基于推广卡尔曼滤波的无源定位系统仿真

在现代电子战、信息战环境中，由于利用目标辐射电磁信息的无源探测定位系统具有自身隐蔽和探测距离远等优点，因此它具有重要的应用价值，并已成为当今非线性跟踪与估计研究领域的热点问题、针对无源定位中状态空间模型非线性和程度较高所引起的滤波发散问题，分析总结了推广卡尔曼滤波(EKF)次优递推滤波过程，最后给出滤波方程及仿真结果．     

编队协同作战中的数据关联技术研究

介绍了编队协同作战系统及其功能,分析了该系统的关键技术之一—数据关联,分析了当前主要的数据关联算法,并指出了其未来发展趋势。     

基于固定延迟平滑算法的机动目标跟踪方法

为进一步提高机动目标的跟踪精度和估值算法的数值稳定性,文章基于平方根UKF（square root UKF：SR-UKF）和固定延迟平滑算法（fixed-lag smoothing）提出一种新的固定延迟平方根UKS算法（fixed-lag square root unscentedkal man smoothing：FL-SR-UKS）。数值仿真说明该算法可以有效处理机动目标跟踪问题,且状态估值精度优于SR-UKF。     

一种基于卡尔曼滤波的修正比例导引率

为分析雷达主动导引头抗干扰性能,建立所需数学模型,对导引头导引精度进行仿真分析。在模拟导引头进行导引所需的距离、速度以及角度信息的干扰噪声情况下,仿真分析了导引头在噪声干扰下的弹道轨迹和导引精度;为进一步提高导引头抗干扰性能,提出基于卡尔曼滤波的修正比例导引率。仿真结果证明此导引率提高了导弹的飞行稳定度和命中率,提高了导引头的抗干扰性能,为导引头试验提供理论支持和相应的试验依据。     

一种基于模型的水声弱信号检测方法(英文)

Detection of weak underwater signals is an area of general interest in marine engineering. A weak signal detection scheme was developed; it combined nonlinear dynamical reconstruction techniques, radial basis function (RBF) neural networks and an extended Kalman filter (EKF). In this method chaos theory was used to model background noise. Noise was predicted by phase space reconstruction techniques and RBF neural networks in a synergistic manner. In the absence of a signal, prediction error stayed low and became relatively large when the input contained a signal. EKF was used to improve the convergence rate of the RBF neural network. Application of the scheme to different experimental data sets showed that the algorithm can detect signals hidden in strong noise even when the signal-to-noise ratio (SNR) is less than −40d B.     

城市道路网络动态OD估计模型

针对以动态交通管理与控制为目标的动态交通需求计算问题,分析了OD量与交叉口转向交通量的动态关系,将其作为新的系统测量量引入,以此建立了同时考虑路段断面交通量和交叉口转向交通量的状态空间模型,得到了基于城市道路网络的动态OD估计模型,给出了考虑不等式约束的卡尔曼滤波递推方程及相应算法过程。利用微观仿真软件Paramics所建立的实验平台对该模型进行了仿真验证。结果表明：应用该模型进行交通量计算,与传统的仅考虑路段断面交通量模型相比,绝对误差平均减少9％,相对误差平均减少20％,而且其能更好地反映交通量真实值随时间变化的情况,计算结果明显优于传统模型。     

An integrated GPS/DR navigation system for AUV

GPS/Dead-reckoning navigation system for autonomous underwater vehicle （AUV） is introduced, which includes navigation overall architecture, hardware and software structure. Dead-reckoning theory is presented in details. And the strong tracking Kalman filter and Singer model are applied to handle the imprecise navigation mode, which can improve the navigation system＇s precision and reliability. Finally, the sea experiments which include autonomous search mission in an unknown area and long distance motion are conducted to demonstrate the reliability and feasibility of the navigation system.     

航向数据去噪中两种滤波方法对比研究

电控罗经因其多种优点得剑广泛的使用,但是由于受多种干扰影响,其航向数据存在不同噪声。利用改进的Sage-Husa自适应滤波和抗野值算法,将其应用到罗经的数据处理中,并与经典kalman进行了比较。通过试验结果中误差方差和Allan方差系数的比较,得到Sage-Husa自适应滤波比经典kalrnan更适用于航向数据的处理。     

Fault detection and isolation for a full-scale railway vehicle suspension with multiple Kalman filters

Reliability and dependability in complex mechanical systems can be improved by fault detection and isolation (FDI) methods. These techniques are key elements for maintenance on demand, which could decrease service cost and time significantly. This paper addresses FDI for a railway vehicle: the mechanical model is described as a multibody system, which is excited randomly due to track irregularities. Various parameters, like masses, spring- and damper-characteristics, influence the dynamics of the vehicle. Often, the exact values of the parameters are unknown and might even change over time. Some of these changes are considered critical with respect to the operation of the system and they require immediate maintenance. The aim of this work is to detect faults in the suspension system of the vehicle. A Kalman filter is used in order to estimate the states. To detect and isolate faults the detection error is minimised with multiple Kalman filters. A full-scale train model with nonlinear wheel/rail contact serves as an example for the described techniques. Numerical results for different test cases are presented. The analysis shows that for the given system it is possible not only to detect a failure of the suspension system from the system's dynamic response, but also to distinguish clearly between different possible causes for the changes in the dynamical behaviour.     

自适应卡尔曼滤波技术在水下机器人运动控制中的应用

水下机器人的位置和速度传感器受环境影响较大，数据滤波问题是运动控制的核心问题之一。建立了水下机器人运动的状态方程和量测方程，并在此基础上采用自适应卡尔曼滤波方法对水下机器人的传感器数据进行了滤波分析，并引入渐消记忆指数加权方法，避免了系统误差和量测误差统计特性的不准确对系统滤波效果的影响。结果表明此方法能达到很好的滤波效果。