基于状态预测自适应一致滤波器的分布式估计融合算法

应用自适应滤波算法改进了基于一致滤波器的估计融合算法以加快节点估计的一致收敛速度,提出了 一种基于状态预测的自适应一致滤波器．在此算法中,节点采用状态预测值作为自适应滤波器的参考信号,应用自 适应算法修正一致滤波器的加权矩阵．仿真结果表明,本文提出的算法不仅能够加快节点估计的一致收敛速度,还 能减小收敛过程中节点的估计误差．     

一种适用于稀疏无线传感器网络的改进分布式UIF算法

分布式无迹信息滤波(Distributed unscented information filter,DUIF)算法是一种有效的非线性分布式状态估计多源信息融合方法,然而当将该算法应用于稀疏无线传感器网络(Wireless sensor networks,WSN)时,稀疏WSN中存在的无效节点会引起使滤波趋于发散的平均一致误差.针对该问题,本文提出一种改进DUIF算法.该算法不改变DUIF算法的级联结构,而是将其底层和上层滤波器分别改进为局部无迹信息滤波器(Local unscented information filter,LUIF)和加权平均一致性滤波器.LUIF对每个节点的局部多源观测信息进行局部融合,得到局部的后验估计信息向量和矩阵,进而将它们作为加权平均一致性滤波器的输入,最终得到不包含平均一致误差的分布式后验估计结果.其中,加权平均一致性滤波器是通过对由LUIF输出的局部后验估 计信息向量和矩阵分别进行平均一致性滤波而得以在改进DUIF算法框架下实现的.同时,在此过程中,相邻节点之间的状态估计互相关信息也被引入改进DUIF算法的输出结果中,进一步增强了滤波的可靠性.仿真实验结果表明,改进DUIF算法能够在稀疏WSN中对机动目标进行有效跟踪,在估计精度和抑制滤波发散方面明显优于标准DUIF算法.     

基于自适应加权融合的分布式滤波算法

针对存在丢包的传感器网络中每个传感器节点对目标估计确信度不同的问题,提出一种基于自适应加权融合的分布式滤波算法.考虑节点在网络中的影响力及其节点属性,将节点重要度与传感器网络节点观测数据间的支持度线性加权,获得每个传感器节点对目标的估计确信度,并将该确信度构成的融合权值引入节点状态估计值的一致性协议中,更新传感器节点对目标的状态估计值,提高分布式滤波算法的估计精度和传感器节点估计值的一致性.仿真结果验证了所提出方法的有效性.     

一种改进的分布式航迹估计融合算法研究

在航迹估计融合算法研究中,加权融合估计算法实现起来特别容易,因此得到了广泛应用。但由于其加权因子直接影响融合结果,一般说来都根据平时经验制定其加权因子,导致算法性能很不稳定,设计起来也不方便。针对此问题,设计了一种改进加权融合算法,并与其它算法进行比较,结果表明,算法稳定性更高,设计更科学,融合效果更好。     

基于分布式估计扩散机制的协作频谱感知方法

研究网络通信频谱优化问题,在分布式网络中,多节点联合估计未知参数.为了满足实时性要求,提出了一种扩散机制的协作频谱感知算法.算法采用扩散矩阵作为加权因子协作更新目标状态估计值,且以较小的均方误差快速实现对信号能量的最优估计.传统的协作感知方法快速性差.提出的算法不需要融合中心,能很好适应拓扑结构动态变化的随机网络.仿真结果表明,改进算法与分布式平均一致性估计算法相比,能够提高实时性并显著改善认知无线电网络的频谱感知性能.     

分布式传感网络的自适应一致性算法

为了提高分布式传感网络的估计精度,提出了一种新的自适应一致性算法。该算法在每次迭代时只需部分节点工作,即进行目标状态的监测。通过节点之间二进制信息的交换来调整每次迭代时的一致性权值,使得每次迭代时工作节点所占的权值更大,进而将该一致性算法与卡尔曼滤波相结合对目标状态进行估计。对该算法进行数值仿真,并与其他一致性加权算法进行比较,验证了该算法的有效性。     

