In recent years, indoor localization becomes more and more essential in our daily life thanks to its interesting applications that cover all domains including security, tourism. Unfortunately, the existing outdoor loc...In recent years, indoor localization becomes more and more essential in our daily life thanks to its interesting applications that cover all domains including security, tourism. Unfortunately, the existing outdoor localization systems fails in indoor environment, which has motivated researchers to develop new localization systems that challenge the indoor environments. In our work, we propose a 3D fingerprinting-based localization system that estimates a source position using acoustic signals. The latter has the advantage of being used in almost roaming devices. No dedicated infrastructure is necessary and the existing infrastructures can then be reused for indoor purposes. The proposed system has been evaluated in experimental tests in an area of dimensions 1.5 m * 1.5 m * 2 m when four microphones were placed at known positions and an artificial fan is turned on. Results show that turbulence affects the precision of estimating the source position by 7% for an accuracy of 8.5 cm.展开更多
文摘In recent years, indoor localization becomes more and more essential in our daily life thanks to its interesting applications that cover all domains including security, tourism. Unfortunately, the existing outdoor localization systems fails in indoor environment, which has motivated researchers to develop new localization systems that challenge the indoor environments. In our work, we propose a 3D fingerprinting-based localization system that estimates a source position using acoustic signals. The latter has the advantage of being used in almost roaming devices. No dedicated infrastructure is necessary and the existing infrastructures can then be reused for indoor purposes. The proposed system has been evaluated in experimental tests in an area of dimensions 1.5 m * 1.5 m * 2 m when four microphones were placed at known positions and an artificial fan is turned on. Results show that turbulence affects the precision of estimating the source position by 7% for an accuracy of 8.5 cm.
文摘随着智能反射表面(Reconfigurable Intelligent Surface, RIS)反射单元数量的增加以及定位范围的扩大,数据维度和计算复杂度也逐渐增大。普通的RIS辅助定位算法已经无法满足高维度和高强度计算的需求。随着深度学习等人工智能技术的发展,众多学者关注用深度学习进行定位。深度学习具有学习能力强、覆盖范围广且高度依赖数据量等优点,可以有效解决数据维度大以及计算量大等问题。考虑视距(Line of Sight, LoS)链路和非视距(Non-Line of Sight, NLoS)链路都存在和仅存在NLoS的定位场景下,引入深度学习技术,采用指纹定位的方法采集位置信息,将其输入到基于多头注意力机制(Multi-Head Attention, MHA)的Transformer网络模型中进行训练,实现RIS辅助定位,挖掘信道状态信息(Channel State Information, CSI)与用户位置之间的映射关系,研究三维场景下RIS辅助定位的定位精度。
