基于RSSI和惯性导航的融合室内定位算法

针对目前对高精度室内定位算法的需求,提出一种基于接收信号强度识别（RSSI）和惯性导航的融合室内定位算法。基于无线传感网中ZigBee节点的RSSI值,采用位置指纹识别算法,对网络中的未知节点进行定位。结合惯性传感单元（IMU）提供的惯性数据,对RSSI定位结果进行融合修正。利用Kalman滤波器,采用状态方程描述待定位节点位置坐标的动态变化规律,从而实现一种以无线传感网络定位为主、IMU为辅的融合定位方法。仿真结果表明,提出的融合定位算法既能改善单独使用RSSI定位受环境干扰较大的问题,又能避免单独使用惯性导航带来的累积误差,极大地提高了定位精度。     

Task distribution algorithm based on community in mobile crowd sensing

A community-based task distribution algorithm was proposed to solve the problem of the validity of mobile crowd sensing (MCS) task distribution.By calculating the minimum spanning tree (MST),the connection parameter (CP) and the community convergence degree (CI) between the mobile nodes,the behavior patterns of the users were abstracted and identified to rationally divide the nodes into different communities.Then,the eigenvalue matching degree of the community behavior patterns with the sensing task was calculated.According to the matching degree,the distribution of the corresponding tasks was completed by the central node of the community.The simulation results show that the proposed method can effectively improve the accuracy of the task distribution and the task completion rate,and save the time cost of the task completion.     

Incentive mechanism based on auction model for mobile crowd sensing network

The selfishness and uncertainty of user behaviors in the mobile crowd sensing network make them unwilling to participate in sensing activities,which may result to a lower sensing task completion rate.To deal with these problems,an incentive mechanism based on auction model was proposed.In order to maximize the utility of each user,the proposed incentive method based on reverse auction (IMRA) leveraged a task-centric method to choose winners,and payed them according to a critical-price strategy.Furthermore,the proposed user-bidirectional interaction incentive mechanism (UBIM) helped drop-out users (buyers) to transfer their unfinished tasks to new users.Simulation results show that,compared with TRAC and IMC-SS,IMRA can achieve a better performance in terms of average user utility and tasks coverage ratio,and the task completion ratio can also be improved by UBIM.     

Accurate map-based indoor navigation on the mobile

We present FootPath, a self-contained, map-based indoor navigation system. Using only the accelerometer and the compass readily available in modern smartphones, we accurately localise a user on her route and provide her with turn-by-turn instructions to her destination. To compensate for inaccuracies in step detection and heading estimation, we match the detected steps onto the expected route using sequence alignment algorithms from the field of bioinformatics. As our solution integrates well with OpenStreetMap, it allows painless and cost-efficient collaborative deployment, without the need for additional infrastructure.     

K-submodular function-based service-limit incentive mechanisms for crowd heterogeneous sensing

Recently, crowd sensing, as a new paradigm, uses mobile devices from users to efficiently fulfill allocated tasks, enabling many novel applications such as location sensing and air pollution monitoring. To achieve high-quality service, extensive user participation is crucial. Most of existing works only apply for the homogeneous task scene where the requester has a budget limit and types of sensors used are homogeneous. On the contrary, we investigate a different scenario where the platform has a service limit and types of sensors are heterogeneous. Specially, we design the two service-limit incentive mechanisms, called SCH and SWH, respectively, by minimizing the total cost for a more general case where the value function is monotone $K$-submodular for participatory users so that given services can be fulfilled. The two mechanisms make it possible to extend current small-scale crowd sensing to large-scale crowd sensing, which have the following properties: individual rationality, task feasibility, computational efficiency, truthfulness, and constant frugality. Finally, we use extensive simulations to validate theoretical properties of our mechanisms.     

Marginal quality‐based long‐term incentive mechanisms for crowd sensing

Crowd sensing is a new paradigm that leverages pervasive sensor‐equipped mobile devices to provide sensing services like forensic analysis, documenting public spaces, and collaboratively constructing statistical models. Extensive user participation is indispensable for achieving good service quality. Nowadays, most of existing mechanisms focus on guaranteeing good service quality based on instantaneous extensive user participation for crowd sensing applications. Little attention has been dedicated to maximizing long‐term service quality for crowd sensing applications due to their heterogeneous interests, preferences, selfish behaviors, and so on. To fill these gaps, a closed‐form expression of the marginal sensing data quality based on the monopoly aggregation in economics is derived in this paper. Furthermore, we design marginal quality‐based incentive mechanisms for long‐term crowd sensing applications not only to enhance extensive user participation by maximizing the expected total profits of mobile users but also to stimulate mobile users to produce high‐quality contents by applying the marginal quality. Finally, simulation results show that our mechanisms outperform the existing solutions. Copyright © 2015 John Wiley & Sons, Ltd.     

