多雷达系统数据融合与航迹预测

为了便于准确定位传感器网络,分布式的单雷达系统首先进行各自的数据处理,包括:地理坐标换算至平面直角坐标;剔除孤立的异常点迹;采用模糊c-均值聚类方法和"动态分区",将单雷达数据中属于同目标的相似点迹归类集合;根据雷达观测和目标运动的特征,在每个点迹集合中设计门限滤波和相关矩阵检验,提取完整连续的目标运动的航迹;结合各航迹特征进行种类分析.接下来对属于不同雷达的航迹两两比较,找出有相交时间段的航迹,采用三次样条对两条航迹的进行内插和外推,再通过模糊综合函数对这两条航迹给出一个相似性度量,并取阈值为0.85.最后得出雷达间各航迹匹配关系.通过该雷达所观测到的航迹的稳定程度来近似估计其观察精度.首先对每一条航迹进行分段拟合得到其剩余方差,然后直接用每一条航迹的剩余方差来衡量雷达的观察精度,最后我们得出雷达的精度排序29107728,7724253720252539.对航迹融合,我们首先采用D-S证据理论并利用分析得到的雷达精度,对表示同一目标的航迹对进行融合.其次试图运用卡尔曼滤波对航迹进行融合:思路一是设法离线估计出噪声矩阵,得出系统噪声方差矩阵和观测噪声方差矩阵,从而用于标准卡尔曼滤波方程;思路二是探究较为实用的自适应滤波,兼顾Sage-Husa自适应滤波算法的高精度与强跟踪自适应滤波算法的可靠性,采用了一种混合算法给出收敛的估计.最终给出了雷达7728和2910的融合算例以及10秒钟的预测轨迹.最后,我们将导弹拦截飞机建模为三维的追逃问题,建立了运动学关系方程,最终归结为最小能量导引律问题.采用"模糊T-S线性模型"以及RH控制方法和伴随技术,在目标作对抗性机动条件下,获得了一个有效拦截的导引律.还对多雷达系统平均处理周期、数据融合系统的航迹处理周期进行了分析,对雷达网络实时性做了评价.     

机动目标的跟踪与反跟踪的建模研究

以机动目标的跟踪与反跟踪为研究对象,分析了目标的状态方程和量测方程,当存在多个目标时,仍需考虑不同目标之间数据关联问题.首先,考虑了雷达1、雷达2和3量测数据的连接和融合问题,建立了机动目标的状态方程和量测方程模型,运用卡尔曼滤波器算法对目标航迹进行了在线追踪,得到目标跟踪航迹.其次,进行一次指数平滑法的时间序列预测和实时灰色预测,得到了其目标的着落轨迹、着落点坐标和时间.最后建立新的动态多目标优化模型,运用计算机仿真对跟踪与反跟踪的目标航迹进行了模拟.     

机动目标跟踪与逃逸分析

在利用地心坐标系的空间匹配算法(ECEFA)实现不同观测雷达空间数据同步的基础上,建立了机动目标跟踪的"当前"统计(CS)模型,通过Kalman滤波,实现了机动目标的有效跟踪.针对混合量测阶段,提出了集中式融合算法,实现了航迹的有效估计;对于机动目标观测断裂问题,提出了航迹粘联算法,有效地提高了航迹的连续性,降低目标状态估计误差.针对所提供的机动目标本身特征,提出了基于修正Hough变换的机动目标航迹起始策略,采用基于概率数据关联(PDA)的机动目标数据关联算法,实现了各目标的有效数据关联,确定了最终的两条航迹;最后,分别从目标的机动跟踪模型与雷达自身属性的角度出发,制订了机动目标的最佳逃逸策略.     

多机协同欺骗组网雷达的多虚假航迹制定方法

为有效欺骗组网雷达并充分利用设备资源,对干扰机进行复用,基于传统多机协同欺骗组网雷达模型设计了一种产生更多虚假航迹的轨迹规划方法,并将变高度平面与雷达-虚假目标射线的交点作为干扰机的航迹点,突破传统模型中干扰机只能做定高飞行的弊端,使真实航迹具有多样性,令敌方更难预测其坐标位置,保证自身安全.同时从几何关系上建立干扰机处于机动状态下的组网雷达、干扰机组、虚假航迹间的数学模型,以9架干扰机产生4条虚假航迹为例给出模型求解流程.     

