超差特征量指标约束下的离散系统待机控制

由目标跟踪过程中出现的实际情形提出一类新的离散系统待机控制问题.将系统跟踪中断(控制失败)概率这一关键性指标转化为一组由滞留时间、超差时间以及随机穿越周期等构成的超差特征量指标集的约束;推导出带状目的域下超差特征量指标的解析表达式,研究了其与区域极点指标之间的相容性条件;当上述指标相容时,利用线性矩阵不等式(LMI),采用差分进化算法(DE)对待机控制策略进行满意优化求解;最后通过一个算例验证了所提方法的有效性.     

连续系统下的一种容偏估计策略

目标跟踪系统中为降低系统复杂度和保证估计的平稳性常常选择尽可能低阶次的模型, 当目标出现较高阶次的机动时, 则很容易丢失目标. 在假定目标的机动时间与强度均有限时, 提出了容偏估计的思想, 将稳态误差系数约束连同区域极点、估计误差方差上界指标一起构成估计系统的约束指标集, 寻求使得稳态误差系数尽可能小的滤波器, 以使得对机动目标跟踪的系统偏差尽可能小. 通过将约束指标集转化为一组双线性矩阵不等式(BMIs),并利用迭代求解线性矩阵不等式(LMIs)近似BMIs的方法, 得到了满足给定指标约束要求的容偏估计策略, 所设计的容偏估计策略可同时保证估计的准确性和精确性的要求, 从而保证了在目标出现机动时, 估计输出具有尽可能小的系统偏差. 最后数值算例对所提出的结论进行了说明.     

线性系统相容指标约束下的容错控制新方法

针对一类具有范数有界不确定性不满足匹配条件的线性连续系统,对易于失效的执行器进行容错控制设计,重新研究了圆域极点指标、H∞指标和H2指标约束下的满意容错控制器的设计问题.基于线性系统的极点配置理论和H2/H∞控制理论,利用线性矩阵不等式( LMI)方法,在假设失效执行器的输出为任意能量有界信号的情况下,分析了这3类指标在相容情况下的取值范围,并在相容指标约束下得到了新的满意容错控制器的设计方法,给出了满意容错控制器的具体设计步骤.所设计的满意容错控制器使闭环系统的极点在一个给定的圆域内,并且保持着系统给定的H2/H∞性能要求.     

超差特征量指标集约束下PID调节器的满意优化

针对一类具有目的域的控制系统,提出将一组由滞留时间、超差时间以及随机穿越周期时间等构成的超差特征量指标纳入满意PID调节器的目标函数集中进行研究。将PID调节器的参数设计转化为局部状态反馈问题,推导出带状目的域下超差特征量指标的解析表达式,并分析了其与稳定裕度、动态误差系数以及稳态输出方差指标之间的相容性;当上述指标相容时,给出了一种利用双群体机制下的差分进化算法和内点法相结合对PID调节器参数进行优化求解的策略;数值算例表明了所提方法的有效性。     

深度指导的无监督领域自适应语义分割

为了提高语义分割精度,解决模型在不同数据域上泛化性差的问题,提出基于深度信息的无监督领域自适应语义分割方法.首先,深度感知自适应框架通过捕捉深度信息和语义信息的内在联系,减小不同域之间的差异;然后,设计了一个轻量级深度估计网络来提供深度信息,通过跨任务交互策略融合深度和语义信息,并在深度感知空间对齐源域和目标域的分布差距;最后,提出基于深度信息的域内自适应策略弥合目标域内部的分布差异,将目标域分为子源域和子目标域,并缩小子源域和子目标域分布差距.实验结果表明,所提方法在SYNTHIA-2-Cityscapes和SYNTHIA-2-Mapillary跨域任务上的平均交并比分别为46.7%和73.3%,与同类方法相比,该方法在语义分割和深度估计精度上均有显著提升.     

基于角色映射和PBNM的多域安全访问控制模型*

在分析多安全域间基于RBAC角色映射和PBNM模型的基础上,提出了多域用户之间的安全访问策略配置模型.该模型通过域间角色映射信息来配置域间访问控制策略,在域内则采用基于PBNM的模型机制配置域内用户安全策略,最终解决了在多域间资源访问控制的安全问题.     

PI控制中衰减度与H∞指标的相容性

研究n阶线性定常系统在PI控制下衰减度与H∞指标的相容性问题.首先基于D-分割法和边界穿越定理,分别推导了描述PI控制器参数稳定域边界、PI控制器满足期望的衰减度指标的参数边界和期望的H∞指标的参数边界的解析表达式.再应用满意控制思想,对上述期望指标的相容性进行了分析,给出了相容指标较好的取值范围.当上述两类指标相容时,可以迅速得到整个PI控制器相容性解集(满意解集).用一个算例说明了所提出的设计方法的有效性.     

