Data Information Fusion From Multiple Access Points for WiFi-Based Self-localization
Data Information Fusion From Multiple Access Points for WiFi-Based Self-localization
复制标题
来自多个接入点的数据信息融合，用于基于 WiFi 的自定位
DOI：
10.1109/lra.2018.2885583
复制
发表时间：
2019
影响因子：
5.2
通讯作者：
H. Asama
中科院分区：
文献类型：
--
作者：
Renato Miyagusuku;A. Yamashita;H. Asama
在这封信中，我们提出了一种新的方法来融合来自多个接入点的信息，以增强基于wifi的自定位。设计基于wifi的定位系统的一种常用方法是学习环境中每个接入点的位置到信号强度映射。然后使用每个映射来计算机器人位置的可能性，条件是感知到的信号强度数据，产生尽可能多的可能性函数。办公楼通常有几十到几百个接入点，因此必须将所有可用的可能性适当地组合成一个单一的、连贯的、联合的可能性，从而产生精确的可能性，但又不能过于自信。虽然大多数研究都集中在学习这些映射和改进数据获取的技术上；对充分融合它们的技术研究一直被忽视。我们的数据信息融合方法基于信息论，产生的联合分布比以前的方法要好得多。此外，通过广泛的测试，我们表明这些联合似然大大提高了系统的定位性能。
In this letter, we propose a novel approach for fusing information from multiple access points in order to enhance WiFi-based self-localization. A common approach for designing WiFi-based localization systems is to learn location-to-signal strength mappings for each access point in an environment. Each mapping is then used to compute the likelihood of the robot's location conditioned on sensed signal strength data, yielding as many likelihood functions as mappings are available. Office buildings typically have from several tens to a few hundreds of access points, making it essential to properly combine all available likelihoods into a single, coherent, joint likelihood that yields precise likelihoods, yet is not overconfident. While most research has focused on techniques for learning these mappings and improving data acquisition; research on techniques to adequately fuse them has been neglected. Our approach for data information fusion is based on information theory and yields considerably better joint distributions than previous approaches. Furthermore, through extensive testing, we show that these joint likelihoods considerably increase the system's localization performance.