Abstract
The thermocline is a relatively narrow vertical region that separates the mixed layer at the surface from the deep-water layer. In this region, the gradient of temperature with respect to depth is higher than in the rest of the water column. The characteristics of the thermocline have strong impact in marine biology, since it may trap high-nutrient organisms, and it also affects sound propagation, with direct impact on underwater acoustic communications and military operations. Under adaptive sampling, Autonomous Underwater Vehicles are practical tools for efficient ocean observation. In this work, we describe an implementation of an Extremum Seeking Controller that performs identification and tracking of thermoclines at its point of highest temperature gradient in a completely autonomous way. The vehicle chosen to perform this tracking was an autonomous vertical profiler, and the algorithms were validated using both real and simulated data.

Abstract
The energy requirements of thruster driven autonomous underwater vehicle (AUV) missions have been growing in recent years. Their complexity and length are continuously increasing due to the growth of undersea exploration. The use of variable buoyancy systems (VBS) can potentially lead to energy savings since consumption is only required for buoyancy changes. As such, energy is only spent during limited periods of time, as opposed to thruster driven systems, where consumption is typically continuous. In this work, an energetic comparison between thruster and VBS driven devices is performed for a specific mission profile and a defined set of parameters. The influence of the mission parameters is studied in order to determine which system leads to the lowest energy consumption. For the case study presented, it is shown that the use of VBS over thrusters can lead to considerable energetic savings. © 2019 IEEE.

Abstract
Autonomous underwater vehicles (AUVs) are becoming increasingly ubiquitous due to the growing needs in exploring Ocean resources. One of the most challenging tasks in this domain relates to the energy these vehicles require, given the increase in the number of scientific payloads and on the mission complexity. One way to potentially reduce the amount of energy consumed during vertical motion is to replace or complement the thruster action with a controlled change of the vehicles floatation, using a variable buoyancy system (VBS). This paper presents the development of an electromechanical VBS for shallow depths, up to 100 m, to be included in an existing AUV. A preliminary mechanical design is presented, along with a mathematical model allowing the calculation of the energy spent by this device, based on the components manufacturers' data. A comparison between the energy consumption using thrusters and the designed VBS is presented. © 2019 IEEE.

Abstract
The lack of penetration of light and electromagnetic radiation beyond a few meters in the ocean makes acoustics the technique of choice for data transmission, target detection and ocean sensing in general. Acoustic transducers are typically based on piezoelectric materials due to the good response at high frequencies. Depending on the application it can be built using ceramics, polymers and composite materials. In the hydrostatic mode PZT ceramics hydrophones have low performance due to the low hydrostatic piezoelectric stress value. On the other hand, PVDF have shown relatively high hydrostatic mode response. This work presents the development of a PVDF hydrophone for deep sea applications. The hydrophone was subjected to a pressure test up to 25 MPa to evaluate the response variation under high hydrostatic pressure. The results show an increase up to 6 dB sensitivity under 15 MPa pressure.

Abstract
In this paper we present a novel method for the acoustic tracking of multiple Autonomous Underwater Vehicles. While the problem of tracking a single moving vehicle has been addressed in the literature, tracking multiple vehicles is a problem that has been overlooked, mostly due to the inherent difficulties on data association with traditional acoustic localization networks. The proposed approach is based on a Probability Hypothesis Density Filter, thus overcoming the data association problem. Our tracker is able not only to successfully estimate the positions of the vehicles, but also their velocities. Moreover, the tracker estimates are labelled, thus providing a way to establish track continuity of the targets. Using real word data, our method is experimentally validated and the performance of the tracker is evaluated.

Abstract
In this article a new Data-Driven formulation of the Particle Filter framework is proposed. The new formulation is able to learn an approximate proposal distribution from previous data. By doing so, the need to explicitly model all the disturbances that might affect the system is relaxed. Such characteristics are particularly suited for Terrain Based Navigation for sensor-limited AUVs, where typical scenarios often include non-negligible sources of noise affecting the system, which are unknown and hard to model. Numerical results are presented that demonstrate the superior accuracy, robustness and efficiency of the proposed Data-Driven approach.