基于压缩感知的卫星干扰源定位

针对传统测向方法实际应用性能较差的问题,提出了一种基于压缩感知的卫星干扰源定位方法。根据干扰信号方位角的空间稀疏性,建立了波达方向( DOA)估计模型,构造出基于阵列流型的过完备原子库和随机单位向量测量矩阵,最后结合信源估计技术和正交匹配追踪( OMP )算法实现干扰目标的方位估计。仿真验证了该方法能同时估计相干干扰信号的强度和角度,适合快速变化的干扰目标定位,并且具有较低的计算复杂度和更好的工程易用性。相关研究对压缩感知理论在卫星干扰源定位中的应用具有一定参考意义。     

IMU辅助的TDOA室内定位

室内环境下,当无线信号受到多径和非视距干扰时,传统的基于到达时间差（Time Difference of Arrival,TDOA）的测距模型定位精度不满足室内定位精度要求。为此,提出利用TDOA与低成本的惯性测量单元（Inertial Measurement Unit,IMU）相结合的定位方法。在视距情况下,只有TDOA系统工作,但在信号受到干扰时,利用IMU能够在短时间内提供一个准确的相对位置信息的特性,采用TDOA算法对其进行辅助定位,并利用卡尔曼滤波器对它们的数据进行预处理,最后使用扩展卡尔曼滤波器对数据进行处理融合。实验结果表明,提出的算法比传统的TDOA定位具有更高的精度。     

Reverberation multi-source localization algorithm based on compressed sensing with dual microphones

In traditional multi-source localization field,it is necessary to guarantee that the number of microphone is more than the number of source.To overcome this constraint,a dual-microphone multi-source localization algorithm based on CS was proposed,where the number of sound source localized successfully was more than 3.The multi-source localization was regarded as the block sparse signal reconstruction in this algorithm,and the full room impulse responses normalized were exploited to construct the compressed observation matrix in frequency domain.In reconstructed block sparse signal,the positions of non-zero blocks were corresponded to the positions of sound sources in space.The simulation shows that compared with the SRP-sub algorithm,in reverberation time 0.6s with dual-microphone,the proposed multi-source localization algorithm based on compressed sensing has higher capability which can reach 80% success rate by using 40 frequency points to localize 3 sound sources.     

自适应环境变化的RSS室内定位方法

根据定位问题的天然稀疏性,提出一种基于两步字典学习的定位方法,依据测量值动态调整字典,使稀疏模型能够自适应RSS的变化。同时提出一种改进的加权l1范数稀疏重构算法,提高低信噪比情况下的重构精度。实验结果表明该方法可以在目标数量未知的情况下实现多目标定位,并具有较强的抗噪声能力。     

基于指纹算法的无线室内定位技术

随着人们对基于位置的服务（LBS）需求日益增大,室内定位逐渐成为用户定位领域的研究热点,而指纹定位因具有定位精度高、普适性强和无需额外设备等优点而受到大多数研究者的青睐。首先详细综述了各种主流室内位置指纹定位技术的定位原理,然后归纳了现有的室内定位算法的原理及发展现状,最后搭建楼宇通道的测试场景,对各种典型室内定位算法进行测试验证和比较分析,为室内定位技术的研究与应用人员提供参考。     

Sequential learning for fingerprint based indoor localization

In this paper, we introduce a novel approach for improving performance of fingerprinting based indoor localization. Our proposal is a two-step procedure in which severe variation in the received signal strength is minimized during the first step via convex optimization, and distance metric learning is then used to estimate a more accurate location. Numerical results show that our proposal outperforms existing techniques in terms of accuracy and reliability.     

模拟人群信任和决策机制的协作频谱感知方法

通过模拟人群内部的信任和决策机制,针对多用户的频谱协作感知一致性问题,提出了一种分布式算法。该算法首先通过网络的历次协作过程预测出各感知用户的动态可信值,据此产生用户的相对可信值,并结合决策机制使得用户之间进行数据交互,随着数据的可信、迭代交互,所有用户状态将趋于一致,最后通过判定算法得出最终结果。算法充分考虑了实际环境中各用户频带感知能力的不平衡性,而且各次级用户只需要进行少量局部数据交换即可实现协作感知,与传统的OR-rule、1-out-of-N rule以及普通迭代法有较大区别。对3种数据篡改攻击进行了分析,并在预测算法的基础上提出了相应的安全策略。理论分析与仿真结果表明,新算法在准确性和安全性上均优于传统合作频谱感知算法,能显著提高频谱感知准确率,同时兼具较强的防攻击能力。     

