标题： 基于RSS的定位中先验信息的有效使用 显示英文标题

标题： On the Effective Usage of Priors in RSS-based Localization

摘要： 本文研究了密集城市环境下的定位问题。在这种环境中，由于接收机（Rx）与卫星之间存在障碍物（如建筑物），导致视线（LOS）链路的可能性较低，全球导航卫星系统无法提供良好的定位精度。因此，必须求助于能够在非视线（NLOS）条件下可靠运行的其他技术。最近，我们提出了一种基于接收信号强度（RSS）指纹和卷积神经网络的算法LocUNet，并展示了它相对于广泛采用的k近邻（kNN）算法以及基于到达时间（ToA）测距的方法的最先进的定位性能。在当前工作中，我们首先认识到LocUNet能够从训练数据中学习Rx位置的潜在先验分布或Rx和发射机（Tx）关联偏好的能力，并将其高性能归因于此。相反，我们证明了基于概率方法的经典方法可以从适当引入此类先验信息中大大受益。我们的研究表明，通过将其与理论上最优的公式进行比较，LocUNet在许多情况下都表现出接近最优的性能。 显示英文摘要

摘要： In this paper, we study the localization problem in dense urban settings. In such environments, Global Navigation Satellite Systems fail to provide good accuracy due to low likelihood of line-of-sight (LOS) links between the receiver (Rx) to be located and the satellites, due to the presence of obstacles like the buildings. Thus, one has to resort to other technologies, which can reliably operate under non-line-of-sight (NLOS) conditions. Recently, we proposed a Received Signal Strength (RSS) fingerprint and convolutional neural network-based algorithm, LocUNet, and demonstrated its state-of-the-art localization performance with respect to the widely adopted k-nearest neighbors (kNN) algorithm, and to state-of-the-art time of arrival (ToA) ranging-based methods. In the current work, we first recognize LocUNet's ability to learn the underlying prior distribution of the Rx position or Rx and transmitter (Tx) association preferences from the training data, and attribute its high performance to these. Conversely, we demonstrate that classical methods based on probabilistic approach, can greatly benefit from an appropriate incorporation of such prior information. Our studies also numerically prove LocUNet's close to optimal performance in many settings, by comparing it with the theoretically optimal formulations.