布站结果,提高惯性+陆基复合导航精度,提出了一种基于遗传算法的陆基导航布站方法.推导了使用惯性+陆基复合导航的Cramer-Rao下界(CRLB),并以其作为遗传算法布站寻优的适应性函数,再结合其他影响布站的因素作为约束条件,形成了基于遗传算法的布站方法.数学仿真表明,通过该方法的布站可得到较为满意的复合导航的精度,在实际工程应用中具有重要意义.     

A branch and bound algorithm for sector allocation of a naval task group

A naval task group (TG) is a collection of naval combatants and auxiliaries that are grouped together for the accomplishment of one or more missions. Ships forming a TG are located in predefined sectors. We define determination of ship sector locations to provide a robust air defense formation as the sector allocation problem (SAP). A robust formation is one that is very effective against a variety of attack scenarios but not necessarily the most effective against any scenario. We propose a 0‐1 integer linear programming formulation for SAP. The model takes the size and the direction of threat into account as well as the defensive weapons of the naval TG. We develop tight lower and upper bounds by incorporating some valid inequalities and use a branch and bound algorithm to exactly solve SAP. We report computational results that demonstrate the effectiveness of the proposed solution approach. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

SOJ干扰条件下雷达网目标跟踪条件CRLB

针对雷达检测概率评估雷达网抗干扰效能方法的局限性,通过对雷达天线和检测概率模型的分析,利用单雷达检测/漏检事件,推导了干扰环境下雷达网的条件CRLB,给出了干扰条件下目标跟踪误差所能达到的一个下界,从而提出了组网雷达基于条件CRLB的效能评估方法,同时从理论上证明了雷达组网的优越性。实验仿真结果表明,SOJ干扰下,利用CRLB可以很明显地证明雷达网融合跟踪比单部雷达性能要好,能比较准确地评估干扰环境下雷达网的性能,因此所述方法具有一定的实际工程意义。     

On‐line parameter estimation for a partially observable system subject to random failure

In this paper, we study the on‐line parameter estimation problem for a partially observable system subject to deterioration and random failure. The state of the system evolves according to a continuous time homogeneous Markov process with a finite state space. The system state is not observable, except for the failure state. The information related to the system state is available at discrete times through inspections. A recursive maximum likelihood (RML) algorithm is proposed for the on‐line parameter estimation of the model. The RML algorithm proposed in the paper is considerably faster and easier to apply than other RML algorithms in the literature, because it does not require projection into the constraint domain and calculation of the gradient on the surface of the constraint manifolds. The algorithm is illustrated by an example using real vibration data. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

高动态环境的GPS信号互模糊函数估计器及其性能分析

为了更可靠地跟踪高动态环境GPS信号参数,提出了一种新的适应高动态环境的GPS参数估计方法———互模糊函数方法。在假定高信噪比的条件下,推导了互模糊函数估计器的估计方差以及Cram er-Rao界,得出了采用互模糊函数法对高动态环境GPS信号的时延和多普勒频移进行联合估计是渐近无偏和渐近有效的结论。     

基于移动Agent的特定数值求解算法研究

在分布式环境下,运用移动Agent技术实现大型复杂系统研究中经常使用的特定数值算法求解,可显著提高计算效率。以分支定界法为例,介绍了其设计思想和实践步骤,并对算法的时间复杂度进行了分析。     

Recursive weighted least squares estimation algorithm based on minimum model error principle

Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matrix and filter parameters are difficult to be determined,which may result in filtering divergence.As to the problem that the accuracy of state estimation for nonlinear ballistic model strongly depends on its mathematical model,we improve the weighted least squares method(WLSM)with minimum model error principle.Invariant embedding method is adopted to solve the cost function including the model error.With the knowledge of measurement data and measurement error covariance matrix,we use gradient descent algorithm to determine the weighting matrix of model error.The uncertainty and linearization error of model are recursively estimated by the proposed method,thus achieving an online filtering estimation of the observations.Simulation results indicate that the proposed recursive esti-mation algorithm is insensitive to initial conditions and of good robustness.     

测量船中心机实时监控专家系统建造技术

测量船中心机实时监控专家系统应用于测量船中心机系统执行海上测量任务的时节，旨在提高整个系统的实时性和可靠性。本文围绕实时专家系统的时序性、时变性、时效性等特征，从系统设计与实现两个方面讨论建造该专家系统的一些关键技术。