Collaborative positioning for swarms:A brief survey of vision,LiDAR and wireless sensors based methods

As positioning sensors,edge computation power,and communication technologies continue to develop,a moving agent can now sense its surroundings and communicate with other agents.By receiving spatial information from both its environment and other agents,an agent can use various methods and sensor types to localize itself.With its high flexibility and robustness,collaborative positioning has become a widely used method in both military and civilian applications.This paper introduces the basic fundamental concepts and applications of collaborative positioning,and reviews recent progress in the field based on camera,LiDAR(Light Detection and Ranging),wireless sensor,and their integration.The paper compares the current methods with respect to their sensor type,summarizes their main paradigms,and analyzes their evaluation experiments.Finally,the paper discusses the main challenges and open issues that require further research.

Wireless Positioning:Technologies,Applications,Challenges,and Future Development Trends

The development of the fifth-generation(5G)mobile communication systems has entered the commercialization stage.5G has a high data rate,low latency,and high reliability that can meet the basic demands of most industries and daily life,such as the Internet of Things(IoT),intelligent transportation systems,positioning,and navigation.The continuous progress and development of society have aroused wide concern.Positioning accuracy is the core demand for the applications,especially in complex environments such as airports,warehouses,supermarkets,and basements.However,many factors also affect the accuracy of positioning in those environments,for example,multipath effects,non-line-of-sight,and clock synchronization errors.This paper provides a comprehensive review of the existing works about positioning for the future wireless network and discusses its key techniques and algorithms,as well as the current development and future directions.We first outline the current traditional positioning technologies and algorithms,which are discussed and analyzed with the relevant literature.In addition,we also discuss application scenarios for wireless localization.By comparing different positioning systems,the challenges and future development directions of existing wireless positioning systems are prospected.

SNWPM:A Siamese Network Based Wireless Positioning Model Resilient to Partial Base Stations Unavailable

Artificial intelligence(AI)models are promising to improve the accuracy of wireless positioning systems,particularly in indoor environments where unpredictable radio propagation channel is a great challenge.Although great efforts have been made to explore the effectiveness of different AI models,it is still an open problem whether these models,trained with the data collected from all base stations(BSs),could work when some BSs are unavailable.In this paper,we make the first effort to enhance the generalization ability of AI wireless positioning model to adapt to the scenario where only partial BSs work.Particularly,a Siamese Network based Wireless Positioning Model(SNWPM)is proposed to predict the location of mobile user equipment from channel state information(CSI)collected from 5G BSs.Furthermore,a Feature Aware Attention Module(FAAM)is introduced to reinforce the capability of feature extraction from CSI data.Experiments are conducted on the 2022 Wireless Communication AI Competition(WAIC)dataset.The proposed SNWPM achieves decimeter-level positioning accuracy even if the data of partial BSs are unavailable.Compared with other AI models,the proposed SNWPM can reduce the positioning error by nearly 50%to more than 60%while using less parameters and lower computation resources.

New Distributed Positioning Algorithm Based on Centroid of Circular Belt for Wireless Sensor Networks

This paper presents a new distributed positioning algorithm for unknown nodes in a wireless sensor network. The algorithm is based exclusively on connectivity. First, assuming that the positions of the anchor nodes are already known, a circular belt containing an unknown node is obtained using information about the anchor nodes that are in radio range of the unknown node, based on the geometric relationships and communication constraints among the unknown node and the anchor nodes. Then, the centroid of the circular belt is taken to be the estimated position of the unknown node. Since the algorithm is very simple and since the only communication needed is between the anchor nodes and the unknown node, the communication and computational loads are very small. Furthermore, the algorithm is robust because neither the failure of old unknown nodes nor the addition of new unknown nodes influences the positioning of unknown nodes to be located. A theoretical analysis and simulation results show that the algorithm does not produce any cumulative error and is insensitive to range error, and that a change in the number of sensor nodes does not affect the communication or computational load. These features make this algorithm suitable for all sizes of low-power wireless sensor networks.

