In this paper, we investigate the employment of reconfigurable intelligent surfaces (RISs) into vehicle platoons, functioning in tandem with a base station (BS) in support of the high-precision location tracking. In particular, the use of a RIS imposes additional structured sparsity that, when paired with the initial sparse line-of-sight (LoS) channels of the BS, facilitates beneficial group sparsity. The resultant group sparsity significantly enriches the energies of the original direct-only channel, enabling a greater concentration of the LoS channel energies emanated from the same vehicle location index. Furthermore, the burst sparsity is exposed by representing the non-line-of-sight (NLoS) channels as their sparse copies. This thus constitutes the philosophy of the diverse sparsities of interest. Then, a diverse dynamic layered structured sparsity (DiLuS) framework is customized for capturing different priors for this pair of sparsities, based upon which the location tracking problem is formulated as a maximum a posterior (MAP) estimate of the location. Nevertheless, the tracking issue is highly intractable due to the ill-conditioned sensing matrix, intricately coupled latent variables associated with the BS and RIS, and the spatialtemporal correlations among the vehicle platoon. To circumvent these hurdles, we propose an efficient algorithm, namely DiLuS enabled spatial-temporal platoon localization (DiLuS-STPL), which incorporates both variational Bayesian inference (VBI) and message passing techniques for recursively achieving parameter updates in a turbo-like way. Finally, we demonstrate through extensive simulation results that the localization relying exclusively upon a BS and a RIS may achieve the comparable precision performance obtained by the two individual BSs, along with the robustness and superiority of our proposed algorithm as compared to various benchmark schemes.

翻译：本文研究了将可重构智能表面（RIS）集成到车辆队列中，与基站（BS）协同工作以支持高精度位置追踪的应用。具体而言，RIS的引入施加了额外的结构化稀疏性，当其与BS初始的稀疏视距（LoS）信道相结合时，促进了有益的群组稀疏性。由此产生的群组稀疏性显著增强了原始直达信道的能量，使得源自相同车辆位置索引的LoS信道能量更加集中。此外，通过将非视距（NLoS）信道表示为稀疏副本，揭示了突发稀疏性。这由此构成了所关注的多样性稀疏性的基本原理。随后，定制了一种多样化动态分层结构化稀疏（DiLuS）框架，用于捕获这对稀疏性的不同先验信息，基于此，位置追踪问题被表述为位置的最大后验（MAP）估计。然而，由于病态感知矩阵、与BS和RIS相关的复杂耦合潜变量、以及车辆队列中的时空相关性，追踪问题高度棘手。为克服这些障碍，我们提出了一种高效算法，即DiLuS支持的时空队列定位（DiLuS-STPL），该算法融合了变分贝叶斯推理（VBI）和消息传递技术，以涡轮迭代方式递归实现参数更新。最后，通过大量仿真结果证明，仅依赖单个BS和单个RIS的定位可达到两个独立BS相当的精度性能，同时所提算法相对于各种基准方案展现出鲁棒性和优越性。