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Formal Reports

Report of results of completed projects or major milestones either in scientific terms or in terms acceptable to a wider audience. Note: Unless linked to the full text, reports are only available to NATO member nations from designated distribution centres. 

Documents

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Analysis of ASW serials conducted during exercise Dynamic Mongoose 15 using MSTPA Analysis of ASW serials conducted during exercise Dynamic Mongoose 15 using MSTPA

Date added: 03/22/2016
Date modified: 03/22/2016
Filesize: Unknown

Analysis of ASW serials conducted during exercise Dynamic Mongoose 15 using MSTPA. Strode, Christopher. CMRE-FR-2015-010. September 2015.

This report presents the results of ASW exercise reconstructions using the Multistatic Tactical Planning Aid (MSTPA). In addition to ingesting standard positional sitreps and tactical narratives, the tool may be used to simulate range dependent sonar performance. This allows for a more detailed analysis of a given serial in which the reasons behind submarine detections may be explored in terms of both acoustic propagation and target strength. The analysis presented here shows excellent agreement between predicted detection opportunities and reported sub-marine detections made by the surface assets. Further, the analysis may be used to explore periods in which submarine detection was predicted but not realised. It is envisaged that this type of analysis may be used to increase the learning opportunities of NATO ASW exercises.

Performance achieved during REP 14 Atlantic and COLLAB/NGAS 14 trials Performance achieved during REP 14 Atlantic and COLLAB/NGAS 14 trials

Date added: 02/22/2016
Date modified: 02/22/2016
Filesize: Unknown

Performance achieved during REP 14 Atlantic and COLLAB/NGAS 14 trials. Sildam, Jüri; Canepa, Gaetano; Munafo, Andrea; Strode, Christopher; LePage, Kevin D.; Goldhahn, Ryan A.. CMRE-FR-2015-024. January 2016.

For the purpose of completeness of bi-static underwater system performance evaluation we propose a new diagnostic test of target perceivability based on known target locations and a set of contacts collected by the sonar system. Assuming exchangeability of target perceivability and target presence or state, our approach is based on a concept of channel capacity (C) defined in terms of maximum mutual information for a given target state distribution and the distribution of contacts defined by a signal-to-noise detection threshold (DT). In our experiments the DT value corresponds to the maximum C value (Cp) defined for an optimal bi-partition of contacts collected over a fixed number of sonar pings within a time window of O(10 minutes). The respective contact partition defines the probability of target detection (Pd) and the probability of false alarms (Pfa) achievable under Cp. The time series of Cp, Pd, and Pfa were obtained by sliding the time window along the target trajectory. We demonstrate a successful test by applying the algorithm to data collected by underwater autonomous vehicle systems operated during two trials in 2014. Target tracks and visually observable target detections match well the observed Pd and Cp peaks.

Context-based reasoning for maritime situation awareness Context-based reasoning for maritime situation awareness

Date added: 02/01/2016
Date modified: 02/01/2016
Filesize: Unknown

Context-based reasoning for maritime situation awareness. Jousselme, Anne-Laure. CMRE-FR-2015-022. December 2015.

The explicit consideration of context in information fusion systems offers the necessary flexibility and adaptability to generalise processes while at the same time improving the interpretation of their outputs. It does however, raise the challenges of adequate context definition and formalisation so that context provides a useful and appropriate contribution to the underlying processing framework. In this report, we propose a formalisation of context-based reasoning from an information fusion perspective, tying together the themes of source quality, uncertainty representation and measurement space versus decision space, all around the central notion of context. We first illustrate the fact that context is a relative notion on the sub-problem of Maritime Situation Awareness. The various levels of processing, the embedded problems, the different granularity levels, the required dynamic of the processing, as well as the place and role of the human are highlighted. The use of context in the literature from different domains (e.g. artificial intelligence, information fusion, natural language processing) is mapped onto the modelling and processing steps. Information and source quality are placed at the core of the reasoning process for a sound consideration of the different quality dimensions and an appropriate representation and processing of uncertainty. The proposed scheme in this report is represented as a Direct Acyclic Graph where context is a central variable influencing the other variables of source quality, of measurement, of decision, of measurement and decision spaces, and of information gathering policy. The proposed scheme is simple enough to provide an appropriate level of abstraction with only 6 compound variables. It is general enough to be applied to different application domains, in particular from information processing to information gathering and source tasking. The proposed framework offers the required generalisation and flexibility of a problem-solving method which can be tuned and adapted to dynamical contextual information. An example of implementation using a Bayesian network is given, and its extension to other uncertainty representations is explored. A Maritime Anomaly Detection problem is illustrated through the proposed approach. We illustrate how the context can then be used to adapt information processing on the fly by changing granularity of the problem or correcting the information based on sources’ performance assessment.

Using Bayesian area search behaviours in autonomous underwater sensor networks for littoral surveillance Using Bayesian area search behaviours in autonomous underwater sensor networks for littoral surveillance

Date added: 02/01/2016
Date modified: 02/01/2016
Filesize: Unknown

Using Bayesian area search behaviours in autonomous underwater sensor networks for littoral surveillance. Munafò, Andrea; Braca, Paolo; Goldhahn, Ryan A.; Ferri, Gabriele; LePage, Kevin D. CMRE-FR-2015-020. December 2015.

IIn this report a multistatic network of AUVs is considered, where a collaborative multi-sensor tracker is coupled with Bayesian search behaviours to go beyond the individual sensor limitations. The Distributed Information FUSION (DIFFUSION) approach is developed based on a Bayes filter implemented in the form of a particle filter within the Random Finite Set (RFS) formulation. The output of the particle filter tracker, namely the target full posterior, is then used by an area search behaviour which is able to drive the AUVs to put the areas of high probability of detections in locations where the target is most likely located. A full validation of the approach is presented including post-processed data obtained during the Exercise Proud Manta 2012 sea trial, and real-time results from the Littoral Continuous Active Sonar 2015 experiment, the first sea trial where all the components presented were operating in the field.

  

The Goal Oriented Decision Support System (GO-DSS) for surveillance asset allocation: integration of different information layers The Goal Oriented Decision Support System (GO-DSS) for surveillance asset allocation: integration of different information layers

Date added: 02/01/2016
Date modified: 02/01/2016
Filesize: Unknown

The Goal Oriented Decision Support System (GO-DSS) for surveillance asset allocation: integration of different information layers. Fabbri, Tommaso; Vicen Bueno, Raul; Grasso, Raffaele. CMRE-FR-2015-016. December 2015.

Data mining, AI and optimisation techniques provide helpful information to substantially improve the decision making process. The above mentioned techniques can be exploited to increase the performances of planning operations. This research details the developments of the Goal Oriented Decision Support System (GO-DSS) as a planning system of N controllable moving assets to optimise the coverage of high-risk areas during counter-piracy operations. By means of machine learning, data fusion and multi-objective evolutionary optimisation, this research shows how heterogeneous in-formation sources can be optimally exploited to improve the performance of the DSS. The research illustrates how Meteorological and Oceanographic (METOC) forecast, pirate attack reports and AIS vessel traffic data are integrated as information sources and details the principal components of the planning tool. Preliminary evaluation scenarios are also provided demonstrating the effectiveness of the approach. Test cases performed during this research and development proves a reduction of more than 60%of the distance between the reported piracy activities and the planned asset trajectories compared to the case in which AIS traffic data are not used.

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