PI控制中衰减度与H∞指标的相容性

研究n阶线性定常系统在PI控制下衰减度与H∞指标的相容性问题.首先基于D-分割法和边界穿越定理,分别推导了描述PI控制器参数稳定域边界、PI控制器满足期望的衰减度指标的参数边界和期望的H∞指标的参数边界的解析表达式.再应用满意控制思想,对上述期望指标的相容性进行了分析,给出了相容指标较好的取值范围.当上述两类指标相容时,可以迅速得到整个PI控制器相容性解集(满意解集).用一个算例说明了所提出的设计方法的有效性.     

基于Vague集的含洞不规则Vague区域关系

模糊区域的空间信息表示和区域关系处理在空间数据库、地理信息系统和人工智能等领域具有重要的意义.引入Vague集的概念和理论对含洞不规则Vague区域关系进行了系统的研究.基于Vague集给出了Vague区域、Vague洞和原子域等概念;将含洞不规则Vague区域分成原子域,研究了原子域间的空间关系;联合原子域关系,给出了含洞不规则Vague区域关系.研究成果可较好地处理含洞不规则Vague区域内的模糊点不确定的隶属信息和复杂的含洞不规则Vague区域的空间关系的表示和分析等问题.     

线列阵高分辨方位估计方法研究

针对水声设备的实际应用,给出了基于实测基阵阵列流形的波束域高分辨方位估计的MUSIC方法.在预形成多波束时,该方法采用适用于任意阵形的自适应波束优化技术,针对水下实际阵列设计超低旁瓣波束以抑制水面上分布的强干扰.为了减小实际阵列模型和理想阵列模型失配所带来的不利影响,在多波束设计时,利用实测阵列流形代替理想阵列流形进行设计,消声水池实验结果验证了该方法可以有效地降低系统中由于阵列模型失配所造成的波束域MUSIC方位估计方法的性能损失.     

Real-Time Target Tracking for Autonomous UAVs in Adversarial Environments: A Gradient Search Algorithm

This paper presents a rule-based intelligent guidance strategy for autonomous pursuit of mobile targets by unmanned aerial vehicles (UAVs) in an area with threats, obstacles, and restricted regions. The probabilistic threat exposure map (PTEM) is used as the mathematical formulation of the area of operation for the guidance strategy to make intelligent decisions based on a set of defined rules. The rules are developed for three objectives in the order of priority as: 1) avoid obstacles/restricted regions; 2) maintain the target proximity; 3) minimize UAV threat exposure level. A least-square estimation and kinematic relations are used to estimate/predict the target states based on noisy position measurements. The work presented herein addresses the same problem as in a previous work by the authors, and aims at improving the computational efficiency without compromising the performance. Simulation results of several pursuit scenarios demonstrate the full capabilities of the strategy and the improvement over the previous work     

Stochastic sensor activation for distributed state estimation over a sensor network

We consider distributed state estimation over a resource-limited wireless sensor network. A stochastic sensor activation scheme is introduced to reduce the sensor energy consumption in communications, under which each sensor is activated with a certain probability. When the sensor is activated, it observes the target state and exchanges its estimate of the target state with its neighbors; otherwise, it only receives the estimates from its neighbors. An optimal estimator is designed for each sensor by minimizing its mean-squared estimation error. An upper and a lower bound of the limiting estimation error covariance are obtained. A method of selecting the consensus gain and a lower bound of the activating probability is also provided.     

MAP-Based Stochastic Diffusion for Stereo Matching and Line Fields Estimation

This paper proposes a stochastic approach to estimate the disparity field combined with line field. In the maximum a posteriori (MAP) method based on Markov random field (MRF) model, it is important to optimize and converge the Gibbs potential function corresponding to the perturbed disparity field. The proposed optimization method, stochastic diffusion, takes advantage of the probabilistic distribution of the neighborhood fields to diffuse the Gibbs potential space iteratively. By using the neighborhood distribution in the non-random and non-deterministic diffusion, both the estimation accuracy and the convergence speed are improved. In the paper, the hierarchical stochastic diffusion is also applied to the disparity field. The hierarchical approach reduces the memory and computational load, and increases the convergence speed of the potential space. The paper also proposes an effective configuration of the neighborhood to be suitable for the hierarchical disparity structure. According to the experiments, the stochastic diffusion shows good estimation performance. The line field improves the estimation at the object boundary, and coincides with the object boundary with the useful contours. The stochastic diffusion is applicable to any kind of field estimation given the appropriate definition of the field and MRF models.     

基于背景感知的显著性目标检测算法

在显著性目标检测算法中,流形排序的检测方法存在先验背景假设和目标检测不完整的问题.针对该问题,在流形排序算法基础上,融入背景鉴别、BING特征估计和权重调整,提出了一种基于背景感知的显著性目标检测算法.首先,通过计算颜色聚类后的边界区域的综合差异度,得到真实背景种子点,从而感知到真实背景区域;再结合图像的BING特征与初始显著图信息,获取目标位置,从而得到完整的前景种子点区域;然后重构前景区域的图模型且利用加权k-壳分解法,来调整前景区域节点之间的连接权重,进而获得清晰的目标边界.实验结果表明,同当前经典的一些算法比较,本文算法在准确率、召回率、F-measure和平均MAE上都优于其余算法.     

