Center for Maritime Research and Experimentation

San Bartolomeo in Galdo, Italy

Center for Maritime Research and Experimentation

San Bartolomeo in Galdo, Italy
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Braca P.,Center for Maritime Research and Experimentation | Marano S.,University of Salerno | Matta V.,University of Salerno | Willett P.,University of Connecticut
IEEE Journal on Selected Topics in Signal Processing | Year: 2013

Tracking an unknown number of objects is challenging, and often requires looking beyond classical statistical tools. When many sensors are available the estimation accuracy can reasonably be expected to improve, but there is a concomitant rise in the complexity of the inference task. Nowadays, several practical algorithms are available for multitarget/multisensor estimation and tracking. In terms of current research activity one of the most popular is the probability hypothesis density, commonly referred to as the PHD, in which the goal is estimation of object locations (unlabeled estimation) without concern for object identity (which is which). While it is relatively well understood in terms of its implementation, little is known about its performance and ultimate limits. This paper is focused on the characterization of PHD estimation performance for the static multitarget case, in the limiting regime where the number of sensors goes to infinity. It is found that the PHD asymptotically behaves as a mixture of Gaussian components, whose number is the true number of targets, and whose peaks collapse in the neighborhood of the classical maximum likelihood estimates, with a spread ruled by the Fisher information. Similar findings are obtained with reference to a naïve, two-step algorithm which first detects the number of targets, and then estimates their positions. © 2007-2012 IEEE.


Williams D.P.,Center for Maritime Research and Experimentation | Fakiris E.,University of Patras
IEEE Transactions on Geoscience and Remote Sensing | Year: 2014

In many remote-sensing applications, measured data are a strong function of the environment in which they are collected. This paper introduces a new context-dependent classification algorithm to address and exploit this phenomenon. Within the proposed framework, an ensemble of classifiers is constructed, each associated with a particular environment. The key to the method is that the relative importance of each object (i.e., data point) during the learning phase for a given classifier is controlled via a modulating factor based on the similarity of auxiliary environment features. Importantly, the number of classifiers to learn and all other associated model parameters are inferred automatically from the training data. The promise of the proposed method is demonstrated on classification tasks seeking to distinguish underwater targets from clutter in synthetic aperture sonar imagery. The measured data were collected with an autonomous underwater vehicle during several large experiments, conducted at sea between 2008 and 2012, in different geographical locations with diverse environmental conditions. For these data, the environment was quantified by features (extracted from the imagery directly) measuring the anisotropy and the complexity of the seabed. Experimental results suggest that the classification performance of the proposed approach compares favorably to conventional classification algorithms as well as state-of-the-art context-dependent methods. Results also reveal the object features that are salient for performing target classification in different underwater environments. © 2014 IEEE.


Mourre B.,Center for Maritime Research and Experimentation | Chiggiato J.,CNR Marine Science Institute
Tellus, Series A: Dynamic Meteorology and Oceanography | Year: 2014

This study compares the ability of two approaches integrating models and data to forecast the Ligurian Sea regional oceanographic conditions in the short-term range (0-72 hours) when constrained by a common observation dataset. The post-processing 3-D super-ensemble (3DSE) algorithm, which uses observations to optimally combine multi-model forecasts into a single prediction of the oceanic variable, is first considered. The 3DSE predictive skills are compared to those of the Regional Ocean Modeling System model in which observations are assimilated through a more conventional ensemble Kalman filter (EnKF) approach. Assimilated measurements include sea surface temperature maps, and temperature and salinity subsurface observations from a fleet of five underwater gliders. Retrospective analyses are carried out to produce daily predictions during the 11-d period of the REP10 sea trial experiment. The forecast skill evaluation based on a distributed multi-sensor validation dataset indicates an overall superior performance of the EnKF, both at the surface and at depth. While the 3DSE and EnKF perform comparably well in the area spanned by the incorporated measurements, the 3DSE accuracy is found to rapidly decrease outside this area. In particular, the univariate formulation of the method combined with the absence of regular surface salinity measurements produces large errors in the 3DSE salinity forecast. On the contrary, the EnKF leads to more homogeneous forecast errors over the modelling domain for both temperature and salinity. The EnKF is found to consistently improve the predictions with respect to the control solution without assimilation and to be positively skilled when compared to the climatological estimate. For typical regional oceanographic applications with scarce subsurface observations, the lack of physical spatial and multivariate error covariances applicable to the individual model weights in the 3DSE formulation constitutes a major limitation for the performance of this multi-model-data fusion approach compared to conventional advanced data assimilation strategies. ©2014 B. Mourre and J. Chiggiato.


