Multi-cluster-center based filtering algorithm and its application to WLAN indoor positioning

Wireless local area network(WLAN) is developing to a ubiquitous technique in daily life.As a related product,WLAN based indoor positioning system is attracting more and more concern.Fingerprint is a mainstream method of wireless indoor positioning.However,it still has some shortcomings of that received signal strength(RSS) is multi-modal and sensitive to environmental factors.These characters would have a negative effect on the performance of positioning system.In this paper,a filtering algorithm based on multi-cluster-center is proposed.We make full use of this algorithm to optimize the training samples at off-line phase to improve the performance of non-linear fitting with the fingerprint feature,and further enhance the positioning accuracy.Finally,we use multiple sets of original WLAN signal samples and signal samples after filtering as the training input of positioning system respectively.After that,the results analysis is demonstrated.Simulation results show that it is a reliable algorithm to enhance the performance of WLAN indoor positioning.     

基于无线局域网信号的城区室内外无缝定位技术

无缝定位技术被认为是移动位置服务的支撑技术,讨论了基于无线局域网（WLAN）的城区室内外无缝定位技术,利用室外公众电话亭WLAN和室内普通WLAN信号,基于实际环境建模的简化无线信号路径衰减模型,通过Cell-ID排序和交会算法,实现在城市室外环境2.1～25 m、室内环境下0.8～4.2 m的定位精度,此技术可应用于城市室内外无缝定位服务。     

Coordinate correction algorithm for WLAN indoor positioning

In the fingerprint matching-based wireless local urea network (WLAN) indoor positioning system,Kalman filter (KF) is usually applied after fingerprint matching algorithms to make positioning results mo...     

Semi-Supervised Clustering Fingerprint Positioning Algorithm Based on Distance Constraints

With the rapid development of WLAN (Wireless Local Area Network) technology, an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online computation. In this paper, it proposes a novel fingerprint positioning algorithm known as semi-supervised affinity propagation clustering based on distance function constraints. We show that by employing affinity propagation techniques, it is able to use a fractional labeled data to adjust similarity matrix of signal space to cluster reference points with high accuracy. The semi-supervised APC uses a combination of machine learning, clustering analysis and fingerprinting algorithm. By collecting data and testing our algorithm in a realistic indoor WLAN environment, the experimental results indicate that the proposed algorithm can improve positioning accuracy while reduce the online localization computation, as compared with the widely used K nearest neighbor and maximum likelihood estimation algorithms.     

基于图像关键帧的Visual-Depth Map建立方法

对于室内视觉定位系统,需要在离线阶段建立Visual Map数据库用来存储图像信息,在线阶段用户通过与Visual Map数据库进行比对来完成用户位置的估计.离线阶段建立的数据库可以采用逐点采样或视频流采样的方式.但是无论何种方式,考虑到数据库中图像信息的相似性,传统方式建立的数据库中存储图像有较多冗余,导致增加了在线阶段的定位时间开销.因此,本文根据Visual Map中的相邻图像间的相似性,提出了一种基于图像关键帧的Visual-Depth Map建立方法,有效地减少了离线数据库的规模.在离线阶段,本文使用Kinect传感器同时获得图像信息和深度信息;然后,通过基于图像相似度的图像关键帧算法对原始图像序列进行筛选,得到关键帧序列,从而实现Visual-Depth Map的建立.在线阶段,用户可以直接输入查询图像与Visual-Depth Map中的图像序列进行检索匹配,找到相似度较高的匹配图像,再通过EPnP算法进行2D-3D的位姿估计,完成用户位置的计算.实验证明,本文所提方法可以在保证较高定位精度的前提下,有效减少离线数据库规模,降低在线阶段的定位时间开销.     

基于生成对抗网络模型的RSS缺失值预测

无线上网（Wi-Fi）定位是目前室内定位中的主流方法,指纹数据库的构建是Wi-Fi定位系统的关键.然而指纹数据库中的接收信号强度（RSS）指纹值会随室内环境的变化而变化,通常需要不断地重新测量指纹值去更新指纹数据库,这就导致了成本高、耗时长,尤其是在定位区域较大的动态环境中是不切实际的.针对此问题,提出了自适应上下文生成对抗网络模型.该模型只需测量指纹数据库中的部分RSS指纹,即"参考点",然后通过学习参考点的分布情况,预测特定位置的缺失指纹.仿真实验结果表明,室内定位精确性显著提高,人力成本大大减少.     

