Low computational complexity family of affine projection algorithms over adaptive distributed incremental networks

This paper presents the problem of distributed estimation in an incremental network based on the family of affine projection (AP) adaptive algorithms. The distributed selective partial update normalized least mean squares (dSPU-NLMS), the distributed SPU-AP algorithm (dSPU-APA), the distributed selective regressor APA (dSR-APA), the distributed dynamic selection of APA (dDS-APA), dSPU-SR-APA and dSPU-DS-APA are introduced in a unified way. These algorithms have low computational complexity feature and close convergence speed to ordinary distributed adaptive algorithms. In dSPU-NLMS and dSPU-APA, the weight coefficients are partially updated at each node during the adaptation. In dSR-APA, the optimum number of input regressors is selected during the weight coefficients update. The dynamic selection of input regressors is used in dDS-APA. dSPU-SR-APA and dSPU-DS-APA combine SPU with SR and DS approaches. In these algorithms, the weight coefficients are partially updated and the input regressors are optimally/dynamically selected at every iteration for each node. In addition, a unified approach for mean-square performance analysis of each individual node is presented. This approach can be used to establish a performance analysis of classical distributed adaptive algorithms as well. The theoretical expressions for stability bounds, transient, and steady-state performance analysis of various distributed APAs are introduced. The validity of the theoretical results and the good performance of dAPAs are demonstrated by several computer simulations.     

基于KFCM增量更新的无线电引信目标识别方法

针对传统无线电引信在复杂电磁环境下作用效果较差的问题,以连续波多普勒引信为例,通过对引信检波输出信号频域的分析,提出一种基于熵的特征提取方法,并利用KFCM算法对信号进行分类识别。由于实际战场环境复杂且不可预测,其背景噪声强度与实验环境下存在差异,因此结合KFCM增量更新特性,使分类模型根据噪声强度变化而实时更新调整,从而达到更好的分类效果。实验结果证明,基于增量更新KFCM算法能显著提高不同信噪比下引信目标识别能力,将KFCM增量更新算法运用到无线电引信抗干扰能取得良好效果。     

复杂城市环境下的全球导航卫星系统/捷联惯性导航系统组合导航算法

针对复杂城市环境下,全球导航卫星系统(GNSS)信号出现频繁短暂失锁或拒止时,对GNSS/捷联惯性导航系统(SINS)组合导航系统的导航精度和鲁棒性影响较大问题,该文提出一种改进的因子图滤波方法。首先使用GNSS接收机内部参数构建信号误差鉴别函数,能实时估计出信号受多径干扰、遮挡等情况下的信号测量性能;同时利用载体运动约束条件构造零速修正因子,对GNSS拒止情况下的系统状态进行更新,避免系统导航性能极速下降。实验结果表明,改进的因子图方法相比经典因子图方法,在城市复杂环境下能提高定位精度63.50%和测速精度42.26%,同时也具有更低的存储量和计算复杂度,特别适用于城市车辆辅助驾驶导航设备中,对导航精度、硬件资源和实时性约束强的场景。     

Attribute Allocation and Retrieval Scheme for Large-Scale Sensor Networks

Wireless sensor network is an emerging technology that enables remote monitoring of large geographical regions. In this paper, we address the problem of distributing attributes over such a large-scale sensor network so that the cost of data retrieval is minimized. The proposed scheme is a data-centric storage scheme where the attributes are distributed over the network depending on the correlations between them. The problem addressed here is similar to the Allocation Problem of distributed databases. In this paper, we have defined the Allocation Problem in the context of sensor networks and have proposed a scheme for finding a good distribution of attributes to the sensor network. We also propose an architecture for query processing given such a distribution of attributes. We analytically determine the conditions under which the proposed architecture is beneficial and present simulation results to demonstrate the same. To the best of our knowledge, this is the first attempt to determine an allocation of attributes over a sensor network based on the correlations between attributes.     

视频监视中背景的提取与更新算法

提出一种视频监视中背景初始化和更新算法。该算法采用在空域中基于象素中值的方法，来完成对背景的初始化：通过差分当前帧和背景帧，来进行当前帧的重构，应用Kalman滤波器完成背景的更新操作。     

使用实时自适应陷波器抑制多个窄带干扰

基于KWAN和MARTIN提出的抑制多个窄带干扰算法,改进了其中的梯度搜索算法,减少了硬件复杂性。KWAN和MARTIN的算法在窄带干扰数量增加时,硬件数量呈几何级数增长。而对于笔者改进的算法来说,硬件数量只需要代数增长就能够实现相同的效果。KWAN和MARTIN的算法在处理3至10个窄带干扰时,所需要的硬件数量分别比新算法多12.5%和124%。新算法的仿真结果显示了它在多种实际的信号处理应用中都是强壮和有效的。     

基于不精确状态信息的服务质量路由技术

传统的服务质量(QoS)路由假设网络节点拥有精确的状态信息,这与实际网络环境并不相符,因此会严重影响算法的性能,基于不精确状态信息的QoS路由技术应运而生。首先论述产生不精确状态信息的各种原因,然后给出了多种解决方案,接着简要描述了迄今为止主要的基于不精确状态信息的QoS路由算法,最后对这些算法进行分析和比较,指出各自的利弊和适用环境。     

A CLASS OF FACTORIZATION UPDATE ALGORITHM FOR SOLVING SYSTEMS OF SPARSE NONLINEAR EQUATIONS

ＡＣＬＡＳＳＯＦＦＡＣＴＯＲＩＺＡＴＩＯＮＵＰＤＡＴＥＡＬＧＯＲＩＴＨＭＦＯＲＳＯＬＶＩＮＧＳＹＳＴＥＭＳＯＦＳＰＡＲＳＥＮＯＮＬＩＮＥＡＲＥＱＵＡＴＩＯＮＳＢＡＩＺＨＯＮＧＺＨＩ（ＩｎｓｔｉｔｕｔｅｏｆＣｏｍｐｕｔａｔｉｏｎａｌＭａｔｈｅｍａｔｉｃ...     

Local convergence of collinear scaling algorithms related to direct least‐change secant update methods for minimization

Collinear scaling algorithms related to direct fixed-scale and rescaled least-change secant update methods for unconstrained minimization are derived. Theorems on local and q-superlinear convergence of these algorithms are presented. These results are extensions of those of Dennis and Walker [14] for direct least-change secant update methods. This revised version was published online in August 2006 with corrections to the Cover Date.     

面向高效加密云数据排序搜索的类别分组索引方法

针对现有可搜索加密领域所遇到的加密密钥维度高、更新不灵活和搜索速度慢等问题,我们提出了一种新型类别分组索引方法——CGIM.新方法将数据分类后,按类提取关键词建立分组索引,并采用分组加密方式实现以若干低维加密密钥代替高维加密密钥以缩短索引和查询请求的加密时间.此外,分组索引方法的每个组向量对应不同的类别,这样不仅可以实现分类更新以改善更新文档的灵活性,而且能够在检索过程中生成针对性陷门,从而进一步提高搜索的速度和效率.理论和实验分析表明,该方法是可行且有效的.