共查询到20条相似文献，搜索用时 171 毫秒
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无线传感器网络WSN的路由存活概率云计算模型的优化设计可以提高WSN网络的网络复杂和数据吞吐量,在WSN网络的路由发射功率能耗具有匹配衰减特性,路由存活概率优化困难。传统方法采用灰色马尔可夫链组合模型进行存活概率预测,云计算精度不高。提出一种基于不变波束形成的WSN路由存活概率云计算优化模型,进行WSN网络云计算路由链路模型的结构设计,在WSN网络中进行任务节点信息表征,采用能量剩余度向量概率密度优化分配算法进行能量分配,把WSN的能量节点信号从阵元域变换到波束域得到阵列域,采用空间协方差矩阵逆的高阶次幂来逼近WSN的数据传输信号模型,将旋转角度控制在半功率主瓣以内,由此得到了云计算模型下WSN路由节点数据传输模型。仿真实验表明,该算法能提高WSN路由的存活概率,从而提高了WSN的吞吐量和通信能力,路由概率寻优中抑制了在WSN网络的路由发射功率能耗的匹配衰减性,通过最小的能量开销实现路由分发速率和负载效益的最大化。 相似文献
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无线传感器网络(Wireless Sensor Network)正成为信息获取的一种新兴手段,而节点自定位技术是其中的一个重要分支。网络节点采集的数据往往需要确定的位置信息才有意义,对目标进行追踪需要预知节点位置,另外,许多网络路由机制,网络拓扑管理也是以预知节点位置信息为基础的。因此,确定节点位置是WSN最基本的功能之一,对WSN应用的有效性起着关键作用。 相似文献
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数据融合是无线传感器网络(WSN)的一个关键技术,目的是减少传感器节点间的数据传输量,降低整个网络的能量消耗和数据冲突,延长全网的生命期.文章针对WSN中数据融合与传统的多传感器数据融合进行比较,介绍了WSN中数据融合的原理和方法,并基于森林防火和贝叶斯方法相结合提出了一种无线传感器网络数据融合的应用,最后给出了WSN... 相似文献
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基于链路同态解析的WSN路由选择算法 总被引：3，自引：0，他引：3
《科技通报》2015,(8)
通过优化无线传感器网络(WSN)的路由选择算法,实现负载均衡,节省WSN节点的能量开销。传统的路由选择算法使用基于汇聚节点随机链路分析的路由选择算法,控制网络中节点的密度实现路由选择,算法受到外界干扰较大,性能不好。提出一种基于链路同态解析的WSN路由选择算法。设计WSN节点的能量调度算法,提取网络传输的数据多路复用器输出端口的数据,找到经过最短路径数目最多的边并将它从网络中移除,采用能量剩余度同态解析模型,把WSN的能量节点变换到链路同态解析波束域中,实现WSN路由选择算法改进,实现WSN节点的能量调度,提高WSN的负载均衡能力。仿真结果表明,该算法能优化WSN的路由选择,节点的平均能量消耗最低,提高了WSN的可靠性和生存周期。 相似文献
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为了增强无线传感网络(WSN)的自适应性,降低WSN功耗,需要进行配网路由修复。传统的路由修复算法采用能量感知模型,设置负载平衡的CTP路由协议估算剩余能量,算法没有考虑路由最大角度的负载平衡问题,路由修复性能不好。提出一种基于负载-功耗联合特征测试的WSN配网路由修复算法,设计了基于ICTP协议的WSN网络模型框架,综合考虑簇头的剩余能量及其与Sink的距离对它的生命周期的影响,进行路由选择设计,采用负载-功耗联合特征测试方法,实现WSN配网路由修复改进。实验结果表明,该算法进行WSN路由修复设计,节点剩余能量方差一直维持在最低,有效地均衡了网络负载,平衡了网络能量消耗,节点生存性较大,提高能量的有效利用率。 相似文献
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本文介绍了一种应用于有源电力滤波器(Active Power Filter,APF)的神经网络自适应谐波电流检测方法.该方法应用自适应噪声抵消技术,并采用RBF神经网络实现噪声抵消.介绍了该RBF神经网络的构造和参数调整算法,应用HATLAB进行仿真研究.仿真结果,该检测方法具有较快的跟踪能力和动态响应速度. 相似文献
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《科技通报》2015,(8)
对卫星通信信道传感器网络的空间节点资源优化控制可以提高卫星通信网络资源的利用率,节省卫星通信网络开销,提高卫星通信传输吞吐能力。提出一种基于稀疏分布的空间节点资源循环迭代控制算法,通过构建空间节点资源的稀疏化控制矩阵,得到最大偏离调度控制量,采用循环迭代思想实现对空间节点资源的循环迭代控制。实现WSN空间节点资源控制模型的设计,构建WSN空间资源任务节点信息表征,得到WSN网络空间状态的最小正特征带状的连接权值,实现对卫星通信WSN网络的空间节点资源循环迭代控制算法的改进。仿真实验结果表明,该算法能提高卫星通信网络资源的利用率,节省卫星通信网络开销,WSN通信节点具有较好的数据吞吐性能,资源分配和控制效果达到最佳状态,展示了其优越性能。 相似文献
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《Journal of The Franklin Institute》2023,360(12):8973-8996
In this paper, the measurement outlier-resistant target tracking problem is investigated in wireless sensor networks (WSNs) with energy harvesting constraints. Each WSN node can acquire energy stochastically from surroundings. No matter whether the WSN node acquires energy or not, the WSN node’s measurement can be transmitted if the energy amount of the WSN node is greater than zero. In such a case, the sensor energy-induced missing measurement (SE-IMM) phenomenon may occur. The objective of this paper is to develop a solution for the considered target tracking problem by devising the filter including a saturation constraint such that, in the simultaneous presence of outliers and the SE-IMM phenomenon, the tracking performance can meet the given performance index. Firstly, the relation between the energy level of the WSN node and its probability distribution is computed recursively. Then, an upper bound of the tracking error covariance is derived which is minimized by appropriately choosing the filter parameter. Finally, the feasibility of the proposed target tracking scheme is validated by conducting a set of comparative experiments and the relationship between the energy of the WSN node and the tracking performance is also disclosed. 相似文献
