基于博弈论组网多机动目标协同跟踪技术研究

在卫星多目标跟踪的背景下,针对传感器网络的协同问题,首次建立了完整的基于移动动态联盟的多目标协同跟踪模型,并针对协同过程中的移动动态联盟形成问题,提出了基于博弈论的联盟形成算法。该协同模型在跟踪多目标时利用移动动态联盟来交换并协调节点间的信息,使节点的行为一致,具有分布式和自组织的特点。仿真实验验证了该协同模型应用在传感器网络多目标跟踪中的有效性。

Abstract：

As to the collaborative technique in sensor network,an integrated multi-target collaborative tracking model was constructed based on mobile dynamic coalition under the background of multi-satellite tracking.Then as to the mobile dynamic coalition formation,a coalition formation method was proposed based on game theory.Using mobile dynamic coalition,this collaborative model exchanged and coordinated the information of nodes to accord their action.So it is distributive and self-organizing.Simulation results validate the effectiveness of collaborative model in sensor network tracking multi-target.     

Sensor planning method for visual tracking in 3D camera networks

Most sensors or cameras discussed in the sensor network community are usually 3D homogeneous, even though their2 D coverage areas in the ground plane are heterogeneous. Meanwhile, observed objects of camera networks are usually simplified as 2D points in previous literature. However in actual application scenes, not only cameras are always heterogeneous with different height and action radiuses, but also the observed objects are with 3D features（i.e., height）. This paper presents a sensor planning formulation addressing the efficiency enhancement of visual tracking in 3D heterogeneous camera networks that track and detect people traversing a region. The problem of sensor planning consists of three issues：（i） how to model the 3D heterogeneous cameras;（ii） how to rank the visibility, which ensures that the object of interest is visible in a camera＇s field of view;（iii） how to reconfigure the 3D viewing orientations of the cameras. This paper studies the geometric properties of 3D heterogeneous camera networks and addresses an evaluation formulation to rank the visibility of observed objects. Then a sensor planning method is proposed to improve the efficiency of visual tracking. Finally, the numerical results show that the proposed method can improve the tracking performance of the system compared to the conventional strategies.     

Method of neural network modulation recognition based on clustering and Polak-Ribiere algorithm

To improve the recognition rate of signal modulation recognition methods based on the clustering algorithm under the low SNR, a modulation recognition method is proposed. The characteristic parameter of the signal is extracted by using a clustering algorithm, the neural network is trained by using the algorithm of variable gradient correction （Polak-Ribiere） so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram. Simulation results show that the recognition rate based on this algorithm is enhanced over 30% compared with the methods that adopt clustering algorithm or neural network based on the back propagation algorithm alone under the low SNR. The recognition rate can reach 90% when the SNR is 4 dB, and the method is easy to be achieved so that it has a broad application prospect in the modulating recognition.     

ROBUST CONSENSUS FOR MULTI-AGENT SYSTEMS OVER UNBALANCED DIRECTED NETWORKS

In this paper, a distributed consensus protocol is proposed for discrete-time single-integer multi-agent systems with measurement noises under general fixed directed topologies, The time-varying control gains satisfying the stochastic approximation conditions are introduced to attenuate noises, thus the closed-loop multi-agent system is intrinsically a linear time-varying stochastic difference system. Then the mean square consensus convergence analysis is developed based on the Lyapunov technique, and the construction of the Lyapunov function especially does not require the typical balanced network topology condition assumed for the existence of quadratic Lyapunov function. Thus, the proposed consensus protocol can be applicable to more general networked multi-agent systems, particularly when the bidirectional and/or balanced information exchanges between agents are not required. Under the proposed protocol, it is proved that the state of each agent converges in mean square to a common random variable whose mathematical expectation is the weighted average of agents＇ initial state values; meanwhile, the random variable＇s variance is bounded.     

