Optimum local decision space partitioning for distributed detection

A distributed detection system is considered that consists of a number of independent local detectors and a fusion center. The decision statistics and performance characteristics (i.e. the false alarm probabilities and detection probabilities) of the local detectors are assumed as given. Communication is assumed only between each local detector and the fusion center and is one-way from the former to the latter. The fusion center receives decisions from the local detectors and combines them for a global decision. Instead of a one-bit hard decision, the authors propose that each local detector provides the fusion center with multiple-bit decision value which represents its decision and, conceptually, its degree of confidence on that decision. Generating a multiple-bit local decision entails a subpartitioning of the local decision space the optimization of which is studied. It is shown that the proposed system significantly outperforms one in which each local detector provides only a hard decision. Based on optimum subpartitioning of local decision space, the detection performance is shown to increase monotonically with the number of partitions     

多传感器决策融合系统中的决策空间优化划分研究

在假定各局部检测器的决策规则已经给定以及在Ｂｈａｔａｃｈａｒｙｙａ距离最大的意义下,对多传感器融合系统中的决策空间优化划分设计进行了研究。首先基于Ｂｈａｔａｃｈａｒｙｙａ距离准则,把对整个系统决策空间的优化划分解耦为分别对各局部检测器决策空间的优化划分;然后从理论上证明了这种划分设计在最大Ｂｈａｔａｃｈａｒｙｙａ距离意义下的最优性,以及这种基于最大Ｂｈａｔａｃｈａｒｙｙａ距离准则进行优化划分设计的合理性;最后,通过对瑞利起伏环境下信号检测融合问题的数值计算表明,本文方法的性能优于基于Ｊ－散度方法的性能。     

基于优化SPOT和D-S证据理论的测试性验证方案

针对现有基于序贯验后加权检验的测试性验证方案对测试性设计指标之间的模糊参数空间考虑不足，以及未能充分运用测试性多源先验信息的问题，提出一种优化序贯验后加权检验和D-S证据理论相结合的测试性验证方案。首先，考虑测试性设计指标之间的模糊参数空间，构建三参数空间复杂假设，并基于Bayes理论研究序贯决策规则，同时确定决策因子以及决策阈值；其次，以测试性指标构成的参数空间为辨识框架，分别构造基于专家信息以及测试性试验数据等先验信息的基本信任分配函数，建立融合多源先验信息的优化序贯验证方案；最后，结合实例进行研究，并与经典验证方案、传统Bayes验证方案、序贯概率比检验方案以及序贯验后加权检验方案进行了对比分析。结果表明，该方案由于考虑了模糊参数空间以及充分融合了多源先验信息，有效解决了模糊参数空间的处理问题，同时所确定的平均故障样本量在决策支持的参数空间均优于其他方法。     

Optimum multisensor fusion of correlated local decisions

A distributed detection system consisting of a number of local detectors and a fusion center is considered. Each detector makes a decision for the underlying binary hypothesis testing problem based on its own observation and transmits its decision to the fusion center where the global decision is derived. The local decision rules are assumed to be given, but the local decisions are correlated. The correlation is generally characterized by a finite number of conditional probabilities. The optimum decision fusion rule in the Neyman-Pearson sense is derived and analyzed. The performance of the distributed detection system versus the degree of correlation between the local decisions is analyzed for a correlation structure that can be indexed by a single parameter. System performance as well as the performance advantage of using a larger number of local detectors degrade as the degree of correlation between local decisions increases     

通信约束下异构多无人机任务分配方法

针对异构多无人机协同执行侦察和打击任务中,存在通信距离、时间延迟等约束条件下的局部任务分配问题,提出了一种基于合同网的分布式多无人机任务分配方法。首先建立了异构集群发现新目标时的局部任务分配问题模型,设计了局部无人机通信网络中的信息一致性算法,实现了任务分配过程中任务发布阶段各无人机的冲突消解。设计了任务分配过程中的联盟构建和无人机资源管理方法,使联盟中各无人机能够以更加平衡的方式消耗资源。仿真结果表明,该方法能够解决通信约束下,异构多无人机执行察打任务时,所触发的针对目标打击任务的任务分配问题,且能够获得最大的系统效能。     

