Broadband passive SONAR systems must detect multiple acoustic sources in environments marked by high noise and limited prior information. Traditional model-based approaches, such as Cell Averaging Constant False Alarm Rate (CA-CFAR), rely on analytical models, which can fail to fully capture complex conditions, creating a need for data-driven methods. We propose a detection framework that integrates a Poisson Random Finite Set (RFS)-based loss function with a Vision Transformer (ViT) architecture. The ViT component processes Bearing Time History (BTH) waterfall data patches, capturing local and global acoustic features, while the Poisson RFS loss naturally accommodates an unknown number of sources. A test-time augmentation (TTA) strategy further boosts performance by exploiting the circular symmetry in bearing data. Experimental results show that our approach improves upon a conventional CFAR detector and CNN-based baselines across Receiver Operating Characteristic (ROC), Precision-Recall, and detection probability metrics. In particular, pairing ViT with the RFS loss yields higher accuracy and robustness to noise, all within a computationally feasible framework for real-time detection tasks.

Quantifying negative information in an efficient way is a challenging task, especially when this information has to be communicated on a network. In this article we leverage the unique properties offered by possibility theory to quantify and approximate the negative information arising in the context of tracking a target with a sensor that has a limited field of view. We also verify experimentally that the corresponding target tracking methodology can be applied in a decentralised manner to a sensor network, while maintaining a performance close to the idealised case where the initial location of the target is better-known.

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.

In this work, we consider the problem of algorithmically predicting rendezvous among vessels based on their trajectory forecasts in a maritime environment. The problem is treated as hypothesis testing on the expected value of the distance between trajectories. We relate this quantity to the first and second degree Wasserstein distances between trajectory forecast distributions. These distributions are obtained using integrated Ornstein-Uhlenbeck process models with the trajectory measurements collected so far. Building upon these results, we propose an algorithm which traverses the trajectories observed so far for detecting rendezvous over a rolling time horizon. We demonstrate the efficacy of the proposed algorithm using simulations.