A low complexity cross-correlation interference mitigation technique for GNSS

The finite length Gold codes used in satellite navigation systems limit their dynamic range, resulting in the introduction of unwanted peaks that can mask out signals of interest. In this paper, a novel cross-correlation interference mitigation technique dealing with this issue is introduced. A brief overview of the multiple access interference problem inherent in satellite navigation systems using the code division multiple access technique is followed by the details of the proposed method. Simulation case studies and analyses of the results detailing weak signal scenarios, carried out entirely using the Global Navigation System Scope, are presented. A comparison of the results is given in the conclusions section along with remarks on the performance of the proposed method and future work to be carried out.

A Single-Frame Super-Resolution Innovative Approach

Super-resolution refers to the process of obtaining a high resolution image from one or more low resolution images. In this work, we present a novel method for the super-resolution problem for the limited case, where only one image of low resolution is given as an input. The proposed method is based on statistical learning for inferring the high frequencies regions which helps to distinguish a high resolution image from a low resolution one. These inferences are obtained from the correlation between regions of low and high resolution that come exclusively from the image to be super-resolved, in term of small neighborhoods. The Markov random fields are used as a model to capture the local statistics of high and low resolution data when they are analyzed at different scales and resolutions. Experimental results show the viability of the method.