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In this paper, based on Stirling’?s polynomial interpolation formula, the Second-order Central Difference Predictive Filter (CDPF2) is proposed for nonlinear estimation. To facilitate the new method, the algorithm flow of CDPF2 is given first. Then, the theoretical deductions demonstrate that the estimated accuracy of the model error and system state for the CDPF2 is higher than that of the conventional PF. In addition, the stochastic boundedness and the error behavior of CDPF2 is analyzed for general nonlinear systems in a stochastic framework. The theoretical analysis presents that the estimation error will remain bounded and the covariance will remain stable if the system?s initial estimation error, disturbing noise terms and model error are small enough, which is the core part of the CDPF2 theory. All of the results have been demonstrated by numerical simulations for a nonlinear example system. 相似文献
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主要研究了视频图像目标跟踪准确性问题。在基于核的颜色特征统计描述及以此建立视觉目标观测概率方法的基础上,提出了一种改进的粒子滤波视频图像目标跟踪算法。首先,本文给出了基于标准粒子滤波的单特征、单目标跟踪算法,然后针对加权样本参数的选择不同,提出改进思路,最后通过与基于均值移位视觉目标跟踪算法的实验结果对比。提出的改进的粒子滤波跟踪算法在稳健性方面有显著地提高,而且若适当选择视觉跟踪参数,在实时性方面能得到有效地保证。 相似文献
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《Journal of The Franklin Institute》2023,360(13):10297-10336
Owing to the effect of measurement noise and sudden changes in the power system, the robustness of state estimation for power system becomes very important. The Unscented Kalman Filter (UKF) is widely used for state estimation. However, it does not consider the influence of different kinds of gross errors. To better deal with gross errors, a robust adaptive UKF with gross error detection and identification (RAUKF-GEDI) is proposed, which uses the robust generalized correntropy loss in the UKF framework. The RAUKF-GEDI detects gross errors by hypothesis testing, and then uses the moving window to identify and classify three kinds of gross errors. Subsequently, the RAUKF-GEDI estimates the magnitudes of the gross errors to further compensate the measurements, and finally uses the compensated measurements to re-estimate the state to obtain precise estimated states. In addition, RAUKF-GEDI also introduces adaptive covariance matching method for state estimation. The RAUKF-GEDI is applied to the state estimation for power systems where the measurements are contaminated by three kinds of gross errors. Finally, the RAUKF-GEDI is also applied to the practical power system of Zhejiang Juchuang Smart Technology Company Park. The results show that the RAUKF-GEDI can detect and identify gross errors and enhance the robustness of UKF. 相似文献
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在对目标进行纯方位跟踪时,伪线性卡尔曼滤波算法是一种有效的跟踪滤波方法,该方法可以很好地对目标运动状态进行估计。通过仿真证明了该方法降低了对模型精度的要求,具有较好的稳定性。 相似文献
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Wuneng Zhou Jinping Mou Tianbo Wang Chuan Ji Jian'an Fang 《Journal of The Franklin Institute》2012,349(6):2004-2018
Based on the conflict and crosstalk avoidance mechanism (CCAM), we propose a sleeping–awaking method for wireless sensor networks (WSNs) in which the maximal degree node (MDN) and all its neighbors run sleep or wake simultaneously while other nodes run the CCAM. This method is said to be the same sleeping–awaking method (SSAM). The SSAM is motivated by the congestion and collision problems of cliques, MDN and its neighbor set in the communicating graph of the WSN. In this communication way, the related protocol about the SSAM is provided accordingly. Under the designed protocol, we get a Markovian switching WSN with both white noise disturbance and multiple time-varying delays. Based on the theory of exponential stability in pth moment, we show that the protocol ensures the WSNs to keep in synchronization with the target function. A numerical example shows that the WSN can keep its target-synchronization even with large time delays. 相似文献
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Najmeh Daroogheh Nader Meskin Khashayar Khorasani 《Journal of The Franklin Institute》2018,355(8):3753-3794
