首页 | 官方网站   微博 | 高级检索  
相似文献
 共查询到19条相似文献,搜索用时 352 毫秒
1.
在多传感器分布式检测系统中,常规融合规则算法要求传感器误差概率已知,且系统中传感器和融合中心同时优化存在一定困难.提出最小二乘融合规则(LSFR)算法,算法不依赖噪声环境稳定性以及传感器的虚警概率与检测概率,融合中心根据各个传感器的硬决策,得到全局的硬决策,并在传感器和融合中心处理达到最优时,获得最佳全局性能.仿真结果表明:对比似然比融合决策算法与Neyman Pearson融合规则(NPFR)算法,LSFR算法全局检测概率显著提高,且在不同数量规模传感器和更多类型的分布式检测系统中具有较好兼容性.  相似文献   

2.
分布式贝叶斯数据融合系统的遗传算法优化   总被引:1,自引:0,他引:1  
数据融合是将多个传感器的信息加以集成,进行综合利用,其性能优于单传感器检测.寻找分布式并联融合系统的全局最优解需要求解一组耦合的非线性方程,运算量随着系统中传感器数量的增加迅速增长,传统方法很难求解.文中分析了融合的结构,重点研究了基于遗传算法的分布式贝叶斯融合系统的全局优化.采用穷举法列举所有可行融合规则,用遗传算法搜索相应规则下的最优解,实现了系统解耦.通过比较各融合规则下的最优解,得出分布式并联融合检测系统的全局最优解.仿真结果表明,该方法求解有效,建立了全局最优的贝叶斯融合检测系统.  相似文献   

3.
分布式多传感器检测系统时间序列数据融合算法研究   总被引:3,自引:0,他引:3  
本文在Chair和Varshney提出的数据融合算法基础上,提出一种分布式多传感器检测系统按时间序列取多组局部决策数据的融合算法及决策规则,并给出系统性能的仿真计算结果。  相似文献   

4.
基于门限自适应的分布式检测融合算法   总被引:2,自引:0,他引:2  
贝叶斯检测融合策略是一种比较传统的分布式检测融合方法,必须给定待检测现象的先验概率和各局部传感器的虚警概率和漏检概率,而在现实应用中,统计量是未知的或者是随时间变化的.因此,研究了一种纽曼一皮尔逊准则下的门限自适应分布式检测系统的融合算法.算法可根据观测数据,自动在线调整门限,使得局部传感器检测达到最佳,从而提高系统的检测性能.计算机仿真的结果表明,算法能较快地收敛,相对局部传感器,融合中心的检测性能也明显地有了提高.  相似文献   

5.
基于N-P准则的水声信号检测系统信息融合   总被引:4,自引:1,他引:4  
多基阵数据融合技术在水声信号处理中具有重要意义,本文给出了基于Neyman Pearson准则的多传感器分布式水声检测信息融合系统.研究了全局最优融合系统以及局部传感器的最优判决准则.在假定各传感器检测独立的情况下,对三传感器的情况进行了仿真.结果表明,检测系统的性能有明显提高.  相似文献   

6.
在分布式WSN系统中,簇内有相当多的无线传感器节点,这些节点可能会部署在各种环境中,采用从单个传感器上所获取信息可靠性不高。为了提高系统的可靠性,需要对多个传感器节点采集数据进行综合,这样就可以有效地提高所获得数据的精度和可信度。研究了在系统节点发生拜占庭故障的情况下,利用现有WSN的数据融合方法以及安全系统中的拜占庭将军问题,提出了一种新的基于OM算法与贝叶斯检测算法的容错检测算法,合理而有效的进行数据融合,减小拜占庭故障对系统的影响,从而使所有节点做出一致决定。通过仿真得出该算法可以保证节点决策具有较高一致性的情况下仍有较高的故障节点减少率。  相似文献   

7.
黄艳   《信息与控制》2007,36(6):0-753
针对水声传感器网络中大延迟、低可靠通信约束下的水声信号分布式检测问题,提出了一种基于时间窗口的自适应融合算法.传感器节点依据声纳接收机的特性计算局部判决并发送给融合中心节点.融合中心节点在时间窗口内,基于已收到的局部判决在线自适应地调整融合规则,从而由最优融合算法得到最终判决.通过仿真,讨论了时间窗口的选择问题以及融合算法的性能.仿真结果表明,新算法具有很高的实用性,能够在动态变化的水声通信条件下保证整个系统高效运行.  相似文献   

