Indoor localization algorithm using clustering on signal and coordination pattern

In recent years, the demand for indoor location-based services has gradually received greater attention. The presence of multipath interference has a tendency to interfere with traditional algorithms calculated based on received signal strength (RSS). The application of virtual tags can greatly reduce deployment costs and enable greater environmental adaptability. However, an excess of ineffectively filtered virtual tags will only lead to greater error in calculation. Therefore, virtual tags are combined with a two-step clustering method to replace the concept of signal hotpoint intersections due to the mutual interdependence of data in space and the influence of neighboring objects. This study improved a two-step location algorithm that combines the advantages of virtual tags and two-step clustering analysis, called clustering-based localization algorithm, offering significant improvement over most traditional localization algorithms. RSS are no longer used as a basis for clustering, and are replaced by the combination of signal and coordination pattern. Two steps of cluster analysis are performed during the filtering process. The first step utilizes the tags’ signals to perform clustering. The second step incorporates tags’ coordinate for filtering. As the clustering-based localization process considers the interactive relationship between coordinate data, it achieves superior results compared to those produced via methods that only use signal strength to select neighboring solutions. This study then constructs a wireless sensor network and assesses the effectiveness of the algorithm.     

多基站下基于信号到达时间的室内三维定位

对基于无线电信号的到达时间(TOA)的定位技术进行了研究.首先,建立了TOA测量值与真实值间的函数关系,通过求解含惩罚项的非线性最小二乘问题实现终端定位和噪声估计,并在已有研究的基础上提出了Alternating Iterative Reweighting(AIR)算法;其次,利用基站之间的几何位置进行筛选,根据每个终端信息完成自适应的最少基站的选择及定位;并结合物理特性实现终端运动轨迹预测;最后,根据基站通信半径筛选出可被定位终端,完成定位精度与终端平均连接度数之间关系的建模.     

Ridge directional singular points for fingerprint recognition and matching

In this paper, a new approach to extract singular points in a fingerprint image is presented. It is usually difficult to locate the exact position of a core or a delta due to the noisy nature of fingerprint images. These points are the most widely used for fingerprint classification and matching. Image enhancement, thinning, cropping, and alignment are used for minutiae extraction. Based on the Poincaré curve obtained from the directional image, our algorithm extracts the singular points in a fingerprint with high accuracy. It examines ridge directions when singular points are missing. The algorithm has been tested for classification performance on the NIST‐4 fingerprint database and found to give better results than the neural networks algorithm. Copyright © 2005 John Wiley & Sons, Ltd.     

Empirical likelihood for balanced ranked-set sampled data

Ranked-set sampling (RSS) often provides more efficient inference than simple random sampling (SRS). In this article, we propose a systematic nonparametric technique, RSS-EL, for hypothesis testing and interval estimation with balanced RSS data using empirical likelihood (EL). We detail the approach for interval estimation and hypothesis testing in one-sample and two-sample problems and general estimating equations. In all three cases, RSS is shown to provide more efficient inference than SRS of the same size. Moreover, the RSS-EL method does not require any easily violated assumptions needed by existing rank-based nonparametric methods for RSS data, such as perfect ranking, identical ranking scheme in two groups, and location shift between two population distributions. The merit of the RSS-EL method is also demonstrated through simulation studies. This work was supported by National Natural Science Foundation of China (Grant No. 10871037)     

Optimal Network Design for Wireless Local Area Network

Network design for wireless local area networks is an important issue in the deployment of these networks. Research activities are presently being undertaken in two major areas: determining the location of base stations (BSs) and assigning the frequency channels for these stations. Our BS location problem selects a set of BSs to provide the best demand area coverage and maximize the signal level and physical area attendance priority. Adequate channel assignment reduces signal interference and improves network throughput. This paper reports a real world experiment where we applied the concepts of two classical outdoor problems namely the optimal BS location problem and the fixed channel assignment problem to build a WLAN into an indoor environment. We propose a mathematical model that we solve by a commercial software and report the computational results.     

利用B样条曲线设计指纹图像滤波模板和快速指纹特征匹配算法

在指纹图像识别过程中,指纹图像的滤波去噪音二值化和指纹特征的匹配是最关键的两个部分.本文针对这两部分的算法设计进行分析和改进,介绍利用B样条造型来设计指纹滤波模板的方法和基于基准点附近的拓扑结构进行快速特征匹配的算法.并且利用改进后的指纹特征匹配算法,在样本指纹库中进行实验,结果表明该算法大大提高了指纹特征匹配的效率和准确性.     

