复杂产品多学科协同设计优化建模方法

复杂产品往往由多个子系统通过复杂的耦合方式组成。针对复杂产品多学科协同设计优化的需求,将复杂产品分解成为分布式层次型系统,建立复杂产品多学科协同优化模型,包括了系统级优化模型、子系统优化模型和子系统分析模型。为解决不确定条件下的复杂产品优化建模问题,在确定性条件下建模方法的基础上引进不确定分析,得到系统目标函数与不确定变量之间的对应关系,建立不确定条件下的复杂产品多学科协同优化模型。最后,给出了复杂产品多学科协同优化建模过程。

Abstract：

Complex products are often composed by a number of subsystems which coupled by some complicated way. In order to meet the need of multidisciplinary collaborative design optimization, the multidisciplinary collaborative optimization model of complex products was established based on distributed hierarchical system, including system-level optimization model, optimization model subsystem and subsystem analysis model. To solve the problem under uncertainty, complex products multidisciplinary collaborative design optimization modeling under uncertainty was concluded based on the introduction of uncertainty analysis to determine the system of objective function and the corresponding relationship between the uncertain variables. Finally, multidisciplinary collaborative optimization modeling process of complex product was provided.     

Adaptive robust control of mobile satellite communication system with disturbance and model uncertainties

The tracking and stable control of a typical shipmounted mobile satellite communication system(MSCS) is studied.Unlike the former studies based on simplified single-axis models,a tri-axis nonlinear model including the kinematic and dynamic features of the MSCS is used as the control object.An adaptive robust controller with trajectory planning is designed to deal with large parametric uncertainties and uncertain nonlinearities of the system.A theoretic performance result is given and proved.The designed adaptive robust controller and other two traditional controllers are tested in the comparative simulations under three different situations.The simulation results show the tracking and stable validity of the proposed controller.     

GPS接收机载波跟踪技术研究及仿真

MATLAB/Simulink对研究高动态GPS接收机的基带信号处理算法极为有效。通过采用载波辅助码跟踪环技术后,GPS接收机高动态性能的关键是通过载波自动频率跟踪环来实现的。在利用Simulink建立载体高动态模型,重点研究高动态GPS接收机三种载波自动频率跟踪技术的基础上,采用带符号的叉积点积自动频率跟踪环,仿真实现了该算法的跟踪误差、失锁概率与环路带宽、载噪比之间的关系。仿真结果表明,带符号的叉积点积自动频率跟踪环算法解决了叉积自动频率跟踪环受符号位影响及叉积点积自动频率跟踪环鉴频范围减半的问题,并能适用于加速度为10g,加加速度为10g/s的高动态环境。

Abstract：

It is very efficient for studying the baseband signal processing algorithms of the high dynamic GPS receiver with MATLAB/Simulink.The key performance of the high dynamic GPS receiver is achieved by the technology of carrier automatic frequency control after taking carrier assistant code tracking loop.By making use of Simulink to set up the high dynamic model and applying signed cross-product and dot-product automatic control based on the key research about thee kinds of carrier automatic tracking technology of the high dynamic GPS receiver,the simulation on the relationship among the tracking error,the lock-loss probability,loop bandwidth and the carrier noise ratio was achieved.The results show that the signed cross-product and dot-product automatic frequency control has solved the problems that cross-product automatic frequency control is affected by the signed bit and the frequency discrimination range of cross-product and dot-product automatic frequency control could reduce to half,and it can also be applied to the high dynamic environment with the acceleration of 10g and the jerk of 10g/s.     

RKP based secure tracking in wireless sensor networks

To enhancing the wireless sensor network＇s security in target tracking and locating application, this article proposes a tracking cluster based mobile cluster distributed group rekeying protocol （MCDGR）. Based on the given sensitivity, sensors can locate the moving object in the monitored area and form a tracking cluster around it. This tracking cluster can follow the target logically, process data on the target and report to the sink node, and thus achieve the tracking function. We introduce a multi-path reinforcement scheme, q-composition scheme and one-way cryptographic hash function based random key predistribution algorithm （RKP）, which can guarantee a high accuracy and security and a low energy consumption on the same time in large-scale sensor networks.     