辅助北斗导航中参考基站服务范围评估研究

为了提高移动定位性能,增强北斗导航系统的应用能力,降低移动网络的投资成本,提出了一种新的辅助卫星移动定位中参考基站服务范围的评估方法,并对辅助北斗导航中参考基站的服务范围进行了评估。首先利用STK建立北斗导航系统模型,模拟出实际的应用场景;然后在该场景下通过改变参考基站与UE的距离,分别计算出二者观测到同一卫星的区间链路数据;最后通过观测到同颗卫星的持续时长分析出参考基站的服务范围。结果表明,参考基站服务范围对基站与UE观测同一颗卫星的可见时长有着较强的影响,当参考基站服务范围超过300km时,就会严重影响到北斗的辅助性能。研究结果为辅助北斗移动定位业务的开展提供先验信息。     

异构网络中多媒体业务的联合呼叫接纳控制

提出了3G与WLAN互连的异构网络中一种多媒体业务的联合呼叫接纳控制算法,针对不同类型用户的业务特性选择合适的优先接入网络,考虑到切换用户及不同业务类型用户的优先级,分别在3G和WLAN中设置了多级的接入门限值．运用多维Markov链模型对该算法的新呼叫阻塞率和切换呼叫掉线率性能进行了理论分析．仿真结果表明,本算法在满足不同类型用户的QoS需求的同时,在新呼叫阻塞率和切换呼叫掉线率之间获得了良好的折中,提高了系统整体性能．     

基于RSSI辅助的精确测距混合定位算法

针对传统的超宽带（UWB）室内定位方法中,UWB信号极易被遮挡,满足不了3个以上有效测距信息,导致无法精确定位的问题,提出了一种基于接收信号强度（RSSI）辅助的精确测距混合定位算法。该算法通过对数正态模型,将无线网络（wireless LAN,WLAN）中测量的RSSI转换为距离信息,通过构建距离差的代价函数,结合单个UWB基站的精确测距,利用搜索方法,实现了在多个RSSI测量值辅助下,一个UWB测距基站便可完成精确定位。该算法与三边测量定位中的最小二乘估计算法和最大似然估计算法对比,定位精度在任意网络环境下均优于最小二乘估计算法,且在定位精度相似的情况下,计算量远少于最大似然估计算法。     

Indoor positioning algorithm based on Markov Monte Carlo

The interference in the received power leads to the problem of the low estimation accuracy of WLAN indoor positioning, so a new method is proposed which constructs the maximum likelihood model and uses the Markov Chain Monte-Carlo sampling method to estimate position coordinates. The method considers taking the interference factor into the model, and uses the random sampling method to solve the estimation problem, which has the advantage of fast convergence and high estimate precision. Furthermore, the Cramer-Rao lower bound (CRLB) of the model is derived. In simulation experiment, the distribution of Cramer-Rao Bound in locating space is given. Finally, simulations show that the MCMC method can estimate the target location accurately. Under the same simulation conditions, the MCMC method achieves greater estimated precision and has a lower computational complexity than the Fletcher-Reeves Method (FR).     

一种面向室内环境变动的人员目标无源定位算法

现有的基于接收信号强度(RSS)的人员目标无源室内定位算法在定位环境变动的情况下难以兼顾人工工作量、时间消耗和定位准确率。针对这个问题,本文提出了基于迁移聚类和坐标融合的变分自编码器(FusVAE)的室内环境变动下人员目标无源定位算法。在环境变动后,采集少量无标签RSS样本,然后使用本文提出的基于度量学习的半监督模糊C均值聚类(SFCMML)对其进行精确聚类和标签标注,对原有的定位模型进行重训练,只需很小的人工和时间代价就可以使原定位模型在新环境下也具有较高的定位准确率。同时,针对变动后环境下采集RSS样本较少的问题,本文提出了基于坐标融合的变分自编码器(FusVAE),对新环境下的RSS样本进行数据增强,丰富了RSS样本的数量和质量,提高了定位模型的泛化能力。实验结果表明,在环境变动的情况下,本文提出的算法的平均定位准确率可达88.6%,和同领域同类型算法相比,具有较高的定位精度和较好的环境变动适应性,更适用于变动环境下的人员目标无源室内定位问题。     

基于高斯过程回归与硬件优化的可见光室内定位设计

为了使可见光室内定位操作更加简单,不依赖于视线视图和位置登记,在硬件方面,使用光电二极管作为接收器,通过组装兼容的光电二极管和紧凑型LED完成硬件方面的优化。在软件算法方面,基于指纹技术,使用高斯过程回归（GPR）算法进行定位。首先对GPR环境进行建模,构建一个强度分布模型。然后,使用贝叶斯理论获得定位的表达形式。实验在3m×3m的平台区域上进行,对25个目标进行测试。与类似的室内定位系统相比,所提系统定位精度更高,实现了厘米级定位。且可以长时间精确定位运动目标,鲁棒性较好。     

基于INS定位和WIFI指纹定位技术的无人机室内定位方法研究

针对无人机室内定位问题,提出了一种基于惯性导航系统（INS）和WIFI指纹定位技术的四旋翼无人机的室内定位方法。该方法首先利用WIFI模块测量的信号接收强度信息,根据WIFI指纹定位原理求出该定位时刻下的无人机位置;同时,利用惯性传感器信息计算出INS定位下的无人机位置,结合两种定位结果得出更为精确的位置。最后,通过实验验证了INS定位和WIFI指纹定位相结合的无人机室内定位方法的有效性,且该方法较两种单独定位方法的定位精度更高。     

Vision-based adaptive control of a 3-RRR parallel positioning system

The macro positioning stage with high-precision and rapid positioning ability plays a crucial role in the macro-micro combination positioning system. In this paper, we develop a practical method for the control of a 3-RRR planar positioning system using online vision measurement as feedback. In this method, a monocular vision system is established to accomplish high-precision online pose measurement for the 3-RRR manipulator. Additionally, a robust and operable adaptive control algorithm, which incorporates a fuzzy controller and a PI controller, is employed to achieve precise and rapid positioning of the 3-RRR positioning system. A series of experiments are conducted to verify the positioning performances of the proposed method, and a conventional PI control algorithm is utilized for comparison. The experimental results indicate that using the proposed control approach, the parallel positioning system obtains high precision and shows higher efficiency and robustness, especially for the time-varying positioning system.     