The determination of two moving heat sources in two-dimensional inverse heat problem

There are some restrictions in the estimation of location and strength problems in recent studies. One of the restrictions is that the problem is limited to the static sources and the single moving source. In other words, there is no method available to estimate the location and the strength of two moving sources presently. Therefore, it is necessary to develop a robust method to estimate the location and the strength of two moving heat sources. In this paper, a numerical algorithm is proposed to determine the problem sequentially. Special feature about this method is that no preselect functional form for the unknown sources is necessary and no sensitivity analysis is needed in the algorithm. Two examples are used to demonstrate the characteristics of the proposed method. From the results, they show that the proposed method is an accurate and efficient method to determine the location and the strength of two moving sources in the inverse heat conduction problem.     

Nonlinear Dynamical Systems and Adaptive Filters in Biomedicine

In this paper we present the application of a method of adaptive estimation using an algebra–geometric approach, to the study of dynamic processes in the brain. It is assumed that the brain dynamic processes can be described by nonlinear or bilinear lattice models. Our research focuses on the development of an estimation algorithm for a signal process in the lattice models with background additive white noise, and with different assumptions regarding the characteristics of the signal process. We analyze the estimation algorithm and implement it as a stochastic differential equation under the assumption that the Lie algebra, associated with the signal process, can be reduced to a finite dimensional nilpotent algebra. A generalization is given for the case of lattice models, which belong to a class of causal lattices with certain restrictions on input and output signals. The application of adaptive filters for state estimation of the CA3 region of the hippocampus (a common location of the epileptic focus) is discussed. Our areas of application involve two problems: (1) an adaptive estimation of state variables of the hippocampal network, and (2) space identification of the coupled ordinary equation lattice model for the CA3 region.     

Integer programming models for maximizing parallel transmissions in wireless networks

In radio communications, a set of links that can transmit in parallel with a tolerable interference is called a compatible set. Finding a compatible set with maximum weighted revenue of the parallel transmissions is an important subproblem in wireless network management. For the subproblem, there are two basic approaches to express the signal to interference plus noise ratio (SINR) within integer programming, differing in the number of variables and the quality of the upper bound given by linear relaxations. To our knowledge, there is no systematic study comparing the effectiveness of the two approaches. The contribution of the paper is two-fold. Firstly, we present such a comparison, and, secondly, we introduce matching inequalities improving the upper bounds as compared to the two basic approaches. The matching inequalities are generated within a branch-and-cut algorithm using a minimum odd-cut procedure based on the Gomory-Hu algorithm. The paper presents results of extensive numerical studies illustrating our statements and findings.     

乘客到达时间不确定的机场动态拼车策略与算法研究

网约车拼车服务作为共享经济领域重要应用,已成为国内外研究热点。针对机场在线拼车平台运营中乘客等待时间过长和车辆行驶成本较高的突出问题,本文提出前瞻式动态拼车匹配策略。该策略将未来随机到达乘客信息纳入当前已到达乘客的拼车匹配决策中,建立了乘客匹配与车辆路径联合优化两阶段随机规划模型。为了在动态环境中实时产生高质量的匹配与路径规划方案,首先基于贝叶斯估计压缩乘客随机到达情景空间,建立了问题的确定性近似最优模型。为了快速求解模型,提出基于订单目的地和乘客期望到达时间相似度的匹配规则,并以此开发改进的差分进化算法。最后,基于某拼车平台真实订单数据,通过对比测试验证了前瞻式匹配策略和改进差分进化算法的有效性与计算效率。     

