Synthetic Sensor Data Generation Exploiting Deep Learning Techniques and Multimodal Information

In recent years, deep learning techniques have revolutionized the field of data generation, including the creation of synthetic sensor data. The ability to generate large quantities of diverse, high-quality data have significant implications in fields such as robotics and computer vision. Synthetic sensor data generation using deep learning techniques involves training a model to generate data that closely resembles real-world sensor data. This is achieved by feeding the model large amounts of real-world data and using it to learn the underlying patterns and structures in the data. Once trained, the model can generate data that are similar in quality and complexity to the original data, but with added variations and noise to increase diversity and realism. Several deep learning techniques such as generative adversarial networks (GANs), variational autoencoders (VAEs), recurrent neural networks (RNNs) have shown impressive results in generating synthetic data for a range of sensors. In this letter, deep learning techniques based on autoregressive convolutional recurrent neural networks (CRNNs) for multivariate time series prediction have been exploited to generate synthetic data for ultrawide band (UWB) and for ultrahigh frequency radio frequency identification (UHF-RFID) sensors. The neural network presented here incorporates measurements from sensors and heterogeneous information, such as the position of the antennas and tags in the environment, to generate synthetic data that can be used to augment real-world data, increasing diversity and robustness of datasets. The deep generation approaches presented here can help researchers generate datasets to validate algorithms without the need for expensive and time-consuming data collection.