Chris Gage

The size dependence of the dielectric constants of barium titanate or other ferroelectric particles can be explored by embedding particles into an epoxy matrix whose dielectric constant can be measured directly. However, to extract the particle dielectric constant requires a model of the composite medium. We compare a finite element model for various volume fractions and particle arrangements to several effective medium approximations, which do not consider particle arrangement explicitly. For a fixed number of particles, the composite dielectric constant increases with the degree of agglomeration, and we relate this increase to the number of regions of enhanced electric field along the applied field between particles in an agglomerate. Additionally, even for dispersed particles, we find that the composite method of assessing the particle dielectric constant may not be effective if the particle dielectric constant is too high compared to the background medium dielectric constant.

This paper presents a system of multiple coordinating autonomous underwater vehicles (AUV) that can localize and track a shark tagged with an acoustic transmitter. Each AUV is equipped with a stereo-hydrophone system that provides measurements of the relative bearing to the transmitter, as well as an acoustic modem that allows for inter-AUV communication and hence cooperative shark state estimation and decentralized tracking control. Online state estimation of the shark's state is performed using a Particle Filter in which measurements are shared between AUVs. The decentralized control system enables the AUVs to circumnavigate a dynamic target, (i.e. the estimated shark location). Each AUV circles the target by tracking circles of different radii and at different phase angles with respect to the target so as to obtain simultaneous sensor vantage points and minimize chance of AUV collision. A series of experiments using two AUVs were conducted in Big Fisherman's Cove in Santa Catalina Island, CA and demonstrated the ability to track a tagged leopard shark (Triakis semifasciata). The performance of the tracking was compared to standard manual tracking performed using an directional hydrophone operated by a researcher in a boat. In an additional experiment, the AUVs tracked an acoustic tag attached to the tracking boat to quantify the error of the state estimation of the system.

This paper presents a prototype system that enables an autonomous underwater vehicle (AUV) to autonomously track and follow a shark that has been tagged with an acoustic transmitter. The AUV's onboard processor handles both real‐time estimation of the shark's two‐dimensional planar position, velocity, and orientation states, as well as a straightforward control scheme to drive the AUV toward the shark. The AUV is equipped with a stereo‐hydrophone and receiver system that detects acoustic signals transmitted by the acoustic tag. The particular hydrophone system used here provides a measurement of relative bearing angle to the tag, but it does not provide the sign (+ or −) of the bearing angle. Estimation is accomplished using a particle filter that fuses bearing measurements over time to produce a state estimate of the tag location. The particle filter combined with a heuristic‐based controller allows the system to overcome the ambiguity in the sign of the bearing angle. The state estimator and control scheme were validated by tracking both a stationary tag and a moving tag with known positions. Offline analysis of these data showed that state estimation can be improved by optimizing diffusion parameters in the prediction step of the filter, and considering signal strength of the acoustic signals in the resampling stage of the filter. These experiments revealed that state estimate errors were on the order of those obtained by current long‐distance shark‐tracking methods, i.e., manually driven boat‐based tracking systems. Final experiments took place in SeaPlane Lagoon, Los Angeles, where a 1‐m leopard shark (Triakis semifasciata) was caught, tagged, and released before being autonomously tracked and followed by the proposed AUV system for several hours.