Direct adaptive control for a class of MIMO nonlinear discrete-time systems

This paper considers the problem of adaptive con-trol for a class of multiple input multiple output （MIMO） nonlinear discrete-time systems based on input-output model with unknown interconnections between subsystems. Based on the Taylor ex-pand technology, an equivalent model in affine-like form is derived for the original nonaffine nonlinear system. Then a direct adap-tive neural network （NN） control er is implemented based on the affine-like model. By finding an orthogonal matrix to tune the NN weights, the closed-loop system is proven to be semiglobal y uni-formly ultimately bounded. The σ-modification technique is used to remove the requirement of persistence excitation during the adaptation. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters.     

NETWORK ANALYSIS OF TERRORIST ACTIVITIES

This paper uses an extensive network approach to "East Turkistan" activities by building both the one-mode and the bipartite networks for these activities.In the one-mode network,centrality analysis and spectrum analysis are used to describe the importance of each vertex.On this basis,two types of core vertices——The center of communities and the intermediary vertices among communities— are distinguished.The weighted extreme optimization(WEO) algorithm is also applied to detect communities in the one-mode network.In the "terrorist-terrorist organization" bipartite network,the authors adopt centrality analysis as well as clustering analysis based on the original bipartite network in order to calculate the importance of each vertex,and apply the edge clustering coefficient algorithm to detect the communities.The comparative and empirical analysis indicates that this research has been proved to be an effective way to identify the core members,key organizations,and communities in the network of "East Turkistan" terrorist activity.The results can provide a scientific basis for the analysis of "East Turkistan" terrorist activity,and thus provide decision support for the real work of "anti-terrorism".     

Best-retransmission count selection for environment optimization over wireless heterogeneous networks

This paper proposes an environment-aware best- retransmission count selected optimization control scheme over IEEE 802.11 multi-hop wireless networks. The proposed scheme predicts the wireless resources by using statistical channel state and provides maximum retransmission count optimization based on wireless channel environment state to improve the packet delivery success ratio. The media access control （MAC） layer selects the best-retransmission count by perceiving the types of packet loss in wireless link and using the wireless channel charac- teristics and environment information, and adjusts the packet for- warding adaptively aiming at improving the packet retransmission probability. Simulation results show that the best-retransmission count selected scheme achieves a higher packet successful delivery percentage and a lower packet collision probability than the corresponding traditional MAC transmission control protocols.     

一种节能的无线传感网异构配置模型

依据对实际传感信息分布与无线传感网络工程需求的分析,提出一种新的无线传感网异构配置模型(WSNHEDPM).对传感节点中的传感模块按需要进行组合,使其与感知物理特征量空间分布特征相适应,以减少信息冗余,降低无线传感节点的能量负担.对两种配置方法进行了配置成本、能量消耗与生存周期方面的理论和实验分析比较.理论和仿真实验表明,该模型使无线传感网在面向实际普通应用环境时更为能量有效和合理.     

Adaptive link layer security architecture for telecommand communications in space networks

Impressive advances in space technology are enabling complex missions, with potentially significant and long term impacts on human life and activities. In the vision of future space exploration, communication links among planets, satel ites, spacecrafts and crewed vehicles wil be designed according to a new paradigm, known as the disruption tolerant networking. In this scenario, space channel peculiarities impose a massive reengineering of many of the protocols usually adopted in terrestrial networks; among them, security solutions are to be deeply reviewed, and tailored to the specific space requirements. Security is to be provided not only to the payload data exchanged on the network, but also to the telecommands sent to a spacecraft, along possibly differentiated paths. Starting from the secure space telecommand design developed by the Consultative Committee for Space Data Systems as a response to agency-based requirements, an adaptive link layer security architecture is proposed to address some of the chal enges for future space networks. Based on the analysis of the communication environment and the error diffusion properties of the authentication algorithms, a suitable mechanism is proposed to classify frame retransmission requests on the basis of the originating event （error or security attack） and reduce the impact of security operations. An adaptive algorithm to optimize the space control protocol, based on estimates of the time varying space channel, is also presented. The simulation results clearly demonstrate that the proposed architecture is feasible and efficient, especially when facing malicious attacks against frame transmission.     

