While the success of improving direction of arrival (DOA) estimation with linear coded covers using a single acoustic vector sensors (AVS) has been established, the extension of this theory to arraybased 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 selfcalibration method in the covariance domain to correct phase and gain errors in each receiving channel. Additionally, we combine selfcalibration 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.