Intelligent control over chaos in the rolling process and its comparison with tracking control

Some existing methods for chaos control in engineering fields are analyzed and their drawbacks are pointed out. A tracking method can solve these problems to some extent, but it still depends on the mathematical model of the system to be controlled. An intelligent method based on fuzzy neural network (FNN) is used to control chaos in engineering fields. The FNN is employed to learn the inherent dynamics from the input and output of chaos, which can be used in the inverse system method, so that the method is free of the exact mathematical model of the system to be controlled. This intelligent method is compared with tracking method in the presence of measurement noise and model error. Simulation results show its superiority and feasibility.     

The Dynamics of Two-State Public Opinion Propagation On Signed Networks

The formation of public opinion on the network is a hot issue in the field of complex network research, and some classical dynamic models are used to solve this problem. The signed network is a particular form of the complex network, which can adequately describe the amicable and hostile relationships in complex real-world systems. However, the methods for studying the dynamic process of public opinion propagation on signed networks still require to be further discussed. In this paper, the authors pay attention to the influence of negative edges in order to design a two-state public opinion propagation mechanism suitable for signed networks. The authors first set the interaction rules between nodes and the transition rules of node states and then apply the model to synthetic and real-world signed networks. The simulation results show that there is a critical value of the negative edge ratio.When the negative edge ratio exceeds this critical value, the evolutionary result of public opinion will change from a consistent state to a split state. This conclusion is also consistent with the distribution result of opinions within communities in the signed network. Besides, the research on the network structural balance shows that the model makes the network evolve in a more balanced direction.     

电力关键基础设施网络仿真模型研究

电力网络模型是关键基础设施模型中的重要组成部分。分析了现实电力网络的拓扑结构和运行原理,根据复杂网络的基本理论,采用基于Agent的建模方法设计实现了关键基础设施中电力网络模型。通过仿真实验,较好的模拟了电力系统输送电能的动态过程和因偶发故障而造成的大规模停电现象,对复杂电力网络的动力学特性进行了初步探索。

Abstract：

The model of electric power network is a major part of critical infrastructure model. The topology of network structure and the principle of net function were analyzed, according to the theory of complex network, applying the agent-based modeling to design and realize the model of electric power network. Through the simulation experiment, it was demonstrated to be better ways to simulate the dynamic progress of electric transmission and the cascading failure phenomenon caused by incidental trouble. The simulation model offers an effective approach to analysis electric power network’s role in critical infrastructure, and makes a step to exploration for complex electric power network’s dynamic character.     

Energy efficient target tracking algorithm using cooperative sensors

Target tracking is one of the applications of wireless sensor networks(WSNs).It is assumed that each sensor has a limited range for detecting the presence of the object,and the network is sufficiently dense so that the sensors can cover the area of interest.Due to the limited battery resources of sensors,there is a tradeoff between the energy consumption and tracking accuracy.To solve this problem,this paper proposes an energy efficient tracking algorithm.Based on the cooperation of dispatchers,sensors in the area are scheduled to switch their working mode to track the target.Since energy consumed in active mode is higher than that in monitoring or sleeping mode,for each sampling interval,a minimum set of sensors is woken up based on the select mechanism.Meanwhile,other sensors keep in sleeping mode.Performance analysis and simulation results show that the proposed algorithm provides a better performance than other existing approaches.     

Vector tracking loops in GNSS receivers for dynamic weak signals

Vehicle positioning with the global navigation satellite system(GNSS) in urban environments faces two problems which are attenuation and dynamic.For traditional GNSS receivers hardly able to track dynamic weak signals,the coupling between all visible satellite signals is ignored in the absence of navigation state feedback,and thermal noise error and dynamic stress threshold are contradictory due to non-coherent discriminators.The vector delay/frequency locked loop(VDFLL) with navigation state feedback and the joint vector tracking loop(JVTL) with coherent discriminator which is a synchronization parameter tracking loop based on maximum likelihood estimation(MLE) are proposed to improve the tracking sensitivity of GNSS receiver in dynamic weak signal environments.A joint vector position tracking loop(JVPTL) directly tracking user position and velocity is proposed to further improve tracking sensitivity.The coherent navigation parameter discriminator of JVPTL,being able to ease the contradiction between thermal noise error and dynamic stress threshold,is based on MLE according to the navigation parameter based linear model of received baseband signals.Simulation results show that JVPTL,which combines the advantages of both VDFLL and JVTL,performs better than both VDFLL and JVTL in dynamic weak signal environments.     

