Comparison of Two Hyperparameter-Free Sparse Signal Processing Methods for Direction-of-Arrival Tracking in the HF97 Ocean Acoustic Experiment

This is the authors' abstract. We don't add key points for this paper.

In this paper, we review and compare the performance of two recently introduced hyperparameter-free sparse signal processing methods namely, the sparse iterative covariance-based estimation method and the sparse Bayesian learning-based relevance vector machine method, for direction-of-arrival (DOA) tracking of multiple signals using an array of sensors. The methods are presented to the readers, in a tutorial style for easy understanding. Hyperparameter-free sparsity-based methods are attractive in practice since tuning of regularization parameters (hyperparameters) is not necessary as they are automatically estimated from the data. The DOA tracking problem is formulated as a snapshot-by-snapshot estimation problem and the implementation of the methods are discussed in detail. A simulation study using a uniform-linear-array is carried out to evaluate the performance of the methods in terms of the root-mean-squared error of the DOA estimates and the probability of resolution with the goal of determining when one is to be preferred over the other. The algorithms are also applied on passive sonar data from the 1997 High-Frequency (HF97) ocean acoustic experiment to demonstrate their usability in a real underwater scenario, as well as their robustness to the modeling assumptions made. We draw new conclusions about the main features of these methods that are important to the underwater acoustic practitioners.