Efficient and accurate sensor network localization

Wireless sensor networks (WSN) have great potential in ubiquitous computing. However, the severe resource constraints of WSN rule out the use of many existing networking protocols and require careful design of systems that prioritizes energy conservation over performance optimization. A key infrastructural problem in WSN is localization—the problem of determining the geographical locations of nodes. WSN typically have some nodes called seeds that know their locations using global positioning systems or other means. Non-seed nodes compute their locations by exchanging messages with nodes within their radio range. Several algorithms have been proposed for localization in different scenarios. Algorithms have been designed for networks in which each node has ranging capabilities, i.e., can estimate distances to its neighbours. Other algorithms have been proposed for networks in which no node has such capabilities. Some algorithms only work when nodes are static. Some other algorithms are designed specifically for networks in which all nodes are mobile. We propose a very general, fully distributed localization algorithm called range-based Monte Carlo boxed (RMCB) for WSN. RMCB allows nodes to be static or mobile and that can work with nodes that can perform ranging as well as with nodes that lack ranging capabilities. RMCB uses a small fraction of seeds. It makes use of the received signal strength measurements that are available from the sensor hardware. We use RMCB to investigate the question: “When does range-based localization work better than range-free localization?” We demonstrate using empirical signal strength data from sensor hardware (Texas Instruments EZ430-RF2500) and simulations that RMCB outperforms a very good range-free algorithm called weighted Monte Carlo localization (WMCL) in terms of localization error in a number of scenarios and has a similar computational complexity to WMCL. We also implement WMCL and RMCB on sensor hardware and demonstrate that it outperforms WMCL. The performance of RMCB depends critically on the quality of range estimation. We describe the limitations of our range estimation approach and provide guidelines on when range-based localization is preferable.     

Motion planning in uncertain environments with vision-like sensors

In this work we present a methodology for intelligent path planning in an uncertain environment using vision-like sensors, i.e., sensors that allow the sensing of the environment non-locally. Examples would include a mobile robot exploring an unknown terrain or a micro-UAV navigating in a cluttered urban environment. We show that the problem of path planning in an uncertain environment, under certain assumptions, can be posed as the adaptive optimal control of an uncertain Markov decision process, characterized by a known, control-dependent system, and an unknown, control-independent environment. The strategy for path planning then reduces to computing the control policy based on the current estimate of the environment, also known as the “certainty-equivalence” principle in the adaptive control literature. Our methodology allows the inclusion of vision-like sensors into the problem formulation, which, as empirical evidence suggests, accelerates the convergence of the planning algorithms. Further we show that the path planning and estimation problems, as formulated in this paper, possess special structure which can be exploited to significantly reduce the computational burden of the associated algorithms. We apply this methodology to the problem of path planning of a mobile rover in a completely unknown terrain.     

On the use of calibration sensors in source localization using TDOA and FDOA measurements

The accuracy of a source location estimate is very sensitive to the presence of the random noise in the known sensor positions. This paper investigates the use of calibration sensors, each of which is capable of broadcasting calibration signals to other sensors as well as receiving the signals from the source and other calibration sensors, to reduce the loss in the source localization accuracy due to uncertainties in sensor positions. We begin the study with deriving the Cramer–Rao lower bound (CRLB) for source localization using time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements when a single calibration sensor is available. The obtained CRLB result is then extended to the more general case with multiple calibration sensors. The performance improvement due to the use of calibration sensors is established analytically. We then propose a closed-form algorithm that can explore efficiently the calibration sensors to improve the source localization accuracy when the sensor positions are subject to random errors. We prove analytically that the newly developed localization method attains the CRLB accuracy under some mild approximations. Simulations verify the theoretical developments.     

