基于运动捕捉系统的UWB室内定位精度标定方法

为了对UWB(Ultra Wide-Band)室内定位系统精度进行有效的评估,提出了一种基于运动捕捉系统的UWB室内定位精度标定方法。该方法的实现是基于两个实验完成的。其一是使用全站仪对运动捕捉系统进行精度验证,通过布尔莎七参数坐标转换模型对实验数据进行处理,表明了运动捕捉系统的精度满足要求,可以用于标定UWB室内定位系统的精度。其二是通过运动捕捉系统来标定UWB室内定位系统的精度,以运动捕捉系统采集的数据为真值,将UWB室内定位系统采集的数据与运动捕捉系统采集的数据进行比较,得出的标定结果符合实际情况,表明了标定方法是有效和可靠的。

基于KFCM-LMC-LSSVM算法的WLAN室内定位方法

针对WLAN室内定位采集指纹点工作量大且定位精度不高的问题,提出一种基于核模糊C均值聚类(kernelized fuzzy C-means,KFCM)、低秩矩阵填充(low-rank matrix completion,LMC)及最小二乘支持向量机(least squares support vector machine,LSSVM)的室内定位算法。首先将指纹点利用KFCM算法进行聚类,使待测点定位到一个区域内。在该区域里运用LMC理论,重构出具有高密度指纹点的指纹库。最后利用LSSVM定位出待测点的物理位置。实验结果表明,采用KFCM-LMC-LSSVM算法不仅减少了构建指纹库的工作量,而且提高了定位精度。

电信大客户WIFI商业应用技术验证

本文针对电信大客户WIFI商业应用进行了技术验证,其中包括无感知上网认证对于手机终端接入WIFI网络的时间的加速效果,以及利用大数据精准营销方式后,用户对于针对性内容的页面点击率,最后验证了上海某商业楼宇室内定位的精度指标,从而有效评估了电信大客户WIFI商业应用技术的实际运行效果,并为下一步电信大客户WIFI商业应用平台推广积累宝贵的实战经验。

A Precise RFID Indoor Localization System with Sensor Network Assistance

Indoor localization is very critical for medical care applications, e.g., the patient localization or tracking inside the building of the hospital. Traditional Radio Frequency Identification(RFID) technologies are very popular in this area since their cost is very low. In such technologies, each tag acts as the transmitter and the Radio Signal Strength Indicator(RSSI) information is measured from the readers. However, RSSI information suffers severely from the multi- path phenomenon. As a result, if in a very large area, the localization accuracy will be affected seriously. In order to solve this problem, we introduce Wireless Sensor Networks(WSNs) with only a few nodes, each of which acts as both transmitter and receiver. In such networks, the change of signal strength(referred as dynamic of RSSI) is leveraged to select a cluster of reference tags as candidates. Then the fi nal target location is estimated by using the RSSI relationships between the target tag and candidate reference tags. Thus, the localization accuracy and scalability are able to be improved. We proposed two algorithms, SA-LANDMARC, and COCKTAIL. Experiments show that the localization accuracy of the two algorithms can reach 0.7m and 0.45 m, respectively. Compared to most traditional Radio Frequency(RF)-based approaches, the localization accuracy is improved at least 50%.

Compressive Sensing Based Wireless Localization in Indoor Scenarios

The sparse nature of location finding in the spatial domain makes it possible to exploit the Compressive Sensing (CS) theory for wireless location.CS-based location algorithm can largely reduce the number of online measurements while achieving a high level of localization accuracy,which makes the CS-based solution very attractive for indoor positioning.However,CS theory offers exact deterministic recovery of the sparse or compressible signals under two basic restriction conditions of sparsity and incoherence.In order to achieve a good recovery performance of sparse signals,CS-based solution needs to construct an efficient CS model.The model must satisfy the practical application requirements as well as following theoretical restrictions.In this paper,we propose two novel CS-based location solutions based on two different points of view:the CS-based algorithm with raising-dimension pre-processing and the CS-based algorithm with Minor Component Analysis (MCA).Analytical studies and simulations indicate that the proposed novel schemes achieve much higher localization accuracy.

Low Power Sensor Design for IoT and Mobile Healthcare Applications

This paper reviews recent advances in radar sensor design for low-power healthcare,indoor real-time positioning and other applications of IoT.Various radar front-end architectures and digital processing methods are proposed to improve the detection performance including detection accuracy,detection range and power consumption.While many of the reported designs were prototypes for concept verification,several integrated radar systems have been demonstrated with reliable measured results with demo systems.A performance comparison of latest radar chip designs has been provided to show their features of different architectures.With great development of IoT,short-range low-power radar sensors for healthcare and indoor positioning applications will attract more and more research interests in the near future.

Precise Transceiver-Free Localization in Complex Indoor Environment

Transceiver-free object localization can localize target through using Radio Frequency(RF) technologies without carrying any device, which attracts many researchers' attentions. Most traditional technologies usually first deploy a number of reference nodes which are able to communicate with each other, then select only some wireless links, whose signals are affected the most by the transceiver-free target, to estimate the target position. However, such traditional technologies adopt an ideal model for the target, the other link information and environment interference behavior are not considered comprehensively. In order to overcome this drawback, we propose a method which is able to precisely estimate the transceiver-free target position. It not only can leverage more link information, but also take environmental interference into account. Two algorithms are proposed in our system, one is Best K-Nearest Neighbor(KNN) algorithm, the other is Support Vector Regression(SVR) algorithm. Our experiments are based on Telos B sensor nodes and performed in different complex lab areas which have many different furniture and equipment. The experiment results show that the average localization error is round 1.1m. Compared with traditional methods, the localization accuracy is increased nearly two times.