8.
郭徽东  章新华 《控制与决策》2004,19(12):1359-1363
在传感器观测噪声不一致或有异常数据存在的条件下,分布式数据融合因没有剔除严重偏离真实值的传感器估计值,从而影响下一步的融合估计.对此,利用概率数据互联的思想,设计以融合中心预测值为中心、传感器节点估计值为观测值的预测域,并引入定向概率数据互联,对进入预测域的传感器估计值分配权重.仿真结果表明,利用概率数据互联思想的多传感器有效地实现了数据融合,其融合精度较传统分布式融合有所提高;在异常数据明显的情况下,算法的效果更加显著.  相似文献   

9.
分布式检测系统的一种软决策融合算法   总被引:2,自引:1,他引:1  
在分布式检测系统中,为了进一步提高系统的性能,各传感器可以向融合中心发送多位二进制判决信息.对于这种发送多位判决信息的软决策融合系统,提出了一种对各传感器观测空间进行再划分的方法,它将各传感器的观测空间按照其检测概率和虚警概率进行再划分.这种划分方法能够简化融合中心的计算,且计算机仿真结果表明,应用该方法后融合系统的检测性能有明显的提高.  相似文献   

10.
系统地阐述了传感器网络环境中几个基本而又重要的信息融合问题的最近进展,包括:最一般条件下全局最优的多传感器分布式统计判决;传感器观测数据或局部估计的最优维数压缩;一般条件下最优线性无偏估计融合公式及其有效算法;传感器观测噪声相关情形下动态系统的卡尔曼滤波融合;容错条件下的区间估计融合.这些结果对传感器网络的设计与应用具有重要意义.  相似文献   

11.
The purpose of decision fusion in a distributed detection system is to achieve a performance that is better than that of local detectors (or sensors). We consider a distributed Bayesian detection system consisting of n sensors and a fusion center, in which the decision rules of the sensors have been given and the decisions of different sensors are conditionally independent. We assume that the decision rules of the sensors can be optimum or suboptimum, and that the probabilities of detection and false alarm of the sensors can be different. Theoretical analysis on the performance of this fusion system is carried out. Conditions for the fusion system to achieve a global risk that is smaller than local risks are obtained  相似文献   

12.
In this paper, we present a fusion rule for distributed multihypothesis decision systems where communication patterns among sensors are given and the fusion center may also observe data. It is a specific form of the most general fusion rule, independent of statistical characteristics of observations and decision criteria, and thus, is called a unified fusion rule of the decision system. To achieve globally optimum performance, only sensor rules need to be optimized under the proposed fusion rule for the given conditional distributions of observations and decision criterion. Following this idea, we present a systematic and efficient scheme for generating optimum sensor rules and hence, optimum fusion rules, which reduce computation tremendously as compared with the commonly used exhaustive search. Numerical examples are given, which support the above results and provide a guideline on how to assign sensors to nodes in a signal detection networks with a given communication pattern. In addition, performance of parallel and tandem networks is compared.  相似文献   

13.
When all the rules of sensor decision are known ,the optimal distributed decision fusion ,which relies only on the joint conditional probability densities , can be derived for very general decision systems. They include those systems with interdependent sensor observations and any network structure. It is also valid for m-ary Bayesian decision problems and binary problems under the Neyman- Pearson criterion. Local decision rules of a sensor with communication from other sensors that are optimal for the sensor itself are also presented ,which take the form of a generalized likelihood ratio test . Numerical examples are given to reveal some interesting phenomena that communication between sensors can improve performance of a senor decision ,but cannot guarantee to improve the global fusion performance when sensor rules were given before fusing.  相似文献   

