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.     

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.     

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．     

噪声方差未知情况下多传感器航迹融合

在多传感器信息融合中,已有的航迹融合算法都是在噪声方差已知情况下基于最优的卡尔曼滤波算法的,而实际应用中噪声方差往往是未知的.针对上述问题,基于扩展记忆因子递推最小平方(EFRLS)估计的滤波方程,研究了噪声方差未知情况下集中式、分布式、混合式多传感器航迹融合方法.并对三种航迹融合算法的跟踪性能和卡尔曼滤波融合算法的性能进行了仿真比较.由于多级式多传感器的航迹融合方法可由本文的方法直接推广,所以只需研究两级的情况就可.     

带未知通信干扰和丢包补偿的多传感器网络化不确定系统的分布式融合滤波

研究了带有未知通信干扰、观测丢失和乘性噪声不确定性的多传感器网络化系统的状态估计问题.通过白色乘性噪声描述系统状态和观测中的随机不确定性,采用一组服从Bernoulli分布的随机变量描述网络传输过程中存在的观测丢失现象,且数据传输中存在未知的网络通信干扰.当发生丢包时,以当前丢失观测的预报值进行补偿.对每个单传感器子系统,应用线性无偏最小方差估计准则设计了不依赖于未知通信干扰的最优线性滤波器.推导了任两个局部滤波误差之间的互协方差阵.进而,应用矩阵加权融合估计算法给出了分布式融合状态滤波器.仿真例子验证了算法的有效性.     

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．     

基于自动删除算法的分布式CFAR检测算法

研究分布式恒虚警(CFAR)检测系统在非均匀干扰背景中进行优化检测.针对多传感器分布式恒虚警检测系统在非均匀干扰背景中容易出现检测概率下降或者虚警率提高的问题,提出了一种基于自动删除算法的分布式恒虚警检测算法.算法是一种基于局部检测统计量的分布式CFAR检测算法,充分利用了局部检测器的观测信息,提高了检测性能,同时采用...     

基于整合概率数据关联的最优传感器序列研究

研究多传感器跟踪系统中传感器处理序列优化问题．首先根据多传感器跟踪系统中多传感器信息的处理特点,提出了基于有限随机集的序列整合概率数据关联（IPDA）滤波算法;然后,通过分析序列IPDA算法中目标生存概率的均值函数,证明了在不同传感器检测概率的多传感器系统中,目标生存概率仅依赖于传感器序列中第1个传感器,并随其检测概率的增大而增大．仿真实验验证了所得结论．     

基于多传感器不完全量测下的机动目标跟踪算法

针对传感器探测概率小于1的不完全量测情况下的非机动目标跟踪问题,提出一种基于多传感器不完全量测下的扩展Kalman滤波算法。首先,利用残差检测的野值剔除方法,确定目标状态估计过程中传感器是否接收到正确的量测数据;其次,基于每个传感器的量测数据,在不完全量测下采用改进的扩展卡尔曼滤波算法分别对目标运动状态进行估计;进而结合多传感器最优加权融合方法求解基于多传感器观测数据的状态估计;最后,将算法应用到光电跟踪系统中。仿真实验得到不完全量测下传感器探测概率对滤波效果的影响,验证了算法的有效性,其跟踪精度接近完全量测下的状态估计精度。     

基于伪测量的分布式最优单步延迟航迹融合估计

融合中心如何处理无序局部数据,对分布式多传感器系统的运行品质至关重要．本文将系统中的局部估计转化为伪测量,将分布式融合估计转化为二级集中式融合估计．将所得的伪测量兼分布式融合估计算法与单步延迟的无序测量数据（out-of-sequencemeasurements,OOSM）最优滤波-A1算法进行组合,得出了分布式多传感器系统的最优单步延迟无序航迹（out-of-sequence tacks,OOST）估计算法,适用于航迹无序局部数据融合估计．该算法具有最优估计性能．     

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.     

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.     

