H∞ filtering for discrete-time systems subject to stochastic missing measurements: a decomposition approach

This paper deals with the problem of H_∞ filtering for discrete-time systems with stochastic missing measurements. A new missing measurement model is developed by decomposing the interval of the missing rate into several segments. The probability of the missing rate in each subsegment is governed by its corresponding random variables. We aim to design a linear full-order filter such that the estimation error converges to zero exponentially in the mean square with a less conservatism while the disturbance rejection attenuation is constrained to a given level by means of an H_∞ performance index. Based on Lyapunov theory, the reliable filter parameters are characterised in terms of the feasibility of a set of linear matrix inequalities. Finally, a numerical example is provided to demonstrate the effectiveness and applicability of the proposed design approach.     

A trustworthy QoS-based collaborative filtering approach for web service discovery

Many network services which process a large quantity of data and knowledge are available in the distributed network environment, and provide applications to users based on Service-Oriented Architecture (SOA) and Web services technology. Therefore, a useful web service discovery approach for data and knowledge discovery process in the complex network environment is a very significant issue. Using the traditional keyword-based search method, users find it difficult to choose the best web services from those with similar functionalities. In addition, in an untrustworthy real world environment, the QoS-based service discovery approach cannot verify the correctness of the web services’ Quality of Service (QoS) values, since such values guaranteed by a service provider are different from the real ones. This work proposes a trustworthy two-phase web service discovery mechanism based on QoS and collaborative filtering, which discovers and recommends the needed web services effectively for users in the distributed environment, and also solves the problem of services with incorrect QoS information. In the experiment, the theoretical analysis and simulation experiment results show that the proposed method can accurately recommend the needed services to users, and improve the recommendation quality.     

一种用于目标跟踪的波形自适应选择算法

提出了一种用于雷达目标跟踪的自适应广义调频波形设计算法。该算法根据跟踪器的动态需求,以广义调频信号为样板波形自适应设计下一时刻的发射波形,其目的是最小化预测的目标跟踪均方误差,并假定高信噪比条件,且目标跟踪运动模型和观测模型均为线性,利用与某一波形相对应的克拉美--罗下限（CRLB）以及卡尔曼滤波器,通过最小化预测的跟踪均方误差来实现广义调频波形的自适应设计。仿真结果表明：在信噪比相同的情况下,与使用固定参数、自适应参数的线性调频波形设计算法相比,所提出的算法能够获得更低的目标跟踪均方误差。     

A New Derivation of the Cubature Kalman Filters

This paper investigates the cubature Kalman filtering (CKF) for nonlinear dynamic systems. This third‐degree rule based filter employs a spherical‐radial cubature rule to numerically compute the integrals encountered in nonlinear filtering problems, thereby removing the requirements of explicitly computing the Jacobians. The cubature rule, however, requires computing the intractable integrals over a high‐dimensional spherical region for multidimensional applications. Moreover, the cubature formula that has been used to construct the spherical cubature formula has some demerits, most notably its inconvenient properties in computation and low estimation accuracy. Aimed at these issues, a general class of CKFs that uses only cubature rules is derived in this paper. It can be shown that the conventional CKF is a special case of the proposed algorithm. The paper also includes higher‐degree CKFs, especially two representative types of the fifth‐degree CKFs. Performance of the proposed algorithms is demonstrated via two target tracking problems. The experimental results, presented herein, illustrate the superior performance of higher‐degree CKFs to conventional nonlinear filters.     

基于中位数的用户信誉度排名算法

针对推荐系统易受Spammer攻击的影响,从而导致对象的实际得分不准确的问题,提出基于中位数的用户信誉度排名算法。通过衡量用户信誉度调整用户打分权重,根据中位数具有不易受极端打分影响的特性,选取用户打分与对象得分差距的中位数作为降低用户信誉度的标准,不断迭代调整用户信誉度以及最终得分直至收敛。在多个真实数据集上的运行结果证明,相比现有排名算法,该算法具有更合理的信誉度分布和更高的排名结果准确度,通过该算法预处理后的数据集在SVD++上运行可以得到更低的均方根误差。     

一种基于图像匹配的地面交通标志实时识别方法

地面交通标志检测识别是智能驾驶领域的一个研究方向,实时性、准确率是该研究的重点。图像匹配的方法是常用的模式识别方法。文中介绍了一种结合先验知识和图像匹配的地面交通标志检测识别方法。算法包括两部分:预处理和检测识别。预处理阶段包括图像压缩、感兴趣区域提取、形态学处理、中值滤波和逆透视等步骤,实现图像降噪和正畸,为检测识别做准备。检测识别阶段包括轮廓提取、面积过滤、图像匹配等步骤,目的是判断待测图像是否含有地面交通标志及其种类。实验证明,该算法实时性好、鲁棒性强、准确率高。     

一种卡尔曼增益约束滤波算法

在滤波过程中,将卡尔曼增益结合约束条件,可以有效地提高滤波精度.论文推导了含有卡尔曼增益约束条件的代价函数,根据Karush-Kuhn-Tucker（KKT）最小化的一阶必要条件,使用高斯牛顿法求出约束卡尔曼增益的最优约束解.最后对具有约束条件的目标跟踪问题进行了仿真实验,结果表明该算法的跟踪精度要高于普通卡尔曼滤波算法.     

BNs-OLS-SARIMA对城市短时交通流的预测*

通过在目标路口构建贝叶斯交通网（BNs）,并对与此交通网相关的交通流建立非平稳季节（SARIMA）模型,采用最小二乘法（OLS）取得相应模型的最佳权重组合,对缺失数据下的城市道路短时交通流进行预测。使用重庆市某路口的交通流数据对模型进行检测,通过多种预测指标对结果进行对比分析,结果表明BNs-OLS-SARIMA把交通流的网络结构与其周期性结合在一起,对短时交通流有良好的预测效果。     

哈尔滨市主要道路交通噪声污染评价

针对哈尔滨市的主要道路交通噪声进行监测,统计并分析整体污染情况、噪声分级状况及空间分布特征。为哈尔滨市今后的道路交通噪声治理工作提供有利的依据,具有较好的实际应用价值。