This paper addresses the problem of multitarget tracking using a network of mobile sensors with unknown positions. In contrast to commonly-used approaches which split the sensor localization and target tracking into two different sub-problems, we propose a holistic approach for joint localization and tracking. The theory of graphical models is used to describe the statistical relationship between sensors, targets, and measurements. To jointly infer the states of sensors and targets, we use the statistical processing of belief propagation.

Cooperative autonomy and data sharing can largely improve the mission performance of robotic networks in underwater surveillance applications. In this paper, we describe the cooperative autonomy used to control the Autonomous Underwater Vehicles (AUVs) acting as sonar receiver nodes in the CMRE Anti-Submarine Warfare (ASW) network. The paper focuses on a track management module that was integrated in the robot autonomy software for enabling the share of information. Track to track (T2T) associations are used for improving track classification and for creating a common tactical picture, necessary for AUV cooperative strategies. We also present a new cooperative data-driven AUV behaviour that exploits the spatial diversity of multiple robots for improving target tracking and for facilitating T2T associations. We report results with real data collected at sea that validate the approach. The reported results are one of the first examples that show the potential of cooperative autonomy and data fusion in realistic underwater surveillance scenarios characterised by limited communications.

This paper presents a Bayesian multi-sensor tracking strategy for a network of autonomous underwater vehicles (AUVs) for the purpose of anti-submarine warfare (ASW). A bistatic configuration and the corresponding acoustic model for the bistatic signal-to-noise ratio (SNR) is used. The Bayesian posterior distribution of the target state based on all available information from sensors and on the acoustic model is reconstructed via particle filtering methods, taking into account the port-starboard ambiguity typical of horizontal line arrays. The posterior distribution is the optimal estimation procedure, the only approximation derives from the particle representation. The effectiveness of the proposed algorithm is demonstrated on a real data set collected by the NATO Centre for Maritime Research and Experimentation (CMRE) during the NATO Proud Manta 2012 exercise (ExPOMA12).

The estimation of sonar system performance in uncertain environments requires the transfer of sound speed, bathymetric and sea surface uncertainty to propagation uncertainty. Currently, methods of estimating the transfer of environmental uncertainty have been based on Monte Carlo simulation of acoustic propagation through an ensemble of environments, or on simplifying assumptions about the physics of propagation either through inhomogeneous media or in the presence of non-separable boundary conditions. In this paper we introduce the polynomial chaos expansion method, which allows the integration of the differential equations of acoustic propagation over a basis expansion that is orthogonal with respect to the probability distribution function describing the environmental variability. The resulting solution converges in a relatively small number of expansion coefficients and may be used to estimate any statistical property of the acoustic field, such as its mean value and its variance