双Markov多假设IMM机动目标跟踪算法

为提高交互式多模型(IMM)算法对机动目标的估计精度,需要增加其模型数量,但模型过多将导致计算量大并降低估计器性能。针对上述问题提出一种基于模型集的双马尔可夫多假设IMM机动目标跟踪算法。该算法用模型集间的马尔可夫转移阵描述模型集之间的大跳变,用模型的马尔可夫转移阵描述模型集内各模型间的小跳变或慢变,以达到细化建模、提高滤波精度的目的。     

Theoretical and experimental study of fatigue growth of interacting cracks

Life assessment of structures weakened by interacting cracks represents an important and very challenging problem. Subsequently, the main objective of this paper is to address this problem by developing a new computational technique. It is based on the classical strip-yield model and plasticity-induced crack closure concept. It also utilises the 3D fundamental solution for an edge dislocation. The crack advance scheme adopts the cycle-by-cycle calculations of the effective stress intensity factors and crack increments. The modelling results are validated against an experimental study focusing on fatigue behaviour of two closely spaced collinear cracks in wide plates with different thicknesses. It is confirmed that non-linear effects associated with crack interaction have a significant influence on fatigue life and cannot be disregarded in life and integrity assessments of structural components.     

改进的交互式多模型粒子滤波跟踪算法

通常的交互多模型卡尔曼滤波(IMMKF)或交互多模型扩展卡尔曼滤波(IMMEKF)对于非高斯问题无能为力;对于非线性问题,其性能不及交互多模型粒子滤波算法(IMMPF)。粒子滤波能够处理非线性/非高斯问题,其与交互式多模型结合用来获得更好的跟踪性能。然而,粒子滤波的主要问题是巨大的计算量,由于粒子滤波通常采用大量的粒子数目,将带来很大的计算负荷。该文提出了一种改进的交互多模型粒子滤波算法,其利用多模型综合使用了卡尔曼滤波和粒子滤波,与常规交互式多模型粒子滤波(IMMPF)相比,大大改善了计算效率。对于非线性/非高斯问题,其性能与IMMPF相当;对于线性问题,其性能与IMMEKF相当,并优于IMMPF的性能。     

基于高斯粒子滤波的当前统计模型跟踪算法

对于非线性系统估计问题,高斯粒子滤波器可以获得近似最优解,与粒子滤波器相比其优点是不需要重采样步骤和不存在粒子退化现象.采用高斯粒子滤波代替当前模型自适应跟踪算法中的卡尔曼滤波,将高斯粒子滤波与当前统计模型的优点相结合,提出了一种新的当前统计模型自适应跟踪算法,用于非线性非高斯系统的机动目标跟踪.MonteCarlo仿真表明,该算法跟踪精度优于标准的交互多模型算法和当前统计模型自适应跟踪算法,实时性好于交互多模型粒子滤波算法.     

改进的交互式当前统计模型算法

交互式多模型(IMM)算法是一种有效的机动目标跟踪算法,但其性能与模型的选择、个数以及参数有关,而当前统计模型(CSM)是一种模型自适应的方法.因此,利用CSM为IMM提供模型,提出一种改进的交互式当前统计模型算法.仿真结果表明,该算法能有效地提高机动目标跟踪精度.     

动态速度渐变技术提升慢波结构注波互作用效率的仿真研究

行波管采用动态速度渐变（DVT）技术能获得很高的效率。本文分析了一种采用DVT技术的慢波结构,并将这一结构与常规设计的慢波结构进行仿真比较,最后对这一结构进行了实验验证,结果证明了这一技术的优越性和可行性。     

Unified Maneuvering Model for Target Tracking by Using Range-rate Measurement

A novel Unified maneuvering model （UMM） for tracking the target performed the complex curvilinear motion in two dimensions is presented, where the model is constructed by time-varying parameters and driven by the along-track and cross-track acceleration in- put, and the range rate plays a key role to update vari- able parameters online. In Cartesian coordinates, by incorporating the cross-track acceleration component which is estimated online by using the range-rate, and the along- track acceleration component which is determined under the assumption of zero-mean first order Markovian process, the proposed UMM exhibits highly self-adjustment capability to compensate the mismatch between the actual motion and the mathematic model adaptively, espe-cially, shows a well capability to approximate the standard Interacting multiple model （IMM） under the situation of complex curvilinear motion and a higher level of the measurement noise. Simulation results validate our theory and show that UMM-based filtering is superior to that using the Current statistic model （CSM）, and has a well approx- imation to IMM, meanwhile, has a low computational load substantially.     

Fusion of Gaussian mixture models for possible mismatches of sensor model

This paper addresses estimation fusion in the presence of possible mismatches of sensor model. The main concerns of the paper lie in two aspects. One is to improve the filter performance of the single sensor when there are possible mismatches about the sensor model. The other one is to adopt a good fusion scheme to combine local estimates. For these purposes, the measurement process of the local sensor is modeled by multiple models firstly, and the IMM (interacting multiple model) estimator is implemented to produce estimates for individual models. Next, we describe the local estimate by a Gaussian mixture rather than a single Gaussian density in the baseline IMM filter. Such a GMM (Gaussian mixture model) representation of the system state allows us to keep the detailed information about the local tracker, which contributes to the further fusion if treated properly. Finally, the fusion of two Gaussian mixtures is done in the probabilistic framework, and a close-form solution is derived without complex numerical operations. Simulation results demonstrate the effectiveness of the proposed approach.     

基于交互式多模型的弹道导弹跟踪

弹道导弹飞行主动段和自由段的运动学模型差别很大,在未准确知道导弹关机点时间先验信息前提下如何对弹道导弹主动段到自由段进行连续跟踪已成为目前亟待解决的问题.针对该问题,提出了一种实时交互多模型跟踪算法,根据弹道导弹主动段和自由段不同受力情况建立相应的跟踪模型.该算法包括两个滤波器:一个为对主动段跟踪效果比较好的CA-EK...     

车载雷达防追尾预警系统中的目标跟踪研究

考虑毫米波雷达测量的误差和噪声的存在,为了提高毫米波雷达汽车防追尾预警系统的可靠性,设计了一个多模型算法实现对机动车辆的准确跟踪。针对汽车在高速公路上最常见的匀速、匀加速及转弯运动模型进行仿真实验,结果表明该算法能够有效地跟踪前方行驶车辆,探知自车与前车之间距离信息、速度信息等,从而降低雷达虚警率。