Barom etric altim eter in wireless com m unication network indoor positioning system

A differential barometric altimetry technology based on the digital pressure sensors is put forward by using the existing mobile phone base station as reference. The height of known base sta- tion is precise. The pressure and temperature of the known base station is measured by sensors and transmitted to users. The absolute height value of user will be calculated by combining the baromet- ric pressure values and temperature values from the base station with the locally measured values. In order to decrease system errors caused by inconsistency between the measured pressure value at base station and the locally measured pressure value, weights correction is applied based on multiple reference stations. The calculated height value is accurate due to eliminating the measured errors caused by irregular changes of atmospheric pressure, with the error less than 1 m. Resolution of ele- vation positioning depends upon the resolution of the pressure sensor, the relationship between which is approximately linear. When the resolution of sensor is 0.01 hPa, the resolution of elevation positioning is about 0. 1 m. In addition, the data frame format at base station is designed in this arti- cle. Experimental results show that the method is accurate, reliable, stable and has the ability to distinguish floors and stair steps.

Positioning a Node of Wireless Sensor Networks in 3 Dimensional Space

To know the location of nodes is very important and valuable for wireless sensor networks (WSN), we present an improved positioning model (3D-PMWSN) to locate the nodes in WSN. In this model, grid in space is presented. When one tag is detected by a certain reader whose position is known, the tag’s position can be known through certain algorithm. The error estimation is given. Emulation shows that the positioning speed is relatively fast and positioning precision is relatively high.

Mobile Positioning System Based on the Wireless Sensor Network in Buildings

Established on the Intel Multi-Core Embedded platform, using 802.11 Wireless Network protocols as the communication medium, combining with Radio Frequency-Communication and Ultrasonic Ranging, imple-ment a mobile terminal system in an intellectualized building. It can provide its holder such functions: 1) Accurate Positioning 2) Intelligent Navigation 3) Video Monitoring 4) Wireless Communication. The inno-vative point for this paper is to apply the multi-core computing on the embedded system to promote its com-puting speed and give a real-time performance and apply this system into the indoor environment for the purpose of emergent event or rescuing.

Wireless Positioning Technologies in CDMA System

Location services not only provide address information, but also locate, monitor and track terminals on a real-time basis. To deliver fast and accurate location services, it is necessary to select an appropriate positioning method. Currently, 3 methods are available for CDMA wireless positioning: network based, Mobile Station (MS) based, and GpsOne positioning.As these methods are different in location time, accuracy, availability, privacy, and operation cost, they shall be selected according to the actual network conditions. Network structure, information bearer protocols, and transport mode make the basis of a wireless positioning system. They can be implemented in different ways, and some details shall be specified by the operators.

Design of 3D Space Following Positioning System Based on ZigBee

The research on positioning system and spatial alignment is a big topic. In this paper, we proposed a design (that) studies two issues. One is the study of range positioning algorithm based on ZigBee communication system. The other one is spatial alignment platform which is controlled with two servos. Hardware and software control system was realized, which also consists of two parts, ZigBee network positioning system and automatic orientation platform.

A KNN-based two-step fuzzy clustering weighted algorithm for WLAN indoor positioning

Although k-nearest neighbors （KNN） is a popular fingerprint match algorithm for its simplicity and accuracy, because it is sensitive to the circumstances, a fuzzy c-means （FCM） clustering algorithm is applied to improve it. Thus, a KNN-based two-step FCM weighted （KTFW） algorithm for indoor positioning in wireless local area networks （WLAN） is presented in this paper. In KTFW algorithm, k reference points （RPs） chosen by KNN are clustered through FCM based on received signal strength （RSS） and location coordinates. The right clusters are chosen according to rules, so three sets of RPs are formed including the set of k RPs chosen by KNN and are given different weights. RPs supposed to have better contribution to positioning accuracy are given larger weights to improve the positioning accuracy. Simulation results indicate that KTFW generally outperforms KNN and its complexity is greatly reduced through providing initial clustering centers for FCM.

