基于RSSI的位置分类算法--粒子滤波器修正传感器估计
RSSI-Based Location Classification Using a Particle Filter to Fuse Sensor Estimates
摘要：
对于网络物理生产系统（CPPS）来说，本地化变得越来越重要，因为无线和移动设备被认为是不可或缺的一部分。虽然基于接收信号强度指示符（RSSI）在无线通信系统中定位目标是通常的解决方案，但是它受到传感器质量的限制。我们考虑的情况下，一辆车进出一个房间，并建议使用粒子滤波器的传感器融合，使我们能够将非理想的模型，并实现高质量的位置估计。然后，我们使用机器学习（ML）来分类车辆位置。我们的研究结果表明，粒子滤波器的位置输出是一个更好的输入到分类器比原始RSSI数据，我们实现了更高的精度，同时减少了ML必须考虑的功能的数量。我们还比较了多种ML算法的性能，并表明SVM为给定任务提供了整体最佳性能。
英文摘要：
For Cyper-Physical Production Systems (CPPS), localization is becoming increasingly important as wireless and mobile devices are considered an integral part. While localizing targets in a wireless communication system based on the Received Signal Strength Indicators (RSSIs) is a usual solution, it is limited by sensor quality. We consider the scenario of a car moving in and out of a chamber and propose to use a particle filter for sensor fusion, allowing us to incorporate non-idealities in our model and achieve a high-quality position estimate. Then, we use Machine Learning (ML) to classify the vehicle position. Our results show that the location output of the particle filter is a better input to the classifiers than the raw RSSI data, and we achieve improved accuracy while simultaneously reducing the number of features that the ML has to consider. We also compare the performance of multiple ML algorithms and show that SVMs provide the overall best performance for the given task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
