In this contribution, it is shown how the number of optical sources in WDM-based optical beamforming networks can be reduced. In optical beamforming networks based on several optical carriers and a dispersive medium a correspondence is established between each optical carrier and each antenna element. However, it is feasible to reduce the number of optical sources of the architecture if the optical carriers are reused by means of the combination of dispersive and non-dispersive time delays.
To sum up, this contribution shows how WDM optical beamforming architectures can be simplified combining dispersive and non-dispersive time delays to allow the use of photonic beamforming techniques in large antenna arrays. The number of optical sources of the beamformer, as well as its total size and cost, can be highly reduced using this technique. Experimental results validating the feasibility of the technique are provided.
We describe a novel spatial filter that produces a low-pass modulation transfer function (MTF), evenwhen the filter uses a high pupil aperture. Furthermore, we show that the filter's MTF has low sensitivity to focus errors. Numerical simulations are reported.
We present a phase mask that substantially reduces the influence of focus error of an optical system; while preserving light gathering power, and lateral resolution. Numerical simulations and first experimental results are shown.
Automatic object segmentation in highly noisy image sequences, composed by a translating object over a background having a different motion, is achieved through joint motion-texture analysis. Local motion and/or texture is characterized by the energy of the local spatio-temporal spectrum, as different textures undergoing different translational motions display distinctive features in their 3D (x,y,t) spectra. Measurements of local spectrum energy are obtained using a bank of directional 3rd order Gaussian derivative filters in a multiresolution pyramid in space- time (10 directions, 3 resolution levels). These 30 energy measurements form a feature vector describing texture-motion for every pixel in the sequence. To improve discrimination capability and reduce computational cost, we automatically select those 4 features (channels) that best discriminate object from background, under the assumptions that the object is smaller than the background and has a different velocity or texture. In this way we reject features irrelevant or dominated by noise, that could yield wrong segmentation results. This method has been successfully applied to sequences with extremely low visibility and for objects that are even invisible for the eye in absence of motion.
Hispars is an European EUCLID investigation projected devoted to evaluation of Artificial Neural Networks for defense pattern applications. Three demonstrators representing three military operational contexts (Air-to- Ground, Ground Battlefield, Naval Threat Evaluation) have been defined and developed. A set of operational processing chains have been selected, and for each of them, ANN methods have been proposed and evaluated on real data set at each level of processing, in comparison to those classical techniques used in existing equipment.