Learning accurate very fast decision trees from uncertain data streams

Most existing works on data stream classification assume the streaming data is precise and definite. Such assumption, however, does not always hold in practice, since data uncertainty is ubiquitous in data stream applications due to imprecise measurement, missing values, privacy protection, etc. The goal of this paper is to learn accurate decision tree models from uncertain data streams for classification analysis. On the basis of very fast decision tree (VFDT) algorithms, we proposed an algorithm for constructing an uncertain VFDT tree with classifiers at tree leaves (uVFDTc). The uVFDTc algorithm can exploit uncertain information effectively and efficiently in both the learning and the classification phases. In the learning phase, it uses Hoeffding bound theory to learn from uncertain data streams and yield fast and reasonable decision trees. In the classification phase, at tree leaves it uses uncertain naive Bayes (UNB) classifiers to improve the classification performance. Experimental results on both synthetic and real-life datasets demonstrate the strong ability of uVFDTc to classify uncertain data streams. The use of UNB at tree leaves has improved the performance of uVFDTc, especially the any-time property, the benefit of exploiting uncertain information, and the robustness against uncertainty.     

An accurate compact model for CMOS cross-shaped Hall effect sensors

The paper describes a new design-oriented compact model for horizontal Hall effect devices. This model is based on the physics of semi-conductor and is simplified according to some assumptions verified through experimental results and/or numerical simulations. Compared to existing models, it has the advantage to take most of the first-order effects into account while allowing fast simulations. A procedure to extract the parameters of the model is given and simulation results are compared to experimental data measured on a cross-shaped sensor designed in a standard 0.35 μm CMOS technology. The maximum error between simulation results and experimental data is less than 1%.     

高准确度的传感器建模方法及应用

通过对纸张定量传感器采用插值法、最小二乘法、最佳一致逼近法建立的模型的比较,提出了一种高准确度的传感器建模方法,并对所建模型的统计特性进行了分析,其所建模型已在纸机上应用。该法也可用于其它类传感器模型的建立。     

Hybrid regression and isophote curvature for accurate eye center localization

Multimedia Tools and Applications - The eye center localization is a crucial requirement for various human-computer interaction applications such as eye gaze estimation and eye tracking. However,...     

基于无线传感器网络的室内精确定位算法

提出了一种基于无线传感器网络的用于室内的高精度定位算法。采用最小二乘法对测量地区的环境参数进行拟合,获得适用的损耗模型。并根据室内的实际情况提出了筛选可靠节点和使用高斯模型处理数据的方法,从而挑选出更为合适的接收信号强度指示器（RSSI）值进行定位计算。最后,对计算结果进行优化,进一步提高定位精度。实验表明：和现有的改进Euclidean算法相比,该算法定位精度高,且设备要求低,对于室内定位具有一定实用价值。     

GPS‐level accurate camera localization with HorizonNet

This paper investigates the problem of position estimation of unmanned surface vessels (USVs) operating in coastal areas or in the archipelago. We propose a position estimation method where the horizon line is extracted in a 360° panoramic image around the USV. We design a convolutional neural network (CNN) architecture to determine an approximate horizon line in the image and implicitly determine the camera orientation (the pitch and roll angles). The panoramic image is warped to compensate for the camera orientation and to generate an image from an approximately level camera. A second CNN architecture is designed to extract the pixelwise horizon line in the warped image. The extracted horizon line is correlated with digital elevation model data in the Fourier domain using a minimum output sum of squared error correlation filter. Finally, we determine the location of the maximum correlation score over the search area to estimate the position of the USV. Comprehensive experiments are performed in field trials conducted over 3 days in the archipelago. Our approach provides excellent results by achieving robust position estimates with global positioning system (GPS)‐level accuracy in previously unvisited test areas.     

