Sunday, December 17, 2017
      CMRE Facebook page  CMRE LinkedIn page  CMRE PAO Youtube page
   
Text Size
CMRE banner

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

Order by : Name | Date | Hits [ Descendent ]

RADARSAT mapping of BORA winds in the Adriatic Sea RADARSAT mapping of BORA winds in the Adriatic Sea

Date added: 03/01/2005
Date modified: 08/14/2012
Filesize: Unknown

RADARSAT mapping of BORA winds in the Adriatic Sea. Askari, Farid ; Signell, Richard P. SR-422. March 2005.

We examine the meteorological phenomena associated with the BORA wind system over the Adriatic Sea using RADARSAT synthetic aperture radar (SAR) imagery. A Bora wind event spins off a number of related atmospheric phenomena which manifest in SAR imagery as: high- intensity jets in the Gulfs of Trieste and Kvarneric, low-intensity shadow regions and island wakes along the Croatian coasts, and high-intensity atmospheric barrier jet along the western Adriatic coast. A key element in this study is the high resolution wind mapping from SAR imagery using scatterometer inversion algorithms and characterizing the Bora morphology. The high-resolution imaging allows for identification of topographically-controlled features and diagnosis of regions of strong vorticity and 3D motion.

Probabilistic modelling of sidescan sonar texture using pairwise pixel interactions Probabilistic modelling of sidescan sonar texture using pairwise pixel interactions

Date added: 06/01/2005
Date modified: 08/14/2012
Filesize: Unknown

Probabilistic modelling of sidescan sonar texture using pairwise pixel interactions. Myers, Vincent L. SR-424. June 2005.

Probability models used to describe sidescan sonar background pixels are usually represented using Rayleigh or K-distributions. While useful for many purposes, these distributions do not capture information which characterises non-independent pixel amplitudes which typify many textures found in modern high-resolution sidescan sonar data. Such models are necessary if we are to compute sensor and mission performance metrics such as probabilities of detection and classification. This report describes a flexible technique for describing sidescan sonar image textures using Markov Random Fields. Pairwise pixel interactions are expressed using gray level difference histograms and, due to the equivalence of Markov and Gibbs random fields, are used as sufficient statistics to a Gibbs probability distribution. The parameters of the Gibbs distribution are found using stochastic approximation and complex textures such as sand ripples and sea grass are successfully represented using this method. The Gibbs model is also shown to be capable of seabed segmentation based on texture, as well as capable of designing a simple constant-false-alarm-rate detector.

Comparison between predictions made by the SUPREMO sonar performance model and measured data Comparison between predictions made by the SUPREMO sonar performance model and measured data

Date added: 07/01/2005
Date modified: 08/14/2012
Filesize: Unknown

Comparison between predictions made by the SUPREMO sonar performance model and measured data. Prior, M.K. : Baldacci, A. SR-429. July 2005.

Predictions of acoustic reverberation and target echo intensity, made by the SUPREMO sonar performance model, are compared with measured data gathered in the Malta Plateau region of the Mediterranean Sea. The model’s ability to predict these quantities is assessed and used as a measure of model performance. It is shown that the model, when given satisfactory input descriptions of the ocean environment, is capable of predicting reverberation intensity to modal errors of magnitude around -1dB with a spread of ±4dB around the modal values. Furthermore, the model is shown to be able to predict trends in signal-to-background intensity ratio as pulse centre frequency is varied between values of 1100Hz, and 1700Hz with bandwidths of 200Hz and 800Hz. The model-measured agreement observed indicates the suitability of the SUPREMO model for use within an environmentally adaptive, low-frequency, active sonar system.

Multistatic sensor placement with the complementary use of Doppler sensitive and insensitive waveforms Multistatic sensor placement with the complementary use of Doppler sensitive and insensitive waveforms

Date added: 07/01/2005
Date modified: 08/14/2012
Filesize: Unknown

Multistatic sensor placement with the complementary use of Doppler sensitive and insensitive waveforms. Doug Grimmett. SR-427. July 2005.

Distributed multistatic active sonar systems have the potential to greatly improve surveillance capability against threat submarines. The geometric diversity of such systems provides an increased number of complementary detection opportunities to counter the underwater threat. The utilization of both Doppler sensitive and insensitive waveforms within such a multistatic network further improves the detection diversity and provides a more robust surveillance capability. This report describes the issues relevant to achieve this detection diversity, and shows the potential improvements of this approach. A simplified sonar signal excess model is described, which provides a capability to evaluate distributed sensor placements using both waveform types.

Side scan sonar image segmentation through multi-resolution texture features: a case study over the Luce Bay site during Northern Light 2003 Side scan sonar image segmentation through multi-resolution texture features: a case study over the Luce Bay site during Northern Light 2003

Date added: 08/01/2005
Date modified: 08/14/2012
Filesize: Unknown

Side scan sonar image segmentation through multi-resolution texture features: a case study over the Luce Bay site during Northern Light 2003. Grasso, Raffaele ; Spina, Francesco ; Hagen, Per Espen ; Bjorn, Halvin. SR-413. August 2005.

The segmentation of a side scan sonar (SSS) data set acquired in the Luce Bay site during the Northern Light '03 exercise by a Hugin autonomous underwater vehicle (AUV) equipped with an EdgeTech SSS, is presented and discussed. The segmentation was performed through the analysis of the image texture using a set of un-decimated wavelet transform features followed by classification using a supervised classifier based on the Mahalanobis distance in the feature space. The classifier was trained on four main sea floor classes, including low and high reflectivity sediments and sand ripples having two different ripple wavelengths, which are well discriminated in the feature space. A simple post-processing with geographic information system (GIS) tools was applied to smooth the data and convert pixels into polygons for a final output in AML/S57, the format adopted for data upload in Command and Control Information Systems (CCIS). The final thematic map showing the classification of the whole data set contains two distinct areas, the first in the inner leg (IL), showing no appreciable variations in the sea bed characteristics and the second, namely the outer leg (OL), having more variability.

User Login