基于残差加权的三维DV-Hop改进WSN定位算法

针对无线传感网诸多具体应用中需要节点位置信息的实际需求,提出一种基于残差加权的三维DV-Hop改进定位算法的解决方案。该方案通过引入残差函数将提高定位精度的问题转化为等式约束条件下残差最小化的求解问题,采用最小二乘准则对待定节点与锚节点的最小跳数进行平均加权处理,并利用二次规划法将其最终转化为无约束条件下最小化的问题。经理论分析得出了三维DV-Hop改进定位算法的模型,实现待定节点的坐标估计并提高了定位精度。仿真结果表明,在相同通信半径、不同锚节点比例的情况下,改进三维DV-Hop定位算法的性能得到了明显提高。     

基于决策信息的毫米波/红外复合制导信息融合

针对复杂环境下的毫米波/红外复合制导,提出一种基于决策信息的改进加权融合算法.通过分析导弹运行状态和受干扰情况,获得制导的决策信息.对经无迹卡尔曼滤波(UKF)估计分别得到的毫米波/红外量测信息进行改进的自适应加权融合,得到融合后的制导信息.仿真实验表明,所提方法融合精度高,能有效提高复合制导的性能.     

一种兼顾性能与能耗的DV-Hop改进算法

现有DV-Hop改进算法对节能考虑不够,研究提高精度并节能的改进方法很有必要。在边定位边扩充锚节点的定位方式中,跳数和扩充锚节点生成时的轮数(简称升级代数)都影响节点平均跳距的估计。提出结合这两个因素进行加权的节点平均跳距估计方法以提高精度;从锚节点组合的有效性和节点早定位能减少因等待锚节点信息所耗的时间和能量两方面考虑,进一步提出有效锚节点组合中含两个初始锚节点和一个扩充锚节点时即可正确定位的优选组合。以定位误差率和完成定位所需轮数为评价指标,通过仿真将新算法与其他DV-Hop算法进行性能比较,结果表明新算法能提高定位精度并节能。     

网络瞄准环境下异步航迹融合策略及融合算法研究

多个网络节点的异步航迹融合是实现网络瞄准作战方式的关键技术之一．本文有效利用各网络节点的异步航迹信息, 提高瞄准的精度. 首先分析了网络瞄准环境下异步航迹融合的主要方式, 在此基础上结合工程实际, 提出了3种可适用于网络瞄准的异步航迹融合策略; 然后根据最优估计理论, 分别给出了不同策略下的异步融合算法与实现步骤; 最后通过仿真验证了所提出方法在解决异步航迹融合问题上的有效性, 并分析了不同航迹融合周期 对系统融合性能的影响.     

Distributed Filtering Algorithm Based on Tunable Weights Under Untrustworthy Dynamics

Aiming at effective fusion of a system state estimate of sensor network under attack in an untrustworthy environment, distributed filtering algorithm based on tunable weights is proposed. Considering node location and node influence over the network topology, a distributed filtering algorithm is developed to evaluate the certainty degree firstly. Using the weight reallocation approach, the weights of the attacked nodes are assigned to other intact nodes to update the certainty degree, and then the weight composed by the certainty degree is used to optimize the consensus protocol to update the node estimates. The proposed algorithm not only improves accuracy of the distributed filtering, but also enhances consistency of the node estimates. Simulation results demonstrate the effectiveness of the proposed algorithm.      

传感器网络一致性分布式滤波算法

为了改善分布式传感器网络的估计性能,提出了一种基于状态预测一致的滤波算法.在对局部估计值进行一致化处理的基础上,重点研究了利用邻居节点前一时刻的估计值对当前局部状态预测值进行修正来提高估计精度.给出了一种一致性增益的选择方法,利用李雅普诺夫方法得到了算法收敛的充分条件,并讨论了影响算法收敛速度的因素.仿真结果表明了算法的有效性,并发现节点度较大的传感器在网络估计中发挥着重要作用,可通过调整这类节点的一致性系数来改善算法性能.     

基于扩散卡尔曼滤波算法的目标跟踪估计

针对节点网络上的目标跟踪,提出一种基于扩散Kalman滤波算法的分布式跟踪估计.假设该节点网络系统按照线性状态空间模型演进,网络中的每个节点获取与未观察到的状态线性相关的测量值;对于每个测量值和每个节点,采用来自邻近区域的数据计算出一个局部状态估计值;采用一个基于扩散矩阵和连接矩阵的扩散步骤,将前面计算得到的邻域估计值...     

