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.     

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.     

多传感器数据融合与航迹预测

多传感器数据融合技术是未来军事电子领域一个重要趋势.根据6个观测雷达的观测数据进行了数据融合算法的研究.在提取目标航迹对时,对每个雷达的数据依据一定的判定条件(时间变化,角度变化在一定范围内等),分别提取出不同的目标航迹对.在提取同一目标的航迹对时,先将目标航迹的一些异常点弃除,然后把时间重合的两段航迹提取出来,通过样条插值进行时间配准,共提取出多条相关的航迹组有3组.在使用雷达探测目标时,由于技术条件和方法等的限制,使雷达数据存在各种误差.利用卡尔曼滤波自适应算法估计出观测位置的噪声方差,对雷达偏差进行修正后,采用联合卡尔曼滤波算法对多条航迹进行融合,接着利用ARMA模型预测目标在未来10秒内的轨迹,最后,对目标在被锁定后的轨迹做出预测,结合导弹的爆炸范围求得导弹击中飞机的概率约为49.54%.     

基于动态规划的高阶隐马氏模型推广的Viterbi算法

首先通过Hadar等价变换方法将高阶隐马氏模型转换为与之等价的一阶向量值隐马氏模型,然后利用动态规划原理建立了一阶向量值隐马氏模型的Viterbi算法,最后通过高阶隐马氏模型和一阶向量值隐马氏模型之间的等价关系建立了高阶隐马氏模型基于动态规划推广的Viterbi算法．研究结果在一定程度上推广了几乎所有隐马氏模型文献中所涉及到的解码问题的Viterbi算法,从而进一步丰富和发展了高阶隐马氏模型的算法理论．     

多无人机对组网雷达的协同干扰

针对无人机编队与组网雷达之间的欺骗干扰问题,结合无人机编队与组网雷达的工作特性,首先采用分层规划的思想建立了无人机编队的航迹搜寻、协同规划、安全约束模型,根据0-1规划的方法制定出匀速直线等约束下无人机编队的协同策略,其次利用改进的航迹搜寻模型进行虚假航迹搜寻,使固定数量的无人机编队新增出4条虚假航迹.     

部分状态可见的隐马尔可夫模型的Viterbi算法

本文研究了隐马尔可夫模型的Viterbi算法,在已知隐马尔可夫模型的部分状态、初始概率分布、状态转移概率矩阵和观测概率矩阵的条件下,由此Viterbi算法给出最优状态序列的估计.相对于已有的算法,本文的算法考虑了部分可见状态对初始条件和递推公式的影响,并且本文的算法能保证预测的状态序列是整体最优的.最后,我们将本文的算法应用于故障识别,从而验证所设计算法的可行性.     

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.     

耦合隐藏马尔科夫模型的算法研究

本文在隐藏马尔科夫的基础上介绍了耦合隐藏马尔科夫模型,基于隐藏马尔科夫的算法计算了耦合隐藏马尔科夫模型的对数似然函数,提出了耦合隐藏马尔科夫模型的EM算法及Viterbi算法,用以估计模型的参数及隐藏状态序列,最后利用实例讨论了该算法的准确性及有效性.     

多模型自适应控制研究概述

多模型自适应控制,作为传统自适应控制算法的改进,在处理具有较大不确定性系统时具有显著的性能提升,也在近二十年中吸引了越来越多的研究关注.文章首先对多模型自适应控制的研究进展做了整体简要的探讨,然后分别针对常用的多模型自适应控制算法进行了深入讨论,从算法思想,流程和主要结论等方面作了详细的分析.最后,分析指出了多模型算法研究工作中尚存在的问题和进一步的研究方向.     

Bias phenomenon and compensation for PDA/JPDA algorithms

Probabilistic Data Association (PDA) and Joint PDA (JPDA) algorithms are approaches for target tracking which have received considerable attention. It has been observed for some years that they both yield biased tracks in a multitarget environment. However, most work assumes no false alarms and the rejection phenomenon of the JPDA algorithm has not been reported. In this paper, the general procedure of multitarget tracking and the PDA/JPDA algorithms are first described. Their bias phenomenon is simulated and investigated. It is observed that

(1) the JPDA algorithm has less bias than the PDA algorithm in a clean environment. Both of them yield coalescence

(2) the JPDA algorithm has coalescence and rejection bias phenomenon while the PDA algorithm has only coalescence phenomenon in a clutter environment.

