This paper develops computationally efficient algorithms for the analysis of acoustic data to localize a target through improved angle of arrival estimation. The passive target localization problem has a wide range of applications in wireless communication, navigation, acoustic sensor networks, indoor localization, to name a few. We have focused on novel formulations and solution methods for target localization using Time Differences of Arrival (TDOA) among distinct pairs of passive sensor nodes in an acoustic sensor network with known sensor positions.

Routing in uncertain environments is challenging as it involves a number of contextual elements, such as different environmental conditions (forecast realizations with varying spatial and temporal uncertainty), changes in mission goals while en route, and asset status. In this paper, we use an approximate dynamic programming method with Q-factors to determine a cost-to-go approximation by treating the weather forecast realization information as a stochastic state. These types of algorithms take a large amount of offline computation time to determine the cost-to-go approximation, but once obtained, the online route recommendation is nearly instantaneous and several orders of magnitude faster than previously proposed ship routing algorithms. The proposed algorithm is robust to the uncertainty present in the weather forecasts. We compare this algorithm to a well-known shortest path algorithm and apply the approach to a real-world shipping tragedy using weather forecast realizations available prior to the event.