Agenda
MSc SPS Thesis presentation
- Thursday, 22 May 2025
- 13:00-15:00
- HB17.140 (Seminar room)
Acoustic array processing with planar coded cover
Yuan yuan
While the success of improving direction of arrival (DOA) estimation with linear coded covers using a single acoustic vector sensor (AVS) has been established, the extension of this theory to array-based systems remains unexplored. To address this gap, we employ a specially designed coded cover and leverage compressed sensing (CS) and compressed covariance sensing (CCS) methods, extending their application from single AVS systems to an array-based acoustic measurement system. Our results demonstrate that a 14×10 coded cover with 12 PU probes enables accurate localization of 100 sound sources in 3D, even at a signal to noise ratio (SNR) as low as 10 dB, showcasing the scalability and robustness of this approach.
To further enhance localization accuracy, we implement a self-calibration method in the covariance domain to correct phase and gain errors in each receiving channel. Additionally, we combine self-calibration with CCS to improve resolution and reduce side lobes. For geometric mismatch, we first investigate the sparsity-cognizant total least-squares (STLS) with multiple measurement vectors (MMV) variant of the fast iterative shrinkage-thresholding algorithm (FISTA) method. Then a grid-searching strategy is employed to compensate for these mismatches, ensuring better estimation accuracy. Experimental validation confirms that these techniques significantly enhance DOA estimation under non-ideal conditions, contributing to the advancement of acoustic sensing and localization methodologies.
