In this paper, the 3D ego-motion parameters estimation problem using radar-network odometry (RNO) is presented. A pipeline for RNO-based 3D ego-motion parameters estimation is proposed and demonstrated. Moreover, two different radar-network-based algorithms for elevation estimation of targets are analyzed and utilized to improve the accuracy of the RNO-based 3D ego-motion parameters estimation. The performance of the proposed solutions is quantitatively evaluated with different simulated scenarios. Finally, the proposed solutions are tested in a scenario of forward-looking synthetic aperture radar image reconstruction for high resolution radar imaging.