Adaptive Sensor Selection with Deterministic Priors for DoA Tracking

Abstract

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 adaptively choosing sensors for each snapshot during DoA tracking. We have devised a greedy algorithm for sensor selection, incorporating a submodular cost function based on our proposed deterministic prior models for DoA. Notably, we show that this selection algorithm is equally efficient compared to the conventional greedy method that relies on exact knowledge of the DOAs. We also introduce a modified version of a conventional CS-reconstruction algorithm that takes advantage of prior information to reduce the required number of measurements and computational time. We demonstrate that the tracking accuracy is improved when using the deterministic priors for sensor selection and subsequent reconstruction.