Simulasi Sistem Pendeteksi Objek Pada Pesawat Dengan Menggunakan Teknologi SAR (Synthetic Aperture Radar) - Mohammed Ikrom Asysyakuur; Denden Mohammad Ariffin; Arief Suryadi Satyawan; Ni Nyoman Ayu Marlina; Nafisun Nufus; Raden Aditya Satria Nugraha; Ema Ema (Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO), 2021)
To map an object in the form of the contours of an area will be difficult if you use a passive sensor system such as a camera because of its limitations to penetrate clouds, fog, and erratic weather. Therefore, better technology is needed to map an object from above the earth's surface or the air. Synthetic Aperture Radar (SAR) is a mapping technique using radar to produce high-resolution maps of the earth's contours or describe an object and present information in the form of images or images. SAR can work in any weather conditions, whether in the rain, snow, or even fog. Another SAR capability is to be able to detect objects with a fairly good level of accuracy. Based on the above, research and development of SAR technology is very necessary. In this research, preliminary studies on SAR technology have been carried out. This research is intended to complement the capabilities of drones or unmanned aerial vehicles (UAVs) both for imaging the contours of the earth and activities related to society 5.0 so that the application can be used for modern agriculture, forestry, marine, and border observation activities. The goal is to simulate the detection of objects that are on the ground. There are two simulated SAR-based object detection methods, namely Range Migration Algorithm and Back Projection Algorithm. This simulation was built using a computer with an AMD A8 processor, 8 GB of memory, and MATLAB 2019 software. The simulation results show that the system design for both algorithms can work well at a frequency of 4 GHz with a resolution range of 3m. The image displayed in this simulation is in 2-D form. The average processing time of the two algorithms to be able to detect objects is 103.2 seconds. The image displayed in this simulation is in 2-D format. While the average processing time of the two algorithms to be able to detect objects is 103.2 seconds.