基于RFID技术的室内定位系统的设计

单纯使用GPS系统无法解决复杂环境下的定位问题,利用各类无线与传感器技术实现室内定位系统成为近年来的研究热点。RFID技术具有成本低、寿命长、通信速率高、覆盖范围广、部署方便等优良特性,因而成为极具前景的室内定位手段。基于RFID技术,研制了以MSP430为主控芯片、兼容CC1101与CC2500两款射频模块的RFID标签与读卡器,采用LAND-MARC算法,实现了一套完整的室内定位系统。通过反复实验分析与参数配置改进,提高了系统的定位精度与稳定性,可应用于人员、资产管理系统等场景。     

Research on indoor localization algorithm based on kernel principal component analysis

An indoor localization algorithm based on kernel principal component analysis (KPCA) was proposed.It applied KPCA to train the original location fingerprint (OLF) and extract the nonlinear feature of the OLF data at the offline stage,such that the information of all AP was more efficiently utilized.At the online stage,an improved weight k-nearest neighbor algorithm for positioning which could automatically choose neighbors was proposed.The experiments were carried out in a realistic WLAN environment.The results show that the algorithm outperforms the existing methods in terms of the mean error and localization accuracy.Moreover,it requires less times of RSS acquisition and AP number.     

一种基于RFID标签阵列的室内定位方法

针对基于天线阵列测角的射频识别(Radio Frequency Identification, RFID)定位系统成本高、无法估计目标姿态的问题,提出了一种基于标签阵列的姿态检索的定位算法。首先,分析了天线方向角、标签阵列姿态角和标签阵列信号到达角之间的关系,建立了标签阵列姿态检索的定位模型;其次,对标签阵列中相位误差来源进行了分析,利用标签间的相位差对标签阵列信号进行重构,降低了读写器收发电路、标签电路及多径效应引入的相位误差对定位精度的影响;同时,分析了标签阵列中的互耦效应对相位的影响,提出了互耦效应相位校正方法,提升了标签阵列的测角精度;最后,提出了一种标签阵列姿态估计算法,使用多个天线的方向角实现对标签阵列姿态角估计和定位。实验结果表明,系统在室内环境下平均定位误差为0.2 m,标签阵列的姿态角估计平均误差为4.35°。     

基于激光测距扫描的室内定位方法研究

在未知室内环境下,基于迭代最近点(ICP)匹配的同步建图与定位(SLAM)算法较为复杂,运算量较大,在微小型飞行器ARM嵌入式控制系统上实现难以保证实时性。本文提出了一种基于激光测距扫描的室内飞行定位方法,用室内环境的特征点替代激光扫描数据进行ICP匹配,减少了匹配时间,提高了SLAM算法的快速性。在环境特征点提取过程中,提出了一种真实特征点筛选机制,减少因虚假特征点导致的匹配误差,保证了匹配精度。设计搭建了试验平台,试验结果表明,本文提出的方法能够满足微小型飞行器室内飞行定位快速性和准确性的要求。     

超宽带室内定位算法

为了解决室内密集多径环境下人员定位精度差这一问题,设计并实现了一种基于脉冲超宽带与能量检测接收机的信号到达时间估计算法。根据在IEEE802.12.4a标准室内家居环境下得到的超宽带信号,深入分析了信号的偏度、翘度与均方差特性。根据信号特性与信噪比之间的关系,算法能够实现高精度的信号传播时间估计。该算法充分考虑了积分周期与信号传播环境如视距与非视距对信号传播时间造成的影响。通过实验分析对比,该算法对信号传播时间估计具有更好的鲁棒性,目标运动速度和形变具有很好的鲁棒性,能够提供更高的精度。相较于阈值估计算法,该算法在低信噪比环境下最优能够提高10 ns的估计精度。     

Multiregional secure localization using compressive sensing in wireless sensor networks

Security and accuracy are two issues in the localization of wireless sensor networks (WSNs) that are difficult to balance in hostile indoor environments. Massive numbers of malicious positioning requests may cause the functional failure of an entire WSN. To eliminate the misjudgments caused by malicious nodes, we propose a compressive‐sensing–based multiregional secure localization (CSMR_SL) algorithm to reduce the impact of malicious users on secure positioning by considering the resource‐constrained nature of WSNs. In CSMR_SL, a multiregion offline mechanism is introduced to identify malicious nodes and a preprocessing procedure is adopted to weight and balance the contributions of anchor nodes. Simulation results show that CSMR_SL may significantly improve robustness against attacks and reduce the influence of indoor environments while maintaining sufficient accuracy levels.