14.
Optimal decision fusion given sensor rules   总被引:3,自引:0,他引:3  
When all the rules of sensor decision are known,the optimal distributed decision fusion,which relies only on the joint conditional probability densities, can be derived for very general decision systems. They include those systems with interdependent sensor observations and any network structure. It is also valid for m-ary Bayesian decision problems and binary problems under the Neyman-Pearson criterion. Local decision rules of a sensor withfrom other sensors that are optimal for the sensor itself are also presented, which take the form of a generalized likelihood ratio test. Numerical examples are given to reveal some interesting phenomem that communication between sensors can improve performance of a senor decision,but cannot guarantee to improve the global fusion performance when sensor rules were given before fusing.  相似文献   

15.
The performance of a distributed Neyman-Pearson detection system is considered. We assume that the decision rules of the sensors are given and that decisions from different sensors are mutually independent conditioned on both hypotheses. The purpose of decision fusion is to improve the performance of the overall system, and we are interested to know under what conditions can a better performance be achieved at fusion center, and under what conditions cannot. We assume that the probabilities of detection and false alarm of the sensors can be different. By comparing the probability of detection at fusion center with that of each of the sensors, with the probability of false alarm at fusion center constrained equal to that of the sensor, we give conditions for a better performance to be achieved at fusion center  相似文献   

16.
基于模糊评判的决策级信息融合算法的研究   总被引:9,自引:1,他引:9  
文章针对水电故障诊断系统中普遍采用的传感器阀值判断方法引起的信息损失问题,将决策级信息融合技术应用于故障诊断系统中。在模糊综合评判技术和软判决融合结构下,提出了一种新的决策级信息融合算法。该算法以合成运算和全局决策融合来自多传感器的局部判决以获取所处理对象的综合决策分析,并通过在丰满水电仿真系统的故障诊断系统中的实际应用表明该算法优于传统的故障检测方法。  相似文献   

17.
This paper proposes and characterizes a sequential decision aggregation system consisting of agents performing binary sequential hypothesis testing and a fusion center which collects the individual decisions and reaches the global decision according to some threshold rule. Individual decision makers’ behaviors in the system are influenced by other decision makers, through a model for social pressure; our notion of social pressure is proportional to the ratio of individual decision makers who have already made the decisions. For our proposed model, we obtain the following results: First, we derive a recursive expression for the probabilities of making the correct and wrong global decisions as a function of time, system size, and the global decision threshold. The expression is based on the individual decision makers’ decision probabilities and does not rely on the specific individual decision-making policy. Second, we discuss two specific threshold rules: the fastest rule and the majority rule. By means of a mean-field analysis, we relate the asymptotic performance of the fusion center, as the system size tends to infinity, to the individual decision makers’ decision probability sequence. In addition to theoretical analysis, simulation work is conducted to discuss the speed/accuracy tradeoffs for different threshold rules.  相似文献   

18.
In this paper we tackle distributed detection of a non-cooperative target with a Wireless Sensor Network (WSN). When the target is present, sensors observe an unknown random signal with amplitude attenuation depending on the distance between the sensor and the target (unknown) positions, embedded in white Gaussian noise. The Fusion Center (FC) receives sensors decisions through error-prone Binary Symmetric Channels (BSCs) and is in charge of performing a (potentially) more-accurate global decision. The resulting problem is a one-sided testing with nuisance parameters present only under the target-present hypothesis. We first focus on fusion rules based on Generalized Likelihood Ratio Test (GLRT), Bayesian and hybrid approaches. Then, aimed at reducing the computational complexity, we develop fusion rules based on generalizations of the well-known Locally-Optimum Detection (LOD) framework. Finally, all the proposed rules are compared in terms of performance and complexity.  相似文献   

19.
The paper considers a sensor network whose sensors observe a common quantity and are affected by arbitrary additive bounded noises with a known upper bound. During the experiment, any sensor can communicate only a finite and given number of bits of information to the decision center. The contributions of the particular sensors, the rules of data encoding, decoding, and fusion, as well as the estimation scheme should be designed to achieve the best overall performance in estimation of the observed quantity by the decision center. An optimal algorithm is obtained that minimizes the maximal feasible error. It is shown that it considerably outperforms the algorithm proposed in recent papers in the area and examined only in the idealized case of noiseless sensors.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司    京ICP备09084417号-23

京公网安备 11010802026262号