Harrison C.H.,Center for Maritime Research and Experimentation | Harrison C.H.,University of Southampton
Journal of the Acoustical Society of America | Year: 2013

The energy flux formulation of waveguide propagation is closely related to the incoherent mode sum, and its simplicity has led to development of efficient computational algorithms for reverberation and target echo strength, but it lacks the effects of convergence or modal interference. By starting with the coherent mode sum and rejecting the most rapid interference but retaining beats on a scale of a ray cycle distance it is shown that convergence can be included in a hybrid formulation requiring minimal extra computation. Three solutions are offered by evaluating the modal intensity cross terms using Taylor expansions. In the most efficient approach the double summation of the cross terms is reduced to a single numerical sum by solving the other summation analytically. The other two solutions are a local range average and a local depth average. Favorable comparisons are made between these three solutions and the wave model Orca with, and without, spatial averaging in an upward refracting duct. As a by-product, it is shown that the running range average is very close to the mode solution excluding its fringes, given a relation between averaging window size and effective number of modes which, in turn, is related to the waveguide invariant. © 2013 Acoustical Society of America.


Williams D.P.,Center for Maritime Research and Experimentation
IEEE Journal of Oceanic Engineering | Year: 2015

In this paper, a new unsupervised algorithm for the detection of underwater targets in synthetic aperture sonar (SAS) imagery is proposed. The method capitalizes on the high-quality SAS imagery whose high resolution permits many pixels on target. One particularly novel component of the method also detects sand ripples and estimates their orientation. The overall algorithm is made fast by employing a cascaded architecture and by exploiting integral-image representations. As a result, the approach makes near-real-time detection of proud targets in sonar data onboard an autonomous underwater vehicle (AUV) feasible. No training data are required because the proposed method is adaptively tailored to the environmental characteristics of the sensed data that are collected in situ. To validate and assess the performance of the proposed detection algorithm, a large-scale study of SAS images containing various mine-like targets is undertaken. The data were collected with the MUSCLE AUV during six large sea experiments, conducted between 2008 and 2012, in different geographical locations with diverse environmental conditions. The analysis examines detection performance as a function of target type, aspect, range, image quality, seabed environment, and geographical site. To our knowledge, this study-based on nearly 30 000 SAS images collectively covering approximately 160 km2 of seabed, and involving over 1100 target detection opportunities-represents the most extensive such systematic, quantitative assessment of target detection performance with SAS data to date. The analysis reveals the variables that have the largest impact on target detection performance, namely, image quality and environmental conditions on the seafloor. Ways to exploit the results for adaptive AUV surveys using through-the-sensor data are also suggested. © 2015 IEEE.


Maresca S.,Center for Maritime Research and Experimentation | Braca P.,Center for Maritime Research and Experimentation | Horstmann J.,Helmholtz Center Geesthacht | Grasso R.,Center for Maritime Research and Experimentation
IEEE Transactions on Geoscience and Remote Sensing | Year: 2014

In the last decades, great interest has been directed toward low-power high-frequency (HF) surface-wave radars as long-range early warning tools in maritime-situational-awareness applications. These sensors, developed for ocean remote sensing, provide an additional source of information for ship detection and tracking, by virtue of their over-the-horizon coverage capability and continuous-time mode of operation. Unfortunately, they exhibit many shortcomings that need to be taken into account, such as poor range and azimuth resolution, high nonlinearity, and significant presence of clutter. In this paper, radar detection, multitarget tracking, and data fusion (DF) techniques are applied to experimental data collected during an HF-radar experiment, which took place between May and December 2009 on the Ligurian coast of the Mediterranean Sea. The system performance is defined in terms of time on target (ToT), false alarm rate (FAR), track fragmentation, and accuracy. A full statistical characterization is provided using one month of data. The effectiveness of the tracking and DF procedures is shown in comparison to the radar detection algorithm. In particular, the detector's FAR is reduced by one order of magnitude. Improvements, using the DF of the two radars, are also reported in terms of ToT as well as accuracy. © 2013 IEEE.