Autonomous localization based energy saving mechanism in indoor MANETs using ACO

The biggest challenge in MANETs is to find most efficient routing due to the changing topology and energy constrained battery operated computing devices. It has been found that Ant Colony Optimization (ACO) is a special kind of optimization technique having characterization of Swarm Intelligence (SI) which is highly suitable for finding the adaptive routing for such a type of volatile network. The proposed ACO routing algorithm uses position information and energy parameters as a routing metric to improve the performance and lifetime of network. Typical routing protocols have fixed transmission power irrespective of the distance between the nodes. Considering limiting factors, like small size, limited computational power and energy source, the proposed solution excludes use of GPS for identifying the distance between nodes for indoor MANETs. The distance between nodes can be determined by using Received Signal Strength Indicator (RSSI) measurements. Thus, an intelligent ACO routing algorithm with location information and energy metric is developed to adaptively adjust the transmission power and distribute the load to avoid critical nodes. Proposed Autonomous Localization based Eligible Energetic Path_with_Ant Colony Optimization (ALEEP_with_ACO) algorithm ensures that nodes in the network are not drained out of the energy beyond their threshold, as the load is shared with other nodes, when the energy of a node in the shortest path has reached its threshold. Hence, the total energy expenditure is reduced, thus prolonging the lifetime of network devices and the network. We simulated our proposal and compared it with the classical approach of AODV and other biological routing approaches. The results achieved show that ALEEP_with_ACO presents the best Packet Delivery Ratio (PDR), throughput and less packet drop specially under high mobility scenarios.     

An Algorithm for Combined Code and Carrier Phase Based GPS Positioning

The Global Positioning System (GPS) is a satellite based navigation system. GPS satellites transmit signals that allow one to quite accurately estimate the location of GPS receivers. In GPS a typical technique for kinematic position estimation is relative positioning where two receivers are used, one receiver is stationary and its exact position is known, the other is roving and its position is to be estimated. We describe the physical situation and give the mathematical model based on the difference of the measurements at the stationary and roving receivers. The model we consider combines both the code and carrier phase measurements. We then present a recursive least squares approach for position estimation. We take full account of the structure of the problem to make our algorithm efficient, and use orthogonal transformations to ensure numerical reliability of the algorithm. Real data test results suggest our algorithm is effective. An additional benefit of this approach is that the drawbacks of double differencing are avoided. The paper could also serve as a straightforward introduction for numerical analysts to an interesting area of GPS.This revised version was published online in October 2005 with corrections to the Cover Date.     

改进二元分布估计算法求解置换流水车间调度问题

针对置换流水车间调度这类组合最优化问题的求解,提出了一种改进二元分布估计算法(Improved binary estimation distribution algorithm, I-EDA)。算法以二元分布估计算法为架构,使用NEH(Nawaz-Enscore-Ham)启发式算法生成初始解,提高了初始解的质量;通过对优势解的统计采样构建位置矩阵模型和链接矩阵模型,依照两个矩阵模型的合并概率组合链接区块产生子代。提出了NEH插入式重组策略和基于位置概率的交换策略和两种全新局部搜索机制替代原二元分布估计算法的相邻交换法,以进一步筛选优势解。最后通过对Reeves标准测试集的仿真实验和算法比较验证了所提出算法的有效性。     

基于单目视觉的SLAM算法研究

利用一种新型的人工路标系统-MR二维码,提出了基于单目视觉和里程计的SLAM方法.首先介绍了MR二维码系统,然后在对机器人运动模型和视觉传感器观测模型进行分析和验证的基础上,给出了一种实用的里程计位置估计误差模型.机器人移动过程中,利用扩展卡尔曼滤波器对视觉信息与里程计信息进行融合.在室内环境下进行了实际实验,实验结果表明该算法可提高机器人定位和构建地图的精度,验证了算法的有效性.     

Detection and localization of hidden radioactive sources with spatial statistical method

The detection of radioactive materials has become a critical issue for environmental services, public health, and national security. This paper proposes a spatial statistical method to detect and localize a hidden radioactive source. Based on a detection system of multiple radiation detectors, the statistical model assumes that the counts of radiation particles received by those detectors are spatially distributed of Poisson distribution, and each comprises a signal and a background. By considering the physical law of signal degradation with distance, the paper provides a numerical method to compute the maximum likelihood estimates of the strength and location of the source. Based on these estimates, a likelihood ratio statistic is used to test the existence of the source. Because of the special properties of the model, the test statistic does not converge asymptotically to the standard chi-square distribution. Thus a bootstrap method is proposed to compute the p-value in the test. The simulation results show that the proposed method is efficient for detecting and localizing the hidden radioactive source.     

