Simulation of imaging radar for obstacle avoidance and enhanced vision

One of the main advantages of millimeter wave (MMW) imaging radar systems results from the fact that their imaging performance does nearly not depend on atmospheric effects such as fog, rain and snow. That is the reason that MMW radar seems to be one of the most promising sensors for enhanced vision systems (EVS), which can aid the pilot during approach, landing and taxiing, especially under bad weather conditions. Compared to other imaging devices (TV, IR etc.), MMW radar systems deliver a lower image resolution and update rate, and have a worse signal to noise ratio. Moreover, the commonly proposed method of the perspective view projection (''out the window view'') in EVS applications results in some imaging errors and artefacts. These sensor specific effects should be taken into account during the presently conducted EVS research and development. To get the opportunity of studying imaging radar systems in ground based research environments, we have developed a new type of a MMW radar sensor simulator. Our approach is based on detailed terrain and/or airport data bases, as they are available for normal visual simulations or VR applications. We have augmented these data bases with some specific attributes which describe object surface properties with respect to MMW. Our approach benefits from the state of the art of high speed computer graphics hard- and software (e.g. z-buffering, lighting, materials, texture mapping). It is implemented in C/C++ and uses the OpenGL graphic standard and the SGI Performer data base handler. It runs on every SGI graphic workstation, and achieves an image update rate of about 20 Hz, which is more than actual available radar systems deliver. One of the main advantages of our approach is, that it can be integrated easily in emergent multisensor based enhanced vision systems and it is a usefull tool for EVS research and development.