We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOAs predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver.

Autonomy, cooperation and data fusion can increase the performance of robotic networks in many underwater applications. In this paper, we describe a novel occupancy grid (OG) based perception layer, and its use for controlling a network of autonomous underwater vehicles (AUVs), sensorised with passive sonars. Data fusion between the robots' bearing-only measurements (typical of passive sonars) enables the estimate of target position. The developed OG framework exploits networking and the spatial diversity provided by the multi-robot system. The perception layer was integrated in the intelligent Cooperative Autonomous Decision Making Engine (iCADME) control architecture and validated for the first time in the Robotics Experimentation and Prototyping MUS (REPMUS) Exercise, held in Portugal in September 2021. Our robotic network participated in a technical demonstration, whose main objective was to localise a bottomed submarine which emitted a periodic help request acoustically during a simulated distress situation. We report results which are one of the first examples to demonstrate how cooperative robotics, supported by data fusion, can be effective in a passive sonar scenario. They also confirm the viability of adopting such solutions in real-world applications, characterised by poor communications and challenging environments. What was achieved at REPMUS21 clearly demonstrates how a network of cooperative robots can improve search & rescue operations of a submarine.

Cooperation among multiple autonomous surface and underwater vehicles is an important capability for detection and tracking of underwater objects. Cooperative autonomy in the underwater environment, however, is challenged by the communication bandwidth. In this work, we propose a selective communication scheme that underpins collaborative surveillance under communication constraints. This scheme classifies signal reflections of sonar pulses that are detected by on-board sensor processing as contacts with the object of interest or background using a convolutional neural network. This network is trained using previously labelled contact spectrograms obtained during three sea trials carried out between 2016-2018. The classification scores at the CNN output are ordered to select the few contacts that the underwater modem bandwidth allows for transmission to the network. First, we evaluate the accuracy of the data-driven information selection scheme using recall scores and similar performance measures. Then, we find the accuracy in Bayesian recursive filtering (tracking) of these contacts for different communication rates using established error metrics. The results suggest that the selective scheme yields a favourable surveillance performance communication cost trade-off.

The necessity of long-range underwater surveillance has strongly increased in the last decades, and low-frequency active sonar (LFAS) systems seem to fulfill this need. However, in littoral environments with shallow water LFAS may suffer from an elevate number of false alarms. In this context, the capability to distinguish between object-generated and clutter-generated detections is crucial. This paper describes a multiobject tracking framework based on the sum-product algorithm that exploits the information provided by a convolutional neural network that classifies the LFAS detections. The effectiveness of the proposed approach is demonstrated both in a simulated and in a real underwater scenario.

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 addresses the problem of localizing an unknown number of static sources emitting unknown signals from time-difference of arrival (TDOA) measurements. Based on the framework of random finite sets and finite set statistics, we formulate the Bayesian estimation problem and develop a particle-based localization algorithm that overcomes the challenges related to the highly non-linear TDOA measurement model, the data associations uncertainty, and the uncertainty in the number of sources to be localized. Simulation results confirm that the number of sources can be determined correctly and accurate location estimates can be obtained when the number of false alarms is low and the probability of detection is high.

The authors discuss the need to provide a realistic model of a generic noise probability density function (PDF), in order to optimize the signal detection in non-Gaussian environments. The target is to obtain a model depending on a few parameters (that are quick and easy to estimate), and is so general that it is able to describe many kinds of noise (e.g., symmetric or asymmetric, with variable sharpness). To this end, a new HOS-based model is introduced, which is derived from the generalized Gaussian function, and depends on three parameters: kurtosis, for representing variable sharpness, and left and right variances (whose combination provides the same information of skewness) for describing the deviation from symmetry. This model is applied to the design of a locally optimum detection (LOD) test. Promising experimental results are presented which are derived from the application of the test to detecting signals corrupted by real underwater acoustic noise.

An acoustic underwater communication problem is considered at low frequency ranges (up to 1 kHz), where propagation performances are improved. This range is dominated by ship noise components and is expected to be non-Gaussian. Classical detector performances may decay in the presence of non-Gaussianity. Spectrum-based and bispectrum-based methods have been used and compared to statistically characterize shipping noise and then to design non-conventional detectors; the main process properties are stationarity, frequency composition and coupling, Gaussianity. Analysis results are reported and refer to real acoustic data recording background traffic and a known-target-ship passage.>

The architecture of a distributed vision system (DVS) based on a combination of multiple modules of standard and extended Kalman filters is presented. It exploits a representation of static and dynamic knowledge for estimation purposes. Spatial constraints describe how observed image features lead to estimate parameters (i.e., in the present application, the density and position of monitored people in the monitored scene); time constraints are used to describe knowledge on dynamic evolution of the mentioned estimated variables. Using dynamic knowledge allows the system to track groups of people, dynamically interacting each others, on the image plane over time. Experimental results, deriving from an extensive test phase carried out on real-life images of an underground station, confirm that integration of different spatial and temporal constraints is an efficient approach for optimizing parameter estimation in DVSs.>