Mr. Manjunath Thindlu Rudrappa | Engineering | Best Researcher Award
Mr. Manjunath Thindlu Rudrappa, Fraunhofer Institute for High Frequency Physics and Radar Techniques, Germany
Manjunath Thindlu Rudrappa is an accomplished researcher specializing in radar signal processing, object tracking, and space object characterization. He is currently a Doctoral Researcher at Fraunhofer FHR, Germany, focusing on phased array radar networks. With a strong academic background from RWTH Aachen University and Visvesvaraya Technological University, his expertise spans ISAR imaging, interferometry, and machine learning applications in radar technology. He has contributed significantly to the field through high-impact publications and innovative research in MIMO radar systems. Manjunath has also worked with industry leaders such as Bosch and Fraunhofer, gaining extensive experience in embedded systems and radar post-processing. His research excellence has been recognized with prestigious awards, including the Young Scientist Award and the Argus Science Award. Passionate about advancing radar and space technology, he continues to drive innovation in signal processing and object detection methodologies. 🚀📡
Publication Profile
📚 Education
Manjunath earned his Bachelor of Engineering (B.E.) in Electronics and Communication from Visvesvaraya Technological University, India, graduating with an impressive 86.41% aggregate. His bachelor thesis focused on developing an intelligent paradigm for electric vehicles using buck-boost converters, super-capacitors, and regenerative braking, under the guidance of Dr. Bhakthavatsalam and Mr. Gowranga K.H from IISc Bangalore. He pursued his Master of Science (M.Sc.) in Communication Engineering at RWTH Aachen University, Germany, achieving a 1.5 aggregate. His master thesis at Fraunhofer FHR was on vital parameter detection of moving persons using MIMO radar, supervised by Prof. Dr.-Ing Peter Knott and Dr.-Ing Reinhold Herschel. Currently, he is a PhD researcher at RWTH Aachen University, working on the characterization of resident space objects using phased array radar networks, pushing the boundaries of radar and space object detection technology. 🎓📡
💼 Experience
Manjunath began his career as an Embedded Software Engineer at Robert Bosch Engineering and Business Solutions Limited (2014–2017) in India, working on software development for automotive systems. Moving to Bosch Engineering GmbH, Germany, he served as an Embedded Application Software Developer (2018–2019), specializing in software solutions for automotive applications. His transition to Fraunhofer FHR in Germany marked his entry into radar research, where he worked as a Work Student (2019–2020) on vital parameter estimation, detection, tracking, and clustering. Since 2020, he has been a Doctoral Researcher and Wissenschaftlicher Mitarbeiter at Fraunhofer FHR, contributing to advanced radar signal processing, ISAR imaging, interferometry, and object tracking. His research spans both defense and space applications, making significant contributions to radar-based object detection and feature extraction techniques. 🔬🚀
🏆 Awards & Honors
Manjunath has received prestigious recognitions for his contributions to radar signal processing and communication technology. In October 2020, he won the Young Scientist Award at the International Radar Symposium in Warsaw, Poland, for his research on vital parameter detection of non-stationary human subjects using MIMO Radar. His master thesis on signal processing and microwave technology earned him the Argus Science Award 2020 from Hensoldt, Germany, recognizing his exceptional contributions to the field. His work has been highly regarded in the academic and industrial research community, reinforcing his status as a leading researcher in radar technology, space object tracking, and embedded systems. 🏅📡
🔬 Research Focus
Manjunath’s research is centered on radar signal processing, object tracking, and space object characterization. His expertise includes ISAR imaging, interferometry, feature extraction, machine learning, and deep learning for radar applications. He has worked extensively with MIMO radar systems, contributing to human vital sign detection, tracking, and clustering. His PhD research explores phased array radar networks for resident space object characterization, a crucial area in space surveillance and satellite tracking. Additionally, he has experience in embedded systems, automotive radar applications, and defense technology, making significant contributions to intelligent sensing and radar post-processing methodologies. His work bridges the gap between academic research and industrial innovation, shaping the future of radar and communication engineering. 🌍📡🚀
Publication Top Notes
1️⃣ Moving human respiration sign detection using mm-wave radar via motion path reconstruction – Cited by: 17 | Year: 2021 📡👤💨
2️⃣ Vital parameters detection of non-stationary human subject using MIMO radar – Cited by: 11 | Year: 2020 📡🔬🧍
3️⃣ Distinguishing living and non-living subjects in a scene based on vital parameter estimation – Cited by: 8 | Year: 2021 🔍👤🏠
4️⃣ Characterisation of resident space objects using multistatic interferometric inverse synthetic aperture radar imaging – Cited by: 4 | Year: 2024 🛰️📡📊
5️⃣ 3D reconstruction of resident space objects using radar interferometry and nonuniform fast Fourier transform from sparse data – Cited by: 4 | Year: 2022 🌍📡📉
6️⃣ Improvements of GESTRA—A phased-array radar network for the surveillance of resident space objects in low-Earth orbit – Cited by: 2 | Year: 2023 🚀🛰️📶
7️⃣ RSO feature extraction using Super Resolution Wavelets and Inverse Radon Transform – Cited by: 1 | Year: 2022 📡📊📉
8️⃣ High-resolution human clustering based on complex signal correlation coefficients – Cited by: 1 | Year: 2022 🏠📡📊
9️⃣ Characterisation of Resident Space Objects and Synchronisation Error Compensation in Multistatic Interferometric Inverse Synthetic Aperture Radar Imaging – Year: 2025 🛰️📡📊
🔟 Clusterung von Detektionen – Year: 2022 📡📍🔍
Conclusion
Mr. Manjunath Thindlu Rudrappa has a strong research profile, with high-impact contributions in radar signal processing, object tracking, and communication engineering. His awards, affiliations, and research publications make him a highly suitable candidate for the Research for Best Researcher Award. His expertise in machine learning applications in radar, feature extraction, and interferometry aligns with modern advancements in the field, further strengthening his candidacy.