RFI Analysis in Radar Data using DSP: A Software Approach Mentees: Brian Chen and Amber Sahdev Mentors: Gonzalo Cucho, Benjamin Eng and Yamuna Phal PI: Prof. Lara Waldrop I. Introduction IV. Implementation The Arecibo Observatory (AO) is a radio telescope in Puerto Rico, with the primary uses in radio astronomy and atmospheric sciences. Nowadays, the use of radio frequency signals for active radio applications (e.g. cellular, wifi) are more and more frequent. Those unfortunately adversely affect 1st stage 1 Figure 1: Arecibo Telescope.[2] measurements of passive radio systems such as Arecibo Observatory, as it is accompanied by a corresponding increased unintentional Radio Frequency Interference (RFI). Figure 4: (a) Time domain (b) Frequency domain. Since RFI analysis involves the observations of frequencies, we started out by implementing Fourier Transform of a signal. Signal is in (a) time domain, (b) frequency domain, with two major frequencies located at 430 MHz and 419 MHz. II. Problem Statement Incoherent scatter radar (ISR) experiments allow us to determine the density of electrons and ions in the ionosphere through spectrum analysis[1]. Since RFI is capable of modifying or influencing the original data those density calculations can be also affected. Figure 2 shows the data from AO @ 430 MHz experiment. The white region at the center is related to density of electrons and ions. RFI was reported at 419 MHz (as highlighted). 2nd stage Combining the two domains, we created a spectrogram. The red parts correspond to signal with strong intensity, while the yellow being weaker. To getting started on how learn how to use CUDA, we implemented these techniques using C++ and FFTW library. Figure 5: Spectrogram of test signal 3rd stage Figure 2: RFI at 419 MHz in ISR experiment in AO. [2] III. Objective & Methodology [Main objective] Generate a basic framework of signal processing techniques to analyze RFI in radar data. In order to reach our goal, we defined the following stages: [1st Stage] Learn the basic concepts of the Fourier Transform (relation between time-frequency domains) and implement it on test signals using MATLAB software. [2nd Stage] Implement a spectrogram of the test signal in MATLAB and C++ by using FFTW libraries. [3rd Stage] Learn how to use a GPU through CUDA to implement the previous techniques and analyze big data samples from Arecibo. Figure 6: Spectrogram of AO real Data Using CUDA libraries and a NVIDIA Tesla M2090 GPU we have processed AO data centered at 430 MHz, sampled at 25 MHz (bandwidth). This is a preliminary result for one pulse (20ms). V. Conclusions Acknowledgments We have developed a basic framework for pre-processing techniques that can be applied to RFI detection. Several stages are needed after these steps to identify the RFI type and parameters as well as for its mitigation which would be our future work. We have learnt how to do basic Fourier transform operations on signals and build a spectrogram using several programs such as MATLAB , C++, and CUDA. These skills will become useful in signal analysis, as they can be applied for radio frequency interference detections. We appreciate the support of our mentors for teaching us about the necessary knowledge and skills required for research. We also thank PURE committee for this chance to work with graduate mentors and to let us have a first-hand experience in research. References [1] Upper Atmospheric observations at the Arecibo Observatory: Examples obtained using new capabilities, B. Isham, C.A. Tepley, M.P. Sulzer, and Q.H. Zhou [2] Arecibo Observatory website: http://www.naic.edu