EE Seminar: Direction-of-Arrival Estimation Using SubSpace Methods for Sparse Arrays
Electrical Engineering Systems Seminar
Speaker: Yoav Amiel
M.Sc. student under the supervision of Dr. Wasim Huleihel
Sunday, 2nd February 2025, at 15:00
Room 011, Kitot Building, Faculty of Engineering
Direction-of-Arrival Estimation Using SubSpace Methods for Sparse Arrays
Abstract
Sparse arrays enable resolving more direction of arrivals (DoAs) than antenna elements using non-uniform arrays. This is typically achieved by reconstructing the covariance of a virtual large uniform linear array (ULA), which is then processed by subspace DoA estimators. However, these methods assume that the signals are non-coherent and the array is calibrated; the latter is often challenging to achieve in sparse arrays, where one cannot access the virtual array elements. In some real scenarios such as Track-Before-Detect (TBD), the receiver has no access to prior information on the sources, not even their number, which is typically needed or added as a hidden assumption to classic algorithms input. In this thesis, we propose Sparse-SubspaceNet, which leverages deep learning to enable subspace-based DoA recovery from sparse miscalibrated arrays with coherent sources without receiver prior information but for the number of sources, and suggest a scheme for learning the number of sources from covariance matrix eigenvalues distribution. Sparse-SubspaceNet utilizes a dedicated deep network to learn from data how to compute a surrogate virtual array covariance that is divisible into distinguishable subspaces. By doing so, we learn to cope with coherent sources and miscalibrated sparse arrays, while preserving the interpretability and the suitability of model-based subspace DoA estimators.
השתתפות בסמינר תיתן קרדיט שמיעה לתלמידי תואר שני ושלישי = עפ"י רישום שם מלא + מספר ת.ז. בדף הנוכחות שיועבר באולם במהלך הסמינר