一种基于校正无线电传播时间的室内三维定位模型

对室内终端进行基于无线通信基站的三维定位时,由于无线电在传播过程中,存在建筑物或树木遮挡、反射、折射等物理现象,导致基站到终端传播时间的测量值与精确值之间存在误差.为了减少上述误差对定位精度的影响,运用无线定位原理建立了一种基于校正无线电传播时间的改进的室内三维定位模型.通过对2016年全国研究生数学建模问题C题所提供的不同场景的数据分析,发现基站与终端无线电传播时间的测量值与其精确值之间存在线性关系,而且同一场景下近似有相同的线性关系,场景不同线性关系一般不同.基于这一现象,对基本的定位模型进行了合理的修正形成了改进的定位模型,数值实验表明改进的定位模型有很好的定位精度和鲁棒性.     

基于声矢量水听器阵列误差的自校正算法

对声矢量水听器阵列的各类误差进行了分类,推导了各类误差对阵列信号模型的影响因子,通过Monte Carlo实验分析对比了各类误差对阵列DOA估计性能的影响,然后将方向性误差和位置误差归结为幅度误差和相位误差,在传统声压阵列误差校正模型和算法的基础上,得到矢量阵列误差自校正的优化模型及自校正算法,最后,通过仿真实验和外场实验的数据处理表明,自校正算法具有良好的参数估计性能,具有一定的工程实用性.     

A multiple template approach for robust tracking of fast motion target

Target tracking is very important in computer vision and related areas. It is usually difficult to accurately track fast motion target with appearance variations. Sometimes the tracking algorithms fail for heavy appearance variations. A multiple template method to track fast motion target with appearance changes is presented under the framework of appearance model with Kalman filter. Firstly, we construct a multiple template appearance model, which includes both the original template and templates affinely transformed from original one. Generally speaking, appearance variations of fast motion target can be covered by affine transformation. Therefore, the affine transform-enhanced templates match the target of appearance variations better than conventional models. Secondly, we present an improved Kalman filter for approximate estimating the motion trail of the target and a modified similarity evaluation function for exact matching. The estimation approach can reduce time complexity of the algorithm and keep accuracy in the meantime. Thirdly, we propose an adaptive scheme for updating template set to alleviate the drift problem. The scheme considers the following differences: the weight differences in two successive frames; different types of affine transformation applied to templates. Finally, experiments demonstrate that the proposed algorithm is robust to appearance variation of fast motion target and achieves real-time performance on middle/low-range computing platform.     

Model recovery for Hammerstein systems using the auxiliary model based orthogonal matching pursuit method

This article investigates parameter and order identification of a block-oriented Hammerstein system by using the orthogonal matching pursuit method in the compressive sensing theory which deals with how to recover a sparse signal in a known basis with a linear measurement model and a small set of linear measurements. The idea is to parameterize the Hammerstein system into the linear measurement model containing a measurement matrix with some unknown variables and a sparse parameter vector by using the key variable separation principle, then an auxiliary model based orthogonal matching pursuit algorithm is presented to recover the sparse vector.The standard orthogonal matching pursuit algorithm with a known measurement matrix is a popular recovery strategy by picking the supporting basis and the corresponding non-zero element of a sparse signal in a greedy fashion. In contrast to this, the auxiliary model based orthogonal matching pursuit algorithm has unknown variables in the measurement matrix. For a K-sparse signal, the standard orthogonal matching pursuit algorithm takes a fixed number of K stages to pick K columns (atoms) in the measurement matrix, while the auxiliary model based orthogonal matching pursuit algorithm takes steps larger than K to pick K atoms in the measurement matrix with the process of picking and deleting atoms, due to the gradually accurate estimates of the unknown variables step by step.The auxiliary model based orthogonal matching pursuit algorithm can simultaneously identify parameters and orders of the Hammerstein system, and has a high efficient identification performance.     

On the Ranked-Set Sampling M-Estimates for Symmetric Location Families

The ranked-set sampling (RSS) is applicable in practical problems where the variable of interest for an observed item is costly or time-consuming but the ranking of a set of items according to the variable can be easily done without actual measurement. In this article, the M-estimates of location parameters using RSS data are studied. We deal mainly with symmetric location families. The asymptotic properties of M-estimates based on ranked-set samples are established. The properties of unbalanced ranked-set sample M-estimates are employed to develop the methodology for determining optimal ranked-set sampling schemes. The asymptotic relative efficiencies of ranked-set sample M-estimates are investigated. Some simulation studies are reported.