This thesis covers the development of an imaging radar capable of detecting hidden obstacles for terrain mapping for use on an autonomous off-road vehicle. Off-road environments pose several challenges for autonomous vehicles. This thesis focuses on one such problem; obstacles hidden in vegetation. The thesis starts with an extensive literature review of sensor technologies, such as daylight cameras, IR and spectral cameras and LiDAR sensors. All of these sensors have their strengths and weaknesses, but they all lack the ability to penetrate and detect truly hidden obstacles. This is where radar sensors are exceptional. Radar sensors used for similar purposes are evaluated and analyzed.Large variations in configurations were found in the literature with regard to frequency and imaging techniques.Conventional mapping radars typically operate at high frequencies, which has proved to drastically reduce their ability to penetrate materials. To understand the large variations in configuration in both frequency and imaging techniques, literature reviews of each of these subjects were also undertaken. To address the questions remaining from the literature review, a comprehensive radar systems analysis is presented. Starting with measurements and analysis of the attenuation of radar signals in the relevant frequencies, before focusing the efforts on contrast between obstacles and vegetation. Concluding that a low frequency system operation in the 1-6 GHz interval might be able to detect hidden obstacles. For investigation of the latter a synthetic aperture radar measurement setup was developed, using a linear rail and a vector network analyzer. A short presentation of image formation algorithms is presented, with a particular focus on time-domain backprojection. Methods for increasing cross-range resolution with limited increase in system complexity were investigated and simulated. Concluding that a time-domain multiple-input multiple-output configuration is suitable for the application. A demonstrator system is developed based on the previous analysis and simulations. All the necessary considerations for developing a functional system based on simulations are presented, including system design and control.The demonstrator itself is built using mostly connectorized commercially available components, but some system components had to be custom made for the application. Considerable performance limiting factors were addressed and mitigated. Finally, the complete demonstrator system is tested in both controlled and complex environments, starting with initial imaging results to ensure system functionality and compare measured performance to the simulated results. The systems ability to detect obstacles in highly controlled environments is tested and found to be very good. The system was mounted on a test vehicle and combining the radar with the vehicles navigation enabled system testing in complex environments. Two test cases are presented; lightly occluded and fully occluded obstacles. In the case with lightly occluded obstacles, the system performed well, detecting the obstacles unambiguously. The case with highly occluded obstacles posed a larger challenge. It is argued that the reason for the ambiguous obstacle detection in this case is a sub-optimal placement of the radar on the vehicle and the rough mapping technique implemented. It is believed that the radar is capable of detecting these obstacles if these issues are resolved.