Deep Neural Network-Based In-Vehicle People Localization Using Ultra-Wideband Radar

In this paper, we present an in-vehicle people localization technique using a deep neural network (DNN) model that is trained by the experimental data. First, an impulse radio ultra-wide band (IR-UWB) radar is installed inside the vehicle, and received signals are acquired by changing the arrangement of people sitting. Then, on the acquired data, we apply the DNN to train a classifier, which can predict whether a person is sitting or not in each seat. To design a network suitable for our system, we evaluate the performance by changing the type of activation function, the number of layers, and the number of nodes in each hidden layer of the DNN. In addition, we compare the performance of the proposed method with conventional machine learning algorithms such as support vector machine (SVM) and decision tree-based methods. From our measured signals, the proposed DNN-based method can classify all possible cases according to the location and number of people with an accuracy of 99%. Moreover, the advantage of our proposed method is that there is no need to extract features from a given radar signal.