Fingerprinting based localization is attracting increasing attention in recent years, yet the great majority of relative works focus on static multi-sensor circumstances. This letter, instead, considers a dynamical fingerprinting localization scenario for the device with intermittently emission status, whereby the fingerprints are obtained by the combination of RSS and AoA via the popular massive-antenna deployment. Taking account of the dynamical behaviour of the target and its spatial sparsity, a novel Sparse-constrained Sequential Sensing (SCSS) framework is designed. The target’s emission status and sparse location are jointly captured by a Random Finite Set (RFS), and its a posteriori distributions are recursively estimated via the Bayesian statistical inference technique. Numerical simulations demonstrate that our SCSS technique can detect the presence of the device precisely, and also improve tracking accuracy significantly when compared to static counterparts.

中文翻译：

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基于指纹的稀疏约束序列传感：联合检测和跟踪海量天线

近年来，基于指纹的定位越来越受到关注，但绝大多数相关工作都集中在静态多传感器环境上。相反，这封信考虑了具有间歇发射状态的设备的动态指纹定位场景，其中指纹是通过流行的大规模天线部署通过 RSS 和 AoA 的组合获得的。考虑到目标的动态行为及其空间稀疏性，设计了一种新颖的稀疏约束序列感知（SCSS）框架。目标的发射状态和稀疏位置由随机有限集（RFS）联合捕获，其后验分布通过贝叶斯统计推断技术递归估计。