Object Detection and 3D Estimation via an FMCW Radar Using a Fully Convolutional Network
This work addresses object detection and 3D estimation for radar-based systems, but it is incremental as it applies existing deep learning methods to radar data with a specific normalization technique.
The paper tackles object detection and 3D position estimation using an FMCW radar by employing a deep learning framework with a fully convolutional network and a normalization method for radar signals. Experimental results show successful detection and 3D estimation of a car in noisy environments.
This paper considers object detection and 3D estimation using an FMCW radar. The state-of-the-art deep learning framework is employed instead of using traditional signal processing. In preparing the radar training data, the ground truth of an object orientation in 3D space is provided by conducting image analysis, of which the images are obtained through a coupled camera to the radar device. To ensure successful training of a fully convolutional network (FCN), we propose a normalization method, which is found to be essential to be applied to the radar signal before feeding into the neural network. The system after proper training is able to first detect the presence of an object in an environment. If it does, the system then further produces an estimation of its 3D position. Experimental results show that the proposed system can be successfully trained and employed for detecting a car and further estimating its 3D position in a noisy environment.