论文标题
使用DOA的无线信号在线室内本地化
Online Indoor Localization Using DOA of Wireless Signals
论文作者
论文摘要
无线移动设备或机器人在室内和GPS限制的环境中的定位是一个困难的问题，尤其是在传统的相机和基于激光雷达的替代感应和本地化方式可能失败的动态场景中。我们提出了一种估计移动机器人在环境中部署的静态无线传感器节点（WSN）的位置。该方法采用了一种新型的粒子滤波器，该滤镜与移动机器人的迁移率模型结合使用高斯概率（DOA）估算方向更新其权重。我们通过广泛的模拟和公共现实世界测量数据集评估和验证了所提出的方法，并与标准的最先进的本地化方法进行了比较。结果显示，高计算效率可以平衡仪表级的本地化精度，从而使其能够在线使用而无需像典型的基于指纹的本地化算法那样专用离线阶段。
Localization of a wireless mobile device or a robot in indoor and GPS-denied environments is a difficult problem, particularly in dynamic scenarios where traditional cameras and LIDAR-based alternative sensing and localization modalities may fail. We propose a method for estimating the location of a mobile robot in relation to static wireless sensor nodes (WSN) deployed in the environment. The method employs a novel particle filter that updates its weights using a Gauss probability over Direction of Arrival (DOA) estimate in conjunction with the mobile robot's mobility model. We evaluate and validate the proposed method in terms of accuracy and computational efficiency through extensive simulations and public real-world measurement datasets, comparing with standard state-of-the-art localization approaches. The results show considerably high meter-level localization accuracy balanced by the high computational efficiency, enabling it to use online without a need for a dedicated offline phase as in typical fingerprint-based localization algorithms.
