Wireless Perception using Deep Learning for Vehicular and Human Detection

Abstract

In the wireless perception of events, Radio Sensing (RS) has been positioned as an alternative technology that does not have major limitations under several environmental conditions or lack of line of sight. However, there are techniques and approaches to the RS that have not yet been widely explored and that can improve its sensing capabilities for characterization and recognition applications. Furthermore, within the new paradigm of joint radio sensing and communications (JRSAC), it is a priority to find alternatives that facilitate the integration of these two technologies. In this dissertation, I present an RS platform based on the analysis of Doppler signatures of a radio-frequency continuous wave signal. Specifically, the platform was used for two relevant wireless perception applications: the detection of human falls and the detection of approaching vehicles. In fall detection, I present a systematic analysis of the influence of antenna orientation on RS systems and how it directly affects classification performance. For this, I compared the performance of two classification algorithms based on deep learning (DL): a long-short-term memory (LSTM) network and a convolutional neural network (CNN). Both models were tested using data collected from experiments with different antenna orientations and activities. The highest accuracy rates achieved were 92.10% for the implemented algorithms. This represents that the majority of events were correctly identified. The results obtained contribute to improving the design of the sensing platforms and increase the accuracy to identify fall events. On the other hand, the same platform was used for the detection of approaching vehicles in a vehicle-to-vehicle dispersion scenario. In this case, the feasibility of using an alternative sensing approach where a vehicle receives the signal transmitted by a second vehicle to characterize the event was verified. Combining this approach with the DL models, an accuracy of 98.60% detecting vehicles was achieved with the LSTM and 94.50% with the CNN. The high accuracy rates demonstrate the potential of Doppler signatures and DL algorithms in detecting oncoming vehicles.