Change search
ReferencesLink to record
Permanent link

Direct link
Discrimination between two classes of radar emitters based on the radio frequency distributions
2005 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The subject of this thesis is to investigate methods for discrimination between two classes of radar emitters on the basis of the distribution of the radio frequencies. For the first class, the radio frequency is uniformly distributed within a certain frequency band. For the second class, the radio frequency belongs to a set of discrete frequencies where each frequency is equally probable. Two methods has been investigated. The first method is a recursive algorithm that applies multiple Kalman filters. The second method is a clustering algorithm. It is a non-recursive algorithm that for each acquired measurement processes all measurements in a batch. To compare the different methods, the posterior probability of each class was calculated given a sequence of measurements. The posterior probability represents all knowledge of the emitters, and constitutes the base for further decisions. The results show that both methods are capable of discriminating between the two classes. For the method that applies multiple Kalman filters, the probability of each class quickly converges towards the desired results. The number of discrete frequencies is chosen as 10 in the simulations, and the number of required measurements is then approximately 30. For the clustering algorithm, the number of required measurements is substantially larger than for the method with the multiple Kalman filters.

Place, publisher, year, edition, pages
Keyword [en]
Keyword [sv]
URN: urn:nbn:se:ltu:diva-44149ISRN: LTU-EX--05/095--SELocal ID: 1f2eb16d-8c28-40af-a37c-c07ecbd9bf5cOAI: diva2:1017424
Subject / course
Student thesis, at least 30 credits
Educational program
Electrical Engineering, master's level
Validerat; 20101217 (root)Available from: 2016-10-04 Created: 2016-10-04Bibliographically approved

Open Access in DiVA

fulltext(961 kB)0 downloads
File information
File name FULLTEXT01.pdfFile size 961 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

ReferencesLink to record
Permanent link

Direct link