论文标题
贝叶斯优化辅助神经网络培训技术无线电定位
Bayesian Optimisation-Assisted Neural Network Training Technique for Radio Localisation
论文作者
论文摘要
基于无线电信号（室内）本地化技术对于智能工厂和仓库等物联网应用很重要。通过机器学习，尤其是神经网络方法，可以实现从信号特征到目标位置的更准确的映射。但是，不同的无线电协议（例如WiFi，蓝牙等）在传输信号中具有不同的功能，可以用于本地化。此外，神经网络方法通常依靠精心构图的模型和广泛的培训过程来在单个本地化方案中获得令人满意的性能。以上在确定神经网络模型结构或超参数的过程中构成了一个主要挑战，以及从可用数据中选择培训功能。本文提出了基于贝叶斯优化的神经网络模型高参数调整和训练方法。自适应选择模型超参数和训练功能，可以通过最少的手动模型训练设计来实现。借助提出的技术，训练过程以一种更自动和有效的方式进行了优化，从而增强了神经网络在本地化中的适用性。
Radio signal-based (indoor) localisation technique is important for IoT applications such as smart factory and warehouse. Through machine learning, especially neural networks methods, more accurate mapping from signal features to target positions can be achieved. However, different radio protocols, such as WiFi, Bluetooth, etc., have different features in the transmitted signals that can be exploited for localisation purposes. Also, neural networks methods often rely on carefully configured models and extensive training processes to obtain satisfactory performance in individual localisation scenarios. The above poses a major challenge in the process of determining neural network model structure, or hyperparameters, as well as the selection of training features from the available data. This paper proposes a neural network model hyperparameter tuning and training method based on Bayesian optimisation. Adaptive selection of model hyperparameters and training features can be realised with minimal need for manual model training design. With the proposed technique, the training process is optimised in a more automatic and efficient way, enhancing the applicability of neural networks in localisation.