Williams D.P.,Center for Maritime Research and Experimentation
IEEE Transactions on Geoscience and Remote Sensing | Year: 2015

A new unsupervised approach for characterizing seafloor in side-looking sonar imagery is proposed. The approach is based on lacunarity, which measures the pixel-intensity variation, of through-the-sensor data. No training data are required, no assumptions regarding the statistical distributions of the pixels are made, and the universe of (discrete) seafloor types need not be enumerated or known. It is shown how lacunarity can be computed very quickly using integral-image representations, thereby making real-time seafloor assessments on-board an autonomous underwater vehicle feasible. The promise of the approach is demonstrated on high-resolution synthetic-aperture-sonar imagery of diverse seafloor conditions measured at various geographical sites. Specifically, it is shown how lacunarity can effectively distinguish different seafloor conditions and how this fact can be exploited for target-detection performance prediction in mine-countermeasure operations. © 1980-2012 IEEE.


Zimmer W.M.X.,Center for Maritime Research and Experimentation
Journal of the Acoustical Society of America | Year: 2013

Passive acoustic monitoring is the method of choice to detect whales and dolphins that are acoustically active and to monitor their underwater behavior. The NATO Science and Technology Organization Centre for Maritime Research and Experimentation has recently implemented a compact passive acoustic monitor (CPAM), consisting of three arrays of two hydrophones each that are combined in a fixed three-dimensional arrangement and that may be towed at depths of more than 100 m. With its volumetric configuration, the CPAM is capable of estimating the three-dimensional direction vector of arriving sounds and under certain conditions on relative geometry between the whale and hydrophone array, the CPAM may also estimate the range to echolocating animals. Basic ranging methods assume constant sound speed and apply straightforward geometry to obtain depth and distance to the sound source. Alternatively, ray-tracing based methods may be employed to integrate the information provided by real sound speed profiles. Both ranging methods combine measurements of sound arrival angles and surface reflection delays and are easily implemented in real-time applications, whereby one could promote the ray-tracing approach as the preferred method because it may integrate real sound speed profiles. © 2013 Acoustical Society of America.


Nielsen P.L.,Center for Maritime Research and Experimentation
Proceedings of Meetings on Acoustics | Year: 2013

Estimating the seabed geoacoustic properties at various fidelity levels has been a research topic for several decades. The majority of the applied seabed characterization techniques often require significant involvement of surface vessels, complex experimental setup and human interaction. Technical advances in underwater autonomy and the development of energy efficient electronics provide new opportunities to optimize underwater environmental surveys in particular of the seabed. In 2012, the CMRE conducted the GLASS'12 experiment in the Mediterranean Sea with the objective to investigate the feasibility of utilizing a hybrid autonomous underwater vehicle equipped with a compact nose array for long-duration seabed characterization over large areas. The vehicle has the capability of operating in traditional propulsion and glider mode, and the nose-mounted array consists of a 5-element vertical and 4-element tetrahedral array. The sound sources used as information carrier were ambient noise, e.g. sea surface generated noise and loud distant sources of opportunity. The experimental setup together with the newly developed autonomous equipment will be presented and examples of inferred reflection loss and sub-bottom profiling from the ambient noise are compared to ground truth measurements. © 2013 Acoustical Society of America.


Pallotta G.,Center for Maritime Research and Experimentation | Vespe M.,Center for Maritime Research and Experimentation | Bryan K.,Center for Maritime Research and Experimentation
Entropy | Year: 2013

Understanding maritime traffic patterns is key to Maritime Situational Awareness applications, in particular, to classify and predict activities. Facilitated by the recent build-up of terrestrial networks and satellite constellations of Automatic Identification System (AIS) receivers, ship movement information is becoming increasingly available, both in coastal areas and open waters. The resulting amount of information is increasingly overwhelming to human operators, requiring the aid of automatic processing to synthesize the behaviors of interest in a clear and effective way. Although AIS data are only legally required for larger vessels, their use is growing, and they can be effectively used to infer different levels of contextual information, from the characterization of ports and off-shore platforms to spatial and temporal distributions of routes. An unsupervised and incremental learning approach to the extraction of maritime movement patterns is presented here to convert from raw data to information supporting decisions. This is a basis for automatically detecting anomalies and projecting current trajectories and patterns into the future. The proposed methodology, called TREAD (Traffic Route Extraction and Anomaly Detection) was developed for different levels of intermittency (i.e., sensor coverage and performance), persistence (i.e., time lag between subsequent observations) and data sources (i.e., ground-based and space-based receivers). © 2013 by the authors; licensee MDPI, Basel, Switzerland.

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