基于决策不确定性的多目标跟踪传感器管理

针对高杂波、电子干扰环境,在量测驱动的多目标滤波框架下提出了一种基于决策不确定性的传感器管理方法。首先,根据部分可观测马尔科夫决策过程的理论,给出了基于Rényi信息增量的传感器管理一般方法。其次,综合考虑决策过程的信息完整性、信息质量、信息的内涵等因素,在量测驱动的自适应滤波框架下,基于目标运动态势评估多目标决策不确定性水平,并选取最大决策不确定性目标。最后,以最大决策不确定性目标的信息增量最大化为准则进行传感器分配方案的求解。仿真实验表明所提方法能够有效抑制电子干扰、杂波对多目标跟踪及传感器分配的影响,与基于威胁的传感器管理方法相比,所提方法的平均最优子模式分配（OSPA）距离及平均计算时长均显著降低,且在高杂波、电子干扰情形下具有较高的可靠性。     

Optimal Detection and Performance of Distributed Sensor Systems

Global optimization of a distributed sensor detection system withfusion is considered, where the fusion rule and local detectors aresolved to obtain overall optimal performance. This yields coupledequations for the local detectors and the fusion center.The detection performance of the distributed system with fusionis developed. The globally optimal system performance is comparedwith two suboptimal systems. Receiver operating characteristics(ROCs) are computed numerically for the problem of detecting aknown signal embedded in non-Gaussian noise.     

Asynchronous distributed detection

Binary parallel distributed-detection architectures employ a bank of local detectors to observe a common volume of surveillance, and form binary local decisions about the existence or nonexistence of a target in that volume. The local decisions are transmitted to a central detector, the data fusion center (DEC), which integrates them to a global target or no target decision. Most studies of distributed-detection systems assume that the local detectors are synchronized. In practice local decisions are made asynchronously and the DFC has to update its global decision continually. In this study the number of local decisions observed by the central detector within any observation period is Poisson distributed. An optimal fusion rule is developed and the sufficient statistic is shown to be a weighted sum of the local decisions collected by the DFC within the observation interval. The weights are functions of the individual local detector performance probabilities (i.e., probabilities of false alarm and detection). In this respect the decision rule is similar to the one developed by Chair and Varshney for the synchronized system. Unlike the Chair-Varshney rule, however, the DFC's decision threshold in the asynchronous system is time varying. Exact expressions and asymptotic approximations are developed for the detection performance with the optimal rule. These expressions allow performance prediction and assessment of tradeoffs in realistic decision fusion architectures which operate over modern communication networks     

Optimal m-ary data fusion with distributed sensors

An optimal data fusion rule is derived for an m-ary detection problem. Each detector determines a local decision using a local decision rule and transmits the local decision to the fusion center. Considering the reliability of local detectors, local decisions are combined to produce the final decision. In this study, based upon the maximum posterior probability concept, optimal decision rules for m-ary detection problems are proposed for the local detector and the data fusion center     

Bayesian edge detector for SAR imagery using discontinuity-adaptive Markov random feld modeling

Synthetic aperture radar（SAR）image is severely affected by multiplicative speckle noise,which greatly complicates the edge detection.In this paper,by incorporating the discontinuityadaptive Markov random feld（DAMRF）and maximum a posteriori（MAP）estimation criterion into edge detection,a Bayesian edge detector for SAR imagery is accordingly developed.In the proposed detector,the DAMRF is used as the a priori distribution of the local mean reflectivity,and a maximum a posteriori estimation of it is thus obtained by maximizing the posteriori energy using gradient-descent method.Four normalized ratios constructed in different directions are computed,based on which two edge strength maps（ESMs）are formed.The fnal edge detection result is achieved by fusing the results of two thresholded ESMs.The experimental results with synthetic and real SAR images show that the proposed detector could effciently detect edges in SAR images,and achieve better performance than two popular detectors in terms of Pratt＇s fgure of merit and visual evaluation in most cases.     

