3 accepted papers
The challenge of sensor selection involves choosing a subset of the available measurements to estimate data while aiming to minimize estimation errors. Traditionally, convex relaxation methods with solvers like the projected subgradient (PSG) algorithm have been employed to address this problem. How…
Compressive sensing (CS) techniques for estimating the direction-of-arrival (DoA) stand apart from traditional approaches due to their ability to derive DoA information from just a single snapshot, eliminating the need for a large number of snapshots. This research addresses the challenge of adaptiv…
In the problem of sensor selection, observations from L out of N sensors are chosen for data estimation, with the objective of minimizing an error metric. It is well known that a greedy selection (GS) method can achieve an error metric which is not worse than 1/e from that of the optimal algorithm,…