Underwater Source-Level Estimation using Sparsity-Cognizant Source-Location Mapping

In this work, we present a method to estimate the acoustic source gain of targets in a shallow-water environment, using passive sonar. This is accomplished by using a two-step approach. The first step is to localize the target, which is accomplished by using the Sparsity-Cognizant Source Location Mapping (scSLM) algorithm, developed at the Space and Naval Warfare Systems Center Pacific. This algorithm has been shown to be robust to environmental mismatch when localizing multiple targets. Moreover, it yields an estimate of the source levels. Unfortunately, the bias in these estimates can be large depending on the values of the algorithm's tuning parameters. The second step of our approach is introduced to decrease the bias in the acoustic source gain estimates. By using the locations obtained by scSLM and the environmental model, we develop an estimator that yields improved acoustic source gain estimates. Numerical tests are used to illustrate the quality of the acoustic source gain estimates for multiple broadband targets in a shallow-water environment.