New results on the performance of distributed Bayesian detectionsystems

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     

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.     

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.     

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％。     

分布式自动删除平均恒虚警率检测技术

根据自动删除平均算法提出了一种新的分布式多传感器的目标检测算法. 在该方法中, 首先根据自动删除平均算法(Censored cell-averaging, CCA)得到各传感器的杂波/噪声电平估计, 然后将检测单元电平与得到的杂波/噪声电平估计值相比较, 得到有无目标的局部判决,并将其传送到融合中心. 融合中心采用"k/N'融合准则得到有无目标的全局判决. 其中, 自动删除平均算法的优势明显, 它不需要干扰的先验信息, 可以容纳的干扰目标数不会像顺序统计量OS (k) (Order statistics)方法那样受指定k值的限制, 更接近实际. 自动删除平均算法还可以检测本身可能是目标的干扰. 在假定目标服从Swerling 2型起伏的情况下, 导出了相应的检测概率与虚警概率解析表达式. 多种检测器数值和图表分析的比较结果表明了该方法的有效性和优越性.     

基于门限自适应的分布式检测融合算法

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.     

Generalized Choquet fuzzy integral fusion

Sensor fusion plays an important role in many application domains. No single source of information (decision or feature) can provide the absolute solution when detection and recognition problems become more complex and computationally expensive (e.g., in land mine detection). However, complementary information can be derived from multiple sources. In this paper, we build a decision-based fusion system based on the uncertainty approach utilizing an extension of the Choquet fuzzy integral (generalized Choquet fuzzy integral, GCFI). The difference between the standard Choquet fuzzy integral and the GCFI is that the GCFI integrates vectors of fuzzy numbers instead of vectors of numeric membership values. The system is applied to a land mine detection problem. The fuzzy vectors represent uncertainty in both the confidence and location estimates of several detection algorithm outputs. The results show a huge improvement in the probability of detection and a reduction in the false alarm rate over the best algorithm and two numeric fusion schemes, i.e., the average confidence and a decision level fusion with the numeric Choquet fuzzy integral. The GCFI obtains 100% probability of detection at 0.02 false alarm rate per square meter on a large test set, whereas the best detection algorithm and the average confidence achieve only 91% and 96% probability of detection at that rate. Additionally, at 0.02 false alarm rate, decision level fusion with the numeric Choquet fuzzy integral reaches only 87% probability of detection.     

Optimal MAC based cooperative spectrum sensing in cognitive radio networks

This paper considers a cognitive radio (CR) system in non-ideal fading wireless channels and pro-poses cooperative spectrum sensing schemes based on coherent multiple access channels (MAC),serving as an alternative way to improve the cooperative spectrum sensing performance and provide space diversity for spec-trum sensing.Sufficient statistics are transmitted using a common channel from the secondary users (SUs) to a fusion center (FC) where the global decision is obtained.The optimal scaling factors of the proposed schemes are obtained by maximizing the detection probability under a target false alarm probability and a transmit power constraint.Because the proposed optimal MAC scheme has high computational complexity,a sub-optimal solu-tion based on maximization of the deflection coefficient (DC) is also proposed.Simulation results show that the proposed algorithms can significantly improve the spectrum sensing performance and approach the detection baseline.     

一种新的主动声纳模糊均值CFAR检测器

为了改善主动声纳的检测性能,本文研究了非高斯分布混响背景下采用模糊逻辑方法进行恒虚警检测设计的问题,提出了一种二元分布式模糊均值恒虚警检测器,两个子检测单元分别计算映射到虚警空间的隶属函数值,通过融合中心得到全局隶属函数值,实现背景混响功率水平估计,从而进行目标判决.仿真结果表明,基于代数和融合准则的检测器性能是最稳健的,相比传统的二进制“与”逻辑以及“或”逻辑,能够提供更好的检测效果.     

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.     

基于模糊评判的决策级信息融合算法的研究

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。     

基于分布式融合的FDI攻击信号快速检测方法

研究带宽受限下信息物理系统中虚假数据注入(false data injection,FDI)攻击的检测问题.首先,将执行器遭受的FDI攻击信号建模为系统的未知输入信号,基于给定的$H_\infty$性能指标,设计局部残差产生器以实时逼近攻击信号.其次,为提高检测系统预警速度,在分布式融合框架下将所有经对数量化后的残差信号发送至检测中心,并设计优化目标将分布式加权融合准则的求解问题转化为线性矩阵不等式形式下的凸优化问题.与单个传感器情况下的检测方法相比,基于分布式融合方法所确定的检测阈值更加精准,从而可大幅度提高对攻击信号的预警速度.最后,通过移动目标系统的仿真验证所提方法的有效性.     

