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Drifters are energy-efficient platforms for monitoring rivers and oceans. Prior work largely focused on free-floating drifters that drift passively with flow and have little or no controllability. In this paper we propose steerable drifters that use multiple rudders for modulating the hydrodynamic forces and thus maneuvering. A dynamic model for drifters with multiple rudders is presented. Simulation is conducted to examine the behavior of the drifter in two different flow conditions, uniform flow and parabolic flow. When there is no difference in relative flow between the rudders, as in uniform flow, the drifter can only be controlled until its velocity approaches that of the water. However, when present, local flow differentials can be exploited to initiate motion lateral to the ambient flow and control the trajectory of the drifter to some degree. The motion of the drifter is further classified as belonging to one of three major modes, rotational, oscillatory, and stable. The behavior of the drifter in a simulated river was mapped for different rudder angles. Identifying the parameters that induce each mode lays the groundwork for developing a feedback control scheme for the drifter.

The problem of localizing a moving target arises in various forms in wireless sensor networks. Deploying multiple sensing receivers and using the time-difference-of-arrival (TDOA) of the target’s emitted signal is widely considered an effective localization technique. Traditionally, TDOA-based algorithms adopt a centralized approach where all measurements are sent to a predefined reference node for position estimation. More recently, distributed TDOA-based localization algorithms have been shown to improve the robustness of these estimates. For target models governed by highly stochastic processes, the method of nonlinear filtering and state estimation must be carefully considered. In this work, a distributed TDOA-based particle filter algorithm is proposed for localizing a moving target modeled by a discrete-time correlated random walk (DCRW). We present a method for using data collected by the particle filter to estimate the unknown probability distributions of the target’s movement model, and then apply the distribution estimates to recursively update the particle filter’s propagation model. The performance of the distributed approach is evaluated through numerical simulation, and we show the benefit of using a particle filter with online model learning by comparing it with the non-adaptive approach.