System design and optimization for radar based biomedical applications

Radar-based systems have a very important potential for the interpretation of motion-based objects using the doppler effect. In recent years, various radar-based studies have been conducted to characterize the tissues and organs, following the movement-indexed vital signs. In this study, it is aimed to make a radar system which can be used in various biomedical applications. Antennas used in the radar system have a very important place in order to perform radar based applications properly. The parameters such as operating frequency, gain and directivity of these antennas should be suitable for the desired application. Transmission line equations and an electromagnetic design program (CST) were used for the design of these antennas and the antennas of the desired performance were designed and optimized. Using the CST program, patch width, patch length, slit width, slit length and geometric variations of the microstrip antenna were made. Dimensional changes on the antennas were compared with the initial state of the antenna. The operating frequency of the radar antenna, which was 2.36 GHz before the process, was brought to the desired level of 2.40 GHz. The radar antenna gain was improved from 3.02 dB to 3.67 dB. Receiver and transmitting antennas with improved performance have been added to the radar system. The modulator circuit, low pass filter circuit and ardunio and frequency counter circuit are designed together with the microwave components installed in accordance with the operating principle of the radar system. In addition, an analogue / digital converter and radar system were connected to the computer and the system was seen to be working properly. A moving obstacle was placed in front of the radar system and tests were performed. It has been determined that the signal amplitude increases in the time domain while the object being moved closer to the radar system and decreases as the object moves away from the radar system. It has been concluded that with appropriate signal processing techniques, these movements can be interpreted based on biomedical application.