  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.
