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This paper presents a technique for dynamically reconfiguring search spaces in order to enable Bayesian autonomous search and tracking missions with moving targets. In particular, marine search and rescue scenarios are considered, highlighting the need for space reconfiguration in situations where moving targets are involved. The proposed technique improves the search space configuration by maintaining the validity of the recursive Bayesian estimation. The advantage of the technique is that autonomous search and tracking can be performed indefinitely, without loss of information. Numerical results first show the effectiveness of the technique with a single search vehicle and a single moving target. The efficacy of the approach for coordinated autonomous search and tracking is shown through simulation, incorporating multiple search vehicles and multiple targets. The examples also highlight the added benefit to human mission planners resulting from the technique's simplification of the search space allocation task.

Abstract-This paper presents a hybrid particle-element approach, HyPE, suitable for recursive Bayesian searching-andtracking (SAT). The hybrid concept, to synthesize two recursive Bayesian estimation (RBE) methods to represent and maintain the belief about all states in a dynamic system, is distinct from the concept behind "mixed approaches", such as Rao-Blackwellized particle filtering, which use different RBE methods for different states. HyPE eliminates the need for computationally expensive numerical integration in the prediction stage and allows space reconfiguration, via remeshing, at minimal computational cost. Numerical examples show the efficacy of the hybrid approach, and demonstrate its superior performance in SAT scenarios when compared with both the particle filter and the element-based method.

This paper compares some of the common tools and techniques that enable state-of-the-art systems to provide high-level control of mobile sensor networks. There is currently a great deal of interest in employing unmanned and autonomous vehicles in intelligence, surveillance, and reconnaissance operations. Although this paper addresses issues common to all mobile sensor networks, the applications presented are typically associated with autonomous vehicles. We focus speciflcally on three high-level areas: I. mission deflnition languages that allow human users to compose missions deflned in terms of tasks, 2. communicationaddressing degradation and loss and relationship to underlying system architecture design, and 3. task allocation among the assets.