This paper presents a mechanism for evaluating the Root Mean Square Error (RMSE) of a Minimum Variance Unbiased Estimator (MVUE) of a target state in 3D space using acoustic measurements. The target state is represented by $(\theta, \phi, r)$ and it is estimated using Time Difference of Arrival measurements at the sensors and we assume that the sound-speed c is unknown. We then examine the interaction between azimuth angle $\theta$ on range RMSE, and the impacts of measurement noise variance on RMSE of $(\theta, \phi, r, c)$ estimates. These results and analytical formulations can be used as a baseline to evaluate proper 3D array geometry design, as well as inform the potential RMSE improvements when using a biased minimum mean square error (MMSE) estimator over an unbiased (MVUE) one for the same set of measurements.

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.

This work formalizes the Route Planning Problem (RPP), wherein a set of distributed assets (e.g., ships, submarines, unmanned systems) simultaneously plan routes to optimize a team goal (e.g., find the location of an unknown threat or object in minimum time and/or fuel consumption) while ensuring that the planned routes satisfy certain constraints (e.g., avoiding collisions and obstacles). This problem becomes overwhelmingly complex for multiple distributed assets as the search space grows exponentially to design such plans. The RPP is formalized as a Team Discrete Markov Decision Process (TDMDP) and we propose a Multi-agent Multi-objective Reinforcement Learning (MaMoRL) framework for solving it. We investigate challenges in deploying the solution in real-world settings and study approximation opportunities. We experimentally demonstrate MaMoRL's effectiveness on multiple real-world and synthetic grids, as well as for transfer learning. MaMoRL is deployed for use by the Naval Research Laboratory - Marine Meteorology Division (NRL-MMD), Monterey, CA.

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.

We consider the problem of point target detection on images and focal plane arrays (FPA). Imaging sensors are becoming ubiquitous tools in several applications, such as biomedical systems, autonomous surveillance systems, target tracking systems, and robotics. In these applications, matched filter and template matching are commonly used detection strategies, however, these approaches are unable to provide sub-pixel accuracy and avenues for adaptive pixel-width selection for computationally efficient image processing. In this paper, we derive the maximum likelihood estimator (MLE) of target location on images. The proposed MLE is optimal under the assumption that the FPA contains a point target that has its signal intensity spread in multiple image pixels in the form of a Gaussian point spread function (PSF) with known standard deviation. Further, we derive the Cramér-Rao lower bound (CRLB) of the estimate and present the hypothesis test for target acceptance, resulting in a novel maximum likelihood detector (MLD) for images. Simulation results are provided to validate the performance of the proposed MLE and MLD; it is shown that the MLE is efficient in very low SNR values, starting at -15 dB, and the MLD achieves probability of detection of near unity with zero false alarms starting at 0 dB.

The knowledge of the principles and goals under which an adversary organization operates is required to predict its future activities. To implement successful counter-actions, additional knowledge of the specifics of the organizational structures, such as command, communication, control, and information access networks, as well as responsibility distribution among members of the organization, is required. In this paper, we employ a Hidden Markov Random Field (HMRF) model and a graph matching algorithm to discover the attributes of and relationships among organizational members, assets, environment areas, and mission tasks. We focus on identifying the mapping between hypothesized nodes of enemy command organization and tracked individuals and resources. This also allows us to compute the posterior energy function quantifying the belief that the observed data has been generated by a particular organization. The experiment results show that our probabilistic model and the Simulated Annealing search algorithm can accurately identify the different organizational structures and achieve correct node mappings among organizational members.

An efficient, exact algorithm is developed for optimizing the performance of nested block joins. The method uses both dynamic programming and branch-and-bound. In the process of deriving the algorithm, the class of resource allocation problems for which the greedy algorithm applies has been extended. Experiments with this algorithm on extremely large problems show that it is superior to all other known algorithms by a wide margin.>