On the performance of distributed Neyman-Pearson detection systems

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     

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％。     

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.     

基于N-P准则的水声信号检测系统信息融合

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．     

A new multiple decisions fusion rule for targets detection in multiple sensors distributed detection systems with data fusion

Currently, multiple sensors distributed detection systems with data fusion are used extensively in both civilian and military applications. The optimality of most detection fusion rules implemented in these systems relies on the knowledge of probability distributions for all distributed sensors. The overall detection performance of the central processor is often worse than expected due to instabilities of the sensors probability density functions. This paper proposes a new multiple decisions fusion rule for targets detection in distributed multiple sensor systems with data fusion. Unlike the published studies, in which the overall decision is based on single binary decision from each individual sensor and requires the knowledge of the sensors probability distributions, the proposed fusion method derives the overall decision based on multiple decisions from each individual sensor assuming that the probability distributions are not known. Therefore, the proposed fusion rule is insensitive to instabilities of the sensors probability distributions. The proposed multiple decisions fusion rule is derived and its overall performance is evaluated. Comparisons with the performance of single sensor, optimum hard detection, optimum centralized detection, and a multiple thresholds decision fusion, are also provided. The results show that the proposed multiple decisions fusion rule has higher performance than the optimum hard detection and the multiple thresholds detection systems. Thus it reduces the loss in performance between the optimum centralized detection and the optimum hard detection systems. Extension of the proposed method to the case of target detection when some probability density functions are known and applications to binary communication systems are also addressed.     

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.     

衰落环境中认知无线电分布式合作频谱检测

为了有效提高认知无线电中频谱检测性能是认知无线电技术的关键一步,构建了认知网络中主用户和一组次用户之间的频谱检测模型,提出了OR/AND两种检测融合准则,并对两种检测融合准则下的检测概率进行了比较。基于准则,进一步具体研究了Rayleigh衰落环境中,针对信噪比(SNR)变化,分布式合作节点数量变化等不同系统参数对提高频谱检测性能的影响。给出了Rayleigh衰落环境下,各参数影响接收机工作特征(ROC)和整体检测性能的仿真分析。结果表明:一定数量的节点可有效抵消衰落对ROC性能造成的影响,使其在低SNR条件下仍获得较高的检测概率。     

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.     

Distributed decision fusion under nonideal communication channels with adaptive topology

Multi-sensor decision fusion has attracted some attention in information fusion field, meanwhile, the distributed target detection has been a well-studied topic in the multi-sensor detection theory. This paper investigates the increase in detection reliability that an adaptive network (with adaptive topologies and nonideal channels and decision fusion rules) can provide, compared with a fixed topology network. We consider a network, consisting of K-local uncertainty sensors and a Fusion Center (FC) tasked with detecting the presence or absence of a target in the Region of Interest (ROI). Sensors transmit binary modulated local decisions over nonideal channels modeled as Gaussian noise or fading channels. Assuming that the signal intensity emitted by a target follows the isotropic attenuation power model, we consider three classes of network topology architectures: (1) serial topology; (2) tree topology, and (3) parallel topology. Under the Neyman–Pearson (NP) criterion, we derive the optimal threshold fusion rule with adaptive topology to minimize the error probability. Extensive simulations are conducted to validate the correctness and effectiveness of the proposed algorithms.     

基于粒子滤波的分布式故障诊断

针对非线性、非高斯环境下多传感器的系统故障诊断问题,提出了一种新的基于粒子滤波的分布式故障诊断方法。通过粒子滤波得到的状态估计值的全概率分布信息可用于故障检测。首先建立系统分布式故障诊断模型,由于通信限制,假设各传感器只能向信息融合中心传输二进制数。在各观测值独立同分布的条件下,提出了分布式故障诊断算法,包括本地判决的设计和融合中心的准则设计。仿真结果表明了所提出算法的有效性和优越性。     

Decentralized M-ary detection via hierarchical binary decision fusion

We consider the distributed M-ary detection problem. The M-ary decision-making process is implemented via a sequence of binary decision-making processes. The resulting binary decisions represent a hierarchical partition of the M-ary object space, which is organized in the form of a binary decision tree. This approach breaks a complex M-ary decision fusion problem into a set of much simpler binary decision fusion problems. We first develop a method for partitioning the M-ary object space. We then obtain the optimal decision rules that the fusion center and the sensors employ at the internal nodes of the binary decision tree. The results are illustrated in an example.     

Agent oriented intelligent fault diagnosis system using evidence theory

Multi sensors fusion is a very important process for fault diagnosis system. Information obtained from multi sensors need to be fused because no single sensor can get all the information for fault diagnosis. Moreover, information from different sensors may be uncertainty, inaccuracy, or even conflicting. Evidence theory can be used for information fusion, which is regarded as an extension form of Bayesian reasoning, but it has a better fusion result by simple reasoning process using belief function without knowing the prior probability. All the information collected from multi sensors in the system can be described as the evidence for diagnosis so that the fault diagnosis problem can then be modeled as a problem of evidence fusion and decision. In this paper, the classical Dempster-Shafer evidence theory is discussed, and the disadvantages of the combination rule are also analyzed. The notion of support degree of focal element is suggested in order to evaluate the conflicts between multi sensors. The new combination rule is then built to allocate the conflicted information from multi sensors based on the support degree of focal element. Furthermore, the decision rules for fault diagnosis are also proposed, as well as the architecture of the agent oriented intelligent fault diagnosis system. Finally, a case study is given to illustrate the performance of the proposed model.     

Distributed detection of a non-cooperative target via generalized locally-optimum approaches

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.     

The sufficient condition for lossless linear transformation for distributed estimation with cross-correlated measurement noises

In most distributed fusion algorithms, the measurement noises in different sensors are often assumed to be uncorrelated, but in practical occasions the assumption may not be met and the measurement noises are often cross-correlated between sensors. So the lossless distributed fusion algorithms with the assumption of uncorrelated measurement noises usually cannot keep their lossless performance in practical applications. Therefore, in the case of cross-correlated measurement noises, the lossless compression rule for distributed estimation is proposed. We prove in theory that the sufficient condition of the lossless compression is the transformation matrix is of full column rank. Using the transformation matrix constructed by the proposed rule, the distributed fusion can achieve the performance of the centralized one. In addition, under this rule two optimal fusion algorithms are proposed and their performances are analyzed.     

分布式检测系统的一种软决策融合算法

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.     

多传感器检测系统的 多步反馈融合算法

通过分析分布式检测系统的工作原理，提出了一种新的局部检测器融合方法，该方法利用检测器局部判决结果与融合中心融合结果的误差多步累积平均值对检测器下一步判决进行修正，从而达到提高检测器检测概率的目的。分析表明，该算法能有效提高系统的性能。     

Optimal sensor rules and unified fusion rules for multisensor multi-hypothesis network decision systems with channel errors

In this paper, for general jointly distributed sensor observations, we present optimal sensor rules with channel errors for a given fusion rule. Then, the unified fusion rules problem for multisensor multi-hypothesis network decision systems with channel errors is studied as an extension of our previous results for ideal channels, i.e., people only need to optimize sensor rules under the proposed unified fusion rules to achieve global optimal decision performance. More significantly, the unified fusion rules do not depend on distributions of sensor observations, decision criterion, and the characteristics of fading channels. Finally, several numerical examples support the above analytic results and show some interesting phenomena which can not be seen in ideal channel case.     

Min-max optimal data encoding and fusion in sensor networks

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.     

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。