Optimal decision f usion given sens or rules

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.     

Optimal decision fusion given sensor rules

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.     

Unified fusion rules for multisensor multihypothesis network decision systems

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.     

Integrated optimization methods in multisensor decision and estimation fusion

This paper presents a significant integrated optimization point of view behind the following three successful decision and estimation fusion results: 1) a unified fusion rule for networked sensor decision systems; 2) optimal sensor data quantization for estimation fusion and 3) integrated multi-target data association tracking systems. More precisely speaking, the integrated optimization method in 1) derives a unified objective function optimizing only sensor rules given a unified fusion rule; the method in 2) derives a unified objective function optimizing both the sensor quantization rule and the final estimation in the MSE sense, and the method in 3) integrates all associated targets and their valid observations into a whole random measurement matrix dynamic system so that the optimal random matrix Kalman filtering can be applied to estimate the states of all associated targets.     

An efficient sensor quantization algorithm for decentralized estimation fusion

In this paper, we consider the design problem of optimal sensor quantization rules (quantizers) and an optimal linear estimation fusion rule in bandwidth-constrained decentralized random signal estimation fusion systems. First, we derive a fixed-point-type necessary condition for both optimal sensor quantization rules and an optimal linear estimation fusion rule: a fixed point of an integral operation. Then, we can motivate an iterative Gauss–Seidel algorithm to simultaneously search for both optimal sensor quantization rules and an optimal linear estimation fusion rule without Gaussian assumptions on the joint probability density function (pdf) of the estimated parameter and observations. Moreover, we prove that the algorithm converges to a person-by-person optimal solution in the discretized scheme after a finite number of iterations. It is worth noting that the new method can be applied to vector quantization without any modification. Finally, several numerical examples demonstrate the efficiency of our method, and provide some reasonable and meaningful observations how the estimation performance is influenced by the observation noise power and numbers of sensors or quantization levels.     

资源受限的无线传感器网络基于衰减信道的决策融合

研究了无线传感器网络中衰减信道下的决策融合规则.由于信道衰减,由节点传输到融合中心的本地决策会丢失或产生差错,要求融合中心的融合规则能够结合信道模型作出最优判决.在Rayleigh分布的信道模型下,对一系列融合算法作了理论和仿真分析.似然比融合算法性能最优,但是它占用的系统资源大,需要预知的信息多,性价比不高,不适合资源受限的无线传感器网络.提出了3种次优算法,它们比似然比规则耗费的信息代价要小.在不同的信噪比(signal-to-noise ratio,简称SNR)范围下,它们的性能有各自的优劣.综合分析发现,在资源受限的无线传感器网络中,最终选择的融合规则应在性能、耗费资源量和复杂度之间获得折衷.     

无线传感器网络中基于衰减信道的决策融合

无线传感器网络中的链路通信质量对上层应用有重要的影响.为此,本文研究了基于衰减信道的多传感器决策融合问题,提出了最优似然比融合规则,并推导出适用于无线传感器网络的二种次优融合规则,最后讨论了相同传感器和相同信道模型下的融合情况.仿真结果表明此方法能比较好的适应在无线传感器网络中信息融合时所遇到的链路衰减和噪声干扰等问题.     

分布式多传感器决策系统融合律的等价类

在任何融合律定后最优传感器律能求得的假设下,我们分析了导致融合律之间等价性和优越性的条件,应用如上结果,欲获全局最优的系统性能,我们可以划分所有可能的融合律为若干等价类和比较某些等价类之间的性能,于是有价值的融合律等价类数目将大大减少,而且上面的分析并不依赖于观测数据的统计性质和优化系统性能的目标。     

传感器网络中信息融合的几个基本问题

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．     

基于Kalman滤波的自回归滑动平均信号信息融合Wiener滤波器

应用Kalman滤波方法,在按矩阵加权线性最小方差最优信息融合规则下,提出了带白色观测噪声的多通道ARMA信号的多传感器信息融合Wiener滤波器.它可统一处理信息融合滤波、平滑和预报问题.为了计算最优加权阵,提出了计算局部滤波误差互协方差阵的公式.同单传感器情形相比,可提高估计精度.一个带三传感器的目标跟踪系统的仿真例子说明了其有效性.     

分布式贝叶斯检测融合优化算法及其计算机仿真

该文研究了利用分布式多传感器获得全局决策的分布式信号检测问题。在这种检测系统中各传感器将其各自关于观测对象的决策传送至融合中心,融合中心根据融合规则给出全局决策。研究重点是基于贝叶斯准则的分布式并联检测融合系统的数据融合理论,给出了使系统全局最优的融合规则和传感器决策规则,提出了对融合规则和传感器决策规则进行优化计算的非线性高斯一赛德尔算法,具体讨论了两相同传感器、两个不同传感器和三个相同传感器在具有独立观测时的数据融合问题。给出了利用本文所提算法对上述几种情况进行计算机仿真的仿真实例。仿真结果表明：融合系统的性能相对传感器有显著改善,采用三个相同传感器的融合系统,其贝叶斯风险下降了26．5％。     