Image segmentation algorithm based on high-dimension fuzzy character and restrained clustering network

An image segmentation algorithm of the restrained fuzzy Kohonen clustering network （RFKCN） based on high- dimension fuzzy character is proposed. The algorithm includes two steps. The first step is the fuzzification of pixels in which two redundant images are built by fuzzy mean value and fuzzy median value. The second step is to construct a three-dimensional （3-D） feature vector of redundant images and their original images and cluster the feature vector through RFKCN, to realize image seg- mentation. The proposed algorithm fully takes into account not only gray distribution information of pixels, but also relevant information and fuzzy information among neighboring pixels in constructing 3- D character space. Based on the combination of competitiveness, redundancy and complementary of the information, the proposed algorithm improves the accuracy of clustering. Theoretical anal- yses and experimental results demonstrate that the proposed algorithm has a good segmentation performance.     

Flocking of multi-robot systems with connectivity maintenance on directed graphs

Analysis and design techniques for cooperative flocking of nonholonomic multi-robot systems with connectivity maintenance on directed graphs are presented. First, a set of bounded and smoothly distributed control protocols are devised via carefully designing a class of bounded artificial potential fields （APF） which could guarantee the connectivity maintenance, col ision avoidance and distance stabilization simultaneously during the system evolution. The connectivity of the underlying network can be preserved, and the desired stable flocking behavior can be achieved provided that the initial communication topology is strongly connected rather than undirected or balanced, which relaxes the constraints for group topology and extends the previous work to more generalized directed graphs. Furthermore, the proposed control algorithm is extended to solve the flocking problem with a virtual leader. In this case, it is shown that al robots can asymptotically move with the desired velocity and orientation even if there is only one informed robot in the team. Finally, nontrivial simulations and experiments are conducted to verify the effectiveness of the proposed algorithm.     

CONTINUOUS AUXILIARY COVARIATE IN ADDITIVE HAZARDS REGRESSION FOR SURVIVAL DATA

This paper considers the additive hazards iliary covariate information to improve the efficiency regression analysis by utilizing continuous aux- of the statistical inference when the primary covariate is ascertained only for a randomly selected subsample. The authors construct a martingale based estimating equation for the regression parameter and establish the asymptotic consistency and normality of the resultant estimators. Simulation study shows that the proposed method can greatly improve the efficiency compared with the estimator which discards the auxiliary covariate information in a variety of settings. A real example is also provided as an illustration.     

基于节点通信度的信息加权一致性滤波

协同目标跟踪是无人机集群等多传感器网络的典型应用。在分布式传感器网络目标跟踪过程中,目标状态估计的一致性直接影响到跟踪有效性。针对目标跟踪过程中网络节点之间一致性迭代次数受限的问题,提出了一种基于节点通信度的信息加权一致性滤波算法,设计了用节点通信度来充分衡量传感器节点在网络中的通信拓扑状况,并构建了非对称一致性权值的选取机制,可在复杂拓扑结构网络中实现快速一致性跟踪。典型目标跟踪场景仿真验证表明,所提算法相比经典的信息加权一致性滤波算法,目标跟踪的不一致程度降低了20%以上,有效提升了分布式跟踪的一致性速度。     

Distributed event region fault-tolerance based on weighted distance for wireless sensor networks

Event region detection is the important application for wireless sensor networks (WSNs), where the existing faulty sensors would lead to drastic deterioration of network quality of service. Considering single-moment nodes fault-tolerance, a novel distributed fault-tolerant detection algorithm named distributed fault-tolerance based on weighted distance (DFWD) is proposed, which exploits the spatial correlation among sensor nodes and their redundant information. In sensor networks, neighborhood sensor nodes will be endowed with different relative weights respectively according to the distances between them and the central node. Having syncretized the weighted information of dual-neighborhood nodes appropriately, it is reasonable to decide the ultimate status of the central sensor node. Simultaneously, readings of faulty sensors would be corrected during this process. Simulation results demonstrate that the DFWD has a higher fault detection accuracy compared with other algorithms, and when the sensor fault probability is 10%, the DFWD can still correct more than 91% faulty sensor nodes, which significantly improves the performance of the whole sensor network.     

