Indoor and outdoor collaborative positioning technology based on Beidou and Bluetooth positioning

In the field of deep learning, the quantity and quality of the dataset are crucial for training high-performance neural network models. In situations with insufficient data samples, the trained network may exhibit poor generalization. To overcome this challenge, this paper proposes a Bayesian inference-optimized approach using Generative Adversarial Networks (GANs) for data augmentation. By training GANs on the original dataset, the model can generate data samples that are similar but distinct from the original data. Unlike traditional data augmentation methods, GANs not only generate more samples but also produce diverse and realistic data, thereby enhancing neural network recognition performance in subsequent small sample environments.