Nonparametric rank detectors under K-distributed clutter in radar applications

This correspondence deals with a comparative analysis of parametric detectors versus rank ones for radar applications, under K-distributed clutter and nonfluctuating and Swerling II target models. We show that the locally optimum detectors (LODs) (optimum for very low signal-to-clutter ratio (SCR)) under K-distributed clutter are not practical detectors; on the contrary, asymptotically optimum detectors (optimum for high SCR) are the practical ones. The performance analysis of the parametric log-detector and the nonparametric (linear rank) detector is carried out for independent and identically distributed (IID) clutter samples, correlated clutter samples, and nonhomogeneous clutter samples. Some results of Monte Carlo simulations for detection probability (P/sub d/) versus SCR are presented in curves for different detector parameter values.     

Quickest detection for sequential decentralized decision systems

Quickest detection procedures are techniques used to detect sudden or abrupt changes (also called disorders) in the statistics of a random process. The goal is to determine as soon as possible that the change occurred, while at the same time minimizing the chance of falsely signaling the occurrence of a disorder before the change. In this work the distributed quickest detection problem when the disorder occurs at an unknown time is considered. The distributed local detectors utilize a simple summing device and threshold comparator, with a binary decision at the output. At the fusion center, the optimal maximum likelihood (ML) procedure is analyzed and compared with the more practical Page procedure for quickest detection. It is shown that the two procedures have practically equivalent performance. For the important case of unknown disorder magnitudes, a version of the Hinkley procedure is also examined. Next, a simple method for choosing the thresholds of the local detectors based on an asymptotic performance measure is presented. The problem of selecting the local thresholds usually requires optimizing a constrained set of nonlinear equations; our method admits a separable problem, leading to straightforward calculations. A sensitivity analysis reveals that the resulting threshold settings are optimal for practical purposes. The issue of which sample size to use for the local detectors is investigated, and the tradeoff between decision delay and communication cost is evaluated. For strong signals, it is shown that the relative performance deteriorates as the sample size increases, that is, as the system cost decreases. Surprisingly, for the weak signal case, lowering the system cost (increasing the sample size) does not necessarily result in a degradation of performance     

引入稀疏原子特征融合的滑动轴承摩擦故障状态监测

从信息融合理论出发，将特征的稀疏表达作为特征融合参数，提出一种结合K奇异值分解(KSVD）和最大相关最小冗余准则(mRMR）的轴承摩擦故障特征融合算法。该算法采用KSVD对信号稀疏化，将稀疏系数对应的字典原子作为特征融合的参数，用以表达非线性故障信息；针对字典原子集的优化选择问题，基于互信息的mRMR提出一种确定最优原子集的原子数目的准则；最后，通过最大化原则融合稀疏系数，提取故障状态监测的有效信息。轴承摩擦故障模拟实验的结果表明，所提方法能够更好地融合不同特征的故障信息，相比于单特征和其他融合特征方法，提高了约12%的故障识别率。      

结合核相关滤波器和深度学习的运动相机中无人机目标检测

针对无人机与相机快速相对运动造成的运动模糊问题，以及小型无人机外观信息缺失和背景复杂造成漏警和虚警问题，提出了一种新的无人机检测-跟踪方法。针对成像尺寸小于32像素×32像素的无人机目标，提出改进的多层特征金字塔的分类和目标框回归器作为目标检测器，克服漏警。利用检测结果初始化基于核相关滤波的目标跟踪器，并持续修正跟踪结果，跟踪结果为剔除检测器虚警提供依据。在跟踪过程中，引入对观测场景纹理自适应的相机运动补偿策略实现目标重定位。多场景下的实验结果表明：提出的方法在对高速运动小目标的检测和跟踪指标上显著优于传统方法，且运动补偿机制的引入进一步增强了方法在极端复杂场景下的鲁棒性。     