基于节点通信度的信息加权一致性滤波

协同目标跟踪是无人机集群等多传感器网络的典型应用。在分布式传感器网络目标跟踪过程中,目标状态估计的一致性直接影响到跟踪有效性。针对目标跟踪过程中网络节点之间一致性迭代次数受限的问题,提出了一种基于节点通信度的信息加权一致性滤波算法,设计了用节点通信度来充分衡量传感器节点在网络中的通信拓扑状况,并构建了非对称一致性权值的选取机制,可在复杂拓扑结构网络中实现快速一致性跟踪。典型目标跟踪场景仿真验证表明,所提算法相比经典的信息加权一致性滤波算法,目标跟踪的不一致程度降低了20%以上,有效提升了分布式跟踪的一致性速度。     

基于动态AHP的海上移动节点网络选择算法

针对海上异构无线网络中现有的节点选择算法存在的实时有效性和与移动节点匹配性较差的问题,提出了一种基于动态层次分析(dynamic analytic hierarchy process, DAHP)法和协同学原理的网络接入选择算法。该算法在满足多因素判决和静态节点网络选择的基础上,采用DAHP法确定接入判决指标的主观权重,运用协同学相关原理确定客观权重,利用接收功率确定移动节点的位置权重,并通过对主、客观和位置权重进行再分配,实现了对移动节点周围的实时网络状态与任务通信需求的兼顾,提高了算法实时性和与移动节点的匹配性。仿真结果表明,该算法相对于基于传统AHP和协同学原理的网络选择算法、基于距离的移动节点算法,能够在降低节点业务阻塞率的同时提高通信通畅度与实时性。     

作战体系复杂网络研究

运用仿真方法,探索信息化战争作战体系拓扑网络的连接机制与分布规律,实验发现这一分布是由δ分布、指数分布与幂律分布(尾部)构成的混合分布;当网络规模较大时,节点之间的连接机制成为网络分布形式是否具有幂律尾的唯一决定因素;当尾部接近幂律分布时,常常是近似服从具有长尾的SF分布,但与同规模基于优先连接机制形成的SF网络相比,拥有大得多的Hub节点,分布标度指数λ一般取值于[1.4,2.2]之间.     

Performance analysis of mobile ad hoc networks under flooding attacks

Due to their characteristics of dynamic topology, wireless channels and limited resources, mobile ad hoc networks are particularly vulnerable to a denial of service (DoS) attacks launched by intruders. The effects of flooding attacks in network simulation 2 (NS2) and measured performance parameters are investigated, including packet loss ratio, average delay, throughput and average number of hops under different numbers of attack nodes, flooding frequency, network bandwidth and network size. Simulation results show that with the increase of the flooding frequency and the number of attack nodes, network performance sharply drops. But when the frequency of flooding attacks or the number of attack nodes is greater than a certain value, performance degradation tends to a stable value.     

传感器网络中一种能量高效的数据收集算法

为了在无线传感器网络中降低能耗和最大化网络生存期,提出一种能量高效的数据收集算法(energy efficient data gathering algorithm, EEDGA)。该算法利用移动代理模型在网络中进行数据收集。首先,EEDGA根据监测精度的要求控制活动节点的数量;然后,通过求最小支配集得到具体的工作节点;最后,利用蚁群算法规划移动代理迁移的最优路线,移动代理以渐进方式收集活动节点的监测数据。仿真结果表明,与典型算法相比,该算法具有更低的能耗和更长的网络生存期。