Optimization of a serial distributed detection system with 2 bits communication constraint

In this paper, we consider a serial distributed detection system of two sensors in which the first sensor is allowed to communicate 2 bits to the second sensor (global detector). We use the J divergence to optimize the system and the optimization is obtained by varying the binary thresholds of the first sensor to obtain the maximum value of the global probability of detection for a specified global probability of false alarm. The numerical results obtained for the J divergence does not satisfy the expected performance improvement. Motivated by this, we propose a new more direct optimization procedure. The numerical results indicate the performance superiority of the proposed procedure over that of the J divergence method. Moreover, the performance of J divergence and the proposed method is better than that of the distributed system in which 1 bit hard decision is communicated between sensors.     

Cooperative spectrum sensing against noise uncertainty using Neyman–Pearson lemma on fuzzy hypothesis test

In this paper, we consider the problem of cooperative spectrum sensing in the presence of the noise power uncertainty. We propose a new spectrum sensing method based on the fuzzy hypothesis test (FHT) that utilizes membership functions as hypotheses for the modeling and analyzing such uncertainty. In particular, we apply the Neyman–Pearson lemma on the FHT and propose a threshold-based local detector at each secondary user (SU) in which the threshold depends on the noise power uncertainty. In the proposed scheme, a centralized manner in the cooperative spectrum sensing is deployed in which each SU sends its one bit decision to a fusion center. The fusion center makes a final decision about the absence/presence of a primary user (PU). The performance of the PU's signal detection is evaluated by the probability of signal detection for a specific signal to noise ratio when the probability of false alarm is set to a fixed value. The performance of the proposed algorithm is compared numerically with two classical threshold-based energy detectors. Simulation results show that the proposed algorithm considerably outperforms the methods with a bi-thresholds energy detector and a simple energy detector in the presence of the noise power uncertainty.     

基于贝叶斯信息融合的解析余度辅助机内测试决策

利用不同传感器之间的解析关系,产生某传感器的余度信号辅助机内测试(BIT)决策,在虚警率(或漏报率)较高的BIT决策中融合其他可靠性较高的传感器信息.对余度信号的先验分布、虚警率、漏报率进行建模,经残差分析后,给出残差决策结果和BIT结果的后验分布,选择贝叶斯风险小者作为最终决策;同时,给出了贝叶斯融合需满足的条件.实验分析结果表明,该方法增力了BIT决策的可信性,有助于BIT虚警剔除和漏报检测.     

基于Alamouti编码的协作频谱检测性能分析

针对感知无线电系统的频谱检测问题,设计Alamouti空时编码与时分多址方式相结合的协作通信上报方案,并对其检测性能进行分析。推导系统误告警概率、漏告警概率和误告警概率下限的理论表达式。分析结果表明,该方案的上报误码率会随本地用户交互信道质量的改善而下降,编码增益可达3 dB以上,系统误告警概率的下限降低。     

基于数据融合确保目标检测精度的传感器节点布置

在使用无线传感器网络进行目标检测时,如何布置尽可能少的传感器节点而同时实现高的正确检测概率和低的误警率,是关键问题之一.采用数据融合技术,能实现传感器节点之间的协同,从而大幅提高目标检测精度.提出了用于目标检测的精度模型,分析了数据融合半径与传感器节点密度之间的关系,设计聚类方法将目标点组织成布置单元,从高密度单元到低密度单元布置传感器节点覆盖目标区域.仿真结果表明,算法在保证检测精度的同时能有效减少所使用的传感器节点数目.     

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.     

分布式自适应CMLWCA CFAR 检测方法

针对分布式多传感器系统中不同传感器的信噪比会影响检测决策，提出一种利用各传感器信噪比决定其权值的自适应删除均值加权单元平均（CMLWCA）恒虚警率（CFAR）检测的新方法．在假定目标服从Swerling II起伏的情况下，导出了相应的检测概率与虚警概率闭式解．多种检测器数值分析的比较结果表明了该方法的有效性和优越性．     

Sleep–wake up scheduling with probabilistic coverage model in sensor networks1

Energy optimisation is one of the important issues in the research of wireless sensor networks (WSNs). In the application of monitoring, a large number of sensors are scattered uniformly to cover a collection of points of interest (PoIs) distributed randomly in the monitored area. Since the energy of battery-powered sensor is limited in WSNs, sensors are scheduled to wake up in a large-scale sensor network application. In this paper, we consider how to reduce the energy consumption and prolong the lifetime of WSNs through wake-up scheduling with probabilistic sensing model in the large-scale application of monitoring. To extend the lifetime of sensor network, we need to balance the energy consumption of sensors so that there will not be too much redundant energy in some sensors before the WSN terminates. The detection probability and false alarm probability are taken into consideration to achieve a better performance and reveal the real sensing process which is characterised in the probabilistic sensing model. Data fusion is also introduced to utilise information of sensors so that a PoI in the monitored area may be covered by multiple sensors collaboratively, which will decrease the number of sensors that cover the monitored region. Based on the probabilistic model and data fusion, minimum weight probabilistic coverage problem is formulated in this paper. We also propose a greedy method and modified genetic algorithm based on the greedy method to address the problem. Simulation experiments are conducted to demonstrate the advantages of our proposed algorithms over existing work.