论文标题
NLOS与UWB本地化的神经网络模型缓解缓解措施
NLOS Ranging Mitigation with Neural Network Model for UWB Localization
论文作者
论文摘要
机器人的定位对于导航和路径计划至关重要，例如需要环境地图的情况。多年来，由于引入低成本UWB模块提供了厘米级的准确性，多年来，用于室内位置系统的Ultra Wideband（UWB）一直在越来越受欢迎。但是，在环境中存在障碍的情况下，UWB的非视线（NLOS）测量将产生不准确的结果。由于低成本UWB设备没有提供通道信息，因此我们提出了一种方法来决定通过使用神经网络（NN）模型提供的一些信号强度信息来确定测量是否在视线（LOS）之内（LOS）。该模型的结果是测量值是LOS的概率，该测量是通过加权最高方（WLS）方法定位的。我们的方法在大厅测试数据中将本地化精度提高了16.93％，使用从办公室培训数据中提取的所有输入的NN模型，在走廊测试数据上，将本地化精度提高了16.93％。
Localization of robots is vital for navigation and path planning, such as in cases where a map of the environment is needed. Ultra-Wideband (UWB) for indoor location systems has been gaining popularity over the years with the introduction of low-cost UWB modules providing centimetre-level accuracy. However, in the presence of obstacles in the environment, Non-Line-Of-Sight (NLOS) measurements from the UWB will produce inaccurate results. As low-cost UWB devices do not provide channel information, we propose an approach to decide if a measurement is within Line-Of-Sight (LOS) or not by using some signal strength information provided by low-cost UWB modules through a Neural Network (NN) model. The result of this model is the probability of a ranging measurement being LOS which was used for localization through the Weighted-Least-Square (WLS) method. Our approach improves localization accuracy by 16.93% on the lobby testing data and 27.97% on the corridor testing data using the NN model trained with all extracted inputs from the office training data.