基于协议同步水下传感器网络目标协同定位算法

为了解决水下目标的定位问题,讨论了一种基于水下传感器阵列网络的目标协同定位算法。该算法在实现水下节点同步的基础上,通过建立目标位置与距离差测量值的统计模型后依据最大似然准则完成目标定位。定位算法的实现采用分布-集中相结合的处理方法,在提高定位精度的同时大大节省了水下节点通信能耗。通过仿真实验验证了算法的有效性和可行性,实验结果表明该方法具有较好的同步及目标定位精度。

Abstract：

A collaborative target location algorithm for underwater acoustic sensor networks （UASN） was proposed. The algorithm was achieved based on time synchronization for high transmitting delay for UASN. Target location was estimated by maximum-likelihood methods based on proposed statistical model which was established by the relation between target position and measured range difference. The proposed algorithm adopts the distributed-centralized computation methods,which degrade the transmitting energy comparing with traditional centralized methods. The result of simulation shows the application validity and the more location performance of the algorithm.     

基于多智能体与市场理论的多机载平台传感器管理

针对作战飞机编队多目标攻击中的传感器管理问题,提出了一种基于多智能体和市场理论的多机载平台传感器管理方法。该方法在多智能体的框架下建立飞机编队多传感器多目标分配模型,将目标分配和传感器管理问题转化为市场理论中的“性价比”最优问题,建立了基于“性价比”最高的任务规划模型、以任务为驱动的传感器目标配对模型以及多平台集中式融合跟踪模型,并利用市场拍卖算法完成传感器-目标分配问题。仿真结果证明了该方法的有效性,与基于信息论的传感器管理方法比较证明了方法的优越性。研究结论有助于提高作战飞机编队的进攻效率,从而提升整体的攻击能力。     

杂波环境下可移动主被动传感器长时调度方法

针对杂波环境下的多目标跟踪问题, 基于可移动主被动传感器系统, 提出了一种辐射控制的长时调度方法。首先, 建立调度模型, 对多目标运动状态和量测结果、传感器调度动作等进行数学描述; 同时, 基于雷达工作原理和截获概率的思想, 提出改进的辐射风险量化方法。随后, 利用高斯混合概率假设密度滤波算法预测长时跟踪精度, 利用所提改进的量化方法预测长时辐射代价, 并利用改进的灰狼优化算法求解传感器调度方案。最后, 执行调度方案获得多目标量测信息, 采用联合广义标签多伯努利滤波算法计算目标估计状态。仿真实验表明, 所提调度方法在保证跟踪精度的基础上, 能够实现对辐射代价的有效控制, 与其他方法相比具有明显的优势。     

面向跟踪任务需求的主动传感器调度方法

以多传感器多目标跟踪为背景,针对跟踪任务需求中辐射风险控制问题,提出一种面向跟踪任务需求的主动传感器调度方法。该方法首先结合不敏卡尔曼滤波,给出了仅考虑跟踪任务需求的传感器调度策略;然后建立基于部分可观马尔可夫决策过程的辐射模型,并采用隐马尔可夫模型滤波器动态更新传感器辐射;最后考虑跟踪任务需求和传感器约束,将辐射风险控制下传感器调度问题转化为非线性约束下寻优问题。仿真实验结果验证了所提方法有效性。     

基于分支剔除的低轨星座实时传感器调度算法

针对低轨星座目标连续跟踪的传感器资源调度问题,通过对调度约束因素的分析,提取跟踪精度、资源松弛度和资源分配均衡因子三个优化参数,建立了传感器实时调度模型;并建立长时调度决策树,将标准代价搜索方法和分支剔除技术相结合,提出了一种基于分支剔除的实时传感器调度算法。仿真实验表明,标准代价搜索和分支剔除技术的引入明显降低了调度算法的运算量,尤其对于多目标和大步长的情况,且调度传感器跟踪目标的误差略小于短时调度方法。     

