У нас вы можете посмотреть бесплатно Radarconf'22: Unlimited Sampling for FMCW Radars : a Proof of Concept или скачать в максимальном доступном качестве, которое было загружено на ютуб. Для скачивания выберите вариант из формы ниже:
Если кнопки скачивания не
загрузились
НАЖМИТЕ ЗДЕСЬ или обновите страницу
Если возникают проблемы со скачиванием, пожалуйста напишите в поддержку по адресу внизу
страницы.
Спасибо за использование сервиса ClipSaver.ru
This the video of the talk presented at IEEE Radarconf 2022 Unlimited Sampling for FMCW Radars : a Proof of Concept Abstract: High-resolution FMCW radar systems are becoming an integral aspect of applications ranging from automotive safety and autonomous driving to health monitoring of infants and the elderly. This integration provides challenging scenarios that require radars with extremely high dynamic range (HDR) ADCs; these ADCs need to avoid saturation while offering high-performance and high-fidelity data-acquisition. The recent concept of Unlimited Sensing allows one to achieve high dynamic range (HDR) acquisition by recording low dynamic range, modulo samples. Interestingly, oversampling of these folded measurements, with a sampling rate independent of the modulo threshold, is sufficient to guarantee their perfect reconstruction for band-limited signals. This contrasts with the traditional methodology of increasing the dynamic range by adding a programmable-gain amplifier or operating multiple ADCs in parallel. This paper demonstrates an FMCW radar prototype that utilises the unlimited sampling strategy. Our hardware experiments show that even with the use of a modulo measurements of lower precision, the US reconstruction is able to match the performances of the conventional acquisition. Furthermore, our real-time processing capability demonstrates that our “proof-of-concept” approach is a viable solution for HDR FMCW radar signal processing, thus opening a pathway for future hardware-software optimization and integration of this technology with other mainstream systems. This is a joint work with Ayush Bhandari (Imperial College, London, UK) thomas-feuillen.com/ for more detail about my research.