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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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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.

Radar sensor network - low observable target acquisition campaign Radar sensor network - low observable target acquisition campaign

Date added: 01/15/2016
Date modified: 01/15/2016
Filesize: Unknown

Radar sensor network - low observable target acquisition campaign.  Errasti, Borja ; Braca, Paolo CMRE-FR-2015-015. December 2015.

The availability of real radar data of low observable maritime targets is a key element in the performance assessment of the techniques and algorithms developed as part of CMRE's programme on Data Knowledge and Operational Effectiveness (DKOE). CMRE has performed an experiment based on the deployment of two cooperating RHIBs carrying GPS recorders and acquiring data with the Radar Sensor Network, a bistatic configurable radar system. The acquired data is valuable attending to two different aspects: First, the data can be used to analyse and compare the performance of the target detection and tracking algorithms. Second, it can be used for asset planning as information about detection probabilities and ranges can be extracted from the featured dataset. This report describes the performed operations and illustrates the acquired data. It is also intended as a user guide for the dataset.

Simulating seabed characterisation using the CMRE high-resolution low-frequency synthetic aperture mine-hunting sonar Simulating seabed characterisation using the CMRE high-resolution low-frequency synthetic aperture mine-hunting sonar

Date added: 01/15/2016
Date modified: 01/15/2016
Filesize: Unknown

Simulating seabed characterisation using the CMRE high-resolution low-frequency synthetic aperture mine-hunting sonar.  Nielsen, Peter L.; Hollett, Reginald D.; Troiano, Luigi; Canepa, Gaetano.  CMRE-FR-2015-023. December 2015.

Reliable sonar performance estimates and probability of mine burial prediction are two very important, not yet completely solved, problems in mine hunting planning and operations. Both sonar performance and mine burial depend on the seabed properties, which often are considered as the most difficult underwater environmental information to obtain. Buried mines are particularly difficult to detect and classify, because of the complex interaction between the acoustic field, seabed and mine. The Centre for Maritime Research and Experimentation (CMRE) has established a quay-side test facility to evaluate a unique low-frequency mine hunting sonar for seabed characterisation. The sonar is wide band and the monostatic source-receiver units are composed of a transducer matrix where the elements are partly operating individually. A frequency invariant shading technique is applied to both source and receive units to obtain constant main lobe amplitude and vertical beamwidth with minimum side lobes. This beamforming technique reduces the impact of multi-path arrivals and allows for direct backscatter measurements from frequency independent patch sizes of the seabed. A technique for acoustic remote sensing of the seabed properties is proposed in this report. The technique is based on traditional matched-field processing of direct path backscattering from a fluid seabed, and the algorithm provides a best estimate of the seabed properties with associated uncertainties. The acoustic backscattered field is calculated by a state-of-the-art low-frequency backscattering model called BLASST, developed at CMRE. The estimated seabed properties and their uncertainties are obtained from synthetically generated scenarios to evaluate the seabed information contents in the sonar backscattered intensity. The results are considered as guidelines in preparation of measurements at the quayside rail facility in 2016. The intention is to apply the algorithm to data acquired by a prototype version of the sonar system installed at the quayside rail, and the performance of the seabed characterisation algorithm will be assessed and recommendations of future development and procedures will be proposed. Ground truth environmental surveys have been conducted at the quayside rail, and the data have been analysed and are presented in this report to support the results from the proposed environmental characterisation algorithm.

Scalable multi-target tracking for large sensor networks Scalable multi-target tracking for large sensor networks

Date added: 12/23/2015
Date modified: 12/23/2015
Filesize: Unknown

Scalable multi-target tracking for large sensor networks. Meyer, Florian; Braca, Paolo; Hlawatsch, Franz; Willett, Peter K. CMRE-FR-2015-019. December 2015.

We propose a method for multisensor-multitarget tracking with excellent scalability in the number of targets (which is assumed known), the number of sensors, and the number of measurements per sensor. Our method employs belief propagation based on a "detailed" factor graph that involves both target-related and measurement-related association variables. Using this approach, an increase in the number of targets, sensors, or measurements leads to additional variable nodes in the factor graph but not to higher dimensions of the messages. We observed very low runtimes of the proposed method; e.g., our MATLAB simulation of a scenario of 30 targets and 10 sensors without gating required less than one second per time step. The performance of the proposed method in terms of mean optimal subpattern assignment (OSPA) error compares well with that of state-of-the-art methods whose complexity scales exponentially with the number of targets. In particular, we observed that our method can outperform the sequential multisensor joint probabilistic data association filter (JPDAF) and performs similar to the Monte Carlo JPDAF.

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