Bias compensated algorithms are then presented using the polynomial regression method. Simulations are carried out to select the order of polynomial regression. Monte Carlo simulations also demonstrate the effectiveness of the compensated PDA/JPDA algorithms.     

A multi-space data association algorithm for target tracking systems

In this paper, a multi-space data association algorithm based on the wavelet transform is proposed. In addition to carrying out the traditional hard logic data association in measurement space, the new algorithm updates the state of the target in the pattern space. Such a function significantly reduces the complicated environment misassociation effects on the data association. Simulation results show that the performance of the multi-spaced data association is much better than the existing data association algorithms in complicated clutter environments, such as the nearest-neighbor standard filter (NNSF), the probabilistic data association (PDA) and the joint probabilistic data association (JPDA). The computation of the multiple-space data association is much less than the aforementioned other existing data associations, and this new data association does not need any priori information of the environment. In complicated clutter environments, compared with the other data association, the new data association proposed in this paper is very robust, reliable and stable.     

Design and analysis of fractal detector for high resolution radars

Recently, fractal geometry has been used as a tool for improving the detection of targets in radar systems. The fractal dimension is utilized as a feature to distinguish between target and clutter in fractal detectors. In this paper, a general model is proposed for the target and clutter signals in high resolution radar (HRR). The fractal dimensions of the clutter and the target plus clutter are evaluated. Performing statistical tests on the distribution of the fractal dimension, it is proved that a gaussian distribution can approximately model the distribution of the fractal dimension for HRR signals. The fractal detector is designed based on the gaussian distribution of the fractal dimension and its performance is compared with a semi-optimum detector. It is demonstrated that the fractal detector has great capabilities in the rejection of colored clutter. Moreover, we show that the fractal detector is CFAR, i.e., the probability of false alarm remains approximately constant in different interference powers.     

Unscented fuzzy-controlled current statistic model and adaptive filtering for tracking maneuvering targets

A novel adaptive algorithm for tracking maneuvering targets is proposed. The algorithm is implemented with fuzzy-controlled current statistic model adaptive filtering and unscented transformation. A fuzzy system allows the filter to tune the magnitude of maximum accelerations to adapt to different target maneuvers, and unscented transformation can effectively handle nonlinear system. A bearing-only tracking scenario simulation results show the proposed algorithm has a robust advantage over a wide range of maneuvers and overcomes the shortcoming of the traditional current statistic model and adaptive filtering algorithm.     

基于多特征信息自适应融合的视频目标跟踪算法

针对单一视觉特征跟踪的局限性,提出一种根据场景变化动态建立目标模型的粒子滤波视觉跟踪算法,方法首先选择简单且具有互补性的色彩与纹理特征描述表示当前图像,然后在粒子滤波框架下,利用民主融合策略进行信息融合,从而提高目标观测模型的鲁棒性;分析和实验表明, 算法对视频运动目标的任意平移、转动、部分遮挡、光照变化以及相似物干扰等情况下的跟踪均具有较好的效果.     

Multiresolutional decomposition and modeling with an application to joint probabilistic data association

A multiresolutional approach for measurement decomposition and system modeling is presented in this paper. The decomposition is performed in both spatial and time domains and provides an excellent platform for developing computationally efficient algorithms. Using multiresolutional decomposition and modeling, a multiresolutional joint probabilistic data association (MR-JPDA) algorithm is developed for multiple target tracking. Monte Carlo simulations demonstrate that the computation of the MRJPDA algorithm is much less than the traditional joint probabilistic data association (JPDA) algorithm with a comparable performance.     

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.     

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.     

A multi-objective-ACO-based data association method for bearings-only multi-target tracking

The study is concerned with data association of bearings-only multi-target tracking using two stationary observers in a 2-D scenario. In view of each target moving with a constant speed, two objective functions, i.e., distance and slope differences, are proposed and a multi-objective-ant-colony-optimization-based algorithm is then introduced to execute data association by minimizing the two objective functions. Numerical simulations are conducted to evaluate the effectiveness of the proposed algorithm in comparison with the data association results of the joint maximum likelihood (ML) method under different noise levels and track figurations.     

对机动再入目标的跟踪方法

一、前言 本文叙述利用自适应滤波对一类机动再入目标进行实时跟踪的方法,这里所说的机动再入目标,是指当目标再入到稠密大气层之下时,利用气动力系数的突变进行机动。比如,当目标作无机动飞行时,目标的对称轴方向与飞行速度方向(工程上称这两方向的夹