多无人机对组网雷达的协同干扰规划

无人机的航行轨迹对组网雷达的协同干扰起着重要作用.基于无人机的虚假航迹点与雷达所成的曲面方程,考虑到无人机飞行时的速度、角度、高度和转弯半径等约束条件,建立了无人机虚假航迹优化模型.分别在无人机匀速运动、变速运动和虚假航迹点缺失的情况下,求解出最少的无人机数量和每架飞机的飞行轨迹.仿真结果表明,虚假航迹优化模型与实际情况相符合,为无人机的飞行轨迹提供了一些参考.     

基于粒子滤波算法的航迹误差分析

当前针对飞行预测的研究主要采用的是kalman算法,在解决非线性问题时存在着只能近似线性的而不够精确的问题.采用近年来受到广泛关注的粒子滤波算法,针对RNAV航路进行分析,结论中得到了对飞行误差仿真分析并对比了卡尔曼滤波仿真效果,证实了粒子滤波在航迹预测中更好的准确性.     

多无人机协同任务策略优化

从研究多无人机协同任务的系统资源分配、任务分配、航线规划、轨迹优化等问题入手,建立了多基地多无人机协同侦察模型.针对问题,首先利用"栅格化聚拢"的思想对目标点进行过滤优化,进而对目标群和无人机基地进行了任务分配,而后结合蚁群算法、贪心算法、最短路径算法等思想,通过Matlab平台,计算出能够让无人机停留在雷达探测区域总时间最少的最优策略.     

机动目标的跟踪与反跟踪综述

结合竞赛中存在的问题,得出以下结论:机动量估计关键在于通过被污染的雷达测量信息估计出目标的加速度随时间的变化,其难点是如何准确描述目标的运动模型;跟踪模型选择合适程度直接影响了数据关联和航迹预测的准确程度,这两者决定了了雷达系统的性能;机动目标的跟踪与反跟踪是一种博弈的关系,既相互对立又相互促进,不断推进和提出雷达新的作战需求.     

多无人机对组网雷达的协同干扰问题研究

在现代战场中,航迹欺骗干扰技术是针对组网雷达提出的协同干扰技术,旨在利用多架无人机在同一时刻对组网雷达中的部分雷达实施相互关联的虚假目标欺骗干扰.充分考虑了实际情况下无人机飞行模态、飞行速度、飞行高度的约束限制,对雷达的位置分布以及虚假航迹线进行可视化,并将三维空间模型投影到二维平面后进行建模分析.主要针对一个虚拟的无人机航迹欺骗干扰问题,建立时空模型,通过构建可达交互矩阵和雷达扫描算法,结合0-1整数规划,得到无人机协同飞行的最优策略.     

目标散布区对导弹目标选择概率影响的仿真模型

当反舰导弹飞近目标散布区时,导弹搜索雷达必须开机搜索目标,导弹根据目标反射信号的几何或能量特性,利用某一算法从被探测到的,包括敌方施放的假目标在内的所有目标中选择某一目标作为导弹的攻击目标.目标散布区的外形和大小对导弹选择真实目标概率的影响是一个尚未得到深入研究的问题.推导了目标散布区对导弹正确选择目标概率的影响函数;在考虑了目标舰及时提供假目标的仿真模型的基础上,给出了七种导引头选择目标的算法及仿真模型.仿真结果表明,目标散布区影响反舰导弹雷达的开机时间,从而影响导引头选择真实目标的概率及其抗电子干扰能力.     

A multiple template approach for robust tracking of fast motion target

Target tracking is very important in computer vision and related areas. It is usually difficult to accurately track fast motion target with appearance variations. Sometimes the tracking algorithms fail for heavy appearance variations. A multiple template method to track fast motion target with appearance changes is presented under the framework of appearance model with Kalman filter. Firstly, we construct a multiple template appearance model, which includes both the original template and templates affinely transformed from original one. Generally speaking, appearance variations of fast motion target can be covered by affine transformation. Therefore, the affine transform-enhanced templates match the target of appearance variations better than conventional models. Secondly, we present an improved Kalman filter for approximate estimating the motion trail of the target and a modified similarity evaluation function for exact matching. The estimation approach can reduce time complexity of the algorithm and keep accuracy in the meantime. Thirdly, we propose an adaptive scheme for updating template set to alleviate the drift problem. The scheme considers the following differences: the weight differences in two successive frames; different types of affine transformation applied to templates. Finally, experiments demonstrate that the proposed algorithm is robust to appearance variation of fast motion target and achieves real-time performance on middle/low-range computing platform.     

