Short Range Millimeter-Wave Inverse Synthetic Aperture Radar Imaging

Millimeter-wave radar imaging has potential uses in applications such as material analysis and medical in-vivo scanning of tissue. This thesis is about measuring objects using a millimeter wave radar, implement radar imaging algorithms and evaluate the resulting images. The measured objects are plates and rods made of metal and a rod made of acrylic glass (PMMA). The objects are measured using the inverse synthetic aperture radar technique, in which the radar is stationary and and the scene is either moving in a line or rotating in front of the radar, in our case at a few decimeters distance. A pulse generating circuit is used as transmitter and the receiver is a sampling oscilloscope. Multiple imaging algorithms, including backprojection and gridding, are implemented in Matlab. In order to test the algorithms, scripts are implemented to create simulated ideal data from line and rotation measurements. Data for multiple point scatterers is simulated and used in the imaging algorithms to identify if the scatterers are correctly resolved. Using this approach the implemented methods are shown to work as intended. Using strip-map measurement data for two separated metal plates it is shown that an image can be produced where the objects are resolved with correct width, spacing and down-range position. Both the backprojection and gridding algorithms are shown to produce higher quality images with a resolution of less than one centimeter when used in spotlight mode. For gridding it is found that objects with a small distance-to-radius ratio are distorted, which limits the object size for a specific distance.While radars previously mostly have been used in military and aviation, development of smaller cheaper radars in recent years has lead to a high interest for using radars as an alternative to other sensors such as IR-sensors and cameras. Some new areas where radars are being tested are mapping the surroundings of self driving cars to avoid collisions, high resolution imaging, material analysis and gesture recognition to allow control of computers simply by making hand gestures in front of it. Another useful application of radars is medical scanning of tissue. This technique could for example be used to detect skin cancer. In this thesis we have investigated how high quality images of objects can be created using a radar. To create an image we need to fill the pixels in the image with information from different points in space. This information needs to be gathered with some kind of sensor. If we take the example of a camera, it has many light sensitive sensors side by side and the information in each of these sensors is later displayed as a unique pixel. Most radars only work as a single sensor and gather information from a large area in front of it. To be able to create an image, a technique called synthetic aperture radar is used. In this method the radar scans a certain area in space. The data from all the different antenna positions can then be combined to create an artificial sensor matrix similar to that in a camera. After processing, the data then corresponds to unique pixels in the image. The benefit of using radar to create images is that they can see through materials that cameras can not, for example smoke and fog but also solid materials like soil can be penetrated. Since the radar transmits its own signal it does not require external light like a camera does. Furthermore, the range to objects can be calculated. In this thesis two different methods for converting measurement data into images were implemented and tested. The first one is called backprojection and is a time domain method, which means that processing is done on the measured time dependent signal. The positions of the radar and object for different measurements are considered and combined to produce an image. The other method is called gridding, in which the measured signal is first transformed into the frequency domain. A frequency domain signal is described by what frequencies build up the signal, rather than a time domain signal in which the signal level at different times is described. The image is produced by rearranging the data in the frequency domain according to the measurement geometry and then returning to the time domain (or spatial domain). With these two methods we were able to produce images with high resolution. We also learned what the different limitations of the two methods were. The backprojection is slow but achieves the best images, while also being flexible since it works for any measurement data. The gridding method is faster but it has certain criteria of the measurement setup, for example the distance to the object needs to be large compared to its size