针对规则更新操作的测试数据包选取算法*

防火墙规则集中存在的配置错误主要来源于规则的添加、删除等更新操作。因此进行规则更新时,需要使用测试算法判断更新操作的正确性。现有的测试算法仅从被添加或被删除规则的顶点选取测试数据包,不能检测出所有因规则冲突而导致的配置错误。基于此,提出了一种针对规则更新操作的测试数据包选取算法PCRU。该算法从两处选取测试数据包,即被添加或者被删除的规则的顶点和规则冲突区域。理论分析和仿真实验表明,与现有测试算法相比,在进行规则更新时,PCRU算法只需使用少量的测试数据包,即可检测出所有因规则冲突而导致的配置错误。     

多传感器最优信息融合白噪声反卷积滤波器

应用现代时间序列分析方法和白噪声估计理论,基于线性最小方差意义下按标量加权最优信息融合准则,对于带白色和有色观测噪声的多传感器单通道系统,提出了分布式融合白噪声反卷积滤波器.它由局部白噪声反卷积滤波器加权构成.可统一处理融合滤波、平滑和预报问题.给出了计算局部滤波误差互协方差公式,可用于计算最优加权.同单传感器情形相比,可提高融合滤波器精度.它可应用于石油地震勘探信号处理.一个3传感器信息融合Bernou lli-Gaussian白噪声反卷积滤波器的仿真例子说明了其有效性.     

LSFR算法在多传感器分布式检测中的优化研究

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.     

一种基于属性序的决策规则挖掘算法

针对面向领域用户的决策规则挖掘问题,用属性序描述领域用户的需求和兴趣,模拟人脑分辨事物的过程,提出了一种属性序下的分层递阶决策规则挖掘算法.该算法在给定属性序下输出的决策规则集不仅具有唯一性,且对任意待识别样本不会作出矛盾的决策.实例和仿真实验结果表明了算法的有效性和可行性.     

概念层次中基于粗糙集的优化可信规则获取

随着系统中数据量剧增,规则太多以及不同决策者对规则有不同层次需求等问题,概念层次提供了一种解决方法。讨论条件属性具有概念层次的情况下,利用粗糙集理论分析属性在不同层次组合下的正域和规则关系,自顶向下提出了概念层次中基于粗糙集的优化可信规则获取的算法。该算法改进了现有的属性约简策略,借助描述子实现属性约简并获取优化可信规则。考虑到层次上正域为空和正域没有新增对象的特殊情况,提高了规则获取的效率。最后通过实例分析说明该算法的可行性。     

Unexpected properties and optimum-distributed sensor detectors fordependent observation cases

Optimum-distributed signal detection system design is studied for cases with statistically dependent observations from sensor to sensor. The common parallel architecture is assumed. Here, each sensor sends a decision to a fusion center that determines a final binary decision using a nonrandomized fusion rule. General L sensor cases are considered. A discretized iterative algorithm is suggested that can provide approximate solutions to the necessary conditions for optimum distributed sensor decision rules under a fixed fusion rule. The algorithm is shown to converge in a finite number of iterations, and the solutions obtained are shown to approach the solutions to the original problem, without discretization, as the variable step size shrinks to zero. In the formulation, both binary and multiple-bit sensor decisions cases are considered. Illustrative numerical examples are presented for two-, three-, and four-sensor cases, in which a common random Gaussian signal is to be detected in Gaussian noise     

优势关系下模糊目标信息系统的决策规则优化

利用基于优势关系的模糊粗糙集模型,讨论了模糊决策信息系统中优化序决策规则的获取问题。利用优势关系定义了模糊目标信息系统中对象的三种属性约简。给出了它们的判定定理,构造相应的区分函数,利用布尔推理技术计算对象的属性约简,得到三类新的优化序决策规则。     

序决策信息系统中区间决策规则的获取

在序决策信息系统中, 定义区间为支配一个特定的对象同时又被另一个特定的对象所支配的所有对象的集合. 以区间为基本知识颗粒, 建立新的优势关系粗糙集模型, 并由此获取决策值为特定区间范围的区间决策规则. 提出区间的约简的概念, 构造区分函数计算区间的约简, 并由此计算优化区间决策规则. 该方法比初始的优势关系粗糙集方法适应性更强, 且所得区间决策规则可直接应用于序信息系统的分类问题.

     

衰落信道对无线传感器网络决策融合的影响分析

户外环境监测中的无线信道非常复杂,受到多径衰落和噪声等多重因素的影响,严重降低了信号的接收质量。对衰落信道特性的深入研究,有助于网络更好地接收信号,提高系统检测性能。详细分析了信道衰落的影响因子,研究了衰落信道下信号的传输性能,仿真了衰落信道对无线传感器网络检测概率的影响,得出了决策融合中节点上传决策的最佳位数。仿真结果表明,衰落信道下的检测概率较理想信道有较大差距,且上传一位决策值是最佳融合策略。