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.

Compressed Sensing is a new signal processing methodology that allows to reconstruct sparse signals using a relatively small number of samples in the form of random projections. These samples are collected at a much lower rate than Nyquist rate. This paper focuses on the application of Compressed Sensing in Cognitive Radar systems that use wide operating frequency bandwidths for spectrum sensing and sharing. Compressed sensing can provide a significant reduction in acquisition time reducing the cost for high resolution analog-to-digital converters with large dynamic range, and high speed signal processors.

This work deals with the analysis of a multisensor radar system in the context of maritime border control scenario. The system is composed by a receiver that exploits the signal emitted by two non co-operative transmitters of opportunity: a UMTS base station and a FM radio station. An algorithm for selecting the transmitter for the tracking of a radar target is proposed. This algorithm can provide a significant aid to harbour protection and can reduce the computational load of surveillance operations.

This work deals with the Sequential Cramér-Rao Lower Bound (SCRLB) for sequential target state estimators for a bistatic tracking problem. In the context of tracking, the SCRLB provides a powerful tool, enabling one to determine a lower bound on the optimal achievable accuracy of target state estimation. The bistatic SCRLBs are analyzed and compared to the monostatic counterparts for a fixed target trajectory. Two different kinematic models are analyzed: constant velocity and constant acceleration. The derived bounds are also valid when the target trajectory is characterized by the combination of these two motions.