在恶劣环境下通过压缩感知实现传感器节点精准定位
Robust Node Localization for Rough and Extreme Deployment Environments
- 基于压缩感知构建节点识别与定位模型
- 少数量锚点即可实现高鲁棒性定位
- 适用于水面等极端部署场景
大量应用需要大规模低功耗无线传感器网络部署。然而，一些环境因跨技术干扰强烈、极端天气（如暴雨、高温）或剧烈运动，导致节点间无线链路质量差且不可预测，严重影响定位精度。针对此类场景中目标节点位置估计误差大的问题，本文以水面传感器部署为背景，提出将节点识别与鲁棒定位建模为压缩感知问题，设计相应算法，并优化锚点配置以提升定位鲁棒性。数值结果表明，所提方法在少量锚点条件下同时实现高效节点识别与精确位置估计。由于仅依赖目标到锚点的距离，方法具有广泛适用性，可实现抗干扰、高鲁棒性的定位。
原文摘要 · Abstract (English)
Many applications have been identified which require the deployment of large-scale low-power wireless sensor networks. Some of the deployment environments, however, impose harsh operation conditions due to intense cross-technology interference, extreme weather conditions (heavy rainfall, excessive heat, etc.), or rough motion, thereby affecting the quality and predictability of the wireless links the nodes establish. In localization tasks, these conditions often lead to significant errors in estimating the position of target nodes. Motivated by the practical deployments of sensors on the surface of different water bodies, we address the problem of identifying susceptible nodes and robustly estimating their positions. We formulate these tasks as a compressive sensing problem and propose algorithms for both node identification and robust estimation. Additionally, we design an optimal anchor configuration to maximize the robustness of the position estimation task. Our numerical results and comparisons with competitive methods demonstrate that the proposed algorithms achieve both objectives with a modest number of anchors. Since our method relies only on target-to-anchor distances, it is broadly applicable and yields resilient, robust localization.
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