Digitální knihovnaUPCE
 

Fakulta elektrotechniky a informatiky / Faculty of Electrical Engineering and Informatics

Stálý URI pro tuto komunituhttps://hdl.handle.net/10195/3847

Práce obhájené před rokem 2008 jsou uloženy pouze v kolekci Vysokoškolské kvalifikační práce

Procházet

Autor: search.filters.author.Pidanič, JanAutor: search.filters.author.Bhatta, AbhishekDatum zahájení: 2013Datum ukončení: 2019

Search Results

Nyní se zobrazuje 1 - 2 z 2
  • Náhled
    Článekpeer-reviewedpublished versionOmezený přístup
    Statistical Analysis of Ground Clutter from FM Based Passive Radar
    (Springer, 2019) Bhatta, Abhishek; Pidanič, Jan; Mishra, Amit
    Statistical characterization of ground clutter information for radar systems is a topic that requires a detailed understanding. In this work, we characterize the ground clutter information from data captured by two Frequency Modulation (FM) based passive radar systems placed at different locations in the Czech Republic. Since the target models are statistically defined by the Swerling models, each of which is based on a particular distribution, we attempted to connect the ground clutter with some of the known distributions. The analysis is performed in three steps. The empirical distribution is fitted with some of the well-known distributions. Two different goodness-of-fit tests (named chi-squared test and Kolmogorov-Smirnov test) are performed on the obtained data by comparing the empirical distribution with the fitted distributions to determine which distribution is the best fit. The observations are analyzed and the results are considered in detail.
  • Náhled
    Konferenční objektpeer-reviewedpostprintOtevřený přístup
    Classification of CommSense data using learning algorithms
    (Institution of Engineering and Technology, 2017) Bhatta, Abhishek; Amit, Kumar Mishra; Pidanič, Jan
    In our previous work we have shown the design of a Communication based Sensing (CommSense) system. The current work presents analysis of the data captured by a CommSense system. Analysis is performed using Support Vector Machines (SVM) and a Multi-layer Perceptron (MLP) which are commonly used supervised learning algorithms. The predicted results are presented in the form of a confusion matrix and an analysis is presented showing the percentage of error in prediction.