Distributed linear estimation over sensor networks

We consider a network of sensors in which each node may collect noisy linear measurements of some unknown parameter. In this context, we study a distributed consensus diffusion scheme that relies only on bidirectional communication among neighbour nodes (nodes that can communicate and exchange data), and allows every node to compute an estimate of the unknown parameter that asymptotically converges to the true parameter. At each time iteration, a measurement update and a spatial diffusion phase are performed across the network, and a local least-squares estimate is computed at each node. The proposed scheme allows one to consider networks with dynamically changing communication topology, and it is robust to unreliable communication links and failures in measuring nodes. We show that under suitable hypotheses all the local estimates converge to the true parameter value.     

Distributed Sparse Signal Estimation in Sensor Networks Using H∞-Consensus Filtering

This paper is concerned with the sparse signal recovery problem in sensor networks, and the main purpose is to design a filter for each sensor node to estimate a sparse signal sequence using the measurements distributed over the whole network. A so-called l₁-regularized H_∞ filter is established at first by introducing a pseudo-measurement equation, and the necessary and sufficient condition for existence of this filter is derived by means of Krein space Kalman filtering. By embedding a high-pass consensus filter into l₁-regularized H_∞ filter in information form, a distributed filtering algorithm is developed, which ensures that all node filters can reach a consensus on the estimates of sparse signals asymptotically and satisfy the prescribed H_∞ performance constraint. Finally, a numerical example is provided to demonstrate effectiveness and applicability of the proposed method.      

Distributed EM Algorithm for Gaussian Mixtures in Sensor Networks

This paper presents a distributed expectation–maximization (EM) algorithm over sensor networks. In the E-step of this algorithm, each sensor node independently calculates local sufficient statistics by using local observations. A consensus filter is used to diffuse local sufficient statistics to neighbors and estimate global sufficient statistics in each node. By using this consensus filter, each node can gradually diffuse its local information over the entire network and asymptotically the estimate of global sufficient statistics is obtained. In the M-step of this algorithm, each sensor node uses the estimated global sufficient statistics to update model parameters of the Gaussian mixtures, which can maximize the log-likelihood in the same way as in the standard EM algorithm. Because the consensus filter only requires that each node communicate with its neighbors, the distributed EM algorithm is scalable and robust. It is also shown that the distributed EM algorithm is a stochastic approximation to the standard EM algorithm. Thus, it converges to a local maximum of the log-likelihood. Several simulations of sensor networks are given to verify the proposed algorithm.      

Decentralized observers with consensus filters for distributed discrete-time linear systems

This paper presents a decentralized observer with a consensus filter for the state observation of discrete-time linear distributed systems. Each agent in the distributed system has an observer with a model of the plant that utilizes the set of locally available measurements, which may not make the full plant state detectable. This lack of detectability is overcome by utilizing a consensus filter that blends the state estimate of each agent with its neighbors’ estimates. It is proven that the state estimates of the proposed observer exponentially converge to the actual plant states under arbitrarily changing, but connected, communication and pseudo-connected sensing graph topologies. Except these connectivity properties, full knowledge of the sensing and communication graphs is not needed at the design time. As a byproduct, we obtained a result on the location of eigenvalues, i.e., the spectrum, of the Laplacian for a family of graphs with self-loops.     

Bounded consensus for multiagent systems by event‐triggered data transmission,time delay,and predictor‐based control

This paper investigates the consensus issue of multiagent systems with data transmission time delay. The state measurement of each local agent is directly sent to a private event‐trigger and further authorized to be broadcasted to its neighbors via communication network only when the threshold of the event‐trigger is violated. Since the controller always receives discrete‐time neighbor information with data transmission time delay, a predictor is employed to estimate the continuous‐time neighbor state. Based on the estimated state, a novel consensus protocol is mainly proposed for achieving the bounded consensus of the multiagent systems. By the proposed method, the asynchronous neighbor information is allowed and the margin of data transmission time delay is also given. Furthermore, it has been proved that the unwanted Zeno phenomena can be naturally excluded. Numerical example is provided to demonstrate the effectiveness of the proposed method.