On optimal fuzzy c-means clustering for energy efficient cooperative spectrum sensing in cognitive radio networks

This work explores the scope of Fuzzy C-Means (FCM) clustering on energy detection based cooperative spectrum sensing (CSS) in single primary user (PU) cognitive radio network (CRN). PU signal energy sensed at secondary users (SUs) is forwarded to the fusion center (FC). Two different combining schemes, namely selection combining (SC) and optimal gain combining are performed at FC to address the sensing reliability problem on two different optimization frameworks. In the first work, optimal cluster center points are searched for using differential evolution (DE) algorithm to maximize the probability of detection under the constraint of meeting the probability of false alarm below a predefined threshold. Simulation results highlight the improved sensing reliability compared to the existing works. In the second one, the problem is extended to the energy efficient design of CRN. The SUs act here as amplify-and-forward (AF) relays and PU energy content is measured at the FC over the combined signal from all the SUs. The objective is to minimize the average energy consumption of all SUs while maintaining the predefined sensing constraints. Optimal FCM clustering using DE determines the optimal SU amplifying gain and the optimal number of PU samples. Simulation results shed a light on the performance gain of the proposed approach compared to the existing energy efficient CSS schemes.     

Optimal decentralized control in the random access multipacketchannel

A decentralized control algorithm is sought that maximizes the stability region of the infinite-user slotted multipacket channel and is easily implementable. To this end, the perfect state information case in which the stations can use the instantaneous value of the backlog to compute the retransmission probability is studied first. The vest throughput possible for a decentralized control protocol is obtained, as well as an algorithm that achieves it. These results are then applied to derive a control scheme when the backlog is unknown, which is the case of practical relevance. This scheme, based on a binary feedback, is shown to be optimal, given some restrictions on the channel multipacket reception capability     

一种多特征分类识别算法融合的网络钓鱼识别技术

针对页面特征提取实时性差的问题进行了研究,提出将特征分类,并行提取、检测、再融合结果的方法。首先提取三个类别的主要特征,包括文本、视觉和网络链接;然后,分别利用了贝叶斯算法、EMD算法以及网络爬虫来进行分类;并且基于后验概率来确定权值的最终选取。最后,把这三个分类结果进行融合。通过对贝叶斯、加权和加权贝叶斯的比较,从正确率、漏报率和误报率对算法进行评估,实验表明采用加权贝叶斯的方法来进行融合计算效果最佳,能够提供较高的准确率和较低的误报和漏报,提高检测的精度和实时性。     

基于自适应检测长度的双门限能量感知算法

针对认知环境中能量感知的噪声不确定性问题,提出了一种基于自适应检测长度的双门限能量感知算法。算法首先根据噪声不确定性大小设置上下判决门限。当检测统计量位于双门限之外时直接判决,否则增加采样数并再次比较,直到得出判决结果或采样数达到上限;为了尽量减小由于采样数增加带来的系统能量开销的增加,给出了系统能量开销与吞吐量折中的最佳采样数上限。从理论上分析了算法的优越性,并进行了仿真验证,结果表明,该算法尽管增加了一定的能量开销,但是可以显著地提高系统检测性能。     

线性扫频干扰检测算法及抗干扰方法研究

提出一种考虑虚警概率和漏检概率指标的低复杂度线性扫频干扰检测算法,并对算法性能进行了理论分析,为实际通信系统判断受扰情况提供依据;提出一种基于Q学习的抗扫频干扰算法,可在无线通信系统遭遇扫频干扰时,自主选择最佳通信信道和最长驻留时间。仿真结果表明所提检测算法可有效检测出线性扫频干扰信号,以较低复杂度得到与理论分析结果相近的检测性能。所提抗扫频干扰学习算法可在干扰环境中自主选择通信信道,高效规避扫频干扰,实现持续可靠的信息传输。     

State estimation for stochastic time‐varying multisensor systems with multiplicative noises: Centralized and decentralized data fusion

In this paper, the state estimation problems, including filtering and one‐step prediction, are solved for uncertain stochastic time‐varying multisensor systems by using centralized and decentralized data fusion methods. Uncertainties are considered in all parts of the state space model as multiplicative noises. For the first time, both centralized and decentralized estimators are designed based on the regularized least‐squares method. To design the proposed centralized fusion estimator, observation equations are first rewritten as a stacked observation. Then, an optimal estimator is obtained from a regularized least‐squares problem. In addition, for decentralized data fusion, first, optimal local estimators are designed, and then fusion rule is achieved by solving a least‐squares problem. Two recursive equations are also obtained to compute the unknown covariance matrices of the filtering and prediction errors. Finally, a three‐sensor target‐tracking system is employed to demonstrate the effectiveness and performance of the proposed estimation approaches.