This paper deals with the problem of estimating the directions of arrival (DOA) of multiple source signals from a single observation vector of an array data. In particular, four estimation algorithms based on the theory of compressed sensing (CS), i.e., the classical ℓ 1 minimization (or Least Absolute Shrinkage and Selection Operator, LASSO), the fast smooth ℓ 0 minimization, and the Sparse Iterative Covariance-Based Estimator, SPICE and the Iterative Adaptive Approach for Amplitude and Phase Estimation, IAA-APES algorithms, are analyzed, and their statistical properties are investigated and compared with the classical Fourier beamformer (FB) in different simulated scenarios. We show that unlike the classical FB, a CS-based beamformer (CSB) has some desirable properties typical of the adaptive algorithms (e.g., Capon and MUSIC) even in the single snapshot case. Particular attention is devoted to the super-resolution property. Theoretical arguments and simulation analysis provide evidence that a CS-based beamformer can achieve resolution beyond the classical Rayleigh limit. Finally, the theoretical findings are validated by processing a real sonar dataset.

Underwater surveillance has traditionally been carried out by means of surface and undersea manned vessels equipped with advanced sensor systems. This approach is often costly and manpower intensive. Marine robotics is an emerging technological area that enables the development of advanced networks for underwater surveillance applications. In contrast with the use of standard assets, these advanced networks are typically composed of small, low-power, and possibly mobile robots, which have limited endurance, processing and wireless communication capabilities. When deployed in a region of interest, these robots can cooperatively form an intelligent network achieving high performance with significant features of scalability, adaptability, robustness, persistence and reliability. Such networks of robots can be the enabling technology for a wide range of applications in the maritime domain. However, they also introduce new challenges for underwater distributed sensing, data processing and analysis, autonomy and communications. The main thrust of this study is to review the underwater surveillance scenario within a framework of four research areas: (i) underwater robotics, (ii) acoustic signal processing, (iii) tracking and distributed information fusion, and (iv) underwater communications networks. Progress in each of these areas as well as future challenges is presented.

Surveillance in Anti-Submarine Warfare (ASW) has traditionally been carried out by means of submarines or frigates with towed arrays. These techniques are manpower intensive. Alternative approaches have recently been suggested using distributed stationary and mobile sensors, such as Autonomous Underwater Vehicles (AUVs). In contrast with the use of standard assets, these small, low-power and mobile devices have limited processing and wireless communication capabilities. However, when deployed in a spatially separated network, these sensors can form an intelligent network achieving high performance with significant features of scalability, robustness and reliability. The Distributed InFormation FUSION (DIFFUSION) strategy, in which the local information is shared among sensors, is one of the key aspects of this intelligent network.In this paper we propose two DIFFUSION schemes, in which the information shared among sensors consists of i) contacts, generated by the local detection stage; and ii) tracks generated by the local tracking stage. In the first DIFFUSION scheme contacts are combined at each nodes using the optimal Bayesian tracking based on the random finite set (RFS) formulation. In the second DIFFUSION scheme tracks are combined using the track-to-track association/fusion (T2T) procedure, then a sequential decision based on the association events is exploited. A full validation of the DIFFUSION schemes is conducted by the NATO Science and Technology Organization -Centre for Maritime Research and Experimentation during the sea trials Exercise Proud Manta 2012-2013 using real data. Performance metrics of DIFFUSION and of local tracking/detection strategies are also evaluated in terms of Time-on-Target (TOT) and False Alarm Rate (FAR).We demonstrate the benefit of using DIFFUSION against the local non-cooperative strategies. In particular DIFFUSION improves the level of TOT (FAR) with respect to the local tracking/detection strategies. Specifically, the TOT is increased over 90 − 95% while the FAR is reduced of two order of magnitude. The problem of communication failures, data not available from the collaborative AUV during certain periods of time, is also investigated. The robustness of DIFFUSION with respect to these communication failures is demonstrated, and the related performance results are reported here. Specifically, with 75% of communication failures the TOT is over 90 − 95% with a relatively small increase of the FAR with respect to the case of perfect communication.Index Terms-Collaborative data fusion, antisubmarine warfare, multistatic active sonar, target tracking, underwater sensor networks, autonomous underwater vehicles, real-world experimentation 1530-437X (c)

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