On the RCS Estimation from Camera Images

In this paper, we present an approach for supervised training of a neural network operating on camera images to estimate the receive power of an automotive radar. The training requires the association of pixels from camera images and radar spectra via an image-warping layer. We evaluate the influence of forward and backward image warping, known from classical image processing, on the application of receive power estimation. It is found that the implementation via backward image warping results in a biased estimation of the receive power whilst the forward image warp results in an unbiased estimation.