基于改进MSCKF的无人机室内定位方法

针对无人机室内定位容易出现漂移的问题,提出基于改进多状态约束卡尔曼滤波器（MSCKF）的无人机（UAV）室内定位方法. 该方法在MSCKF的框架下,提出高鲁棒性、低时延的标志点检测方法. 利用在世界坐标系下坐标已知的标志点计算得到无人机位姿,实现惯性测量单元(IMU)信息与单目视觉信息融合以及无人机位姿修正. 对提出的定位方法进行测试. 测试结果表明,该方法的定位误差小于0.266 m,与OpenVins和LARVIO开源算法相比,定位精度提高了54.6%以上.     

室内智能移动机器人ZigBee无线网络定位技术

针对目前室内机器人定位方法不能同时满足定位精度、定位范围和复杂环境条件下定位的情况,提出了一种利用ZigBee无线网络、基于接收到信号强度测距的定位方法。研究通过搭建一个ZigBee无线网络室内定位系统,实时提供机器人的位置坐标,为机器人实现路径规划提供保障。通过实验对定位系统中的各项参数进行标定,并分析了影响定位精度的因素。实验结果表明,ZigBee无线网络定位系统在室内复杂环境下可实现定位,在无障碍物的条件下精度可达到0.25 m,可以满足室内机器人的定位精度要求,定位范围可根据实际需求进行扩展。     

基于模拟退火聚类的室内定位算法

针对大型室内场景下现有指纹定位算法运算复杂度高、定位精度低的问题,提出一种基于模拟退火聚类的室内定位算法。该算法采用模拟退火聚类的方法完成对指纹空间的聚类和划分,有效降低了指纹匹配所需的候选指纹数量,并消除了具有一定特征相似性的奇异点,从而降低了运算复杂度,提高了定位精度。实验结果表明,该算法可计算出定位环境下指纹空间的最优聚类数从而确保定位精度,较K 均值聚类定位算法和KNN算法定位精度高,且定位精度不受初始值影响。     

Visible light assisted relay communication system and a power allocation scheme

When the indoor visible light communication link is interrupted due to occlusion blocking, the signal needs to be forwarded through the auxiliary relay, and therefore, an indoor visible light communication auxiliary relay system model is established. First, the positional constraint relationship of indoor light source on the roof when there is link blockage in the indoor visible light communication system is analyzed by using the office ceiling light as the signal light source and relay, and the asymmetrically multiplexing DC offset optical orthogonal frequency division multiplexing is used to realize the auxiliary relay signal multiplexing, which ensures the occlusion signal relay transmission while maintaining its target terminal. Then the genetic algorithm is used to optimize the distribution of the power at the source and the relay to optimize the system performance, and the BER performance of the equal power allocation scheme is compared in the occlusion case. The results show that when the direct communication link is blocked, the system loss of the auxiliary relay system with the optimal power allocation scheme is 3 dB less than the system with equal power allocation.     

漫游场景下HeNB和WLAN间的跨层无缝切换方案

家庭基站是一种小型低功率蜂窝基站,用于室内场所的无线接入．它和无线局域网技术都具有相同的无线接入功能,在并存的情况下不可避免地会发生切换．为了对这两种技术进行更好的融合,在漫游场景下,提出一种无线局域网和家庭基站间的跨层无缝切换方案．该方案使用预认证的方法,并对已有的预注册方案进行改进,使得切换过程拥有较低的时延,同时使用包存储转发功能保证了低丢包率．给出了具体的信令流程．最后,对本方案和其他方案在通信开销,切换时延以及丢包率等方面进行了详细的比较分析．分析结果表明,本方案的通信开销、切换时延和丢包率都小于现有的两种主流方案．     

Algorithm for recursive Bayesian localization triggered by temporalseries measurement information

To improve the robustness of a position system and reduce the localization error, this paper proposes a fingerprint positioning method based on the recursive Bayesian. To solve the blindness and unreliability of the location fingerprint data in an offline phase, the fingerprint database based on the sample variance is developed to measure the confidence of sampling values and reduce the impact of environmental factors, improving the reliability for online localization. The proposed method provides the target position at the current moment by utilizing the Markov model that is established by the constraint relationship between moments in the source movement, which avoids the jump problem of the position estimation and poor robustness and improves the localization accuracy. Extensive experimental results demonstrate that the average localization error norm of the proposed algorithm is no more than 0.927m, indicating significantly lower errors than other traditional schemes (often by more than 30 percent).