Health monitoring of nonlinear systems is broadly concerned with the system health tracking and its prediction to future time horizons. Estimation and prediction schemes constitute as principle components of any health monitoring technique. Particle filter (PF) represents a powerful tool for performing state and parameter estimation as well as prediction of nonlinear dynamical systems. Estimation of the system parameters along with the states can yield an up-to-date and reliable model that can be used for long-term prediction problems through utilization of particle filters. This feature enables one to deal with uncertainty issues in the resulting prediction step as the time horizon is extended. Towards this end, this paper presents an improved method to achieve uncertainty management for long-term prediction of nonlinear systems by using particle filters. In our proposed approach, an observation forecasting scheme is developed to extend the system observation profiles (as time-series) to future time horizon. Particles are then propagated to future time instants according to a resampling algorithm instead of considering constant weights for the particles propagation in the prediction step. The uncertainty in the long-term prediction of the system states and parameters are managed by utilizing dynamic linear models for development of an observation forecasting scheme. This task is addressed through an outer adjustment loop for adaptively changing the sliding observation injection window based on the Mahalanobis distance criterion. Our proposed approach is then applied to predicting the health condition as well as the remaining useful life (RUL) of a gas turbine engine that is affected by degradations in the system health parameters. Extensive simulation and case studies are conducted to demonstrate and illustrate the capabilities and performance characteristics of our proposed and developed schemes. 相似文献
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《Journal of The Franklin Institute》2023,360(13):9663-9688
A novel hierarchical coordination control strategy (HCCS) is offered to guarantee the stability of four-wheel drive electric vehicles (4WD-EVs) combining the Unscented Kalman filter (UKF). First, a dynamics model of the 4WD-EVs is established, and the UKF-based estimator of sideslip angle and yaw rate is constructed concurrently. Second, the equivalent cornering stiffness coefficients are jointly estimated to consider the impact of vehicle uncertainty parameters on the estimator design. Afterwards, a HCCS with two-level controller is presented. The sideslip angle and yaw rate are controlled by an adaptive backstepping-based yaw moment controller, and the computational burden is relieved by an improved adaptive neural dynamic surface control technology in the upper-level controller. Simultaneously, the optimal torque distribution controller of hub motors is developed to optimize the adhesion utilization ratio of tire in the lower-level controller. Finally, the proposed HCCS has shown effective improvement in the trajectory tracking capability and yaw stability of the 4WD-EVs under various maneuver conditions compared with the traditional Luenberger observer-based and the federal-cubature Kalman filter-based hierarchical controller. 相似文献
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The conventional interacting multiple model (IMM) algorithm will increase the computational load when applying a large number of models, meanwhile, it cannot yield accurate estimation results with a small number of models. Furthermore, the unknown target acceleration is regarded as an additional process noise to the target model, and its time-varying variance is hard to be approximated. The paper proposes a fuzzy-logic adaptive variable structure multiple model (FAVSMM) algorithm for tracking a high maneuvering target. The algorithm can optimize the model parameters using the model probability and construct an optimal model set quickly, and the fuzzy-logic IMM algorithm included in the FAVSMM algorithm is adopted for states estimation. The simulation results show that the proposed algorithm can match well with the actual target trajectory with less computational complexity and better accuracy. 相似文献
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针对传统图像预处理中图像信息损失的问题,提出基于小波变换的图像去噪和增强的算法。实验证明基于小波变换的图像预处理方法能在去噪和增强的同时,保留了图像在时间和空间域的信息,为视频录播后续的目标检测与跟踪提供了高质量图像,提高了录播系统的跟踪准确性。 相似文献