A study of the vestigial records of cosmic rays in lunar rocks using a thick section technique

An experimental technique has been developed for systematic measurements of fossil tracks along selected planes cut from grains and rocks. With controlled etching, the technique allows successive revelation of tracks in different minerals in the same section, a typical sequence being olivine, anorthite, clinopyroxene. It thus becomes possible to study precisely the cosmic ray track density variations over dimensions much greater than those of individual crystals. The technique also provides accurate information on the relative recording characteristics of different minerals present in a rock and cosmic ray tracks can be studied with a minimum interference of tracks due to spontaneous fission of uranium and transuranic elements. Continuous chains of sections, each section measuring approximately 1 cm., have been cut along several different planes in fifteen rocks from Mare Tranquillitatis, Oceanus Procellarum and Fra Mauro region. The cosmic ray track measurements from these sections have provided dramatic evidence for a number of processes affecting lunar rocks. The statistical, and non-uniform nature of erosion by micrometeorite bombardment can be seen in sections intersecting exposed surface which show regions of very steep track density gradients interspersed with eroded regions having lower track densities. The thick section technique permits determination of the energy spectrum of VH nuclei from track density gradients that extend over distances limited only by the dimensions of the rock, and, more important, in samples of identical orientation. The latter is particularly important in higher energy regions (deeper within the rock) where variations in crystal orientation cause track density differences of the same order as real changes in the gradient. Also in the near surface regions of rocks where low energy particles produce steep track density gradient, the thick section method has proved indispensable since it permits accurate depth determinations not possible in the spot sampling procedure. In this paper the technique of studying track profiles in thick sections is described. Although developed primarily for studying lunar samples, the thick section technique is also useful for similar studies in meteorites, particularly for gas-rich meteorites containing irradiated grains. In contrast to single grain studies, thick sections preserve the grain boundaries and permit accurate depth—density measurements. In addition thick section studies have revealed occasional large uniformly irradiated lithic fragments which would not have been possible to discover by spot sampling methods.     

A dynamic programming method for matching rail-track monitoring data

A commonly used method of monitoring the condition of rail track is to run an inspection vehicle over the track at intervals of about 3 months. Measurements of several geometric properties of the track are automatically recorded about every 900 mm, resulting in long sequences of data (signals) arising from runs of up to 100 km. Condition monitoring is done by comparing the results of a current run with those of a previously recorded reference run. Before this can be done, the two signals need to be aligned so that corresponding distance measurements in each signal actually refer to the same point on the track. A procedure for matching the two signals is presented, which has at its heart a dynamic programming method. The procedure is demonstrated on data from rail tracks in Australia.     

Multidimensional assignment formulation of data association problems arising from multitarget and multisensor tracking

The ever-increasing demand in surveillance is to produce highly accurate target and track identification and estimation in real-time, even for dense target scenarios and in regions of high track contention. The use of multiple sensors, through more varied information, has the potential to greatly enhance target identification and state estimation. For multitarget tracking, the processing of multiple scans all at once yields high track identification. However, to achieve this accurate state estimation and track identification, one must solve an NP-hard data association problem of partitioning observations into tracks and false alarms in real-time. The primary objective in this work is to formulate a general class of these data association problems as multidimensional assignment problems to which new, fast, near-optimal, Lagrangian relaxation based algorithms are applicable. The dimension of the formulated assignment problem corresponds to the number of data sets being partitioned with the constraints defining such a partition. The linear objective function is developed from Bayesian estimation and is the negative log posterior or likelihood function, so that the optimal solution yields the maximum a posteriori estimate. After formulating this general class of problems, the equivalence between solving data association problems by these multidimensional assignment problems and by the currently most popular method of multiple hypothesis tracking is established. Track initiation and track maintenance using anN-scan sliding window are then used as illustrations. Since multiple hypothesis tracking also permeates multisensor data fusion, two example classes of problems are formulated as multidimensional assignment problems.This work was partially supported by the Air Force Office of Scientific Research through AFOSR Grant Numbers AFOSR-91-0138 and F49620-93-1-0133 and by the Federal Systems Company of the IBM Corporation in Boulder, CO and Owego, NY.     

