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.     

Stereo-camera-based object detection using fuzzy color histograms and a fuzzy classifier with depth and shape estimations

This paper proposes a new method of detecting an object containing multiple colors with non-homogeneous distributions in complex backgrounds and subsequently estimating the depth and shape of the object using a stereo camera. To extract features for object detection, this paper proposes fuzzy color histograms (FCHs) based on the self-splitting clustering (SSC) of the hue-saturation (HS) color space. For each scanning window in a pyramid of scaled images, the FCH is obtained by accumulating the fuzzy degrees of all of the pixels belonging to each cluster. The FCH is fed to a fuzzy classifier to detect an object in the left image captured by the stereo camera. To find the matched object region in the right image, the left and right images are first segmented using the SSC-partitioned HS space. The depth of the object is then found by performing stereo matching on the segmented images. To find the shape of the object, a disparity map is built using the estimated object depth to automatically determine the stereo matching window size and disparity search range. Finally, the shape of the object is segmented from the disparity map. The experimental results of the detection of different objects with depth and shape estimations are used to verify the performance of the proposed method. Comparisons with different detection and disparity map construction methods are performed to demonstrate the advantage of the proposed method.     

指纹巡检系统在煤矿井下的使用

为进一步加强煤矿企业安全生产工作,基于瓦斯检查人员按照巡检图表执行瓦斯检查过程中,存在＂空班漏检＂的情况,神州煤业公司引入指纹巡检系统,利用指纹识别身份,通过指纹和设备绑定、巡检点和地址卡绑定,保证巡检人员的真实性、巡检数据的唯一性,有效防止巡检人员＂空班漏检＂等行为,使瓦斯＂一班三检＂制度落到实处。     

基于块方向图的指纹中心点定位

提出了一种基于块方向图的指纹中心点定位方法,该方法先利用灰度梯度法得到指纹块方向,再利用块搜索得到指纹的大致中心点位置,最后根据块的Poincare index算法得到指纹确定的中心点位置,该算法简单、快速、鲁棒性好。     

第三代核主泵屏蔽电机的关键部件金属材料国产化综述

材料的国产化是设备国产化的基础,第三代核主泵屏蔽电机部分兲键材料在国内均为首次制造,能否实现国产化将会成为制约主泵电机国产化的瓶颈问题。为推迚主泵电机兲键材料的国产化迚程,同时满足后续项目的需求,依托国家科技重大专项,对部分兲键材料的国产化研究迚行了专项立项研究,取得了突破性的迚展。通过与联合单位的合作,迚行了材料试制及试验研究,实现了屏蔽套、电机轴,飞轮护环,高强度支撑筒及推力盘等兲键部件材料的国产化制造,为核主泵电机的国产化奠定了材料基础。本文主要论述了国家重大专项立项研究的兲键部件金属材料国产化情况。     

Energy-efficient WiFi scanning for localization

WiFi radio signals are commonly used for the localization of mobile devices. However, frequent scanning of WiFi reduces user experiences because it consumes significant energy on mobile devices. In this paper, we propose an energy-efficient WiFi scan system for localization. The proposed system preserves the quality of service of each application and minimizes the energy consumption of WiFi scanning. The main idea is that the system adaptively controls the dwell time of beacon-listening in given locations. We predict the number of scanned access points (APs) according to locations and optimize the dwell time of beacon-listening to obtain the minimum number of scanned APs. The evaluation shows that the proposed system saves the energy consumption of WiFi scans by 33.6% and 45.7%, according to the number of scanned APs, while not decreasing the accuracy of localization in indoor and outdoor scenarios.     

Enhanced radio tomographic imaging with heterogeneous Bayesian compressive sensing

This work explores the novel use of Bayesian compressive sensing (BCS) in radio tomographic imaging (RTI), which aims at addressing the performance degradation of shadow fade imaging due to multipath interferences, through the sophisticated efforts on enhancing BCS with the capability of heterogeneous-noise-variance learning. Our contribution is twofold. Firstly, we incorporate a hierarchical model of heterogeneous noise variances into sparse Bayesian learning, which can contribute to the enhancement of BCS in terms of noise-variance awareness. Then, under our enhanced BCS (namely heterogeneous BCS) framework, we develop two learning algorithms for the RTI reconstruction. Theoretical analysis will show the potential advantages of using our heterogeneous BCS in mitigating the effect of multipath interferences, as well as in improving the RTI performance with our learning algorithms. Finally, the experimental results in the context of device-free localization and tracking are reported to demonstrate the effectiveness of the proposed approach.     

Received signal strength based positioning for multiple nodes in wireless sensor networks

We address the problem of locating multiple nodes in a wireless sensor network with the use of received signal strength (RSS) measurements. In RSS based positioning, transmit power and path-loss factor are two environment dependent parameters which may be uncertain or unknown. For unknown transmit powers, we devise two-step weighted least squares (WLS) and maximum likelihood (ML) algorithms for node localization. The mean square error of the former is analyzed in the presence of zero-mean white Gaussian disturbances. When both transmit powers and path-loss factors are unavailable, two nonlinear least squares estimators, namely, the direct ML approach and combination of linear least squares and ML algorithm, are developed. Numerical examples are also included to evaluate the localization accuracy of the proposed estimators by comparing with two existing node positioning methods and the Cramér–Rao lower bound.