Ultra-wideband (UWB) is the state-of-the-art and most popular technology for wireless localization. Nevertheless, precise ranging and localization in non-line-of-sight (NLoS) conditions is still an open research topic. Indeed, multipath effects, reflections, refractions and complexity of the indoor radio environment can easily introduce a positive bias in the ranging measurement, resulting in highly inaccurate and unsatisfactory position estimation. This article proposes an efficient representation learning methodology that exploits the latest advancement in deep learning and graph optimization techniques to achieve effective ranging error mitigation at the edge. Channel Impulse Response (CIR) signals are directly exploited to extract high semantic features to estimate corrections in either NLoS or LoS conditions. Extensive experimentation with different settings and configurations have proved the effectiveness of our methodology and demonstrated the feasibility of a robust and low computational power UWB range error mitigation.
翻译:超广频系(UWB)是无线定位的最先进和最受欢迎的技术,然而,非视线条件下的精确测距和定位仍然是一个开放的研究课题,事实上,室内无线电环境的多病原效应、反射、折射和复杂性很容易在测距中引入积极的偏差,导致非常不准确和不令人满意的定位估计。本篇文章提出一种高效的代议学习方法,利用深层次学习和图形优化技术的最新进展,在边缘实现有效的测距误差减缓。 频道脉冲反应信号被直接利用来提取高语义特征来估计非视线或LOS条件下的校正。 与不同环境和配置进行的广泛实验证明了我们的方法的有效性,并展示了强健和低计算功率UWB测距差差的可行性。