Tracking elementary particles near their primary vertex: A combinatorial approach

Colliding beams experiments in High Energy Physics rely on solid state detectors to track the flight paths of charged elementary particles near their primary point of interaction. Reconstructing tracks in this region requires, per collision, a partitioning of up to 10³ highly correlated observations into an unknown number of tracks. We report on the successful implementation of a combinatorial track finding algorithm to solve this pattern recognition problem in the context of the ALEPH experiment at CERN. Central to the implementation is a 5-dimensional axial assignment model (AP5) encompassing noise and inefficiencies of the detector, whose weights of assignments are obtained by means of an extended Kalman filter. A preprocessing step, involving the clustering and geometric partitioning of the observations, ensures reasonable bounds on the size of the problems, which are solved using a branch & bound algorithm with LP relaxation. Convergence is reached within one second of CPU time on a RISC workstation in average.     

Optimal track class assignment for a railroad network

The track class and speed for North American railroads are governed by regulations from the Federal Railroad Administration. Better quality of track permits greater operational speeds and lower transportation cost but also requires greater maintenance expenditure. In this study, we considered the trade-off between maintenance and transportation costs, and developed an optimization framework determining the optimal assignment for track class based upon the characteristics of the track, traffic demand, and maintenance budget. We also presented a solution algorithm by using Lagrangian Relaxation techniques to improve the solution efficiency. This tool can help railroads maintain tracks at their most appropriate level so as to provide reliable and cost-effective services.     

Neural network-based state fusion and adaptive tracking for maneuvering targets

An adaptive algorithm for tracking maneuvering targets is proposed. This algorithm is implemented with two filters and a multilayer feedforward neural network using state fusion, together with the current statistic model and adaptive filtering. The neural network fuses automatically all the state information of the two filters and tunes adaptively the system variance for one of the two filters to adapt to different target maneuvers when the two filters track the same maneuvering target in parallel. Simulation results show that the adaptive algorithm tracks very well maneuvering targets over a wide range of maneuvers with high precision, in both one and three-dimensional cases.     

Target torque estimation for gearshift in dual clutch transmission with uncertain parameters

The target torque of engaging clutches during gearshift is a key factor that affects the dynamic response of powertrains equipped with the dual clutch transmissions (DCT). This paper investigates a method to estimate the target torque of engaging clutches under conditions where engine torque and measurement signals contain white noise and some vehicle parameters (the radius of wheel and rolling friction coefficient) are uncertain. To compute the target torque accurately, the state of system should be estimated when the uncertain parameters exist. The vehicle powertrain is modeled as the 3DOF system when one clutch is closed and the 4DOF system when two clutches are open, while the measured signals include speeds of the engine, transmission, and vehicle (rotational speed of wheels). In addition to traditional extended Kalman filter (EKF), both the joint extended Kalman filter (JEKF) and dual extended Kalman filter (DEKF) are used to estimate the target torque. The simulation results show that DEKF and JEKF provide much higher accuracy in the estimation of target torque than EKF when some parameters of the model are uncertain, so as to produce a better ride performance of the transmission during gearshift, i.e. reduction of power interruption and compressed shifting time. Furthermore, the DEKF provides higher accuracy than the JEKF in estimating uncertain parameters.     

Stochastic modelling of tropical cyclone tracks

A stochastic model for the tracks of tropical cyclones that allows for the computerised generation of a large number of synthetic cyclone tracks is introduced. This will provide a larger dataset than previously available for the assessment of risks in areas affected by tropical cyclones. To improve homogeneity, the historical tracks are first split into six classes. The points of cyclone genesis are modelled as a spatial Poisson point process, the intensity of which is estimated using a generalised version of a kernel estimator. For these points, initial values of direction, translation speed, and wind speed are drawn from histograms of the historical values of these variables observed in the neighbourhood of the respective points, thereby generating a first 6-h segment of a track. The subsequent segments are then generated by drawing changes in theses variables from histograms of the historical data available near the cyclone’s current location. A termination probability for the track is determined after each segment as a function of wind speed and location. In the present paper, the model is applied to historical cyclone data from the western North Pacific, but it is general enough to be transferred to other ocean basins with only minor adjustments. A version for the North Atlantic is currently under preparation.