基于遗传算法和最小二乘法的曲面匹配

 针对基于最小二乘法的 ICP曲面匹配算法难以处理待比较曲面的局部大变形问题,提出一种改进算法。即采用遗传算法确定曲面初始相对位置以保证匹配优化结果为全局最优值,利用 ICP算法匹配结果构造偏差阈值,以此阈值过滤点群后再以最小二乘法进行匹配处理,消除局部大变形影响,获得合理的变换矩阵。以此变换矩阵变换初始点群再进行误差计算,从而获得理想的匹配结果。试验表明,该准则对局部大变形的处理效果优于常规的最小二乘 ICP曲面匹配算法。     

Virtual target guidance-based distributed model predictive control for formation control of multiple UAVs

The paper proposes a Virtual Target Guidance (VTG)-based distributed Model Predictive Control (MPC) scheme for formation control of multiple Unmanned Aerial Vehicles (UAVs). First, a framework of distributed MPC scheme is designed in which each UAV only shares the information with its neighbors, and the obtained local Finite-Horizon Optimal Control Problem (FHOCP) can be solved by swarm intelligent optimization algorithm. Then, a VTG approach is developed and integrated into the distributed MPC scheme to achieve trajectory tracking and obstacle avoidance. Further, an event-triggered mechanism is proposed to reduce the computational burden for UAV formation control, which takes into consideration the predictive state errors as well as the convergence of cost function. Numerical simulations show that the proposed VTG-based distributed MPC scheme is more computationally efficient to achieve formation control of multiple UAVs in comparison with the traditional distributed MPC method.     

基于损伤标尺的电子设备预测维修决策优化

基于故障预测信息进行维修决策是预测性维修等新型维修模式的主要特征之一,可以有效提高装备使用可用度、降低寿命周期费用。面向单部件电子系统,针对故障预测中的损伤标尺方法,在完美换件维修的假设下,以单位时间成本、平均使用可用度与平均效费比为指标,提出了一种预测维修决策优化模型。对于与外场可更换单元(LRU)寿命独立的损伤标尺,选择预测距离与Weibull分布的形状参数为决策变量,对于与LRU寿命相关的损伤标尺,选择累积损伤因子与随机标准差为决策变量,应用Monte Carlo方法仿真研究了各个决策变量对维修效能指标的影响。结果表明:应用与LRU寿命独立损伤标尺的预测维修策略的效果优于事后维修策略,但劣于年龄换件策略,揭示了该方法的本质特性;应用与LRU寿命相关损伤标尺的预测维修策略的效果在一定条件下优于年龄换件策略。最后分析了维修决策的优选方法。     

自适应设计空间扩展的高效代理模型气动优化设计方法

对基于Kriging模型气动优化的加点方法和设计空间的构建问题进行了研究。首先,针对高效全局优化（EGO）方法收敛缓慢的问题,提出了一种混合加点方法,该方法通过引入期望提高（EI）阈值控制EI和最小预测值（MP）加点准则,利用先全局再局部的优化思想,提高了EGO方法在确定设计空间内的收敛性。其次,针对设计空间的构建问题,对比了扩大设计变量范围和多轮优化两种不同的设计空间构建方法,分析了设计变量范围对设计空间大小和样本密度的影响,进而提出了自适应设计空间扩展的代理模型优化方法。相对于传统固定设计空间的方法,自适应设计空间扩展的方法在动态的设计空间中进行优化搜索,只在有潜力的维度扩展设计变量范围,通过构建自适应设计空间,实现了样本的高效配置。最后,通过ADODG标准翼型优化算例证实,自适应设计空间优化方法可以大幅提高气动优化设计效率。     

改进的粒子群优化算法在气动设计中的应用

为了提高优化系统的搜索效率,发展出了社会模型这种改进智能优化算法的通用策略,在此基础上,提出了一种基于社会模型的改进粒子群优化(IPSOSM)算法。该算法对社会模型进行了分析并在此指导下,将人工鱼群算法(AFSA)中的聚群行为引入到粒子群优化(PSO)算法中,丰富了粒子之间的优势信息源,增强了粒子的信息共享能力,使得IPSOSM算法能够有效地跳出局部最优。函数测试表明,该算法显著提高了PSO算法的寻优性能。将IPSOSM算法应用到翼型和机翼的气动优化设计之中,取得了良好的结果,从而表明提出的算法简洁有效,具有较好的实用性。