面向抗干扰传感器子集选择的推荐系统方法
A Recommendation System Approach for Interference-Robust Sensor Subset Selection
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中文总结 AI 辅助
本文针对现有基于RSSI的传感器子集选择方法易受声学干扰的问题，提出结合频带声学特征与双塔MLP架构的推荐系统框架，在户外车辆跟踪任务中较RSSI基线提升约20%精度且保持低计算开销。
中文摘要 AI 辅助
本文提出一种用于跟踪任务的传感器子集选择方法。现有研究表明，低成本的声学接收信号强度指示器（RSSI）测量值可用于推荐传感器节点子集，这些节点的昂贵传感模态（如摄像头）能实现高跟踪精度。尽管基于RSSI的方法效率较高，但易受声学干扰影响。我们提出一种受推荐系统启发的框架，该框架利用频带声学特征和双塔多层感知器（Two-Tower MLP）架构对候选传感器子集进行高效评分。户外车辆跟踪部署的实验结果显示，与RSSI基线相比，所提方法可将精度提升约20%，同时保持实时选择性传感所需的低计算开销。
英文摘要
This paper develops a method for sensor-subset selection for tracking. Prior work showed that low-cost acoustic Received Signal Strength Indicator (RSSI) measurements can be used to recommend subsets of sensor nodes whose expensive sensing modalities, such as cameras, can achieve high tracking accuracy. While efficient, RSSI-based approaches are challenged by acoustic interference. We propose a recommendation-system-inspired framework that instead leverages frequency-band acoustic features and a Two-Tower Multi-Layer Perceptron (MLP) architecture to efficiently score candidate sensor subsets. Experimental results on outdoor vehicle-tracking deployments show that the proposed method can improve accuracy by around 20\% over the RSSI baseline while maintaining the low computational overhead required for real-time selective sensing.
发表机构
- University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
机构由 AI 辅助整理，请以论文原文为准。