Toward accurate localization and high recognition performance for noisy iris images

Iris recognition plays an important role in biometrics. Until now, many scholars have made different efforts in this field. However, the recognition performances of most proposed methods degrade dramatically when the image contains some noise, which inevitably occurs during image acquisition such as reflection spots, inconsistent illumination, eyelid, eyelash, hair, etc. In this paper, an accurate iris localization and high recognition performance approach for noisy iris images is presented. After filling the reflection spots using the inpainting method which is based on Navier-Stokes (NS) equations, the Probable boundary (Pb) edge detection operator is used to detect pupil edge initially, which can eliminate the interference of inconsistent illumination, eyelid, eyelash and hair. Besides, the accurate circle parameters are obtained in delicately to reduce the input space of Hough transforms. The iris feature code is constructed based on 1D Log-Gabor filter. Our thorough experimental results on the challenging iris image database CASIA-Iris-Thousand achieve an EER of 1.8272 %, which outperforms the state-of-the-art methods.     

Simultaneous localization and mapping of a wheel-based autonomous vehicle with ultrasonic sensors

An autonomous vehicle should recognize its position when the vehicle is moving, otherwise the vehicle would not operate correctly. In addition, the “kidnapping“ problem sometimes appears when the vehicle starts at an unknown position. The theoretical basis of the solution to this problem, known as simultaneous localization and mapping (SLAM), is now well understood. A number of approaches to SLAM have appeared in the recent literature. The SLAM algorithm consists of two methods. One is a map-building method that makes a map to represent the environment. The other is a mapping method to compute the position of the vehicle in absolute coordinates. However, it is difficult to apply the SLAM algorithm to an actual autonomous vehicle because it needs a certain amount of operating time. In this article, we explain the use of ultrasonic sensors for detecting obstacles in corridors, and a digital magnetic compass, a gyro, and two encoders for immediate localization. Experiments showed that this algorithm can be executed with a high degree of accuracy and reliability in an unknown environment. This algorithm could also solve the “kidnapping“ problem in a fast operating time.     

Managing cohort movement of mobile sensors via GPS-free and compass-free node localization

A critical problem in mobile ad hoc wireless sensor networks is each node’s awareness of its position relative to the network. This problem is known as localization. In this paper, we introduce a variant of this problem, directional localization, where each node must be aware of both its position and orientation relative to its neighbors. Directional localization is relevant for applications that require uniform area coverage and coherent movement. Using global positioning systems for localization in large scale sensor networks may be impractical in enclosed spaces, and might not be cost effective. In addition, a set of pre-existing anchors with globally known positions may not always be available. In this context, we propose two distributed algorithms based on directional localization that facilitate the collaborative movement of nodes in a sensor network without the need for global positioning systems, seed nodes or a pre-existing infrastructure such as anchors with known positions. Our first algorithm, GPS-free Directed Localization (GDL) assumes the availability of a simple digital compass on each sensor node. We relax this requirement in our second algorithm termed GPS- and Compass-free Directed Localization (GCDL). Through experimentation, we demonstrate that our algorithms scale well for large numbers of nodes and provide convergent localization over time, despite errors introduced by motion actuators and distance measurements. In addition, we introduce mechanisms to preserve swarm formation during directed sensor network mobility. Our simulations confirm that, in a number of realistic scenarios, our algorithms provide for a mobile sensor network that preserves its formation over time, irrespective of speed and distance traveled. We also present our method to organize the sensor nodes in a polygonal geometric shape of our choice even in noisy environments, and investigate the possible uses of this approach in search-and-rescue type of missions.     

Mobile robot localization based on effective combination of vision and range sensors

