用频段声学特征提升传感器选子集的抗干扰能力，实现实时高精度跟踪。
A Recommendation System Approach for Interference-Robust Sensor Subset Selection
- 借鉴推荐系统思想，用频段声学特征和双塔MLP评分候选传感器组合。
- 户外实测显示准确率比基于RSSI的方法提升约20%，且计算开销低。
- 适合需要实时、抗干扰传感器选择的智能监控与自动驾驶场景。
本文提出一种用于目标跟踪的传感器子集选择方法。已有研究证明，低成本的声学接收信号强度指示（RSSI）可用于推荐一组传感器节点，使其昂贵的感知模态（如摄像头）实现高跟踪精度。然而，基于RSSI的方法易受声学干扰影响。本文提出一种受推荐系统启发的框架，改用频段声学特征，并采用双塔多层感知机（Two-Tower MLP）架构高效评分候选传感器子集。在室外车辆跟踪部署实验中，该方法相比基线RSSI方案将跟踪准确率提升了约20%，同时保持了实时选择感知所需的低计算开销。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。