Fuzzy adaptive Kalman filter for indoor mobile target positioning with INS/WSN integrated method

Pure inertial navigation system(INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network(WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter(KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system(FIS), and the fuzzy adaptive Kalman filter(FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.

动态无线充电系统自解耦型分段导轨及其双模切换策略研究

为抑制电动汽车动态无线充电系统发射单元间的交叉耦合,降低其对系统谐振参数设计的影响,文中利用螺线管线圈与平面线圈正交解耦特性,提出一种自解耦分段发射导轨,其中螺线管为分级叠层绕制,实现了相邻发射单元间的自解耦。为降低系统输出参数波动,基于互感动态变化规律,确定系统最佳充电区间,并提出一种双模工作模式及其分段切换策略,实现对发射线圈实能顺序的实时控制。为实现双模工作状态的有序变换,设计自解耦检测线圈结构,该检测线圈采用单层螺线管绕制并与接收线圈正交,避免检测信号与电能传输之间的干扰。最后,搭建实验平台对所提方法的有效性进行验证。实验结果表明,系统输出功率平稳性提升了19.5%,系统的最高效率可达92.3%。

基于多绕组变压器的高压无线充电自由定位系统

针对电动车辆无线电能传输(wireless power transfer,WPT)系统存在的半导体性能有限和定位困难的问题,提出了一种多对一高压无线充电自由定位系统。该系统采用逆变器串联输入的设计来适应高压应用场景,同时利用多绕组变压器实现逆变器的等效并联输出和向多路发射回路传输电能的功能,并采用了多对一的设计以扩大电动车的定位范围以实现无线充电的自由定位功能。为分析多绕组变压器的工作机理和研究多对一拓扑的能量传输特性,进行了等效电路分析和MATLAB仿真,并制作实验室原型样机进行了实验验证。基于实验和仿真结果,提出了一种基于多对一WPT拓扑的混合工作模式,可以有效地扩大电动车辆无线充电时的定位范围。研究和分析表明,文中所提出的拓扑结构可以有效地提高系统的输入电压以应用于高压场景,并能有效扩大电动车辆无线充电时的定位范围以实现自由定位。

面向室内语音导航的“附近”关系协同定位方法

针对传统室内定位模式单一,且单一“附近”空间关系无法满足定位需求的问题,提出一种融合“附近”空间关系/无线保真(WiFi)/行人航迹推算(PDR)/地磁的室内定位方法:阐述多源融合语音交互室内定位方法框架;并针对室内环境,建立“附近”空间关系概率密度函数;然后步行构建地磁/WiFi指纹库;最后后通过“附近”空间关系约束粒子及指纹匹配空间范围,在PDR推算位置基础上,利用WiFi/地磁/“附近”空间关系更新粒子权重,实现最优位置估计。实验结果表明,基于粒子滤波融合单一“附近”空间关系/WiFi/PDR/地磁为一种低成本可行的定位方法,平均定位精度1.93 m,有90%可能性定位精度可达2.52 m。

基于改进天鹰的三维室内多层结构定位算法

在面向一些大型商超、医院、教学楼等大规模室内多层结构定位中,针对多层WSN结构的非测距定位问题,提出一种基于改进天鹰的三维室内多层结构定位算法IAODV-HOP算法。首先,为节点划分3类通信半径以细化跳数,同时利用最小均方差和权重因子修正节点的平均跳距。其次,用IAO算法对未知节点坐标进行寻优,通过佳点集策略对种群初始化,解决天鹰算法因初始种群随机分布而导致的种群的质量和多样性难以保证的问题,并且在局部搜索中加入黄金正弦的搜索策略完善种群的位置更新方式,增强了算法的局部搜索能力。通过仿真实验,本文所提算法IAODV-HOP相较于传统3D-DV-Hop、PSO-3DDV-Hop、N3-3DDV-Hop以及N3-ACO-3DDV-Hop算法,归一化平均定位误差分别下降70.33%、62.67%、64%、53.67%,表现出更优的性能,具有更好的稳定性和更高的定位精度。