Most localization algorithms are either range-based or vision-based, but the use of only one type of sensor cannot often ensure successful localization. This paper proposes a particle filter-based localization method that combines the range information obtained from a low-cost IR scanner with the SIFT-based visual information obtained from a monocular camera to robustly estimate the robot pose. The rough estimation of the robot pose by the range sensor can be compensated by the visual information given by the camera and the slow visual object recognition can be overcome by the frequent updates of the range information. Although the bandwidths of the two sensors are different, they can be synchronized by using the encoder information of the mobile robot. Therefore, all data from both sensors are used to estimate the robot pose without time delay and the samples used for estimating the robot pose converge faster than those from either range-based or vision-based localization. This paper also suggests a method for evaluating the state of localization based on the normalized probability of a vision sensor model. Various experiments show that the proposed algorithm can reliably estimate the robot pose in various indoor environments and can recover the robot pose upon incorrect localization. Recommended by Editorial Board member Sooyong Lee under the direction of Editor Hyun Seok Yang. This research was conducted by the Intelligent Robotics Development Program, one of the 21st Century Frontier R&D Programs funded by the Ministry of Knowledge Economy of Korea. Yong-Ju Lee received the B.S. degree in Mechanical Engineering from Korea University in 2004. He is now a Student for Ph.D. of Mechanical Engineering from Korea University. His research interests include mobile robotics. Byung-Doo Yim received the B.S. degree in Control and Instrumentation Engineering from Seoul National University of Technology in 2005. Also, he received the M.S. degree in Mechatroncis Engineering from Korea University in 2007. His research interests include mobile robotics. Jae-Bok Song received the B.S. and M.S. degrees in Mechanical Engineering from Seoul National University in 1983 and 1985, respectively. Also, he received the Ph.D. degree in Mechanical Engineering from MIT in 1992. He is currently a Professor of Mechanical Engineering, Korea University, where he is also the Director of the Intelligent Robotics Laboratory from 1993. His current research interests lie mainly in mobile robotics, safe robot arms, and design/control of intelligent robotic systems.     

一种新型煤矿井下巷道精确定位方法

针对现有煤矿井下基于电磁波的定位系统存在定位精度不高的问题,提出了基于可见光通信的煤矿井下精确定位方法。该方法应用可见光通信技术,采用LED光源广播自身位置信息,依据移动终端接收光信号的强度,计算出移动终端与特定LED光源点的距离,进行二维和三维的精确定位,定位精度可以达到几厘米。相比电磁波定位手段,该方法有效克服了多径干扰现象,定位效果显著。     

用等精度测频方法实现振弦式传感器频率测量

介绍一种用等精度测频实现振弦式传感器频率测量的实用方法 ,该方法能在较大频段范围内实现等精度 ,具有很高的分辨力 ,并且频率测量的分辨力可按要求方便调节。     

An indoor localization solution using Bluetooth RSSI and multiple sensors on a smartphone

Multimedia Tools and Applications - In this paper, we propose an indoor positioning system using a Bluetooth receiver, an accelerometer, a magnetic field sensor, and a barometer on a smartphone....     

On stabilization of uncertain dynamic nonholonomic systems

This paper considers the stabilization problem of uncertain dynamic nonholonomic systems. New robust and adaptive robust control laws are presented with an aim to stabilize the system to the origin with a simple design procedure and no extensive online computations. The designed controllers have been implemented in a nonholonomic wheeled mobile robot, and the application are discussed. Simulation study demonstrates the effectiveness of the proposed method.     

非确定性数据库中空值处理

尽管关系数据库有很多优势,但它缺乏一种处理非确定性数据的能力.目前,已经提出了几种将非确定性结合到关系数据库模型的方法,它们对关系数据库模型做了诸多扩展.但空值问题依旧存在,一些模型根本就没有考虑空值因素.这违背了非确定性数据库要更加真实地反应现实世界的初衷.为此,给出了一种非确定性数据库系统中空值处理方法,改进现有非确定性数据库模型中对空值处理不完善的情况.     

On buckling optimization under uncertain loading combinations

This paper proposes a technique to optimize structural components for buckling when the applied loads are partially unknown or unpredictable. As opposed to the traditional buckling optimization situation where the loading configuration is specified, the load ratios are assumed uncertain and are incorporated as variables in the optimization problem formulation. As a result, the optimal designs obtained are insensitive to load variations within an admissible convex set. Additionally, in order to generalize the results and therefore provide a systematic solution procedure, a theorem concerning the shape of the stability boundary of structures whose buckling loads are the solution of linear eigenproblems is stated and proven. Recevied February 2, 2000