Particle Penetration through Inclined and L-Shaped Cracks

This paper presents a particle penetration model predicting particle penetration coefficient (Pp) through a narrow crack of arbitrary incline angles (θ). The objective was to simulate Pp for outdoor-to-indoor particle penetration for residential infiltration conditions. This model assumes laminar infiltration flow and considers particle deposition from both gravitational sedimentation and Brownian diffusion. For micron-sized particles, modeling results indicate that gravitational sedimentation is the major deposition mechanism. Pp increases monotonically with ∣θ∣ because effective particle sedimentation velocity (vs?cos?θ) decreases monotonically with ∣θ∣. For submicron-sized particles (0.1?μm), Brownian diffusion is the major particle deposition mechanism. Because Brownian diffusion is a nondirectional deposition mechanism, crack inclination did not affect Pp. This study applied this model to estimate Pp for L-shaped cracks, and validated modeling results with experiments. Experimental results indicated that inertial impaction and crack entrance cutoff effects were not significant particle deposition mechanisms for the test micron-sized particles. Gravitational sedimentation was the major deposition mechanism. An L-shaped crack can be simulated as the combination of horizontal and vertical sections. This model agreed reasonably with experimental results.     

Wi-Fi fingerprinting based on collaborative confidence level training

Wi-Fi fingerprinting has been a popular indoor positioning technique with the advantage that infrastructures are readily available in most urban areas. However wireless signals are prone to fluctuation and noise, introducing errors in the final positioning result. This paper proposes a new fingerprint training method where a number of users train collaboratively and a confidence factor is generated for each fingerprint. Fingerprinting is carried out where potential fingerprints are extracted based on the confidence factor. Positioning accuracy improves by 40% when the new fingerprinting method is implemented and maximum error is reduced by 35%.     

智能停车场管理系统的探讨

针对传统停车场存在着收费管理混乱及整体运作效率低等问题,设计了一种基于RFID的智能停车场管理系统。介绍了系统的基本功能、总体结构,并对RFID技术、车位引导技术和车牌自动识别技术进行了详细的探讨。     

一种基于稀疏表示的WLAN室内定位算法

随着科技进步和人民生活水平的提高,越来越多的用户对定位技术需求变得日益迫切。基于WLAN的室内定位技术研究在此背景下应运而生,但是该技术容易受非视距离以及多径影响。而位置指纹算法有效地克服了上述缺点,并得到了广泛应用。提出一种基于稀疏表示的WLAN室内定位算法,以解决位置指纹算法K近邻方法中参数选择问题、不能综合利用全局参考点信息问题,并对其进行了实验仿真。     

基于众包的嵌套流形匹配室内定位方法

随着普适应用的兴起,室内定位变得越来越重要。传统的基于指纹的定位方法需要现场勘测,所需时间及工作量巨大,且需实时更新,以适应室内变化,这大大限制了其应用范围。采用众包形式进行室内信息采集,并记录其在室内的大量路径信息,利用嵌套在路径中的低维流形一致性进行地理位置匹配,以建立位置指纹库。通过高斯粒子滤波器对传感器数据进行去噪,进而解决步长差异问题。定位时,根据用户位置的连续性和路径信息筛选出合理的近邻点,继而实现精确定位。在84m²的会议室进行实验,在不需要现场勘测的情况下,所提方法可达到与传统方法可比的定位精度。该方法可以实时适应环境变化,在2周甚至1个月之后,其定位准确性优于传统定位方法。     

基于RFID的蛛网模型定位算法设计

针对当前室内定位算法所遇到的精度问题,在LANDMARC系统的基础上提出了一种基于有源RFID的蛛网定位算法。文中介绍了蛛网模型的建立,详细阐述了蛛网定位系统的定位原理以及算法的实现,并通过仿真实验对蛛网定位算法的误差进行了分析,论证了该算法的可行性和精确性。     

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.     

MRILoc: Multiresolution Indoor Localization from crowdsourced samples

The demand of Wi-Fi fingerprinting-based indoor localization has been growing steadily for its open-source and low-cost infrastructure. Since site survey is tedious and costly, crowdsourcing is becoming a typical practice for fingerprint database construction. However, erroneous location annotation and non-uniform sample density need to be addressed in sample crowdsourcing. Furthermore, the traditional massive fingerprint databases in large and complex indoor environments require longer processing time and extensive computational complexity, limiting their efficiency and scalability. We propose a Multiresolution Indoor Localization from crowdsourced samples (MRILoc) system to address these challenges. The MRILoc consists of three major offline modules and a new online hierarchical and multiresolution localization algorithm. Firstly, we designe a Sample Reliability Measure Algorithm (SRMA) to identify reliable crowdsourced samples. Secondly, we construct weighted surfaces using reliable fingerprints and downsample fingerprints at the center of grids to resolve the issue of the non-uniform sample density. Thirdly, we construct hierarchical training databases for multiresolution localization. Our online algorithm integrates the K-Nearest Neighbors (KNN) to classify a test sample in different resolution subareas and uses XGBoost regression for the final exact localization approximation if necessary. To evaluate the performance of the proposed method, we conduct experiments on two field measurement datasets. The experiment results show a high average hitting rate and 19% localization accuracy improvement over the peer schemes.     

一种面向室内定位的基站选择优化方法

基于蜂窝网的室内定位由于与通信网络共用基础设施,因此具有覆盖范围广、无需基础设施再投资等突出优点,已成为电信运营商级室内定位的首选,是5G通信领域的研究热点之一。在蜂窝网室内定位场景中,基站的布局将直接影响接收首径的数量、到达时间TOA(Time of Arrivaling)和测量误差等要素,从而影响定位精度。据此,文中提出一种面向室内定位的基站选择优化方法,以减小由于基站布局引入的误差。首先,引入TOA信息去除TDOA定位的虚定位点;其次,针对不同基站选择方案得到的定位结果,利用二次聚类的思想去除孤立点,并根据聚类结果中样本节点数量最多的类确定定位点的位置。实验结果表明,与其他优化方法相比,所提方法的室内定位平均误差降低了15.49%。