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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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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.

An overview of extended target tracking techniques for radar sensor network data An overview of extended target tracking techniques for radar sensor network data

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

An overview of extended target tracking techniques for radar sensor network data. Vivone, Gemine ; Braca, Paolo. CMRE-FR-2015-017. December 2015.

Ship traffic monitoring and port protection represent big challenges and intensive research activities are focused on these topics. Radars are widely exploited technologies for these purposes. The CMRE?s radar sensor network installed in the Gulf of La Spezia, Italy, is an example of a bistatic high resolution radar network to detect and track targets in maritime environment. It consists of an X-band Marine radar and an inverse synthetic aperture radar. In this report, we present several approaches to track extended targets (i.e. targets that occupy more than one radar cell) using data acquired by the radar sensor network. A fully Bayesian solution to the filtering problem for extended target tracking is presented first. Proper measurement models to deal with the radar?s measurement noise and its conversion into Cartesian coordinates are presented for both the monostatic case and the bistatic case. A gamma Gaussian inverse Wishart probability hypothesis density tracker is also provided to address the problem of the multiple extended target tracking in a cluttered environment. The proposed converted measurements model is also integrated in the above-mentioned approach. Finally, a signal processing chain based on a pixel-wise detector and a joint probability data association tracker is proposed to track extended targets reducing the computational burden and meeting the real time requirement.

Big data architectures in support of computational maritime situational awareness: case study in port traffic analysis Big data architectures in support of computational maritime situational awareness: case study in port traffic analysis

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

Big data architectures in support of computational maritime situational awareness: case study in port traffic analysis.Cazzanti, Luca; Davoli, Antonio. CMRE-FR-2015-021. December 2015.

Computational Maritime Situation Awareness (MSA) supports the maritime industry, governments, and international organizations with machine learning and statistical data analysis techniques for analyzing vessel traffic data. A critical challenge of scaling computational MSA to big data regimes is integrating the core learning algorithms with big data technologies while taking into account the semantics of the maritime domain and the needs of the stakeholders. To address this challenge, this report surveys the concepts and technologies from the field of big data and describes why and how they may support the typical tasks and challenges faced by the MSA community. As a concrete, practical example of how big data can support MSA, this report describes a software tool developed by the Centre for Maritime Re-search and Experimentation (CMRE) according to big data principles that analyses large quantities of open source, maritime vessel traffic data, produces summary statistics of activities in ports, and makes them available to end users in easy-to-understand charts.

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