提高水雷探测效果的声纳图像增强算法

对灭雷具系统中显控台对高分辨率图像声纳信号进行转换的计算机辅助探测算法进行了探索,重点介绍了一种能够增强声纳图像中目标信号显示效果的先进算法.从而可以使声纳对目标扫描后的图像更加清晰,与背景对比效果更加明显.为便于理解和比较,同时还引用了已经被国外普遍应用的三种其它算法.     

交互多模型算法的无人机控制系统故障诊断

交互多模型算法是一种广泛用于各种结构与参数变化的动态系统自适应估计的有效方法.在建立无人机控制系统传感器和执行器的全局故障和局部故障模型的基础上,应用此方法对传感器和执行器的各种软硬故障进行诊断,应用所建立的数学模型与方法,对无人机传感器和执行器的局部与全局故障进行了仿真计算.在仿真过程中发现,与一般的多模型方法相比,此方法能够更加准确可靠地诊断故障,无延迟报警,算法简单.仿真结果验证了该方法的有效性.     

混沌时间序列中弱信号的检测

介绍了混沌的基本特性,利用混沌时间序列对RBF神经网络进行训练,则训练好的神经网络对混沌序列未来时刻的值具有一定的预测能力.若混沌序列中包含有目标信号,则其混沌特性将受到影响,导致神经网络对其预测误差的增大.这样可根据预测误差对淹没在混沌杂波及混沌杂波加一定强度的白噪声中的弱目标信号进行检测.仿真结果表明这种方法优于传统的目标检测方法,有着较好的杂波抑制能力.     

图像匹配技术在对岸火力支援中的应用

针对水面舰艇对岸远程火力支援的作战需求和未来的发展趋势,对卫星和空基侦察信息在舰载武器对岸火力支援作战中的应用可行性进行了分析,提出了通过综合应用卫星和舰载无人机(UAV)侦察图像进行景象匹配,精确定位岸上远程目标对射击偏差进行观测修正的技术思路,为水面舰艇对岸上远程固定目标实施精确打击提供了一种可行和有效的手段.     

突防过程的组网雷达干扰资源优化分配

以组网雷达融合中心检测概率为目标函数,建立针对组网雷达系统的干扰资源优化分配模型.并进一步指出该模型运用于飞行器突防时,其不足之处在于"以点带面",以飞行器突防过程中某一点的融合中心检测概率代替整个突防过程时,该评估方法损失过程中的大量信息.在此基础上充分利用突防过程的信息,提出以"融合中心检测概率加权积分"为目标函数,建立基于突防过程的干扰资源优化分配模型.仿真结果表明具有一定的指导意义.     

小波变换在零件边缘检测中的应用

目前对机械零件的尺寸、口径等参数的测量大多采用接触测量,而利用零件图像的边缘信息进行非接触测量,则是一种新的更好的检测方式。用此方法可以对机械零件图像的边缘进行精确的检测,再根据相应的数学关系得到相关参数的值,不但可以提高零件检测的精度,还可以大大节省人力和物力。模拟边缘检测技术的具体过程,依次采用最佳阈值迭代算法、小波变换模极大值法、边缘连接局部处理法对零件图像边缘信息进行边缘检测和提取,并对检测过程进行了重点分述。最后,通过一个具体实验,验证了小波技术与其他技术结合使用所获得的良好效果,从而进一步显示了小波变换在边缘检测技术中应用的巨大潜力。     

多任务时间规划冲突检测方法

摘要：针对多任务的时间规划问题,基于Simple Temporal Networks(STN)时态表示模型和时态推理方法,提出了一种时间冲突检测的智能方法,并对该方法给予了数学证明。实验表明,该方法能够检测出基于STN表示的作战方案的时间冲突,并对任务时间规划提供参考范围,可有效辅助指挥员快速制定作战方案的时间规划。     

Contents and Author Index,Volume 20, 2013

The United States has multiple nuclear detection initiatives to secure against a terrorist nuclear attack, including the Container Security Initiative, installation of radiation detectors at U.S. border points of entry, and establishment of the Domestic Nuclear Detection Office (DNDO). The current nuclear detection system architecture falls short of being able to reliably catch fissile nuclear material in transit, specifically shielded Highly Enriched Uranium (HEU) and Plutonium (Pu), both within the US and abroad. Checkpoints at border crossings can be circumvented, and no adequate system is under development to deter the transport of fissile materials. Using nuclear link-budget calculations, we show why a network relying primarily on handhelds, fixed detectors, and portals is not sufficient. We examine the technical, economic, and operational feasibility of a comprehensive national network incorporating in-vehicle detectors to reliably detect and deter the transport of fissile material inside the vehicle itself.     

How Different Terrorist Attacks Affect Stock Markets

Terrorist attacks adversely affect the Pakistani stock market. However, such effect is short-lived: the market recovers from terrorist shocks in one day. The impact of attack depends on the locations and types of attack. The more severe the attack (i.e. more people killed), the more negative is the KSE-100 index return. Most interestingly, stock market contains information about future attacks. In sum, different tactics of terrorists have varied effects on financial markets, which in turn can predict terrorist attacks.     

Adaptive subspace detection based on two-step dimension reduction in the underwater waveguide

In the underwater waveguide, the conventional adaptive subspace detector (ASD), derived by using the generalized likelihood ratio test (GLRT) theory, suffers from a significant degradation in detection per-formance when the samplings of training data are deficient. This paper proposes a dimension-reduced approach to alleviate this problem. The dimension reduction includes two steps: firstly, the full array is divided into several subarrays; secondly, the test data and the training data at each subarray are transformed into the modal domain from the hydrophone domain. Then the modal-domain test data and training data at each subarray are processed to formulate the subarray statistic by using the GLRT theory. The final test statistic of the dimension-reduced ASD (DR-ASD) is obtained by summing all the subarray statistics. After the dimension reduction, the unknown parameters can be estimated more accurately so the DR-ASD achieves a better detection performance than the ASD. In order to achieve the optimal detection performance, the processing gain of the DR-ASD is deduced to choose a proper number of subarrays. Simulation experiments verify the improved detection performance of the DR-ASD compared with the ASD.