SASS: a bi-spectral panoramic IRST - results from measurement campaigns with the Italian Navy

Ceglie, S. U. de; Moro, Maria Lo; Vita, R.; Neri, Alessandro; Barani, Gianni; Cavallini, Maurizio; Quaranta, Carlo; Colombi, G.

doi:10.1117/12.850042

Cited by 5 publications

(3 citation statements)

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“…Multi-sensor information fusion (MSIF) is widely used in the military [11], medical field [12], fault diagnosis [13], intelligent transportation [14], and many other applications. For flow pattern identification, Toye et al [15] combined X-ray chromatography imaging and capacitance chromatography imaging, and fused the acquired data to understand and study the gas-liquid distribution of the absorption column at the experimental monitoring point.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks and Feature Fusion for Flow Pattern Identification of the Subsea Jumper

Lin,

Xu,

Liu

et al. 2023

Applied Sciences

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The gas–liquid two-phase flow patterns of subsea jumpers are identified in this work using a multi-sensor information fusion technique, simultaneously collecting vibration signals and electrical capacitance tomography of stratified flow, slug flow, annular flow, and bubbly flow. The samples are then processed to obtain the data set. Additionally, the samples are trained and learned using the convolutional neural network (CNN) and feature fusion model, which are built based on experimental data. Finally, the four kinds of flow pattern samples are identified. The overall identification accuracy of the model is 95.3% for four patterns of gas–liquid two-phase flow in the jumper. Through the research of flow profile identification, the disadvantages of single sensor testing angle and incomplete information are dramatically improved, which has a great significance on the subsea jumper’s operation safety.

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Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks and Feature Fusion for Flow Pattern Identification of the Subsea Jumper

Lin,

Xu,

Liu

et al. 2023

Applied Sciences

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“…The third-generation IRST systems utilize the FPAs with step/stare imaging to achieve wide area coverage. The new IRST achieves greater performances than the previous generation thanks to their staring nature, i.e., to longer integration times (milliseconds rather than microseconds) [3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…They also suffer from more false alarms [2].The third-generation IRST systems utilize the FPAs with step/stare imaging to achieve wide area coverage. The new IRST achieves greater performances than the previous generation thanks to their staring nature, i.e., to longer integration times (milliseconds rather than microseconds) [3][4][5][6][7][8].In step/stare imaging applications, a high-resolution narrow FOV sensor is rapidly stepped or indexed across an area of interest, stopping at intervals to stare and collect imagery. Then the images at each location are stitched together to form a large but high-resolution image.…”

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confidence: 99%

Non-Rotationally Symmetric Field Mapping for Back-Scanned Step/Stare Imaging System

Xin

Zhang

et al. 2020

Applied Sciences

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Step/stare imaging with focal plane arrays (FPAs) has become the main approach to achieve wide area coverage and high resolution imaging for long range targets. A fast steering mirror (FSM) is usually utilized to provide back-scanned motion to compensate for the image motion. However, the traditional optical design can just hold one field point relatively stable, typically the central or on-axis field point, on the FPA during back-scanning; all other field points may wander during the exposure due to imaging distortion characteristics of the optical system, which reduces the signal to noise ratio (SNR) of the target. Aiming toward this problem, this paper proposes a non-rotationally symmetric field mapping method for the back-scanned step/stare imaging system, which can make all field points stable on the FPA during back-scanning. First of all, the mathematical model of non-rotationally symmetric field mapping between object space and image space is established. Then, a back-scanned step/stare imaging system based on the model is designed, in which this non-rotationally symmetric mapping can be implemented with an afocal telescope including freeform lenses. Freeform lenses can produce an anamorphic aberration to adjust distortion characteristics of the optical system to control image wander on an FPA. Furthermore, the simulations verify the effectiveness of the method.

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Accuracy test and analysis for infrared search and track system

Cao

Zheng

et al. 2013

Optik

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SASS: a bi-spectral panoramic IRST - results from measurement campaigns with the Italian Navy

Cited by 5 publications

References 0 publications

Convolutional Neural Networks and Feature Fusion for Flow Pattern Identification of the Subsea Jumper

Convolutional Neural Networks and Feature Fusion for Flow Pattern Identification of the Subsea Jumper

Non-Rotationally Symmetric Field Mapping for Back-Scanned Step/Stare Imaging System

Accuracy test and analysis for infrared search and track system

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