IMM理论在超低空探测与跟踪上的应用

以超低空防御为背景 ,以防御系统雷达和红外传感器的融合为主线 ,运用概率互联理论和交互式多模型理论对超低空目标的探测和跟踪问题进行了仿真。经仿真认定 ,该理论不仅可用于目标的探测和预警 ,也可用于导弹的跟踪和制导     

输出/输入比值优势与DEA有效

进一步讨论了多目标规划模型 ( vp)中的 Pareto有效与 DEA有效的关系 ,得到两个重要结论 ,藉此无须求解 DEA模型就能判断某些 Pareto有效 DMU的 DEA有效性。     

基于使命模式的仿真想定动态提炼研究

作战体系对抗仿真想定涉及内容多、结构复杂,在想定构建过程中需要不断对想定内容进行调整优化。为提高作战体系探索性仿真效率,提炼出更优、更符合指挥员意图的武器装备体系配置方案和部署方案的想定,提出了一种基于使命模式的想定动态提炼方法,定义了想定动态提炼所采用的特殊的仿真模式——使命模式,分析了该模式的特点,给出基于使命模式的想定动态提炼的流程和算法,最后结合航空兵对地面目标突防筹划分析,实现了想定动态提炼的过程。实验表明,基于使命模式的想定提炼方法可以对多种想定局部效果的动态分析比较,通过单次仿真即可实现传统蒙特卡洛模式下需要多次仿真才能完成的探索过程,大大提高了探索效率。     

一种基于模型共享的半监督多标签图像学习法

提出一种快速且有效的半监督多标签学习方法:模型共享半监督推举。该方法能发现、共享并组合多个基模型,每个基模型是在某个标签上利用半监督支持向量机(S3VM)上学习的。通过使用模型共享,标签关联被显示地利用且对于每个标签来说只需要少量的基模型即可生成最后的决策结果。在Corel5k和Mediamill数据集上评估方法,实验结果显示的方法与当前流行的监督和半监督多标签学习方法是可比的。     

一种快速的俯视行人检测方法

提出了一种新颖的单目俯视客流图像的快速行人检测方法。该方法是针对俯视行人头部或头肩部特征进行的检测的。根据行人头部在俯视图中的左、右两半对称,左、中、右结构,上、下两部分基本相同等空间分布特征,利用积分图计算、比较目标区域内矩形子块的灰度累加值来实现这类特征的检测;此外,融合头部区域颜色均匀的特征,计算并判别否是行人。实验证明,该检测方法是快速和有效的。     

基于全方位优化算法的改进马田系统分类方法

针对马田系统的若干不足, 提出一种改进的马田系统优化模型, 其核心思想是 根据分类问题的目的和特点提出若干优化目标, 采用优化模型替代正交表和信噪比筛选关键变量. 针对模型的特点, 采用了一种全方位优化算法进行求解. 通过对4个 UCI数据集的算例分析表明, 该方法不仅有较好的分类精度, 且能筛选关键变量, 降维效果明显. 最后对一个实际生产案例 进行了研究, 结果表明该方法在保持高分类效率的情况下, 能够显著减少质量检测变量, 降低成本, 提高生产效率.     

Pre-processing for time domain image formation in SS-BSAR system

A pre-processing procedure is designed for a space-surface bistatic synthetic aperture radar (SS-BSAR) system when a time domain image formation algorithm is employed. Three crucial technical issues relating to the procedure are fully discussed. Firstly, unlike image formation algorithms operating in the frequency domain, a time domain algorithm requires the accurate global navigation satellite system (GNSS) time and position. This paper proposes acquisition of this information using a time-and-spatial transfer with precise ephemeris and interpolation. Secondly, synchronization errors and compensation methods in SS-BSAR are analyzed. Finally, taking the non-ideal factors in the echo and the compatibility of image formation algorithms into account, a matched filter based on the minimum delay is constructed. Experimental result using real data suggest the pre-processing is functioning properly.     

基于修正核函数SVM的网络入侵检测

支持向量机分类方法在小样本、非线性情况下具有较好的泛化性能,在入侵检测系统中有着广泛的应用。针对入侵检测过程中可能出现的由两类样本不平衡造成的分离超平面偏移现象,以核函数所蕴含的黎曼几何为依据,引入一个伪一致性变换函数,对核函数进行修改,提高支持向量机的分类泛化能力,建立基于支持向量机的网络入侵检测系统,并对系统总体结构和运行机制进行了详细的描述。实验仿真表明,该系统可有效地提高入侵检测的准确率,改善由于数据集不平衡造成的支持向量机分类偏移的情况。     

Novel reference range selection method in InlSAR imaging

Aiming at the reference range selection for different antennas in interferometric inverse synthetic aperture radar (InISAR) systems, this paper proposes a respective focusing (RF) method. The reference ranges for echoes of different antennas are selected respectively for RF, which is different from the traditional uniform focusing (UF) with the same reference range applied to all the antennas. First, a comparison between UF and RF for InISAR signal model considering the ranging error is given. Compared with RF, UF has an advantage in overcoming the ranging error differences between different antennas. Then the influence of ranging error upon the interferometric imaging with RF is investigated particularly, and it is found that the ranging error differences between different antennas are far smaller than the wavelength, which is advantageous to imaging. By comparing the capabilities of interferometric imaging between RF and UF, it is concluded that RF is a better choice in conquering problems such as image mismatching and phase ambiguity even with ranging errors. Simulations demonstrate the validity of the proposed method.     

A CLUSTERING ALGORITHM FOR MIXED NUMERIC AND CATEGORICAL DATA

Most of the earlier work on clustering mainly focused on numeric data whose inherent geometric properties can be exploited to naturally define distance functions between data points. However, data mining applications frequently involve many datasets that also consists of mixed numeric and categorical attributes. In this paper we present a clustering algorithm which is based on the k-means algorithm. The algorithm clusters objects with numeric and categorical attributes in a way similar to k-means. The object similarity measure is derived from both numeric and categorical attributes. When applied to numeric data, the algorithm is identical to the k-means. The main result of this paper is to provide a method to update the “cluster centers“ of clustering objects described by mixed numeric and categorical attributes in the clustering process to minimise the clustering cost function. The clustering performance of the algorithm is demonstrated with the two well known data sets, namely credit approval and abalone databases.