In outdoor RF localization systems, particularly where line of sight can not be guaranteed or where multipath effects are severe, information about the terrain may improve the position estimate’s performance. Given the difficulties in obtaining real data, a ray-tracing fingerprint is a viable option. Nevertheless, although presenting good simulation results, the performance of systems trained with simulated features only suffer degradation when employed to process real-life data. This work intends to improve the localization accuracy when using ray-tracing fingerprints and a few field data obtained from an adverse environment where a large number of measurements is not an option. We employ a machine learning (ML) algorithm to explore the multipath information. We selected algorithms random forest and gradient boosting; both considered efficient tools in the literature. In a strict simulation scenario (simulated data for training, validating, and testing), we obtained the same good results found in the literature (error around 2 m). In a real-world system (simulated data for training, real data for validating and testing), both ML algorithms resulted in a mean positioning error around 100 ,m. We have also obtained experimental results for noisy (artificially added Gaussian noise) and mismatched (with a null subset of) features. From the simulations carried out in this work, our study revealed that enhancing the ML model with a few real-world data improves localization’s overall performance. From the machine ML algorithms employed herein, we also observed that, under noisy conditions, the random forest algorithm achieved a slightly better result than the gradient boosting algorithm. However, they achieved similar results in a mismatch experiment. This work’s practical implication is that multipath information, once rejected in old localization techniques, now represents a significant source of information whenever we have prior knowledge to train the ML algorithm.

在室外RF定位系统中，尤其是在不能保证视线或严重的多径影响的地方，有关地形的信息可能会改善位置估算的性能。考虑到获取真实数据的困难，光线跟踪指纹是一个可行的选择。尽管如此，尽管呈现出良好的仿真结果，但使用模拟功能训练的系统的性能仅在用于处理现实数据时才会受到影响。这项工作旨在提高光线跟踪指纹和从无法进行大量测量的不利环境中获得的一些现场数据时的定位精度。我们采用机器学习（ML）算法来探索多径信息。我们选择了随机森林和梯度提升算法；两者都被认为是文献中的有效工具。在严格的模拟场景中（用于训练，验证和测试的模拟数据），我们获得了与文献相同的良好结果（误差约为2 m）。在实际系统中（用于训练的模拟数据，用于验证和测试的实际数据），这两种ML算法均导致平均​​定位误差约为100μm。我们还获得了噪声（人为添加的高斯噪声）和不匹配（具有零子集）特征的实验结果。从这项工作中进行的模拟中，我们的研究表明，使用一些实际数据来增强ML模型可以提高本地化的整体性能。从本文采用的机器ML算法中，我们还观察到，在嘈杂的条件下，随机森林算法比梯度提升算法取得了更好的效果。但是，他们在失配实验中获得了相似的结果。这项工作的实际含义是，一旦我们拥有训练ML算法的先验知识，一旦在旧的定位技术中被拒绝，多径信息便成为重要的信息来源。