Particle filter based information-theoretic active sensing

This work addresses the task of active sensing, or information-seeking control of mobile sensor platforms. Formulation of a control objective in terms of information gain allows mobile sensors to be both autonomous and easily reconfigurable to include a variety of sensor and target models. Tracking a moving target using a camera mounted on a fixed-wing unmanned aircraft is considered, but the control formulation is not specific to this choice of sensor or estimation task. A control formulation is developed which minimizes the entropy of an estimate distribution over a receding horizon subject to stochastic non-linear models for both the target motion and sensors. Previous similar work has been restricted to either a stationary target, a horizon of length one, or Gaussian estimates.The prediction of conditional entropy is shown to be inherently complex, and a computationally efficient sequential Monte Carlo method is developed. The entropy prediction depends on this Monte Carlo method as well as a novel approach for entropy calculation in the context of particle filtering. These methods are verified through simulation and post-processing of experimental flight data.     

Learning Changing Concepts by Exploiting the Structure of Change

This paper examines learning problems in which the target function is allowed to change. The learner sees a sequence of random examples, labelled according to a sequence of functions, and must provide an accurate estimate of the target function sequence. We consider a variety of restrictions on how the target function is allowed to change, including infrequent but arbitrary changes, sequences that correspond to slow walks on a graph whose nodes are functions, and changes that are small on average, as measured by the probability of disagreements between consecutive functions. We first study estimation, in which the learner sees a batch of examples and is then required to give an accurate estimate of the function sequence. Our results provide bounds on the sample complexity and allowable drift rate for these problems. We also study prediction, in which the learner must produce online a hypothesis after each labelled example and the average misclassification probability over this hypothesis sequence should be small. Using a deterministic analysis in a general metric space setting, we provide a technique for constructing a successful prediction algorithm, given a successful estimation algorithm. This leads to sample complexity and drift rate bounds for the prediction of changing concepts.     

Multi-sensor multi-rate fusion estimation for networked systems: Advances and perspectives

In industrial systems, the multi-rate sampling strategy has been widely used due to the advantage in balancing cost and performance as well as the psychical characteristics of the hardware. Accordingly, the analysis and synthesis problems of the multi-rate systems (MRSs) have received considerable research attentions owing to the significant engineering background. Among others, the state estimation problem, aims to estimate the system state based on the contaminated measurement signals, is one of the most important topics in the area of signal processing. In the past decades, plenty of research results have been obtained on the state estimation problems for MRSs. The intent of this survey is to provide a timely and systematic review with respect to the available state estimation algorithms for networked MRSs and the corresponding fusion methods. First, a general state–space model of the MRSs is given and the methods that transform the MRSs into single-rate ones are introduced. Then, the recent advances on the state estimation as well as fusion estimation problems for MRSs are discussed based on the performance indices used. Finally, some future research topics are given in the MRS state estimation problems.     

二值alpha平面辅助的视频对象快速运动估计算法

提出了一种任意形状视频对象的快速运动估计方法.详细分析了alpha平面在视频对象的快速运动估计过程中起到的指导性作用,采用边界扩展和边界掩码技术,提出了一种新的二值alpha平面匹配衡量准则WBAMC (weighted binary alpha-plane matching criterion).结合优先搜索策略,提出了二值alpha平面辅助的视频对象快速运动估计算法BAAME(binary alpha-plane assisted motion estimation),.首先,利用alpha平面和WBAMC准则,将边界宏块的搜索范围缩小至两个搜索起点的单调区域,再采用传统的快速运动估计算法确定其运动向量;然后,用边界宏块的运动向量预测内部宏块的搜索起点;最后,采用快速运动估计算法搜索内部宏块的运动向量.这种方法可与多种空间域和频率域运动估计算法相结合,有效地应用于基于对象的视频编码器中.实验结果表明,对于多种类型的标准测试视频流,BAAME算法始终能够保持较高的估计精度和主观质量,运动补偿的平均PSNR(peak signal-to-noise ratio)较DS(diamosd search)和PSA(pdoety search algorithm)(BAAS(binary alpha-plant assisted search) DS)高出0.1dB～0.SdB,略低于FS(full search),但是其计算复杂度与FS相比降低了20倍.     

Efficiency Research of Planted Rice Area Estimation From NOAA一AVHRR Data in Taihu Domain

运用NOAA-AVHRR资料估算水稻种植面积，是遥感应用领域中一个新的研究方向，结合国家“八五”攻关项目“太湖地区遥感话产”的要求，在太湖地区进行了初步的尝试:(1)根据估产精度要求和NOAA一AVHRR资料校正精度，探讨了运用NOAA一AVHRR资料估产所需的最小区域范围。(2)针对太湖地区的具休地理环境设计了提取水稻种植曲积的技术方案，并在试验区取得了初步成果。