基于虚拟力驱动的传感器网络节点定位

为了解决传感器网络节点三边测量定位的全局优化问题,提出了一种基于虚拟力驱动的节点最优相对定位方法。该方法建立了满足节点位置最优最小二乘估计需要的虚拟势能场模型,给出了一种基于虚拟势能场梯度的虚拟力定义新方法,证明了利用该虚拟力驱动节点定位的收敛性,提供了保证节点定位结果全局最优的方法。仿真分析表明,基于虚拟力驱动的节点相对定位方法能够解决节点相对位置的最优估计问题。     

战术移动Ad Hoc网络下的分布式位置辅助功率控制算法

针对战术移动自组(AdHoc)网络提出一种分布式位置辅助的功率控制算法(LAPCA),它通过位置预测来推算节点的邻节点数目,进而调整信号发射功率,以保持最佳的网络连通性,由此提高整个网络的有效流量。该算法作为一个独立模块可以方便地与已有的移动自组网络路由协议相结合。采用较为适合战术环境的参考点群组移动(RPGM)模型来产生网络仿真场景,从仿真实验结果来看,几乎在所有的RPGM场景下采用该算法后的网络有效流量相对于纯粹的AODV路由协议都得到了提高。     

Bidirectional parallel multi-branch convolution feature pyramid network for target detection in aerial images of swarm UAVs

In this paper, based on a bidirectional parallel multi-branch feature pyramid network (BPMFPN), a novel one-stage object detector called BPMFPN Det is proposed for real-time detection of ground multi-scale targets by swarm unmanned aerial vehicles (UAVs). First, the bidirectional parallel multi-branch convolution modules are used to construct the feature pyramid to enhance the feature expression abilities of different scale feature layers. Next, the feature pyramid is integrated into the single-stage object detection framework to ensure real-time performance. In order to validate the effectiveness of the proposed algorithm, experiments are conducted on four datasets. For the PASCAL VOC dataset, the proposed algorithm achieves the mean average precision (mAP) of 85.4 on the VOC 2007 test set. With regard to the detection in optical remote sensing (DIOR) dataset, the proposed algorithm achieves 73.9 mAP. For vehicle detection in aerial imagery (VEDAI) dataset, the detection accuracy of small land vehicle (slv) targets reaches 97.4 mAP. For unmanned aerial vehicle detection and tracking (UAVDT) dataset, the proposed BPMFPN Det achieves the mAP of 48.75. Compared with the previous state-of-the-art methods, the results obtained by the proposed algorithm are more competitive. The experimental results demonstrate that the proposed algorithm can effectively solve the problem of real-time detection of ground multi-scale targets in aerial images of swarm UAVs.     

基于RBF神经网络的背景估计及红外小目标检测

利用RBF神经网络的函数逼近特性,得到了一种红外图像背景估计算法,进而提出了一种检测红外小目标的方法。利用有目标和没有目标的真实红外图像对此算法进行检测,背景估计效果理想,小目标检测效果理想,证明该算法是可行有效的。     

BP神经网络的收敛问题

研究了BP神经网络的收敛问题。基于随机理论 ,提出了解决网络收敛性问题的随机优选法。该方法不仅在任何条件下都能得到问题的具有一定精度的解答 ,而且收敛速度很快     

求解传感器网络最大生存时间的最大流算法

节能是传感器网络研究的中心问题之一,目的是延长网络的生存时间。因此对于一个给定网络,很自然地关心它的最大生存时间是多少。从网络最大流的角度分析这个问题,给出了求解传感器网络最大生存时间确切值的算法。     

战场态势感知系统评价方法?

战场态势感知系统主要由传感器系统、通信系统以及其他分系统构成,是现代战争系统的基础组成部分。由于战场态势感知活动广泛作用于物理域、信息域和认知域,相关评价研究面临诸多困难和挑战。在介绍战场态势感知系统相关概念的基础上,重点对其评价方法的研究现状进行了综述,并对不同评价方法的特点进行了分析比较,最后,指出了战场态势感知系统评价方法有待进一步研究和解决的问题。     

Minimal LST representations of MAP(n)s: Moment fittings and queueing approximations

A Markovian arrival process of order n, MAP(n), is typically described by two n × n transition rate matrices in terms of rate parameters. While it is straightforward and intuitive, the Markovian representation is redundant since the minimal number of parameters is n² for non‐redundant MAP(n). It is well known that the redundancy complicates exact moment fittings. In this article, we present a minimal and unique Laplace‐Stieltjes transform (LST) representations for MAP(n)s. Even though the LST coefficients vector itself is not a minimal representation, we show that the joint LST of stationary intervals can be represented with the minimum number of parameters. We also propose another minimal representation for MAP(3)s based on coefficients of the characteristic polynomial equations of the two transition rate matrices. An exact moment fitting procedure is presented for MAP(3)s based on two proposed minimal representations. We also discuss how MAP(3)/G/1 departure process can be approximated as a MAP(3). A simple tandem queueing network example is presented to show that the MAP(3) performs better than the MAP(2) in queueing approximations especially under moderate traffic intensities. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 549–561, 2016     

战术互联网子网干扰效果评估

目前对Ad hoc网络攻击的研究大多是针对网络各层协议弱点所设计,战术互联网通信设备运用了许多先进的抗干扰传输技术,协议攻击很难奏效,直接对战术互联网受扰后的网络性能评估很有必要。文章的主要工作是对战术互联网子网进行建模分析,通过设置节点接包时以概率出错来模拟节点受扰情况,分析受扰节点数、节点受扰程度不同时整个战术互联网子网的性能变化。     

基于shearlet与PCNN的多聚焦图像融合方法

针对传统脉冲耦合神经网络(PCNN)模型在多聚焦图像融合领域应用中面临的参数繁杂等问题,提出一种基于剪切波(shearlet)变换与改进型PCNN的多聚焦图像融合方法。相比以往的变换域方法,shearlet具有理想的图像信息捕捉性能以及较低的计算复杂度,因此,可利用shearlet将待融合图像进行多尺度多方向分解。其次,对经典PCNN模型加以改进,综合运用清晰度水平以及协调矩阵完成低频子带图像以及一系列高频子带图像的融合过程。最后,运行shearlet反变换得到最终融合图像。仿真实验选取了若干组待融合图像进行仿真,验证了该方法在主、客观评价两方面的优越性。