The multi-sensor, high accuracy, 4 axes gyro-stabilised turret system (LEOSS-T) designed by LEONARDO SPA - Electronics Division for airborne surveillance applications has been transformed in a 3kW Power Laser Effector substituting one of the sensor with the Optical Head of a 3 kW single mode IPG Fiber Laser. Inside the LEONARDO LASER Facility, we demonstrated that the LEOSS Laser Effector system is able to: detect and track a micro drone; determine and maintain the aim point for the time necessary to obtain the desired effect on the target. Drone Dazzling and shooting-down have been tested. Demonstration setup and results are presented.
Cooled infrared detectors are typically characterized by well-known electro-optical parameters: responsivity, noise equivalent temperature difference, shot noise, 1/f noise, and so on. Particularly important for staring arrays is also the residual fixed pattern noise (FPN) that can be obtained after the application of the nonuniformity correction (NUC) algorithm. A direct measure of this parameter is usually hard to define because the residual FPN strongly depends, other than on the detector, on the choice of the NUC algorithm and the operative scenario. We introduce three measurable parameters: instability, nonlinearity, and a residual after a polynomial fitting of the detector response curve, and we demonstrate how they are related to the residual FPN after the application of an NUC (the relationship with three common correction algorithms is discussed). A comparison with experimental data is also presented and discussed.
Long range imaging systems have applications in vessel traffic monitoring, border and coastal observation, and generic surveillance. Often, sign reading and identification capabilities are required, and medium or long-wave infrared systems are simply not the best solution for these tasks, because of the low scene contrast. Among reflected light imagers, the short-wave infrared has a competitive advantage over the visible and near-infrared spectrum, being less affected by path attenuation, scattering and turbulence. However, predicting a SWIR system long range performance still represents a challenge because of the need of an accurate atmospheric modelling. In this paper, we present the key limiting performance factors for long range applications, and how we used popular atmospheric models to extract the synthetic simulation parameters needed for range performance prediction. We then present a case study for a long range application, where the main requirement is to read a vessel name at distances greater than 10km. The results show a significant advantage of SWIR over visible and near-infrared solutions for long range identification tasks.
The raw output of a generic infrared vision system, based on staring arrays, is spatially not uniform. This spatial noise
can be much greater than the system NETD, and determines a strong drop in system performance.
Therefore we need to model all system non-uniformity (NU) sources to highlight the parameters that should be
controlled by optical and mechanical design, the ones depending on the focal plane array and those that can be corrected
in post-processing.
In this paper, we identify the main NU sources (optical relative irradiance, housing straylight, detector pixel-pixel
differences and non linearity), we show how to model these sources and how they are related to the design and physical
parameters of the system. We then describe the total signal due to these sources at the detector output. Applying different
NUC algorithms to this signal, the final results on the image can be simulated finding a proper correction algorithm. At
the end we show the agreement between the model with the experimental data taken on a real system.
Changing a limited set of parameters, this model can be applied to many third generation thermal imager configurations.
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