基于多通信半径和改进麻雀搜索算法的DV-HOP节点定位研究

在无线传感器网络中,传统DV-Hop(Distance Vector-Hop)算法因跳数和平均跳距计算存在较大偏差,从而对未知节点定位产生较大误差.针对该问题,设计了基于多通信半径和改进麻雀搜索的DV-Hop定位算法.首先采用多通信半径修正节点间的跳数,使跳数值较真实反映两个节点间的距离.其次采用修正的跳数去修正信标节点的平均跳距,从而获得未知节点到各信标节点修正后的距离.最后采用麻雀搜索算法(Sparrow Search Algorithm,SSA)估算未知节点位置,将节点定位问题转化为函数寻优问题.针对麻雀搜索算法前期容易陷入局部最优解,后期寻优精度不高的问题,提出将Levy飞行策略引入麻雀搜索算法中,提升算法的全局寻优能力.仿真结果表明,与传统DV-Hop算法、SSA DV-Hop算法相比,改进SSA DV-Hop算法的定位精度明显提高.

基于2.4 GHz无线技术的RTK定位系统

利用2.4 GHz无线技术,设计RTK(real time kinematic)定位系统.在两种实验场景下,进行对比实验,比较RTK定位系统和传统PVT(position velocity time)定位系统的定位误差.实验结果表明:两种实验场景下,相对于传统PVT定位系统,该文设计的RTK定位系统均有更小的定位误差.该文定位系统可应用于无人机、精准农业等领域.

基于490 nm垂直外腔面发射激光器的长距离水下激光通信系统

水下无线光通信技术为深海勘探和海洋资源开发利用带来了一种效率高且可靠性强的通信新方案.本文采用490 nm垂直外腔面发射激光器作为光源,基于声光外调制技术,采用脉冲位置调制方式(pulseposition modulation,PPM)搭建了长距离水下无线光通信系统.结合光源的优势并经过软判决算法优化PPM解调来提升水下通信性能,采用64 PPM调制,成功实现了96 m的水下传输距离,在50 MHz时隙频率下得到传输的误码率为1.9×10^(-5).同时测量到采用软判决解调相较于硬判决解调在性能增益上有着大约2 dB的提升,验证了软判决算法在提升水下通信性能方面相比硬判决算法的显著优势.

基于RSSI的修正补偿三边定位算法设计与仿真

在无线传感器网络节点定位过程中,三边定位算法常结合测距算法获取未知节点的位置信息。针对现有三边定位算法定位精度低、适用范围小和需要多次发送定位数据包导致节点能耗增大等问题,提出一种修正补偿测量距离的三边定位算法。相比常见发送多次定位数据包然后进行滤波计算降低定位误差的方法,采用对数常态-模型测量节点间距离,发送较少定位数据包,通过判断三锚节点圆的相对位置,对由于测距误差较大导致节点圆不相交的3种情况进行修正补偿,采用基于质心算法的三边定位算法计算未知节点坐标。通过搭建Matlab仿真试验,验证算法可行性及定位精度,试验结果表明,提出的修正补偿三边定位算法综合考虑了定位精度、算法复杂度和节点功耗3者之间的关系,适用范围更广,定位误差<15%。

基于UWB的矿用一体化智能车载终端的设计

设计了1种基于UWB精确定位技术,融合精确定位、4G/5G无线通信、视频监控、数据采集、语音通话等多种功能于一体的智能车载终端。智能车载终端采用安卓平台,以ARM RK3399为控制核心,以UWB通信收发芯片DW1000为精确定位核心,实现了井下车辆的精确定位、智能调度、信息采集、安全预警、语音通话、人机交互等功能。测试结果表明:车载终端最大动态定位误差不大于7.3 m,满足煤矿对智能辅助运输的各项功能需求.