基于GA-SA的低轨星座传感器资源调度算法

针对基于低轨预警系统的多目标跟踪,提出了兼顾跟踪精度与系统效率的传感器资源调度算法。首先,建立了目标跟踪模型。然后,以调度周期内后验克拉美罗下界(posterior Cramer-Rao lower bound,PCRLB)变化率、卫星切换率为指标,建立了传感器调度的混合整数规划模型,在此基础上,采用遗传(genetic algorithm,GA) 模拟退火(simulated annealing,SA)混合算法对调度模型进行优化求解,提高了对解空间的搜索能力与求解速度。最后,仿真试验表明本文调度模型的正确性与GA-SA混合优化算法的有效性。     

基于信息增量的多被动传感器资源分配算法

针对多被动传感器多目标跟踪中的传感器资源分配问题进行研究。讨论了多被动传感器跟踪误差的克拉美-罗下限,在此基础上分析了多被动传感器系统跟踪误差的几何分布。为解决多目标跟踪中的传感器资源分配问题,通过先验信息熵与后验信息熵之间的差值获得信息增量;在此基础上针对多被动传感器系统的特点构建最优化分配模型,将被动传感器组合在不同时刻动态地分配给不同目标。仿真实验表明,与不考虑资源分配的固定跟踪模式相比,该方法能够高效合理地利用有限资源,并且使多被动传感器多目标跟踪系统的整体跟踪性能得到提高。     

多传感器优化部署下的机动目标协同跟踪算法

针对资源有限的传感器网络中目标动态跟踪问题,提出了一种能够自适应选择跟踪传感器的机动目标协同跟踪算法。首先,采用粒子群优化算法优化传感器网络能耗与有效覆盖率,进行传感器位置部署;然后,以最大化候选传感器的Rényi信息增量与最小化传感器间信息传递能耗为适应度函数,采用二进制粒子群优化算法自适应选择最佳跟踪传感器组;最后,利用交互多模型粒子滤波对机动目标位置进行估计并进行分布式融合。仿真结果表明,与现有方法相比,该方法可在非高斯非线性环境下自适应选择最优跟踪传感器,显著提高目标跟踪精度,降低网络能耗。     

基于粒子滤波的机器人定位及动态目标跟踪

提出了一种基于粒子滤波的动态跟踪算法,解决了传统SLAM理论在处理动态目标时误差不断累加的问题。通过分析移动机器人和激光测距仪,里程计的原理,建立了机器人的运动和观测模型。将数据关联的方法用于动态环境中则提高了系统的稳定性和定位的精度。仿真结果表明此算法能够比较精确地估计出机器人的位姿以及动态目标在地图中的位置,为开展将静态与动态相结合的定位与地图构建的研究提供了一种可行方案。     

基于随机集理论的多个声目标融合跟踪

针对杂波环境下,采用多个被动声传感器跟踪多个声目标的应用场合,建立了多个声目标跟踪的随机有限集模型,采用概率假设密度(probability hypothesis density, PHD)粒子滤波对该模型进行求解。针对PHD滤波器只适用于单传感器的问题,提出了一种实现多个声传感器融合跟踪的方法。该方法在序贯PHD滤波器的基础上进行改进,提高了目标检测率,通过仿真实验验证了该方法的有效性。     

基于量子多目标进化算法的多任务Agent联盟生成

多Agent联盟生成是多Agent系统的关键问题之一, 主要研究如何在多Agent系统中动态生成面向任务的最优联盟. 为使Agent能稳定的组织起来完成单Agent不能完成的任务并在成本、资源、利益等方面达到一个良好的平衡性能并达到全局最优, 提出了联盟多目标综合评价模型, 并将量子进化多目标算法应用于多目标多任务Agent联盟问题, 运用编码的映射, 将资源组合和任务分配合并为一个过程, 降低了问题的复杂性. 对比实验结果表明该算法求得的解的质量高, 平衡性好, 能有效避免了联盟死锁和资源浪费.