This paper addresses the problem of optimal acoustic sensor placement for underwater target localization in 3-D using range measurements only. By adopting an estimation theoretical framework, the optimal geometric sensor formation that will yield the best achievable performance in terms of target positioning accuracy is computed by maximizing the determinant of an appropriately defined Fisher information matrix (FIM). For mathematical tractability, it is assumed that the measurements of the ranges between the target and a set of acoustic sensors are corrupted with white Gaussian noise. For the sake of completeness, an explicit analytical result for a generic n-sensor network is first obtained for the case when there is no uncertainty in the prior knowledge about the target position. The result is then extended to the practical case where the target is known to lie inside a region of uncertainty. The optimal sensor configuration thus obtained lends itself to an interesting and useful geometrical interpretation. In addition, the “spreading” of the configuration is shown to depend on the number of range measurements, target depth, and the probability distribution function that characterizes the prior knowledge about the target position. Results are also obtained for the problem of optimal sensor placement with constraints, namely, by considering that the sensors can be either located at the sea surface or distributed between the surface and the seabed. The connection between 2-D and 3-D scenarios is clarified. Simulation examples illustrate the key results derived.

The problem of determining the optimal geometric configuration of a sensor network that will maximize the range-related information available for multiple target positioning is of key importance in a multitude of application scenarios. In this paper, a set of sensors that measures the distances between the targets and each of the receivers is considered, assuming that the range measurements are corrupted by white Gaussian noise, in order to search for the formation that maximizes the accuracy of the target estimates. Using tools from estimation theory and convex optimization, the problem is converted into that of maximizing, by proper choice of the sensor positions, a convex combination of the logarithms of the determinants of the Fisher Information Matrices corresponding to each of the targets in order to determine the sensor configuration that yields the minimum possible covariance of any unbiased target estimator. Analytical and numerical solutions are well defined and it is shown that the optimal configuration of the sensors depends explicitly on the constraints imposed on the sensor configuration, the target positions, and the probabilistic distributions that define the prior uncertainty in each of the target positions. Simulation examples illustrate the key results derived.

In this paper, we address the problem of determining the optimal geometric configuration of an acoustic sensor network that will maximize the angle-related information available for underwater target positioning. In the set-up adopted, a set of autonomous vehicles carries a network of acoustic units that measure the elevation and azimuth angles between a target and each of the receivers on board the vehicles. It is assumed that the angle measurements are corrupted by white Gaussian noise, the variance of which is distance-dependent. Using tools from estimation theory, the problem is converted into that of minimizing, by proper choice of the sensor positions, the trace of the inverse of the Fisher Information Matrix (also called the Cramer-Rao Bound matrix) to determine the sensor configuration that yields the minimum possible covariance of any unbiased target estimator. It is shown that the optimal configuration of the sensors depends explicitly on the intensity of the measurement noise, the constraints imposed on the sensor configuration, the target depth and the probabilistic distribution that defines the prior uncertainty in the target position. Simulation examples illustrate the key results derived.

In present and future society, all individuals must be able to face the problems, risks, advantages and opportunities that will arrive with new paradigms in the labour market, social relations and technology. To reach this goal, a quality and inclusive education together with a proper and complete formation in technology (communications, robotics, programming, computational thinking (CT), etc.) must be imparted at all educational levels. Moreover, all individuals should have the same opportunities to develop their skills and knowledge, as stated in Goal 4 of the Sustainable Development Goals, Sustainable Education. Following this trend, in the present work, a practical experience about how to teach CT using robotics is developed, showing the results and evaluation of the lessons on robotics taught to students in their 4th year of compulsory secondary education, and where the students showed a high degree of apathy and demotivation. The teaching unit was based on an action research approach that includes a careful selection of pedagogical techniques and instruments to attract and keep the attention and interest of the students. In addition to the robotics lessons, a previous computational thinking training with Blockly Games was carried out, which contributed to noticeably increase the students motivation and to introduce them to the programming of robots. Moreover, gamification was used to motivate and evaluate the individual knowledge, and the students were required to present the work performed through a final project. The individual needs of the students were fulfilled with a daily monitoring. The results show that the pedagogical techniques, instruments and evaluation were adequate to increase the motivation of the students and to obtain a significant learning, showing how the teaching of CT may attract students that have lost interest and motivation, while providing them with abilities that will be essential for the learning throughout life.